From e02fc2628c37d0587696abd2af219c991c23e347 Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Wed, 4 Jan 2023 16:06:43 +0800 Subject: [PATCH 01/10] update versions to v23.02-snapshot for CICD jobs (#267) * update versions for CICD jobs Signed-off-by: liyuan * modify versions Signed-off-by: liyuan * cmake version Signed-off-by: liyuan Signed-off-by: liyuan --- .../csp/databricks/generate-init-script-10.4.ipynb | 2 +- .../csp/databricks/generate-init-script.ipynb | 2 +- .../xgboost-examples/on-prem-cluster/kubernetes-scala.md | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/README.md | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/pom.xml | 4 ++-- examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh | 6 +++--- .../micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb | 2 +- examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml | 4 ++-- .../RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt | 8 ++++---- examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh | 2 +- .../notebooks/cuspatial_sample_standalone.ipynb | 2 +- examples/UDF-Examples/Spark-cuSpatial/pom.xml | 4 ++-- .../mortgage/notebooks/python/MortgageETL+XGBoost.ipynb | 2 +- .../mortgage/notebooks/python/MortgageETL.ipynb | 2 +- .../mortgage/notebooks/python/cv-mortgage-gpu.ipynb | 2 +- .../mortgage/notebooks/scala/mortgage-ETL.ipynb | 6 +++--- .../XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb | 2 +- .../XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb | 2 +- 18 files changed, 28 insertions(+), 28 deletions(-) diff --git a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb b/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb index 09033b8e0..e3309ebda 100644 --- a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb +++ b/docs/get-started/xgboost-examples/csp/databricks/generate-init-script-10.4.ipynb @@ -133,7 +133,7 @@ "1. Edit your cluster, adding an initialization script from `dbfs:/databricks/init_scripts/init.sh` in the \"Advanced Options\" under \"Init Scripts\" tab\n", "2. Reboot the cluster\n", "3. Go to \"Libraries\" tab under your cluster and install `dbfs:/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar` in your cluster by selecting the \"DBFS\" option for installing jars\n", - "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.12/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n", + "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n", "5. Inside the mortgage example notebook, update the data paths\n", " `train_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-train.csv')`\n", " `trans_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-trans.csv')`" diff --git a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb b/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb index b0799d5c1..320792c77 100644 --- a/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb +++ b/docs/get-started/xgboost-examples/csp/databricks/generate-init-script.ipynb @@ -133,7 +133,7 @@ "1. Edit your cluster, adding an initialization script from `dbfs:/databricks/init_scripts/init.sh` in the \"Advanced Options\" under \"Init Scripts\" tab\n", "2. Reboot the cluster\n", "3. Go to \"Libraries\" tab under your cluster and install `dbfs:/FileStore/jars/xgboost4j-spark-gpu_2.12-1.7.1.jar` in your cluster by selecting the \"DBFS\" option for installing jars\n", - "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.12/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n", + "4. Import the mortgage example notebook from `https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb`\n", "5. Inside the mortgage example notebook, update the data paths\n", " `train_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-train.csv')`\n", " `trans_data = reader.schema(schema).option('header', True).csv('/data/mortgage/csv/small-trans.csv')`" diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md index 9a869d59e..4c823e7d7 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md @@ -40,7 +40,7 @@ export SPARK_DOCKER_IMAGE= export SPARK_DOCKER_TAG= pushd ${SPARK_HOME} -wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-22.12/dockerfile/Dockerfile +wget https://github.com/NVIDIA/spark-rapids-examples/raw/branch-23.02/dockerfile/Dockerfile # Optionally install additional jars into ${SPARK_HOME}/jars/ diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/README.md b/examples/ML+DL-Examples/Spark-cuML/pca/README.md index ca573aaf1..d0185f7d0 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/README.md +++ b/examples/ML+DL-Examples/Spark-cuML/pca/README.md @@ -48,7 +48,7 @@ It is assumed that a Standalone Spark cluster has been set up, the `SPARK_MASTER ``` bash RAPIDS_ML_JAR=PATH_TO_rapids-4-spark-ml_2.12-22.02.0-cuda11.jar - PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-22.12.0.jar + PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar jupyter toree install \ --spark_home=${SPARK_HOME} \ diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml index 9cc790476..bcdc8192f 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml +++ b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml @@ -21,7 +21,7 @@ com.nvidia PCAExample jar - 22.12.0-SNAPSHOT + 23.02.0-SNAPSHOT 8 @@ -51,7 +51,7 @@ com.nvidia rapids-4-spark-ml_2.12 - 22.12.0-SNAPSHOT + 23.02.0-SNAPSHOT diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh index f5b287351..e0991aa36 100755 --- a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh +++ b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh @@ -15,8 +15,8 @@ # limitations under the License. # -ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/22.12.0-SNAPSHOT/rapids-4-spark-ml_2.12-22.12.0-SNAPSHOT.jar -PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/22.12.0-SNAPSHOT/rapids-4-spark_2.12-22.12.0-SNAPSHOT.jar +ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/23.02.0-SNAPSHOT/rapids-4-spark-ml_2.12-23.02.0-SNAPSHOT.jar +PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.02.0-SNAPSHOT/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar $SPARK_HOME/bin/spark-submit \ --master spark://127.0.0.1:7077 \ @@ -38,4 +38,4 @@ $SPARK_HOME/bin/spark-submit \ --conf spark.network.timeout=1000s \ --jars $ML_JAR,$PLUGIN_JAR \ --class com.nvidia.spark.examples.pca.Main \ -/workspace/target/PCAExample-22.12.0-SNAPSHOT.jar +/workspace/target/PCAExample-23.02.0-SNAPSHOT.jar diff --git a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb index 9e854115b..d900a2ec3 100644 --- a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb +++ b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb @@ -22,7 +22,7 @@ "import os\n", "# Change to your cluster ip:port and directories\n", "SPARK_MASTER_URL = os.getenv(\"SPARK_MASTER_URL\", \"spark:your-ip:port\")\n", - "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-22.12.0.jar\")\n" + "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\")\n" ] }, { diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml index 812bbc778..ea9a25dac 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml @@ -25,7 +25,7 @@ user defined functions for use with the RAPIDS Accelerator for Apache Spark - 22.12.0-SNAPSHOT + 23.02.0-SNAPSHOT 1.8 @@ -37,7 +37,7 @@ cuda11 2.12 - 22.12.0-SNAPSHOT + 23.02.0-SNAPSHOT 3.1.1 2.12.15 ${project.build.directory}/cpp-build diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt index 593312611..e9f365484 100755 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/cpp/CMakeLists.txt @@ -16,7 +16,7 @@ cmake_minimum_required(VERSION 3.23.1 FATAL_ERROR) -file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-22.12/RAPIDS.cmake +file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-23.02/RAPIDS.cmake ${CMAKE_BINARY_DIR}/RAPIDS.cmake) include(${CMAKE_BINARY_DIR}/RAPIDS.cmake) @@ -32,7 +32,7 @@ if(DEFINED GPU_ARCHS) endif() rapids_cuda_init_architectures(UDFEXAMPLESJNI) -project(UDFEXAMPLESJNI VERSION 22.12.0 LANGUAGES C CXX CUDA) +project(UDFEXAMPLESJNI VERSION 23.02.0 LANGUAGES C CXX CUDA) option(PER_THREAD_DEFAULT_STREAM "Build with per-thread default stream" OFF) option(BUILD_UDF_BENCHMARKS "Build the benchmarks" OFF) @@ -84,10 +84,10 @@ set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -w --expt-extended-lambda --expt-relax set(CUDA_USE_STATIC_CUDA_RUNTIME OFF) rapids_cpm_init() -rapids_cpm_find(cudf 22.12.00 +rapids_cpm_find(cudf 23.02.00 CPM_ARGS GIT_REPOSITORY https://github.com/rapidsai/cudf.git - GIT_TAG branch-22.12 + GIT_TAG branch-23.02 GIT_SHALLOW TRUE SOURCE_SUBDIR cpp OPTIONS "BUILD_TESTS OFF" diff --git a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh index c98b916ff..bd675849d 100755 --- a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh +++ b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh @@ -31,7 +31,7 @@ rm -rf $DATA_OUT_PATH # the path to keep the jars of spark-rapids & spark-cuspatial JARS=$ROOT_PATH/jars -JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-22.12.0-SNAPSHOT.jar,$JARS/spark-cuspatial-22.12.0-SNAPSHOT.jar} +JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar,$JARS/spark-cuspatial-23.02.0-SNAPSHOT.jar} $SPARK_HOME/bin/spark-submit --master spark://$HOSTNAME:7077 \ --name "Gpu Spatial Join UDF" \ diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb index 3fa3744a3..1fd1c4855 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb @@ -9,7 +9,7 @@ "source": [ "from pyspark.sql import SparkSession\n", "import os\n", - "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-22.12.0.jar,/data/cuspatial_data/jars/spark-cuspatial-22.12.0-SNAPSHOT.jar\")\n", + "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar,/data/cuspatial_data/jars/spark-cuspatial-23.02.0-SNAPSHOT.jar\")\n", "spark = SparkSession.builder \\\n", " .config(\"spark.jars\", jarsPath) \\\n", " .config(\"spark.sql.adaptive.enabled\", \"false\") \\\n", diff --git a/examples/UDF-Examples/Spark-cuSpatial/pom.xml b/examples/UDF-Examples/Spark-cuSpatial/pom.xml index 1f609009f..dd81a615a 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/pom.xml +++ b/examples/UDF-Examples/Spark-cuSpatial/pom.xml @@ -24,13 +24,13 @@ UDF of the cuSpatial case for the RAPIDS Accelerator The RAPIDS accelerated user defined function of the cuSpatial case for use with the RAPIDS Accelerator for Apache Spark - 22.12.0-SNAPSHOT + 23.02.0-SNAPSHOT 1.8 1.8 8 - 22.12.0-SNAPSHOT + 23.02.0-SNAPSHOT 2.12 3.2.0 ${project.build.directory}/cpp-build diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index a3b93140a..490dc08f2 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -6,7 +6,7 @@ "source": [ "# Dataset\n", "\n", - "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.12/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", + "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "# ETL + XGBoost train & transform\n", "\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index 4551654f5..f0f5bd7b4 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -6,7 +6,7 @@ "source": [ "## Prerequirement\n", "### 1. Download data\n", - "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.12/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", + "Dataset is derived from Fannie Mae’s [Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html) with all rights reserved by Fannie Mae. Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "### 2. Download needed jars\n", "* [rapids-4-spark_2.12-22.12.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.12.0/rapids-4-spark_2.12-22.12.0.jar)\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb index ea128ef9c..336595d59 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb @@ -63,7 +63,7 @@ "Setting default log level to \"WARN\".\n", "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", "2022-11-25 09:34:43,952 WARN resource.ResourceUtils: The configuration of cores (exec = 4 task = 1, runnable tasks = 4) will result in wasted resources due to resource gpu limiting the number of runnable tasks per executor to: 1. Please adjust your configuration.\n", - "2022-11-25 09:34:58,155 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator 22.12.0-SNAPSHOT using cudf 22.12.0-SNAPSHOT.\n", + "2022-11-25 09:34:58,155 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator 23.02.0-SNAPSHOT using cudf 23.02.0-SNAPSHOT.\n", "2022-11-25 09:34:58,171 WARN rapids.RapidsPluginUtils: spark.rapids.sql.multiThreadedRead.numThreads is set to 20.\n", "2022-11-25 09:34:58,175 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator is enabled, to disable GPU support set `spark.rapids.sql.enabled` to false.\n", "2022-11-25 09:34:58,175 WARN rapids.RapidsPluginUtils: spark.rapids.sql.explain is set to `NOT_ON_GPU`. Set it to 'NONE' to suppress the diagnostics logging about the query placement on the GPU.\n" diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index fb4128d35..75319f42e 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -16,8 +16,8 @@ "source": [ "## Prerequirement\n", "### 1. Download data\n", - "\n", - "Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-22.12/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", + "\n", + "Refer to these [instructions](https://github.com/NVIDIA/spark-rapids-examples/blob/branch-23.02/docs/get-started/xgboost-examples/dataset/mortgage.md) to download the dataset.\n", "\n", "### 2. Download needed jars\n", "* [rapids-4-spark_2.12-22.12.0.jar](https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark_2.12/22.12.0/rapids-4-spark_2.12-22.12.0.jar)\n", @@ -27,7 +27,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.12.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\n", "\n", "```\n", "\n", diff --git a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb index c41e3dd72..ab334a6bf 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb @@ -26,7 +26,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.12.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\n", "$ export PYSPARK_DRIVER_PYTHON=jupyter \n", "$ export PYSPARK_DRIVER_PYTHON_OPTS=notebook\n", "```\n", diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb index 485518326..d8395e439 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb @@ -26,7 +26,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-22.12.0.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\n", "\n", "```\n", "\n", From 7aba376288f830f21a8e39b6850ab26ffcdba3a5 Mon Sep 17 00:00:00 2001 From: Matt Ahrens Date: Wed, 4 Jan 2023 19:19:35 -0600 Subject: [PATCH 02/10] Updating Databricks tool example notebooks with output + support for wildcards/S3 as input for qual tool (#264) * Updating Databricks tool example notebooks with output + support for wildcards/S3 as input for qual tool Signed-off-by: Matt Ahrens * Updating formatting for tools notebooks Signed-off-by: Matt Ahrens * Updating formatting for tools notebooks Signed-off-by: Matt Ahrens * Removing mahrens and replacing with user1 and added link to further options Signed-off-by: Matt Ahrens Signed-off-by: Matt Ahrens --- ...tor for Apache Spark] Profiling Tool Notebook Template.ipynb | 2 +- ...for Apache Spark] Qualification Tool Notebook Template.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/databricks/[RAPIDS Accelerator for Apache Spark] Profiling Tool Notebook Template.ipynb b/tools/databricks/[RAPIDS Accelerator for Apache Spark] Profiling Tool Notebook Template.ipynb index 6d7d66d22..9113f27fd 100644 --- a/tools/databricks/[RAPIDS Accelerator for Apache Spark] Profiling Tool Notebook Template.ipynb +++ b/tools/databricks/[RAPIDS Accelerator for Apache Spark] Profiling Tool Notebook Template.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","source":["# Welcome to the Profiling Tool for the RAPIDS Accelerator for Apache Spark\nTo run the tool, you need to enter a log path that represents the DBFS location for your Spark GPU event logs. Then you can select \"Run all\" to execute the notebook. After the notebook completes, you will see various output tables show up below.\n\n## GPU Job Tuning Recommendations\nThis has general suggestions for tuning your applications to run optimally on GPUs.\n\n## Per-Job Profile\nThe profiler output includes information about the application, data sources, executors, SQL stages, Spark properties, and key application metrics at the job and stage levels."],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"5156a76c-7af7-465d-aff4-41a2e54e3595","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["import json\nimport requests\nimport base64\nimport shlex\nimport subprocess\nimport pandas as pd\n\nTOOL_JAR_URL = 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/22.12.0/rapids-4-spark-tools_2.12-22.12.0.jar'\nTOOL_JAR_LOCAL_PATH = '/tmp/rapids-4-spark-tools.jar'\n\n# Profiling tool output directory.\nOUTPUT_DIR = '/tmp' \n\nresponse = requests.get(TOOL_JAR_URL)\nopen(TOOL_JAR_LOCAL_PATH, \"wb\").write(response.content)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"53b4d770-9db6-4bd7-9b93-d036d375eac5","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["dbutils.widgets.text(\"log_path\", \"\")"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"f0e4371a-d2d9-4449-81ed-8f6c61ae8f80","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["eventlog_string=dbutils.widgets.get(\"log_path\") \n\nq_command_string=\"java -Xmx10g -cp /tmp/rapids-4-spark-tools.jar:/databricks/jars/* com.nvidia.spark.rapids.tool.profiling.ProfileMain --csv --auto-tuner -o {} \".format(OUTPUT_DIR) + eventlog_string\nargs = shlex.split(q_command_string)\ncmd_out = subprocess.run(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n\nif cmd_out.returncode != 0:\n dbutils.notebook.exit(\"Profiling Tool failed with stderr:\" + cmd_out.stderr)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"e9e7cecf-c2dc-4a0f-aea1-61a323e4ccc4","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["import os\n\napp_df = pd.DataFrame(columns = ['appId', 'appName'])\n\nfor x in os.scandir(OUTPUT_DIR + \"/rapids_4_spark_profile/\"):\n tmp_df = pd.read_csv(x.path + \"/application_information.csv\")\n app_df = app_df.append(tmp_df[['appId', 'appName']])"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"be0a2da7-1ee3-475e-96f9-303779edfd85","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## GPU Job Tuning Recommendations"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"a1e326ec-5701-4b08-ae0f-7df0c8440038","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["app_list = app_df[\"appId\"].tolist()\napp_recommendations = pd.DataFrame(columns=['app', 'recommendations'])\n\nfor app in app_list:\n app_file = open(OUTPUT_DIR + \"/rapids_4_spark_profile/\" + app + \"/profile.log\")\n recommendations_start = 0\n recommendations_str = \"\"\n for line in app_file:\n if recommendations_start == 1:\n recommendations_str = recommendations_str + line\n if \"### D. Recommended Configuration ###\" in line:\n recommendations_start = 1\n app_recommendations = app_recommendations.append({'app': app, 'recommendations': recommendations_str}, ignore_index=True)\n \ndisplay(app_recommendations)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"4979f78c-44a0-4e54-b803-e5e194b71104","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Per-App Profile"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"1d4f9927-e9d8-4897-b604-f7832dc634aa","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["for x in os.scandir(OUTPUT_DIR + \"/rapids_4_spark_profile/\"):\n print(\"APPLICATION ID = \" + str(x))\n log = open(x.path + \"/profile.log\")\n print(log.read())"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"9a8f1a58-e86f-4bd0-a245-878186feb8b9","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"[RAPIDS Accelerator for Apache Spark] Profiling Tool Notebook Template","dashboards":[{"elements":[{"elementNUID":"be0a2da7-1ee3-475e-96f9-303779edfd85","dashboardResultIndex":0,"guid":"05eef9d3-7c55-4e26-8d1f-fa80338359e6","resultIndex":null,"options":null,"position":{"x":0,"y":0,"height":6,"width":24,"z":null},"elementType":"command"}],"guid":"a9ea7799-040a-484e-a59d-c3cdf5072953","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"91c1bfb2-695a-4e5c-8a25-848a433108dc","origId":2690941040041430,"title":"Executive View","width":1600,"globalVars":{}},{"elements":[],"guid":"0896a45f-af1b-4849-b6c2-2b6abcb8b97b","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"62243296-4562-4f06-90ac-d7a609f19c16","origId":2690941040041431,"title":"App View","width":1920,"globalVars":{}}],"notebookMetadata":{"pythonIndentUnit":2,"widgetLayout":[{"name":"log_path","width":576,"breakBefore":false},{"name":"Apps","width":494,"breakBefore":false}]},"language":"python","widgets":{"log_path":{"nuid":"c7ce3870-db19-4813-b1cb-cead3f4c36f1","currentValue":"/dbfs/","widgetInfo":{"widgetType":"text","name":"log_path","defaultValue":"","label":null,"options":{"widgetType":"text","validationRegex":null}}}},"notebookOrigID":2690941040041407}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"cell_type":"markdown","source":["# Welcome to the Profiling Tool for the RAPIDS Accelerator for Apache Spark\nTo run the tool, you need to enter a log path that represents the DBFS location for your Spark GPU event logs. Then you can select \"Run all\" to execute the notebook. After the notebook completes, you will see various output tables show up below. More options for running the profiling tool can be found here: https://nvidia.github.io/spark-rapids/docs/spark-profiling-tool.html#profiling-tool-options.\n\n## GPU Job Tuning Recommendations\nThis has general suggestions for tuning your applications to run optimally on GPUs.\n\n## Per-Job Profile\nThe profiler output includes information about the application, data sources, executors, SQL stages, Spark properties, and key application metrics at the job and stage levels."],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"5156a76c-7af7-465d-aff4-41a2e54e3595","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["import json\nimport requests\nimport base64\nimport shlex\nimport subprocess\nimport pandas as pd\n\nTOOL_JAR_URL = 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/22.12.0/rapids-4-spark-tools_2.12-22.12.0.jar'\nTOOL_JAR_LOCAL_PATH = '/tmp/rapids-4-spark-tools.jar'\n\n# Profiling tool output directory.\nOUTPUT_DIR = '/tmp' \n\nresponse = requests.get(TOOL_JAR_URL)\nopen(TOOL_JAR_LOCAL_PATH, \"wb\").write(response.content)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"53b4d770-9db6-4bd7-9b93-d036d375eac5","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
Out[59]: 2011685
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
Out[59]: 2011685
"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.widgets.text(\"log_path\", \"\")\neventlog_string=dbutils.widgets.get(\"log_path\") \n\ndbutils.widgets.text(\"output_path\", \"\")\noutputpath_string=dbutils.widgets.get(\"output_path\")"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"f0e4371a-d2d9-4449-81ed-8f6c61ae8f80","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"code","source":["worker_info_path = \"/tmp/worker_info.yaml\"\n\nworker_info = \"\"\"\n system:\n numCores: 32\n memory: 212992MiB\n numWorkers: 5\n gpu:\n memory: 15109MiB\n count: 4\n name: T4\n softwareProperties:\n spark.driver.maxResultSize: 7680m\n spark.driver.memory: 15360m\n spark.executor.cores: '8'\n spark.executor.instances: '2'\n spark.executor.memory: 47222m\n spark.executorEnv.OPENBLAS_NUM_THREADS: '1'\n spark.scheduler.mode: FAIR\n spark.sql.cbo.enabled: 'true'\n spark.ui.port: '0'\n spark.yarn.am.memory: 640m\n\"\"\"\n\nwith open(worker_info_path, 'w') as f:\n f.write(worker_info)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"6c35e478-abe6-49b7-97f9-a8aba71f11d3","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"code","source":["!java -Xmx10g -cp /tmp/rapids-4-spark-tools.jar:/databricks/jars/* com.nvidia.spark.rapids.tool.profiling.ProfileMain --csv --worker-info $worker_info_path --auto-tuner -o $outputpath_string $eventlog_string &> /tmp/prof_debug.log"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"e9e7cecf-c2dc-4a0f-aea1-61a323e4ccc4","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"code","source":["import os\n\napp_df = pd.DataFrame(columns = ['appId', 'appName'])\n\nfor x in os.scandir(outputpath_string + \"/rapids_4_spark_profile/\"):\n tmp_df = pd.read_csv(x.path + \"/application_information.csv\")\n app_df = app_df.append(tmp_df[['appId', 'appName']])"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"be0a2da7-1ee3-475e-96f9-303779edfd85","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"markdown","source":["## GPU Job Tuning Recommendations"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"a1e326ec-5701-4b08-ae0f-7df0c8440038","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["app_list = app_df[\"appId\"].tolist()\napp_recommendations = pd.DataFrame(columns=['app', 'recommendations'])\n\nfor app in app_list:\n app_file = open(outputpath_string + \"/rapids_4_spark_profile/\" + app + \"/profile.log\")\n recommendations_start = 0\n recommendations_str = \"\"\n for line in app_file:\n if recommendations_start == 1:\n recommendations_str = recommendations_str + line\n if \"### D. Recommended Configuration ###\" in line:\n recommendations_start = 1\n app_recommendations = app_recommendations.append({'app': app, 'recommendations': recommendations_str}, ignore_index=True)\n \ndisplay(app_recommendations)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"4979f78c-44a0-4e54-b803-e5e194b71104","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[["app-20220210005817-0212","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1197m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004538-0189","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000414-0117","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2353m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005713-0210","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000744-0123","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002521-0154","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004801-0193","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3158m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002620-0156","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001501-0135","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1365m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001417-0134","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1365m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001930-0143","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005502-0206","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002316-0150","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004324-0185","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005039-0198","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210004834-0194","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2099m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004011-0180","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004656-0191","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001324-0133","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2225m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000856-0125","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000241-0114","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002105-0146","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000312-0115","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003325-0169","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002654-0157","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005425-0205","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1509m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000700-0122","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001109-0129","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002138-0147","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001717-0139","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000018-0110","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002725-0158","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000933-0126","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210004617-0190","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005846-0213","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1197m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210002757-0159","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001038-0128","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000628-0121","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3404m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001959-0144","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000556-0120","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004727-0192","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3354m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005222-0201","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210000825-0124","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005536-0207","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001139-0130","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000343-0116","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003705-0176","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2337m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003359-0170","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005611-0208","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2974m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000119-0112","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3061m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005322-0203","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003900-0179","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004355-0186","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001648-0138","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002938-0162","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005354-0204","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001821-0141","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2530m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210005252-0202","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210002452-0153","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001617-0137","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003254-0168","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004507-0188","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001751-0140","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005642-0209","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210004905-0195","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003751-0177","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002030-0145","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001854-0142","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210002549-0155","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210001239-0132","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2225m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004428-0187","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3796m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002240-0149","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005147-0200","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2974m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220523230623-0000","\nSpark Properties:\n--conf spark.executor.instances=20\n--conf spark.executor.memory=16384m\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=6\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' should be increased since spilling occurred.\n"],["app-20220210000207-0113","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220209235945-0109","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210002349-0151","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002421-0152","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000448-0118","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001005-0127","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003149-0166","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003635-0175","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003223-0167","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004041-0181","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003532-0173","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003607-0174","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003048-0164","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210004145-0183","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003825-0178","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003118-0165","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000049-0111","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210000519-0119","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3061m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210003015-0163","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210005744-0211","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210005111-0199","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004217-0184","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210002904-0161","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3796m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001546-0136","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003503-0172","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210001209-0131","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n"],["app-20220210004114-0182","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210002208-0148","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210002833-0160","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"],["app-20220210003431-0171","\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n"]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"app","type":"\"string\"","metadata":"{}"},{"name":"recommendations","type":"\"string\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
apprecommendations
app-20220210005817-0212\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1197m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004538-0189\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000414-0117\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2353m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005713-0210\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000744-0123\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002521-0154\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004801-0193\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3158m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002620-0156\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001501-0135\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1365m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001417-0134\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1365m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001930-0143\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005502-0206\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002316-0150\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004324-0185\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005039-0198\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210004834-0194\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2099m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004011-0180\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004656-0191\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001324-0133\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2225m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000856-0125\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000241-0114\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002105-0146\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000312-0115\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003325-0169\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002654-0157\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005425-0205\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1509m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000700-0122\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001109-0129\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002138-0147\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001717-0139\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000018-0110\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002725-0158\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000933-0126\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210004617-0190\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005846-0213\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=1197m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210002757-0159\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001038-0128\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000628-0121\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3404m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001959-0144\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000556-0120\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004727-0192\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3354m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005222-0201\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210000825-0124\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005536-0207\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001139-0130\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000343-0116\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003705-0176\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2337m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003359-0170\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005611-0208\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2974m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000119-0112\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3061m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005322-0203\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003900-0179\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004355-0186\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001648-0138\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002938-0162\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005354-0204\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001821-0141\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2530m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210005252-0202\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210002452-0153\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001617-0137\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003254-0168\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004507-0188\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001751-0140\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005642-0209\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210004905-0195\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003751-0177\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002030-0145\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001854-0142\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210002549-0155\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210001239-0132\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2225m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004428-0187\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3796m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002240-0149\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005147-0200\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=2974m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220523230623-0000\nSpark Properties:\n--conf spark.executor.instances=20\n--conf spark.executor.memory=16384m\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.shuffle.partitions=6\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' should be increased since spilling occurred.\n
app-20220210000207-0113\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220209235945-0109\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210002349-0151\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002421-0152\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000448-0118\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001005-0127\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003149-0166\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003635-0175\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003223-0167\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004041-0181\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003532-0173\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003607-0174\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003048-0164\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210004145-0183\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003825-0178\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003118-0165\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000049-0111\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210000519-0119\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3061m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210003015-0163\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210005744-0211\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210005111-0199\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004217-0184\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210002904-0161\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=3796m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001546-0136\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003503-0172\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210001209-0131\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n
app-20220210004114-0182\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210002208-0148\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210002833-0160\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
app-20220210003431-0171\nSpark Properties:\n--conf spark.executor.cores=8\n--conf spark.executor.instances=20\n--conf spark.executor.memoryOverhead=5734m\n--conf spark.rapids.memory.pinnedPool.size=4096m\n--conf spark.rapids.sql.concurrentGpuTasks=2\n--conf spark.sql.files.maxPartitionBytes=4096m\n--conf spark.sql.shuffle.partitions=200\n--conf spark.task.resource.gpu.amount=0.125\n\nComments:\n- 'spark.executor.memoryOverhead' was not set.\n- 'spark.executor.memoryOverhead' must be set if using 'spark.rapids.memory.pinnedPool.size\n- 'spark.sql.shuffle.partitions' was not set.\n- Average JVM GC time is very high. Other Garbage Collectors can be used for better performance.\n
"]}}],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"[RAPIDS Accelerator for Apache Spark] Profiling Tool Notebook Template","dashboards":[{"elements":[{"elementNUID":"be0a2da7-1ee3-475e-96f9-303779edfd85","dashboardResultIndex":0,"guid":"05eef9d3-7c55-4e26-8d1f-fa80338359e6","resultIndex":null,"options":null,"position":{"x":0,"y":0,"height":6,"width":24,"z":null},"elementType":"command"}],"guid":"a9ea7799-040a-484e-a59d-c3cdf5072953","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"91c1bfb2-695a-4e5c-8a25-848a433108dc","origId":2690941040041430,"title":"Executive View","width":1600,"globalVars":{}},{"elements":[],"guid":"0896a45f-af1b-4849-b6c2-2b6abcb8b97b","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"62243296-4562-4f06-90ac-d7a609f19c16","origId":2690941040041431,"title":"App View","width":1920,"globalVars":{}}],"notebookMetadata":{"pythonIndentUnit":2,"widgetLayout":[{"name":"log_path","width":562,"breakBefore":false},{"name":"output_path","width":511,"breakBefore":false}]},"language":"python","widgets":{"log_path":{"nuid":"c7ce3870-db19-4813-b1cb-cead3f4c36f1","currentValue":"/dbfs/user1/profiler_logs","widgetInfo":{"widgetType":"text","name":"log_path","defaultValue":"","label":null,"options":{"widgetType":"text","validationRegex":null}}},"output_path":{"nuid":"a7d1d293-d8c3-452b-9ffb-786ea7a28843","currentValue":"/tmp","widgetInfo":{"widgetType":"text","name":"output_path","defaultValue":"","label":null,"options":{"widgetType":"text","validationRegex":null}}}},"notebookOrigID":2690941040041407}},"nbformat":4,"nbformat_minor":0} diff --git a/tools/databricks/[RAPIDS Accelerator for Apache Spark] Qualification Tool Notebook Template.ipynb b/tools/databricks/[RAPIDS Accelerator for Apache Spark] Qualification Tool Notebook Template.ipynb index db4f756fb..95d50747d 100644 --- a/tools/databricks/[RAPIDS Accelerator for Apache Spark] Qualification Tool Notebook Template.ipynb +++ b/tools/databricks/[RAPIDS Accelerator for Apache Spark] Qualification Tool Notebook Template.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","source":["# Welcome to the Qualification Tool for the RAPIDS Accelerator for Apache Spark\nTo run the tool, you need to enter a log path that represents the DBFS location for your Spark CPU event logs. Then you can select \"Run all\" to execute the notebook. After the notebook completes, you will see various output tables show up below.\n\n## Summary Output\nThe report represents the entire app execution, including unsupported operators and non-SQL operations. By default, the applications and queries are sorted in descending order by the following fields:\n- Recommendation;\n- Estimated GPU Speed-up;\n- Estimated GPU Time Saved; and\n- End Time.\n\n## Stages Output\nFor each stage used in SQL operations, the Qualification tool generates the following information:\n1. App ID\n1. Stage ID\n1. Average Speedup Factor: the average estimated speed-up of all the operators in the given stage.\n1. Stage Task Duration: amount of time spent in tasks of SQL Dataframe operations for the given stage.\n1. Unsupported Task Duration: sum of task durations for the unsupported operators. For more details, see Supported Operators.\n1. Stage Estimated: True or False indicates if we had to estimate the stage duration.\n\n## Execs Output\nThe Qualification tool generates a report of the “Exec” in the “SparkPlan” or “Executor Nodes” along with the estimated acceleration on the GPU. Please refer to the Supported Operators guide for more details on limitations on UDFs and unsupported operators.\n1. App ID\n1. SQL ID\n1. Exec Name: example Filter, HashAggregate\n1. Expression Name\n1. Task Speedup Factor: it is simply the average acceleration of the operators based on the original CPU duration of the operator divided by the GPU duration. The tool uses historical queries and benchmarks to estimate a speed-up at an individual operator level to calculate how much a specific operator would accelerate on GPU.\n1. Exec Duration: wall-Clock time measured since the operator starts till it is completed.\n1. SQL Node Id\n1. Exec Is Supported: whether the Exec is supported by RAPIDS or not. Please refer to the Supported Operators section.\n1. Exec Stages: an array of stage IDs\n1. Exec Children\n1. Exec Children Node Ids\n1. Exec Should Remove: whether the Op is removed from the migrated plan."],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"df33c614-2ecc-47a0-8600-bc891681997f","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["import json\nimport requests\nimport base64\nimport shlex\nimport subprocess\nimport pandas as pd\n\nTOOL_JAR_URL = 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/22.12.0/rapids-4-spark-tools_2.12-22.12.0.jar'\nTOOL_JAR_LOCAL_PATH = '/tmp/rapids-4-spark-tools.jar'\n\n# Qualification tool output directory.\nOUTPUT_DIR = '/tmp/'\n\nresponse = requests.get(TOOL_JAR_URL)\nopen(TOOL_JAR_LOCAL_PATH, \"wb\").write(response.content)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"53b4d770-9db6-4bd7-9b93-d036d375eac5","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"code","source":["dbutils.widgets.text(\"log_path\", \"\")\neventlog_string=dbutils.widgets.get(\"log_path\")\n\nq_command_string=\"java -Xmx10g -cp /tmp/rapids-4-spark-tools.jar:/databricks/jars/* com.nvidia.spark.rapids.tool.qualification.QualificationMain -o {} \".format(OUTPUT_DIR) + eventlog_string\nargs = shlex.split(q_command_string)\ncmd_out = subprocess.run(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n\n\nif cmd_out.returncode != 0:\n dbutils.notebook.exit(\"Qualification Tool failed with stderr:\" + cmd_out.stderr)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"e9e7cecf-c2dc-4a0f-aea1-61a323e4ccc4","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Summary Output"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"bbe50fde-0bd6-4281-95fd-6a1ec6f17ab2","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["summary_output=pd.read_csv(OUTPUT_DIR + \"rapids_4_spark_qualification_output/rapids_4_spark_qualification_output.csv\")\ndisplay(summary_output)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"fb8edb26-e173-47ff-92a1-463baec7c06b","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Stages Output"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"6756159b-30ca-407a-ab6b-9c29ced01ea6","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["stages_output=pd.read_csv(OUTPUT_DIR + \"rapids_4_spark_qualification_output/rapids_4_spark_qualification_output_stages.csv\")\ndisplay(stages_output)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"cdde6177-db5f-434a-995b-776678a64a3a","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## Execs Output"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"4d7ce219-ae75-4a0c-a78c-4e7f25b8cd6f","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["execs_output=pd.read_csv(OUTPUT_DIR + \"rapids_4_spark_qualification_output/rapids_4_spark_qualification_output_execs.csv\")\ndisplay(execs_output)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"998b0c51-0cb6-408e-a01a-d1f5b1a61e1f","inputWidgets":{},"title":""}},"outputs":[],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"[RAPIDS Accelerator for Apache Spark] Qualification Tool Notebook Template","dashboards":[{"elements":[],"guid":"0ed3c80b-b2f6-4c89-9a92-1af2f168d5ea","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"91c1bfb2-695a-4e5c-8a25-848a433108dc","origId":2721260844584915,"title":"Executive View","width":1600,"globalVars":{}},{"elements":[],"guid":"ab4cecf9-0471-4fee-aa33-8927bb7e1bb1","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"62243296-4562-4f06-90ac-d7a609f19c16","origId":2721260844584916,"title":"App View","width":1920,"globalVars":{}}],"notebookMetadata":{"pythonIndentUnit":2,"widgetLayout":[{"name":"log_path","width":1152,"breakBefore":false}]},"language":"python","widgets":{"log_path":{"nuid":"88986aa6-6e67-4d09-aeeb-7c96ea1ea8f1","currentValue":"/dbfs/","widgetInfo":{"widgetType":"text","name":"log_path","defaultValue":"","label":null,"options":{"widgetType":"text","validationRegex":null}}}},"notebookOrigID":2721260844584890}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"cell_type":"markdown","source":["# Welcome to the Qualification Tool for the RAPIDS Accelerator for Apache Spark\nTo run the tool, you need to enter a log path that represents the DBFS location for your Spark CPU event logs. Then you can select \"Run all\" to execute the notebook. After the notebook completes, you will see various output tables show up below. More options for running the qualification tool can be found here: https://nvidia.github.io/spark-rapids/docs/spark-qualification-tool.html#qualification-tool-options.\n\n## Summary Output\nThe report represents the entire app execution, including unsupported operators and non-SQL operations. By default, the applications and queries are sorted in descending order by the following fields:\n- Recommendation;\n- Estimated GPU Speed-up;\n- Estimated GPU Time Saved; and\n- End Time.\n\n## Stages Output\nFor each stage used in SQL operations, the Qualification tool generates the following information:\n1. App ID\n1. Stage ID\n1. Average Speedup Factor: the average estimated speed-up of all the operators in the given stage.\n1. Stage Task Duration: amount of time spent in tasks of SQL Dataframe operations for the given stage.\n1. Unsupported Task Duration: sum of task durations for the unsupported operators. For more details, see Supported Operators.\n1. Stage Estimated: True or False indicates if we had to estimate the stage duration.\n\n## Execs Output\nThe Qualification tool generates a report of the “Exec” in the “SparkPlan” or “Executor Nodes” along with the estimated acceleration on the GPU. Please refer to the Supported Operators guide for more details on limitations on UDFs and unsupported operators.\n1. App ID\n1. SQL ID\n1. Exec Name: example Filter, HashAggregate\n1. Expression Name\n1. Task Speedup Factor: it is simply the average acceleration of the operators based on the original CPU duration of the operator divided by the GPU duration. The tool uses historical queries and benchmarks to estimate a speed-up at an individual operator level to calculate how much a specific operator would accelerate on GPU.\n1. Exec Duration: wall-Clock time measured since the operator starts till it is completed.\n1. SQL Node Id\n1. Exec Is Supported: whether the Exec is supported by RAPIDS or not. Please refer to the Supported Operators section.\n1. Exec Stages: an array of stage IDs\n1. Exec Children\n1. Exec Children Node Ids\n1. Exec Should Remove: whether the Op is removed from the migrated plan."],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"df33c614-2ecc-47a0-8600-bc891681997f","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["import json\nimport requests\nimport base64\nimport shlex\nimport subprocess\nimport pandas as pd\n\n# Download Spark RAPIDS tool jar\nTOOL_JAR_URL = 'https://repo1.maven.org/maven2/com/nvidia/rapids-4-spark-tools_2.12/22.12.0/rapids-4-spark-tools_2.12-22.12.0.jar'\nTOOL_JAR_LOCAL_PATH = '/tmp/rapids-4-spark-tools.jar'\nresponse = requests.get(TOOL_JAR_URL)\nopen(TOOL_JAR_LOCAL_PATH, \"wb\").write(response.content)\n\n# Download S3 jars\nHADOOP_AWS_URL = 'https://repo.maven.apache.org/maven2/org/apache/hadoop/hadoop-aws/2.7.4/hadoop-aws-2.7.4.jar'\nHADOOP_AWS_LOCAL_PATH = '/tmp/hadoop-aws-2.7.4.jar'\nresponse = requests.get(HADOOP_AWS_URL)\nopen(HADOOP_AWS_LOCAL_PATH, \"wb\").write(response.content)\n\nAWS_JAVA_URL = 'https://repo.maven.apache.org/maven2/com/amazonaws/aws-java-sdk/1.7.4/aws-java-sdk-1.7.4.jar'\nAWS_JAVA_LOCAL_PATH = '/tmp/aws-java-sdk-1.7.4.jar'\nresponse = requests.get(AWS_JAVA_URL)\nopen(AWS_JAVA_LOCAL_PATH, \"wb\").write(response.content)\n\n"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"53b4d770-9db6-4bd7-9b93-d036d375eac5","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
Out[38]: 11948376
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
Out[38]: 11948376
"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.widgets.text(\"log_path\", \"\")\neventlog_string=dbutils.widgets.get(\"log_path\")\n\ndbutils.widgets.text(\"output_path\", \"\")\noutputpath_string=dbutils.widgets.get(\"output_path\")"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"acf401a3-12d3-4236-a6c5-8fe8990b153a","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"code","source":["!java -Xmx10g -cp /tmp/rapids-4-spark-tools.jar:/tmp/hadoop-aws-2.7.4.jar:/tmp/aws-java-sdk-1.7.4.jar:/databricks/jars/* com.nvidia.spark.rapids.tool.qualification.QualificationMain -o $outputpath_string $eventlog_string &> /tmp/qual_debug.log"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"65c4e778-5505-4334-ab89-424334803253","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"
","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["\n
"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Summary Output"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"bbe50fde-0bd6-4281-95fd-6a1ec6f17ab2","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["summary_output=pd.read_csv(outputpath_string + \"/rapids_4_spark_qualification_output/rapids_4_spark_qualification_output.csv\")\ndisplay(summary_output)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"fb8edb26-e173-47ff-92a1-463baec7c06b","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[["TPC-DS Like Bench q14b","app-20220209225233-0018","Strongly Recommended",2.7,48971.53,83400.46,112708,27066964,132372,109793,65.66,null,null,null,null,null,null,112701,2939275,700037,26366927,4.16,false,"HashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q14a","app-20220209225013-0017","Strongly Recommended",2.66,51728.96,86381.03,117178,28746000,138110,112636,64.69,null,null,null,null,null,null,117172,2978987,1114076,27631924,4.29,false,"HashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q4","app-20220209224316-0007","Strongly Recommended",2.66,41856.03,69720.96,90167,85324700,111577,90167,65.09,null,null,null,null,null,null,90163,3086516,0,85324700,4.41,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q24b","app-20220209230348-0030","Recommended",2.34,32581.07,43720.92,56748,27574691,76302,56694,60.89,null,null,null,null,null,null,56741,2950634,25998,27548693,4.37,false,"AdaptiveSparkPlan;Subquery;Execute CreateViewCommand;Filter;ColumnarToRow","decimal"],["TPC-DS Like Bench q67","app-20220209232913-0074","Recommended",2.27,144720.72,184416.27,308185,15437192,329137,265300,73.64,null,null,null,null,null,null,308174,3028469,2148108,13289084,3.28,false,"Execute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRow","DecimalType"],["TPC-DS Like Bench q24a","app-20220209230232-0029","Recommended",2.25,33029.22,41483.77,53469,27911960,74513,53202,60.14,null,null,null,null,null,null,53455,3305140,139193,27772767,4.54,false,"AdaptiveSparkPlan;Subquery;Execute CreateViewCommand;Filter;ColumnarToRow","decimal"],["TPC-DS Like Bench q93","app-20220209235239-0100","Recommended",2.18,31203.42,36894.57,50166,29213056,68098,50166,64.73,null,null,null,null,null,null,50161,2973035,0,29213056,3.78,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q23a","app-20220209225917-0027","Recommended",2.17,43196.97,50617.02,74068,45913641,93814,66840,64.81,null,null,null,null,null,null,74064,2975228,4480262,41433379,4.12,false,"HashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q23b","app-20220209230053-0028","Recommended",2.08,46588.14,50761.85,77422,59799857,97350,66526,66.43,null,null,null,null,null,null,77417,3011951,8415627,51384230,4.22,false,"HashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRow","DecimalType;decimal"],["TPC-DS Like Bench q72","app-20220209233659-0079","Recommended",1.97,30920.88,30054.11,41703,25434438,60975,41703,74.77,null,null,null,null,null,null,41694,2971887,0,25434438,3.58,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q64","app-20220209232701-0071","Recommended",1.95,30544.61,29103.38,38925,31852357,59648,37301,55.12,null,null,null,null,null,null,38919,2987987,1328490,30523867,4.55,false,"HashAggregate;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q78","app-20220209234412-0085","Recommended",1.94,24390.79,22946.2,29098,24753911,47337,29098,56.99,null,null,null,null,null,null,29090,2968697,0,24753911,4.73,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q95","app-20220209235427-0102","Recommended",1.9,29920.38,26967.61,37674,23337528,56888,35528,49.19,null,null,null,null,null,null,37667,2958216,1328886,22008642,4.15,false,"Execute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRow","merge_sum"],["TPC-DS Like Bench q87","app-20220209234925-0094","Recommended",1.81,25518.2,20871.79,26279,16348325,46390,26279,52.04,null,null,null,null,null,null,26272,2954066,0,16348325,4.86,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q50","app-20220209231919-0057","Recommended",1.76,26735.89,20549.1,28736,16444045,47285,28736,52.87,null,null,null,null,null,null,28732,3339217,0,16444045,3.51,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q97","app-20220209235552-0104","Recommended",1.76,24057.8,18398.19,23731,11475611,42456,23731,58.69,null,null,null,null,null,null,23727,3009477,0,11475611,4.45,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q75","app-20220209233914-0082","Recommended",1.74,25544.26,18972.73,24939,19831478,44517,24939,43.74,null,null,null,null,null,null,24933,2987306,0,19831478,4.18,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q38","app-20220209231214-0044","Recommended",1.73,23047.06,16910.93,21292,15248682,39958,21292,48.81,null,null,null,null,null,null,21288,3167687,0,15248682,4.86,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q31","app-20220209230826-0037","Recommended",1.68,23595.69,16242.3,20407,12617670,39838,20407,34.76,null,null,null,null,null,null,20399,2942830,0,12617670,4.9,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q74","app-20220209233831-0081","Recommended",1.68,24668.06,16781.93,21747,16692240,41450,21747,46.93,null,null,null,null,null,null,21741,3325282,0,16692240,4.38,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q11","app-20220209224833-0014","Recommended",1.62,26707.24,16820.75,24208,17615125,43528,22143,45.76,null,null,null,null,null,null,24205,2917642,1502045,16113080,4.16,false,"Execute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRow","DecimalType"],["TPC-DS Like Bench q16","app-20220209225524-0020","Recommended",1.62,25717.33,16006.66,22726,18535920,41724,21306,44.67,null,null,null,null,null,null,22720,3028781,1157466,17378454,4.01,false,"Execute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRow","merge_sum"],["TPC-DS Like Bench q51","app-20220209232008-0058","Recommended",1.59,21942.35,13069.64,16925,12278939,35012,16925,47.26,null,null,null,null,null,null,16920,2979507,0,12278939,4.38,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q81","app-20220209234611-0088","Recommended",1.58,23125.59,13612.4,17437,10033526,36738,17393,36.22,null,null,null,null,null,null,17431,3083743,24955,10008571,4.59,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow;Filter","DECIMAL"],["TPC-DS Like Bench q65","app-20220209232803-0072","Recommended",1.57,23403.59,13469.4,18332,10164071,36873,18332,46.41,null,null,null,null,null,null,18324,2941892,0,10164071,3.77,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q30","app-20220209230750-0036","Recommended",1.56,21777.06,12322.93,16070,8447753,34100,16023,33.65,null,null,null,null,null,null,16063,2987480,24435,8423318,4.33,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow;Filter","DECIMAL"],["TPC-DS Like Bench q15","app-20220209225447-0019","Recommended",1.52,23166.12,12074.87,15970,6770100,35241,15970,34.25,null,null,null,null,null,null,15964,2923648,0,6770100,4.09,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q35","app-20220209231035-0041","Recommended",1.51,24552.13,12648.86,18298,10027023,37201,17010,37.15,null,null,null,null,null,null,18293,3059747,705510,9321513,3.9,false,"Execute CreateViewCommand;SortMergeJoin;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q8","app-20220209224648-0011","Recommended",1.51,23372.7,12016.29,15691,6693324,35389,15691,20.01,null,null,null,null,null,null,15686,3133963,0,6693324,4.26,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q69","app-20220209233517-0076","Recommended",1.5,24534.64,12431.35,16589,7246714,36966,16589,27.75,null,null,null,null,null,null,16583,2963952,0,7246714,3.99,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q94","app-20220209235349-0101","Recommended",1.5,24361.44,12284.55,17424,13168326,36646,16352,41.87,null,null,null,null,null,null,17416,3048502,809952,12358374,4.01,false,"Execute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRow","merge_sum"],["TPC-DS Like Bench q54","app-20220209232140-0061","Recommended",1.5,23475.06,11742.93,15568,5555140,35218,15568,28.65,null,null,null,null,null,null,15563,3021018,0,5555140,4.07,false,"Execute CreateViewCommand;ReusedExchange;Subquery;ColumnarToRow",null],["TPC-DS Like Bench q39a","app-20220209231256-0045","Recommended",1.49,22102.22,10863.77,14136,4794226,32966,14136,24.49,null,null,null,null,null,null,14131,2998677,0,4794226,4.32,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q29","app-20220209230713-0035","Recommended",1.49,23597.91,11588.08,15912,10021701,35186,15912,44.09,null,null,null,null,null,null,15905,3057910,0,10021701,3.68,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q47","app-20220209231740-0054","Recommended",1.48,23520.63,11417.36,15246,8996925,34938,15160,35.78,null,null,null,null,null,null,15241,3107687,50299,8946626,4.05,false,"Execute CreateViewCommand;ReusedExchange;Filter;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q59","app-20220209232422-0066","Recommended",1.48,24246.78,11658.21,16091,6749831,35905,16091,34.65,null,null,null,null,null,null,16084,3068245,0,6749831,3.63,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q6","app-20220209224544-0009","Recommended",1.46,22488.01,10526.98,13758,6335474,33015,13756,30.04,null,null,null,null,null,null,13753,2943126,863,6334611,4.26,false,"Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q80","app-20220209234532-0087","Recommended",1.46,25298.12,11841.87,16742,12630126,37140,15856,40.02,null,null,null,null,null,null,16735,3360633,668346,11961780,3.95,false,"Execute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q39b","app-20220209231330-0046","Recommended",1.46,22094.04,10341.95,13457,3807675,32436,13457,30.24,null,null,null,null,null,null,13451,2941727,0,3807675,4.32,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q57","app-20220209232314-0064","Recommended",1.46,22995.99,10722.0,14351,8374423,33718,14237,31.73,null,null,null,null,null,null,14345,2948771,66283,8308140,4.05,false,"Execute CreateViewCommand;ReusedExchange;Filter;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q1","app-20220209224147-0004","Recommended",1.46,21599.35,10007.64,13417,7598550,31607,13321,37.09,null,null,null,null,null,null,13412,3012015,54119,7544431,4.01,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow;Filter","DECIMAL"],["TPC-DS Like Bench q18","app-20220209225644-0022","Recommended",1.44,22216.88,9865.11,13492,7586670,32082,13492,33.42,null,null,null,null,null,null,13484,2962080,0,7586670,3.72,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q45","app-20220209231631-0052","Recommended",1.44,23723.45,10458.54,14455,5768757,34182,14154,33.74,null,null,null,null,null,null,14447,2957022,120068,5648689,3.83,false,"Execute CreateViewCommand;ReusedExchange;BroadcastHashJoin;ColumnarToRow",null],["TPC-DS Like Bench q17","app-20220209225607-0021","Recommended",1.43,24334.61,10564.38,14420,7818278,34899,14420,37.4,null,null,null,null,null,null,14412,3106844,0,7818278,3.74,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q25","app-20220209230506-0031","Recommended",1.43,21671.78,9370.21,12790,7060196,31042,12790,34.97,null,null,null,null,null,null,12784,3060435,0,7060196,3.74,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q49","app-20220209231846-0056","Recommended",1.42,22231.44,9476.55,12466,8785195,31708,12466,26.92,null,null,null,null,null,null,12448,3080539,0,8785195,4.17,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q46","app-20220209231707-0053","Recommended",1.42,22047.85,9385.14,12318,6163042,31433,12318,26.39,null,null,null,null,null,null,12312,3017041,0,6163042,4.2,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q10","app-20220209224756-0013","Recommended",1.42,24919.87,10489.12,15574,7745798,35409,14432,30.14,null,null,null,null,null,null,15567,3236768,567780,7178018,3.66,false,"Execute CreateViewCommand;SortMergeJoin;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q68","app-20220209233444-0075","Recommended",1.41,21674.52,9006.47,11821,5109423,30681,11821,20.54,null,null,null,null,null,null,11814,3004334,0,5109423,4.2,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q5","app-20220209224509-0008","Recommended",1.41,23548.42,9749.57,13729,8967631,33298,13729,41.35,null,null,null,null,null,null,13722,3186659,0,8967631,3.45,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q34","app-20220209231004-0040","Recommended",1.4,20675.69,8427.3,10842,5053339,29103,10842,27.34,null,null,null,null,null,null,10836,2994320,0,5053339,4.49,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q82","app-20220209234649-0089","Recommended",1.4,22206.53,8955.46,12212,7344489,31162,12212,39.29,null,null,null,null,null,null,12199,2973553,0,7344489,3.75,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q85","app-20220209234821-0092","Recommended",1.4,24121.11,9726.88,13468,7684101,33848,13468,29.91,null,null,null,null,null,null,13462,2949755,0,7684101,3.6,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q28","app-20220209230637-0034","Recommended",1.39,24599.41,9839.58,15464,14309580,34439,13823,33.04,null,null,null,null,null,null,15458,3008413,1518296,12791284,3.47,false,"AdaptiveSparkPlan;CollectLimit;HashAggregate;Execute CreateViewCommand;ColumnarToRow","merge_avg;merge_count"],["TPC-DS Like Bench q58","app-20220209232349-0065","Recommended",1.39,22489.0,8770.99,12322,5717106,31260,12322,34.17,null,null,null,null,null,null,12313,3102820,0,5717106,3.47,false,"Execute CreateViewCommand;Subquery;ColumnarToRow",null],["TPC-DS Like Bench q19","app-20220209225718-0023","Recommended",1.38,21689.15,8456.84,11373,5749051,30146,11373,23.22,null,null,null,null,null,null,11364,3161292,0,5749051,3.9,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench q61","app-20220209232531-0068","Recommended",1.38,23308.72,9017.27,12357,4376440,32326,12357,36.63,null,null,null,null,null,null,12349,3022176,0,4376440,3.7,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q71","app-20220209233627-0078","Recommended",1.38,21967.67,8440.32,11371,4987537,30408,11371,21.81,null,null,null,null,null,null,11363,3052139,0,4987537,3.88,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q22","app-20220209225845-0026","Recommended",1.38,22397.03,8535.96,12381,1736064,30933,12381,50.01,null,null,null,null,null,null,12376,2929875,0,1736064,3.22,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q70","app-20220209233555-0077","Recommended",1.37,21798.79,8233.2,10751,4816235,30032,10751,33.51,null,null,null,null,null,null,10746,3034688,0,4816235,4.26,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q83","app-20220209234722-0090","Recommended",1.36,21949.16,8010.83,10956,5253931,29960,10956,26.62,null,null,null,null,null,null,10949,2993826,0,5253931,3.72,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench q98","app-20220209235636-0105","Recommended",1.36,20487.86,7376.13,9674,3164505,27864,9674,21.36,null,null,null,null,null,null,9669,2953354,0,3164505,4.21,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q79","app-20220209234502-0086","Recommended",1.35,21073.77,7578.22,10344,5584295,28652,10344,29.69,null,null,null,null,null,null,10340,3007559,0,5584295,3.74,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q73","app-20220209233802-0080","Recommended",1.35,20143.11,7154.88,9205,3086301,27298,9205,27.46,null,null,null,null,null,null,9201,2929775,0,3086301,4.49,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q91","app-20220209235141-0098","Recommended",1.34,22090.61,7648.38,10351,3106198,29739,10351,23.11,null,null,null,null,null,null,10347,2997584,0,3106198,3.83,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q37","app-20220209231143-0043","Recommended",1.34,22130.6,7605.4,10371,5754398,29736,10371,38.21,null,null,null,null,null,null,10366,3003155,0,5754398,3.75,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q44","app-20220209231559-0051","Recommended",1.34,22359.33,7652.66,10956,7175937,30012,10347,29.11,null,null,null,null,null,null,10950,3029969,398712,6777225,3.84,false,"AdaptiveSparkPlan;Subquery;Execute CreateViewCommand;Filter;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q9","app-20220209224725-0012","Recommended",1.33,21873.81,7315.18,9829,8064543,29189,9829,35.84,null,null,null,null,null,null,9823,3021491,0,8064543,3.91,false,"Execute CreateViewCommand;AdaptiveSparkPlan;Subquery;ColumnarToRow",null],["TPC-DS Like Bench q40","app-20220209231405-0047","Recommended",1.33,24561.7,8167.29,12119,8004983,32729,11272,36.85,null,null,null,null,null,null,12110,3142013,558989,7445994,3.63,false,"Execute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q2","app-20220209224220-0005","Recommended",1.32,21301.46,6890.53,8966,4724046,28192,8966,38.76,null,null,null,null,null,null,8960,3025086,0,4724046,4.32,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench ss_max","app-20220209235735-0107","Recommended",1.31,20784.37,6539.62,9176,5331368,27324,8883,40.82,null,null,null,null,null,null,9171,3049122,169902,5161466,3.79,false,"Execute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRow","merge_count;merge_max"],["TPC-DS Like Bench q60","app-20220209232500-0067","Recommended",1.31,22250.37,6983.62,9811,3834676,29234,9811,19.92,null,null,null,null,null,null,9806,2939560,0,3834676,3.47,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q13","app-20220209224945-0016","Recommended",1.31,20407.71,6360.28,8816,4265966,26768,8816,34.5,null,null,null,null,null,null,8808,2951094,0,4265966,3.59,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q56","app-20220209232242-0063","Recommended",1.3,22798.87,7044.12,9896,3729466,29843,9896,14.44,null,null,null,null,null,null,9890,2971656,0,3729466,3.47,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q76","app-20220209234000-0083","Recommended",1.3,22630.64,6827.35,9835,6264831,29458,9835,33.9,null,null,null,null,null,null,9830,3037202,0,6264831,3.27,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q33","app-20220209230935-0039","Recommended",1.3,21561.95,6491.04,9119,3032680,28053,9119,18.14,null,null,null,null,null,null,9111,2983234,0,3032680,3.47,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q88","app-20220209235013-0095","Recommended",1.3,23989.56,7197.43,9998,6687038,31187,9998,43.92,null,null,null,null,null,null,9987,3212440,0,6687038,3.57,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q7","app-20220209224619-0010","Not Recommended",1.29,20663.5,6143.5,8936,4022166,26807,8936,35.88,null,null,null,null,null,null,8929,2959904,0,4022166,3.2,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q48","app-20220209231816-0055","Not Recommended",1.29,21310.47,6237.52,8514,3969649,27548,8514,33.42,null,null,null,null,null,null,8507,2990347,0,3969649,3.74,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q26","app-20220209230539-0032","Not Recommended",1.28,21556.18,6109.81,8887,4002441,27666,8887,36.79,null,null,null,null,null,null,8876,3012952,0,4002441,3.2,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q12","app-20220209224918-0015","Not Recommended",1.27,19624.36,5459.63,7584,1865587,25084,7584,23.82,null,null,null,null,null,null,7577,3102250,0,1865587,3.57,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q62","app-20220209232605-0069","Not Recommended",1.27,21448.22,5877.77,8574,4218821,27326,8574,36.47,null,null,null,null,null,null,8565,3017055,0,4218821,3.18,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q20","app-20220209225749-0024","Not Recommended",1.26,21250.95,5641.04,7836,2040974,26892,7836,22.51,null,null,null,null,null,null,7829,3039094,0,2040974,3.57,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q99","app-20220209235705-0106","Not Recommended",1.25,22355.32,5801.67,8463,4558275,28157,8463,33.1,null,null,null,null,null,null,8455,2973149,0,4558275,3.18,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q36","app-20220209231114-0042","Not Recommended",1.25,21356.73,5527.26,7756,3642750,26884,7756,30.08,null,null,null,null,null,null,7752,2905924,0,3642750,3.48,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q89","app-20220209235046-0096","Not Recommended",1.25,20969.23,5401.76,7869,4029740,26371,7615,24.99,null,null,null,null,null,null,7858,3012145,129765,3899975,3.44,false,"Execute CreateViewCommand;ReusedExchange;Filter;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q86","app-20220209234857-0093","Not Recommended",1.25,21140.77,5388.22,7493,3505441,26529,7493,29.09,null,null,null,null,null,null,7489,3240380,0,3505441,3.56,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q27","app-20220209230608-0033","Not Recommended",1.25,22114.89,5587.1,8150,3737103,27702,8150,29.62,null,null,null,null,null,null,8143,3067371,0,3737103,3.18,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q66","app-20220209232841-0073","Not Recommended",1.24,24291.91,5938.08,9771,5287383,30230,8612,35.68,null,null,null,null,null,null,9765,2922061,626680,4660703,3.22,false,"Execute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRow","DecimalType"],["TPC-DS Like Bench q53","app-20220209232111-0060","Not Recommended",1.23,22160.97,5233.02,7622,3879496,27394,7368,25.54,null,null,null,null,null,null,7617,2905347,128795,3750701,3.45,false,"Execute CreateViewCommand;ReusedExchange;Filter;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q84","app-20220209234754-0091","Not Recommended",1.23,21195.3,4968.69,7129,3814060,26164,7129,40.65,null,null,null,null,null,null,7123,3029105,0,3814060,3.3,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q63","app-20220209232634-0070","Not Recommended",1.23,21192.66,4957.33,7215,3490147,26150,6980,26.58,null,null,null,null,null,null,7207,2976838,113318,3376829,3.45,false,"Execute CreateViewCommand;ReusedExchange;Filter;ColumnarToRow","decimal;DecimalType"],["TPC-DS Like Bench q43","app-20220209231533-0050","Not Recommended",1.23,19958.18,4641.81,6660,3257940,24600,6660,29.65,null,null,null,null,null,null,6651,2961446,0,3257940,3.3,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench ss_maxb","app-20220209235804-0108","Not Recommended",1.23,19063.41,4414.58,5825,3798491,23478,5825,48.17,null,null,null,null,null,null,5819,3155711,0,3798491,4.13,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q3","app-20220209224250-0006","Not Recommended",1.22,20089.36,4522.63,6489,2898373,24612,6489,26.88,null,null,null,null,null,null,6483,3094853,0,2898373,3.3,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench q21","app-20220209225818-0025","Not Recommended",1.22,20413.61,4531.38,6610,1072926,24945,6610,40.94,null,null,null,null,null,null,6605,3079474,0,1072926,3.18,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow",null],["TPC-DS Like Bench q55","app-20220209232217-0062","Not Recommended",1.22,19494.18,4296.81,6165,2612787,23791,6165,24.78,null,null,null,null,null,null,6158,2995446,0,2612787,3.3,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench q92","app-20220209235212-0099","Not Recommended",1.21,20741.45,4549.54,6384,2095601,25291,6123,25.07,null,null,null,null,null,null,6380,2967129,85419,2010182,3.89,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow;Filter","DECIMAL"],["TPC-DS Like Bench q90","app-20220209235114-0097","Not Recommended",1.21,20450.98,4350.01,6209,2604676,24801,6209,44.95,null,null,null,null,null,null,6204,2976499,0,2604676,3.34,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q32","app-20220209230908-0038","Not Recommended",1.2,20973.26,4366.73,6107,2712962,25340,5909,25.21,null,null,null,null,null,null,6098,3048254,87623,2625339,3.83,false,"Execute CreateViewCommand;ReusedExchange;ColumnarToRow;Filter","DECIMAL"],["TPC-DS Like Bench q42","app-20220209231505-0049","Not Recommended",1.2,21414.78,4436.21,6365,2726759,25851,6365,23.15,null,null,null,null,null,null,6358,3047836,0,2726759,3.3,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench q52","app-20220209232045-0059","Not Recommended",1.19,20523.3,4071.69,5842,2683090,24595,5842,25.46,null,null,null,null,null,null,5827,3074975,0,2683090,3.3,false,"Execute CreateViewCommand;ColumnarToRow",null],["TPC-DS Like Bench q41","app-20220209231439-0048","Not Recommended",1.19,20533.93,4071.06,5649,161564,24605,5649,55.4,null,null,null,null,null,null,5642,2970757,0,161564,3.58,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["TPC-DS Like Bench q96","app-20220209235526-0103","Not Recommended",1.18,20189.67,3776.32,5324,2304428,23966,5324,44.32,null,null,null,null,null,null,5317,2947875,0,2304428,3.44,false,"Execute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRow",null],["DART-ALGO","application_1665146417445_0002","Not Recommended",1.04,375798.12,16253.87,44531,75900172,392052,23675,14.55,null,null,null,null,null,null,16579,58017590,35546441,40353731,3.19,false,"Scan hive ecomm_ops_tables.zzz_dart_browse_ads_prod_mod;Scan hive ecomm_ops_tables.s0s0jzb_browse_shelf_cat_map;CollectLimit",null],["DART-ALGO","application_1665146417445_0003","Not Recommended",1.0,690682.0,0.0,0,0,690682,0,0.0,null,null,null,null,null,null,0,0,0,0,1.0,false,null,null]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"App Name","type":"\"string\"","metadata":"{}"},{"name":"App ID","type":"\"string\"","metadata":"{}"},{"name":"Recommendation","type":"\"string\"","metadata":"{}"},{"name":"Estimated GPU Speedup","type":"\"double\"","metadata":"{}"},{"name":"Estimated GPU Duration","type":"\"double\"","metadata":"{}"},{"name":"Estimated GPU Time Saved","type":"\"double\"","metadata":"{}"},{"name":"SQL DF Duration","type":"\"long\"","metadata":"{}"},{"name":"SQL Dataframe Task Duration","type":"\"long\"","metadata":"{}"},{"name":"App Duration","type":"\"long\"","metadata":"{}"},{"name":"GPU Opportunity","type":"\"long\"","metadata":"{}"},{"name":"Executor CPU Time Percent","type":"\"double\"","metadata":"{}"},{"name":"SQL Ids with Failures","type":"\"double\"","metadata":"{}"},{"name":"Unsupported Read File Formats and Types","type":"\"double\"","metadata":"{}"},{"name":"Unsupported Write Data Format","type":"\"double\"","metadata":"{}"},{"name":"Complex Types","type":"\"double\"","metadata":"{}"},{"name":"Nested Complex Types","type":"\"double\"","metadata":"{}"},{"name":"Potential Problems","type":"\"double\"","metadata":"{}"},{"name":"Longest SQL Duration","type":"\"long\"","metadata":"{}"},{"name":"NONSQL Task Duration Plus Overhead","type":"\"long\"","metadata":"{}"},{"name":"Unsupported Task Duration","type":"\"long\"","metadata":"{}"},{"name":"Supported SQL DF Task Duration","type":"\"long\"","metadata":"{}"},{"name":"Task Speedup Factor","type":"\"double\"","metadata":"{}"},{"name":"App Duration Estimated","type":"\"boolean\"","metadata":"{}"},{"name":"Unsupported Execs","type":"\"string\"","metadata":"{}"},{"name":"Unsupported Expressions","type":"\"string\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["
App NameApp IDRecommendationEstimated GPU SpeedupEstimated GPU DurationEstimated GPU Time SavedSQL DF DurationSQL Dataframe Task DurationApp DurationGPU OpportunityExecutor CPU Time PercentSQL Ids with FailuresUnsupported Read File Formats and TypesUnsupported Write Data FormatComplex TypesNested Complex TypesPotential ProblemsLongest SQL DurationNONSQL Task Duration Plus OverheadUnsupported Task DurationSupported SQL DF Task DurationTask Speedup FactorApp Duration EstimatedUnsupported ExecsUnsupported Expressions
TPC-DS Like Bench q14bapp-20220209225233-0018Strongly Recommended2.748971.5383400.461127082706696413237210979365.66nullnullnullnullnullnull1127012939275700037263669274.16falseHashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q14aapp-20220209225013-0017Strongly Recommended2.6651728.9686381.031171782874600013811011263664.69nullnullnullnullnullnull11717229789871114076276319244.29falseHashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q4app-20220209224316-0007Strongly Recommended2.6641856.0369720.9690167853247001115779016765.09nullnullnullnullnullnull9016330865160853247004.41falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q24bapp-20220209230348-0030Recommended2.3432581.0743720.925674827574691763025669460.89nullnullnullnullnullnull56741295063425998275486934.37falseAdaptiveSparkPlan;Subquery;Execute CreateViewCommand;Filter;ColumnarToRowdecimal
TPC-DS Like Bench q67app-20220209232913-0074Recommended2.27144720.72184416.273081851543719232913726530073.64nullnullnullnullnullnull30817430284692148108132890843.28falseExecute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRowDecimalType
TPC-DS Like Bench q24aapp-20220209230232-0029Recommended2.2533029.2241483.775346927911960745135320260.14nullnullnullnullnullnull534553305140139193277727674.54falseAdaptiveSparkPlan;Subquery;Execute CreateViewCommand;Filter;ColumnarToRowdecimal
TPC-DS Like Bench q93app-20220209235239-0100Recommended2.1831203.4236894.575016629213056680985016664.73nullnullnullnullnullnull5016129730350292130563.78falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q23aapp-20220209225917-0027Recommended2.1743196.9750617.027406845913641938146684064.81nullnullnullnullnullnull7406429752284480262414333794.12falseHashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q23bapp-20220209230053-0028Recommended2.0846588.1450761.857742259799857973506652666.43nullnullnullnullnullnull7741730119518415627513842304.22falseHashAggregate;Subquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRowDecimalType;decimal
TPC-DS Like Bench q72app-20220209233659-0079Recommended1.9730920.8830054.114170325434438609754170374.77nullnullnullnullnullnull4169429718870254344383.58falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q64app-20220209232701-0071Recommended1.9530544.6129103.383892531852357596483730155.12nullnullnullnullnullnull3891929879871328490305238674.55falseHashAggregate;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q78app-20220209234412-0085Recommended1.9424390.7922946.22909824753911473372909856.99nullnullnullnullnullnull2909029686970247539114.73falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q95app-20220209235427-0102Recommended1.929920.3826967.613767423337528568883552849.19nullnullnullnullnullnull3766729582161328886220086424.15falseExecute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRowmerge_sum
TPC-DS Like Bench q87app-20220209234925-0094Recommended1.8125518.220871.792627916348325463902627952.04nullnullnullnullnullnull2627229540660163483254.86falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q50app-20220209231919-0057Recommended1.7626735.8920549.12873616444045472852873652.87nullnullnullnullnullnull2873233392170164440453.51falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q97app-20220209235552-0104Recommended1.7624057.818398.192373111475611424562373158.69nullnullnullnullnullnull2372730094770114756114.45falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q75app-20220209233914-0082Recommended1.7425544.2618972.732493919831478445172493943.74nullnullnullnullnullnull2493329873060198314784.18falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q38app-20220209231214-0044Recommended1.7323047.0616910.932129215248682399582129248.81nullnullnullnullnullnull2128831676870152486824.86falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q31app-20220209230826-0037Recommended1.6823595.6916242.32040712617670398382040734.76nullnullnullnullnullnull2039929428300126176704.9falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q74app-20220209233831-0081Recommended1.6824668.0616781.932174716692240414502174746.93nullnullnullnullnullnull2174133252820166922404.38falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q11app-20220209224833-0014Recommended1.6226707.2416820.752420817615125435282214345.76nullnullnullnullnullnull2420529176421502045161130804.16falseExecute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRowDecimalType
TPC-DS Like Bench q16app-20220209225524-0020Recommended1.6225717.3316006.662272618535920417242130644.67nullnullnullnullnullnull2272030287811157466173784544.01falseExecute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRowmerge_sum
TPC-DS Like Bench q51app-20220209232008-0058Recommended1.5921942.3513069.641692512278939350121692547.26nullnullnullnullnullnull1692029795070122789394.38falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q81app-20220209234611-0088Recommended1.5823125.5913612.41743710033526367381739336.22nullnullnullnullnullnull17431308374324955100085714.59falseExecute CreateViewCommand;ReusedExchange;ColumnarToRow;FilterDECIMAL
TPC-DS Like Bench q65app-20220209232803-0072Recommended1.5723403.5913469.41833210164071368731833246.41nullnullnullnullnullnull1832429418920101640713.77falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q30app-20220209230750-0036Recommended1.5621777.0612322.93160708447753341001602333.65nullnullnullnullnullnull1606329874802443584233184.33falseExecute CreateViewCommand;ReusedExchange;ColumnarToRow;FilterDECIMAL
TPC-DS Like Bench q15app-20220209225447-0019Recommended1.5223166.1212074.87159706770100352411597034.25nullnullnullnullnullnull159642923648067701004.09falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q35app-20220209231035-0041Recommended1.5124552.1312648.861829810027023372011701037.15nullnullnullnullnullnull18293305974770551093215133.9falseExecute CreateViewCommand;SortMergeJoin;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q8app-20220209224648-0011Recommended1.5123372.712016.29156916693324353891569120.01nullnullnullnullnullnull156863133963066933244.26falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q69app-20220209233517-0076Recommended1.524534.6412431.35165897246714369661658927.75nullnullnullnullnullnull165832963952072467143.99falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q94app-20220209235349-0101Recommended1.524361.4412284.551742413168326366461635241.87nullnullnullnullnullnull174163048502809952123583744.01falseExecute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRowmerge_sum
TPC-DS Like Bench q54app-20220209232140-0061Recommended1.523475.0611742.93155685555140352181556828.65nullnullnullnullnullnull155633021018055551404.07falseExecute CreateViewCommand;ReusedExchange;Subquery;ColumnarToRownull
TPC-DS Like Bench q39aapp-20220209231256-0045Recommended1.4922102.2210863.77141364794226329661413624.49nullnullnullnullnullnull141312998677047942264.32falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q29app-20220209230713-0035Recommended1.4923597.9111588.081591210021701351861591244.09nullnullnullnullnullnull1590530579100100217013.68falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q47app-20220209231740-0054Recommended1.4823520.6311417.36152468996925349381516035.78nullnullnullnullnullnull1524131076875029989466264.05falseExecute CreateViewCommand;ReusedExchange;Filter;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q59app-20220209232422-0066Recommended1.4824246.7811658.21160916749831359051609134.65nullnullnullnullnullnull160843068245067498313.63falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q6app-20220209224544-0009Recommended1.4622488.0110526.98137586335474330151375630.04nullnullnullnullnullnull13753294312686363346114.26falseSubquery;Execute CreateViewCommand;Filter;ReusedExchange;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q80app-20220209234532-0087Recommended1.4625298.1211841.871674212630126371401585640.02nullnullnullnullnullnull167353360633668346119617803.95falseExecute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q39bapp-20220209231330-0046Recommended1.4622094.0410341.95134573807675324361345730.24nullnullnullnullnullnull134512941727038076754.32falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q57app-20220209232314-0064Recommended1.4622995.9910722.0143518374423337181423731.73nullnullnullnullnullnull1434529487716628383081404.05falseExecute CreateViewCommand;ReusedExchange;Filter;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q1app-20220209224147-0004Recommended1.4621599.3510007.64134177598550316071332137.09nullnullnullnullnullnull1341230120155411975444314.01falseExecute CreateViewCommand;ReusedExchange;ColumnarToRow;FilterDECIMAL
TPC-DS Like Bench q18app-20220209225644-0022Recommended1.4422216.889865.11134927586670320821349233.42nullnullnullnullnullnull134842962080075866703.72falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q45app-20220209231631-0052Recommended1.4423723.4510458.54144555768757341821415433.74nullnullnullnullnullnull14447295702212006856486893.83falseExecute CreateViewCommand;ReusedExchange;BroadcastHashJoin;ColumnarToRownull
TPC-DS Like Bench q17app-20220209225607-0021Recommended1.4324334.6110564.38144207818278348991442037.4nullnullnullnullnullnull144123106844078182783.74falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q25app-20220209230506-0031Recommended1.4321671.789370.21127907060196310421279034.97nullnullnullnullnullnull127843060435070601963.74falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q49app-20220209231846-0056Recommended1.4222231.449476.55124668785195317081246626.92nullnullnullnullnullnull124483080539087851954.17falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q46app-20220209231707-0053Recommended1.4222047.859385.14123186163042314331231826.39nullnullnullnullnullnull123123017041061630424.2falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q10app-20220209224756-0013Recommended1.4224919.8710489.12155747745798354091443230.14nullnullnullnullnullnull15567323676856778071780183.66falseExecute CreateViewCommand;SortMergeJoin;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q68app-20220209233444-0075Recommended1.4121674.529006.47118215109423306811182120.54nullnullnullnullnullnull118143004334051094234.2falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q5app-20220209224509-0008Recommended1.4123548.429749.57137298967631332981372941.35nullnullnullnullnullnull137223186659089676313.45falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q34app-20220209231004-0040Recommended1.420675.698427.3108425053339291031084227.34nullnullnullnullnullnull108362994320050533394.49falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q82app-20220209234649-0089Recommended1.422206.538955.46122127344489311621221239.29nullnullnullnullnullnull121992973553073444893.75falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q85app-20220209234821-0092Recommended1.424121.119726.88134687684101338481346829.91nullnullnullnullnullnull134622949755076841013.6falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q28app-20220209230637-0034Recommended1.3924599.419839.581546414309580344391382333.04nullnullnullnullnullnull1545830084131518296127912843.47falseAdaptiveSparkPlan;CollectLimit;HashAggregate;Execute CreateViewCommand;ColumnarToRowmerge_avg;merge_count
TPC-DS Like Bench q58app-20220209232349-0065Recommended1.3922489.08770.99123225717106312601232234.17nullnullnullnullnullnull123133102820057171063.47falseExecute CreateViewCommand;Subquery;ColumnarToRownull
TPC-DS Like Bench q19app-20220209225718-0023Recommended1.3821689.158456.84113735749051301461137323.22nullnullnullnullnullnull113643161292057490513.9falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench q61app-20220209232531-0068Recommended1.3823308.729017.27123574376440323261235736.63nullnullnullnullnullnull123493022176043764403.7falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q71app-20220209233627-0078Recommended1.3821967.678440.32113714987537304081137121.81nullnullnullnullnullnull113633052139049875373.88falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q22app-20220209225845-0026Recommended1.3822397.038535.96123811736064309331238150.01nullnullnullnullnullnull123762929875017360643.22falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q70app-20220209233555-0077Recommended1.3721798.798233.2107514816235300321075133.51nullnullnullnullnullnull107463034688048162354.26falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q83app-20220209234722-0090Recommended1.3621949.168010.83109565253931299601095626.62nullnullnullnullnullnull109492993826052539313.72falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench q98app-20220209235636-0105Recommended1.3620487.867376.139674316450527864967421.36nullnullnullnullnullnull96692953354031645054.21falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q79app-20220209234502-0086Recommended1.3521073.777578.22103445584295286521034429.69nullnullnullnullnullnull103403007559055842953.74falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q73app-20220209233802-0080Recommended1.3520143.117154.889205308630127298920527.46nullnullnullnullnullnull92012929775030863014.49falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q91app-20220209235141-0098Recommended1.3422090.617648.38103513106198297391035123.11nullnullnullnullnullnull103472997584031061983.83falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q37app-20220209231143-0043Recommended1.3422130.67605.4103715754398297361037138.21nullnullnullnullnullnull103663003155057543983.75falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q44app-20220209231559-0051Recommended1.3422359.337652.66109567175937300121034729.11nullnullnullnullnullnull10950302996939871267772253.84falseAdaptiveSparkPlan;Subquery;Execute CreateViewCommand;Filter;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q9app-20220209224725-0012Recommended1.3321873.817315.189829806454329189982935.84nullnullnullnullnullnull98233021491080645433.91falseExecute CreateViewCommand;AdaptiveSparkPlan;Subquery;ColumnarToRownull
TPC-DS Like Bench q40app-20220209231405-0047Recommended1.3324561.78167.29121198004983327291127236.85nullnullnullnullnullnull12110314201355898974459943.63falseExecute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q2app-20220209224220-0005Recommended1.3221301.466890.538966472404628192896638.76nullnullnullnullnullnull89603025086047240464.32falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench ss_maxapp-20220209235735-0107Recommended1.3120784.376539.629176533136827324888340.82nullnullnullnullnullnull9171304912216990251614663.79falseExecute CreateViewCommand;AdaptiveSparkPlan;HashAggregate;ColumnarToRowmerge_count;merge_max
TPC-DS Like Bench q60app-20220209232500-0067Recommended1.3122250.376983.629811383467629234981119.92nullnullnullnullnullnull98062939560038346763.47falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q13app-20220209224945-0016Recommended1.3120407.716360.288816426596626768881634.5nullnullnullnullnullnull88082951094042659663.59falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q56app-20220209232242-0063Recommended1.322798.877044.129896372946629843989614.44nullnullnullnullnullnull98902971656037294663.47falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q76app-20220209234000-0083Recommended1.322630.646827.359835626483129458983533.9nullnullnullnullnullnull98303037202062648313.27falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q33app-20220209230935-0039Recommended1.321561.956491.049119303268028053911918.14nullnullnullnullnullnull91112983234030326803.47falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q88app-20220209235013-0095Recommended1.323989.567197.439998668703831187999843.92nullnullnullnullnullnull99873212440066870383.57falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q7app-20220209224619-0010Not Recommended1.2920663.56143.58936402216626807893635.88nullnullnullnullnullnull89292959904040221663.2falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q48app-20220209231816-0055Not Recommended1.2921310.476237.528514396964927548851433.42nullnullnullnullnullnull85072990347039696493.74falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q26app-20220209230539-0032Not Recommended1.2821556.186109.818887400244127666888736.79nullnullnullnullnullnull88763012952040024413.2falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q12app-20220209224918-0015Not Recommended1.2719624.365459.637584186558725084758423.82nullnullnullnullnullnull75773102250018655873.57falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q62app-20220209232605-0069Not Recommended1.2721448.225877.778574421882127326857436.47nullnullnullnullnullnull85653017055042188213.18falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q20app-20220209225749-0024Not Recommended1.2621250.955641.047836204097426892783622.51nullnullnullnullnullnull78293039094020409743.57falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q99app-20220209235705-0106Not Recommended1.2522355.325801.678463455827528157846333.1nullnullnullnullnullnull84552973149045582753.18falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q36app-20220209231114-0042Not Recommended1.2521356.735527.267756364275026884775630.08nullnullnullnullnullnull77522905924036427503.48falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q89app-20220209235046-0096Not Recommended1.2520969.235401.767869402974026371761524.99nullnullnullnullnullnull7858301214512976538999753.44falseExecute CreateViewCommand;ReusedExchange;Filter;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q86app-20220209234857-0093Not Recommended1.2521140.775388.227493350544126529749329.09nullnullnullnullnullnull74893240380035054413.56falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q27app-20220209230608-0033Not Recommended1.2522114.895587.18150373710327702815029.62nullnullnullnullnullnull81433067371037371033.18falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q66app-20220209232841-0073Not Recommended1.2424291.915938.089771528738330230861235.68nullnullnullnullnullnull9765292206162668046607033.22falseExecute CreateViewCommand;ReusedExchange;HashAggregate;ColumnarToRowDecimalType
TPC-DS Like Bench q53app-20220209232111-0060Not Recommended1.2322160.975233.027622387949627394736825.54nullnullnullnullnullnull7617290534712879537507013.45falseExecute CreateViewCommand;ReusedExchange;Filter;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q84app-20220209234754-0091Not Recommended1.2321195.34968.697129381406026164712940.65nullnullnullnullnullnull71233029105038140603.3falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q63app-20220209232634-0070Not Recommended1.2321192.664957.337215349014726150698026.58nullnullnullnullnullnull7207297683811331833768293.45falseExecute CreateViewCommand;ReusedExchange;Filter;ColumnarToRowdecimal;DecimalType
TPC-DS Like Bench q43app-20220209231533-0050Not Recommended1.2319958.184641.816660325794024600666029.65nullnullnullnullnullnull66512961446032579403.3falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench ss_maxbapp-20220209235804-0108Not Recommended1.2319063.414414.585825379849123478582548.17nullnullnullnullnullnull58193155711037984914.13falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q3app-20220209224250-0006Not Recommended1.2220089.364522.636489289837324612648926.88nullnullnullnullnullnull64833094853028983733.3falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench q21app-20220209225818-0025Not Recommended1.2220413.614531.386610107292624945661040.94nullnullnullnullnullnull66053079474010729263.18falseExecute CreateViewCommand;ReusedExchange;ColumnarToRownull
TPC-DS Like Bench q55app-20220209232217-0062Not Recommended1.2219494.184296.816165261278723791616524.78nullnullnullnullnullnull61582995446026127873.3falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench q92app-20220209235212-0099Not Recommended1.2120741.454549.546384209560125291612325.07nullnullnullnullnullnull638029671298541920101823.89falseExecute CreateViewCommand;ReusedExchange;ColumnarToRow;FilterDECIMAL
TPC-DS Like Bench q90app-20220209235114-0097Not Recommended1.2120450.984350.016209260467624801620944.95nullnullnullnullnullnull62042976499026046763.34falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q32app-20220209230908-0038Not Recommended1.220973.264366.736107271296225340590925.21nullnullnullnullnullnull609830482548762326253393.83falseExecute CreateViewCommand;ReusedExchange;ColumnarToRow;FilterDECIMAL
TPC-DS Like Bench q42app-20220209231505-0049Not Recommended1.221414.784436.216365272675925851636523.15nullnullnullnullnullnull63583047836027267593.3falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench q52app-20220209232045-0059Not Recommended1.1920523.34071.695842268309024595584225.46nullnullnullnullnullnull58273074975026830903.3falseExecute CreateViewCommand;ColumnarToRownull
TPC-DS Like Bench q41app-20220209231439-0048Not Recommended1.1920533.934071.06564916156424605564955.4nullnullnullnullnullnull5642297075701615643.58falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
TPC-DS Like Bench q96app-20220209235526-0103Not Recommended1.1820189.673776.325324230442823966532444.32nullnullnullnullnullnull53172947875023044283.44falseExecute CreateViewCommand;AdaptiveSparkPlan;ColumnarToRownull
DART-ALGOapplication_1665146417445_0002Not Recommended1.04375798.1216253.8744531759001723920522367514.55nullnullnullnullnullnull165795801759035546441403537313.19falseScan hive ecomm_ops_tables.zzz_dart_browse_ads_prod_mod;Scan hive ecomm_ops_tables.s0s0jzb_browse_shelf_cat_map;CollectLimitnull
DART-ALGOapplication_1665146417445_0003Not Recommended1.0690682.00.00069068200.0nullnullnullnullnullnull00001.0falsenullnull
"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Stages Output"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"6756159b-30ca-407a-ab6b-9c29ced01ea6","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["stages_output=pd.read_csv(outputpath_string + \"/rapids_4_spark_qualification_output/rapids_4_spark_qualification_output_stages.csv\")\ndisplay(stages_output)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"cdde6177-db5f-434a-995b-776678a64a3a","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":true,"datasetInfos":[],"data":[["app-20220209224509-0008",31,2.95,1184,0,false],["app-20220209224509-0008",33,2.9,1086,0,false],["app-20220209224509-0008",35,3.37,2321523,0,false],["app-20220209224509-0008",41,3.9,172648,0,false],["app-20220209224509-0008",39,5.72,4586824,0,false],["app-20220209224509-0008",38,3.36,694337,0,false],["app-20220209224509-0008",40,4.26,656835,0,false],["app-20220209224509-0008",37,3.36,524190,0,false],["app-20220209224509-0008",32,2.9,641,0,false],["app-20220209224509-0008",36,2.73,7065,0,false],["app-20220209224509-0008",42,3.0,218,0,false],["app-20220209224509-0008",34,2.9,1080,0,false],["app-20220209235114-0097",39,2.71,0,0,true],["app-20220209235114-0097",40,3.54,413,0,false],["app-20220209235114-0097",34,2.95,964,0,false],["app-20220209235114-0097",37,2.71,0,0,true],["app-20220209235114-0097",41,3.0,365,0,false],["app-20220209235114-0097",31,2.95,1058,0,false],["app-20220209235114-0097",32,2.95,694,0,false],["app-20220209235114-0097",35,3.68,2142147,0,false],["app-20220209235114-0097",38,4.34,271,0,false],["app-20220209235114-0097",36,3.68,457864,0,false],["app-20220209235114-0097",33,2.95,900,0,false],["app-20220209230506-0031",35,3.33,93394,0,false],["app-20220209230506-0031",36,3.33,239863,0,false],["app-20220209230506-0031",38,6.91,2696172,0,false],["app-20220209230506-0031",37,3.33,1684494,0,false],["app-20220209230506-0031",40,3.9,205018,0,false],["app-20220209230506-0031",32,2.9,7530,0,false],["app-20220209230506-0031",41,3.0,210,0,false],["app-20220209230506-0031",39,5.67,2129775,0,false],["app-20220209230506-0031",34,2.95,1198,0,false],["app-20220209230506-0031",31,2.95,1284,0,false],["app-20220209230506-0031",33,2.9,1258,0,false],["app-20220209232634-0070",32,2.9,1046,0,false],["app-20220209232634-0070",34,3.72,2226056,0,false],["app-20220209232634-0070",31,2.95,6677,0,false],["app-20220209232634-0070",33,2.95,672,0,false],["app-20220209232634-0070",37,3.0,210,0,false],["app-20220209232634-0070",35,4.3,688894,0,false],["app-20220209232634-0070",36,4.3,566592,113318,false],["app-20220209225845-0026",36,3.0,303,0,false],["app-20220209225845-0026",32,2.9,5690,0,false],["app-20220209225845-0026",31,2.95,1181,0,false],["app-20220209225845-0026",34,3.68,621736,0,false],["app-20220209225845-0026",33,2.9,894,0,false],["app-20220209225845-0026",35,3.9,1106260,0,false],["app-20220209235526-0103",34,3.68,2301205,0,false],["app-20220209235526-0103",31,2.95,1051,0,false],["app-20220209235526-0103",33,2.95,875,0,false],["app-20220209235526-0103",32,2.95,676,0,false],["app-20220209235526-0103",36,4.69,621,0,false],["app-20220209235526-0103",35,2.6,0,0,true],["app-20220209231919-0057",35,3.33,6655624,0,false],["app-20220209231919-0057",36,5.78,9297072,0,false],["app-20220209231919-0057",38,3.0,228,0,false],["app-20220209231919-0057",31,2.9,711,0,false],["app-20220209231919-0057",37,3.9,354848,0,false],["app-20220209231919-0057",34,3.33,134143,0,false],["app-20220209231919-0057",32,2.95,724,0,false],["app-20220209231919-0057",33,2.9,695,0,false],["app-20220209231533-0050",33,3.71,2489960,0,false],["app-20220209231533-0050",34,3.9,765614,0,false],["app-20220209231533-0050",35,3.0,246,0,false],["app-20220209231533-0050",32,2.95,1024,0,false],["app-20220209231533-0050",31,2.95,1096,0,false],["application_1665146417445_0002",4,4.5,233,0,false],["application_1665146417445_0002",3,4.34,2893207,0,false],["application_1665146417445_0002",2,2.6,14571021,7285510,false],["application_1665146417445_0002",1,2.0,51972027,25986013,false],["application_1665146417445_0002",7,4.5,244,0,false],["application_1665146417445_0002",6,3.98,1918109,0,false],["application_1665146417445_0002",5,2.6,4540826,2270413,false],["application_1665146417445_0002",0,1.0,4505,4505,false],["app-20220209230053-0028",40,3.71,4979300,0,false],["app-20220209230053-0028",42,3.94,517898,0,false],["app-20220209230053-0028",38,4.34,53,0,false],["app-20220209230053-0028",44,7.76,730644,0,false],["app-20220209230053-0028",54,3.0,221,0,false],["app-20220209230053-0028",35,3.33,4618718,0,false],["app-20220209230053-0028",36,3.33,59206,0,false],["app-20220209230053-0028",50,7.76,661105,0,false],["app-20220209230053-0028",43,3.33,25052,0,false],["app-20220209230053-0028",37,5.73,8697022,1449502,false],["app-20220209230053-0028",39,2.95,77,0,false],["app-20220209230053-0028",53,2.68,456328,182530,false],["app-20220209230053-0028",45,3.33,34900,0,false],["app-20220209230053-0028",51,8.28,13701110,2997113,false],["app-20220209230053-0028",31,2.95,701,0,false],["app-20220209230053-0028",46,3.37,2366404,0,false],["app-20220209230053-0028",52,8.28,17309649,3786482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App IDStage IDAverage Speedup FactorStage Task DurationUnsupported Task DurationStage Estimated
app-20220209224509-0008312.9511840false
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app-20220209224509-0008353.3723215230false
app-20220209224509-0008413.91726480false
app-20220209224509-0008395.7245868240false
app-20220209224509-0008383.366943370false
app-20220209224509-0008404.266568350false
app-20220209224509-0008373.365241900false
app-20220209224509-0008322.96410false
app-20220209224509-0008362.7370650false
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Showing the first 1000 rows.
"]}}],"execution_count":0},{"cell_type":"markdown","source":["## Execs Output"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"4d7ce219-ae75-4a0c-a78c-4e7f25b8cd6f","inputWidgets":{},"title":""}}},{"cell_type":"code","source":["execs_output=pd.read_csv(outputpath_string + \"/rapids_4_spark_qualification_output/rapids_4_spark_qualification_output_execs.csv\")\ndisplay(execs_output)"],"metadata":{"application/vnd.databricks.v1+cell":{"showTitle":false,"cellMetadata":{},"nuid":"998b0c51-0cb6-408e-a01a-d1f5b1a61e1f","inputWidgets":{},"title":""}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":true,"datasetInfos":[],"data":[["app-20220209224509-0008",24,"ColumnarToRow",null,1.0,0,30,false,"31",null,null,true],["app-20220209224509-0008",24,"ColumnarToRow",null,1.0,0,89,false,"39",null,null,true],["app-20220209224509-0008",24,"WholeStageCodegen (15)","WholeStageCodegen 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App IDSQL IDExec NameExpression NameTask Speedup FactorExec DurationSQL Node IdExec Is SupportedExec StagesExec ChildrenExec Children Node IdsExec Should Remove
app-20220209224509-000824ColumnarToRownull1.0030false31nullnulltrue
app-20220209224509-000824ColumnarToRownull1.0089false39nullnulltrue
app-20220209224509-000824WholeStageCodegen (15)WholeStageCodegen (15)8.0423405095true39Sort96false
app-20220209224509-000824Exchangenull4.258497true36:39nullnullfalse
app-20220209224509-00080Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (8)WholeStageCodegen (8)2.92305965true37Project:Filter:ColumnarToRow66:67:68false
app-20220209224509-000824Unionnull3.0085truenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (22)WholeStageCodegen (22)4.51883976true40HashAggregate77false
app-20220209224509-000824Projectnull3.0060truenullnullnullfalse
app-20220209224509-000824Exchangenull4.280029104true35:39nullnullfalse
app-20220209224509-000824Projectnull3.0040truenullnullnullfalse
app-20220209224509-000824Projectnull3.0054truenullnullnullfalse
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app-20220209224509-000824Scan parquet null3.0536114true34nullnullfalse
app-20220209224509-000824WholeStageCodegen (6)WholeStageCodegen (6)4.51879889true40HashAggregate10false
app-20220209224509-000824Projectnull3.0056truenullnullnullfalse
app-20220209224509-000824SubqueryBroadcastnull3.0173670truenullnullnullfalse
app-20220209224509-000814Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824Projectnull3.0033truenullnullnullfalse
app-20220209224509-000824HashAggregatenull4.5077true40nullnullfalse
app-20220209224509-000824Scan parquet null3.0750661109true35nullnullfalse
app-20220209224509-000824HashAggregatenull4.506true40nullnullfalse
app-20220209224509-000824Projectnull3.0087truenullnullnullfalse
app-20220209224509-000810Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824ColumnarToRownull1.0035false38nullnulltrue
app-20220209224509-000824BroadcastExchangenull3.0171238truenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (5)WholeStageCodegen (5)4.1331502612true38HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin13:14:15:16:17false
app-20220209224509-000824ColumnarToRownull1.00113false34nullnulltrue
app-20220209224509-000824ColumnarToRownull1.0042false31nullnulltrue
app-20220209224509-000822Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (3)WholeStageCodegen (3)2.976027true31Project:Filter:ColumnarToRow28:29:30false
app-20220209224509-00081Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824BroadcastExchangenull3.0156771truenullnullnullfalse
app-20220209224509-000824ColumnarToRownull1.0062false37nullnulltrue
app-20220209224509-000824WholeStageCodegen (13)WholeStageCodegen (13)2.91705586true39Project:Filter:ColumnarToRow87:88:89false
app-20220209224509-000824Scan parquet null3.028348true32nullnullfalse
app-20220209224509-000824Sortnull8.00103true39nullnullfalse
app-20220209224509-000823Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824Projectnull3.00106truenullnullnullfalse
app-20220209224509-000824ColumnarToRownull1.0022false38nullnulltrue
app-20220209224509-000824Unionnull3.008truenullnullnullfalse
app-20220209224509-000812Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824HashAggregatenull4.5010true40nullnullfalse
app-20220209224509-000824Filternull2.8073true33nullnullfalse
app-20220209224509-000824BroadcastExchangenull3.01537110truenullnullnullfalse
app-20220209224509-000824SortMergeJoinnull22.7094true39nullnullfalse
app-20220209224509-000824Projectnull3.0014truenullnullnullfalse
app-20220209224509-000824SubqueryBroadcastnull3.01736101truenullnullnullfalse
app-20220209224509-000824BroadcastExchangenull3.0112844truenullnullnullfalse
app-20220209224509-000816Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824Filternull2.8061true37nullnullfalse
app-20220209224509-000824WholeStageCodegen (16)WholeStageCodegen (16)2.91506623105true35Project:Filter:ColumnarToRow106:107:108false
app-20220209224509-000824Filternull2.8034true38nullnullfalse
app-20220209224509-000824Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209224509-00085Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (20)WholeStageCodegen (20)2.8540111true34Filter:ColumnarToRow112:113false
app-20220209224509-000824SubqueryBroadcastnull3.0173637truenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (14)WholeStageCodegen (14)1.0409098false36ColumnarToRow99false
app-20220209224509-00089Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824Scan parquet null3.01245469true37nullnullfalse
app-20220209224509-00086Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824ColumnarToRownull1.0068false37nullnulltrue
app-20220209224509-000824Filternull2.8021true38nullnullfalse
app-20220209224509-000824TakeOrderedAndProjectnull3.001true41:42nullnullfalse
app-20220209224509-000824BroadcastHashJoinnull5.09015true38nullnullfalse
app-20220209224509-000824BroadcastHashJoinnull5.09057true37nullnullfalse
app-20220209224509-000824Unionnull3.0018truenullnullnullfalse
app-20220209224509-00082Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (7)WholeStageCodegen (7)2.924709959true37Project:Filter:ColumnarToRow60:61:62false
app-20220209224509-00087Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000819Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-00084Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (21)WholeStageCodegen (21)4.13323042579true39HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin80:81:82:83:84false
app-20220209224509-000824Exchangenull4.276878true39:40nullnullfalse
app-20220209224509-000824Scan parquet null3.017303523true38nullnullfalse
app-20220209224509-000815Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824ColumnarToRownull1.0047false32nullnulltrue
app-20220209224509-000824BroadcastHashJoinnull5.09082true39nullnullfalse
app-20220209224509-000824Projectnull3.0066truenullnullnullfalse
app-20220209224509-000824SubqueryBroadcastnull3.0173691truenullnullnullfalse
app-20220209224509-000824BroadcastHashJoinnull5.09084true39nullnullfalse
app-20220209224509-000824Scan parquet null3.01956236true38nullnullfalse
app-20220209224509-000824Scan parquet null3.051575true33nullnullfalse
app-20220209224509-000824Exchangenull4.22064true40:41nullnullfalse
app-20220209224509-000824Projectnull3.0020truenullnullnullfalse
app-20220209224509-000824HashAggregatenull4.5013true38nullnullfalse
app-20220209224509-000824BroadcastHashJoinnull5.09055true37nullnullfalse
app-20220209224509-000824WholeStageCodegen (24)WholeStageCodegen (24)4.5308342true41HashAggregate3false
app-20220209224509-000824Unionnull3.0058truenullnullnullfalse
app-20220209224509-000824Filternull2.8029true31nullnullfalse
app-20220209224509-000824WholeStageCodegen (12)WholeStageCodegen (12)4.53468649true40HashAggregate50false
app-20220209224509-000824HashAggregatenull4.5050true40nullnullfalse
app-20220209224509-000824SubqueryBroadcastnull3.0173664truenullnullnullfalse
app-20220209224509-000824Exchangenull4.2442551true37:40nullnullfalse
app-20220209224509-000813Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824Filternull2.8088true39nullnullfalse
app-20220209224509-000824Filternull2.8046true32nullnullfalse
app-20220209224509-000824Scan parquet null3.074331true31nullnullfalse
app-20220209224509-000824Scan parquet null3.02399100true36nullnullfalse
app-20220209224509-000824WholeStageCodegen (4)WholeStageCodegen (4)2.829145true32Filter:ColumnarToRow46:47false
app-20220209224509-000824Filternull2.8041true31nullnullfalse
app-20220209224509-000824BroadcastHashJoinnull5.09017true38nullnullfalse
app-20220209224509-000824Projectnull3.0093truenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (1)WholeStageCodegen (1)2.932567319true38Project:Filter:ColumnarToRow20:21:22false
app-20220209224509-000824Expandnull3.007true40nullnullfalse
app-20220209224509-000824Exchangenull4.2617911true38:40nullnullfalse
app-20220209224509-000824Projectnull3.0028truenullnullnullfalse
app-20220209224509-000824Projectnull3.0016truenullnullnullfalse
app-20220209224509-000824Filternull2.8067true37nullnullfalse
app-20220209224509-000824ColumnarToRownull1.00108false35nullnulltrue
app-20220209224509-000824Filternull2.80107true35nullnullfalse
app-20220209224509-000824SubqueryBroadcastnull3.0173624truenullnullnullfalse
app-20220209224509-000824Filternull2.80112true34nullnullfalse
app-20220209224509-000824Projectnull3.0083truenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (11)WholeStageCodegen (11)4.1323510952true37HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin53:54:55:56:57false
app-20220209224509-000824Scan parquet null3.013214963true37nullnullfalse
app-20220209224509-000820Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000817Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-00083Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000818Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (3)WholeStageCodegen (3)2.976039true31Project:Filter:ColumnarToRow40:41:42false
app-20220209224509-000824ColumnarToRownull1.0099false36nullnulltrue
app-20220209224509-00088Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (10)WholeStageCodegen (10)2.853872true33Filter:ColumnarToRow73:74false
app-20220209224509-000824WholeStageCodegen (2)WholeStageCodegen (2)2.93627532true38Project:Filter:ColumnarToRow33:34:35false
app-20220209224509-000824HashAggregatenull4.5053true37nullnullfalse
app-20220209224509-000824HashAggregatenull4.503true41nullnullfalse
app-20220209224509-000821Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (23)WholeStageCodegen (23)3.752011495true40HashAggregate:Expand6:7false
app-20220209224509-000824BroadcastExchangenull3.0171226truenullnullnullfalse
app-20220209224509-000811Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209224509-000824WholeStageCodegen (18)WholeStageCodegen (18)12.85337652992true39Project:SortMergeJoin93:94false
app-20220209224509-000824WholeStageCodegen (17)WholeStageCodegen (17)8.00102true39Sort103false
app-20220209224509-000824HashAggregatenull4.5080true39nullnullfalse
app-20220209224509-000824ReusedExchangenull1.0025falsenullnullnullfalse
app-20220209224509-000824ColumnarToRownull1.0074false33nullnulltrue
app-20220209224509-000824Scan parquet null3.0864890true39nullnullfalse
app-20220209224509-000824Sortnull8.0096true39nullnullfalse
app-20220209224509-000824Scan parquet null3.074343true31nullnullfalse
app-20220209235114-009724Projectnull3.0035truenullnullnullfalse
app-20220209235114-009724Filternull2.8052true36nullnullfalse
app-20220209235114-009724Projectnull3.0049truenullnullnullfalse
app-20220209235114-009724Projectnull3.0023truenullnullnullfalse
app-20220209235114-009717Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724BroadcastHashJoinnull5.09016true35nullnullfalse
app-20220209235114-009724Projectnull3.0013truenullnullnullfalse
app-20220209235114-00979Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Scan parquet null3.091360520true35nullnullfalse
app-20220209235114-009724Exchangenull4.296942true36:38nullnullfalse
app-20220209235114-009724Scan parquet null3.051138true33nullnullfalse
app-20220209235114-009724BroadcastHashJoinnull5.09046true36nullnullfalse
app-20220209235114-009724Projectnull3.0015truenullnullnullfalse
app-20220209235114-009724WholeStageCodegen (6)WholeStageCodegen (6)3.8441805343true36HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:Filter:ColumnarToRow44:45:46:47:48:49:50:51:52:53false
app-20220209235114-009724HashAggregatenull4.507true40nullnullfalse
app-20220209235114-009724BroadcastExchangenull3.0140155truenullnullnullfalse
app-20220209235114-009724Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209235114-009721Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009720Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Projectnull3.0017truenullnullnullfalse
app-20220209235114-009724Projectnull3.0057truenullnullnullfalse
app-20220209235114-009714Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Projectnull3.0047truenullnullnullfalse
app-20220209235114-00973Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009722Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724WholeStageCodegen (8)WholeStageCodegen (8)4.51726true40HashAggregate7false
app-20220209235114-00978Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724TakeOrderedAndProjectnull3.002true40:41nullnullfalse
app-20220209235114-009724Filternull2.8036true33nullnullfalse
app-20220209235114-009724HashAggregatenull4.5044true36nullnullfalse
app-20220209235114-009724Scan parquet null3.050860true34nullnullfalse
app-20220209235114-009724WholeStageCodegen (7)WholeStageCodegen (7)4.54240true38HashAggregate41false
app-20220209235114-009724Projectnull3.004truenullnullnullfalse
app-20220209235114-009713Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724WholeStageCodegen (5)WholeStageCodegen (5)3.8414436929true35HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:Filter:ColumnarToRow10:11:12:13:14:15:16:17:18:19false
app-20220209235114-009723Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Projectnull3.0045truenullnullnullfalse
app-20220209235114-009724WholeStageCodegen (1)WholeStageCodegen (1)2.951922true31Project:Filter:ColumnarToRow23:24:25false
app-20220209235114-009724Projectnull3.0011truenullnullnullfalse
app-20220209235114-009724Filternull2.8024true31nullnullfalse
app-20220209235114-009724AdaptiveSparkPlannull1.001falsenullnullnullfalse
app-20220209235114-009724ColumnarToRownull1.0031false32nullnulltrue
app-20220209235114-009724BroadcastHashJoinnull5.09014true35nullnullfalse
app-20220209235114-009724WholeStageCodegen (3)WholeStageCodegen (3)2.951534true33Project:Filter:ColumnarToRow35:36:37false
app-20220209235114-009724HashAggregatenull4.5041true38nullnullfalse
app-20220209235114-00974Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Projectnull3.0029truenullnullnullfalse
app-20220209235114-009724WholeStageCodegen (9)WholeStageCodegen (9)3.0963true40Project4false
app-20220209235114-00977Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009712Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Filternull2.8058true34nullnullfalse
app-20220209235114-009715Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009716Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-00971Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724BroadcastHashJoinnull5.09050true36nullnullfalse
app-20220209235114-009719Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-00975Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724BroadcastHashJoinnull5.09048true36nullnullfalse
app-20220209235114-009724BroadcastNestedLoopJoinnull3.005true40nullnullfalse
app-20220209235114-009724BroadcastExchangenull3.0133133truenullnullnullfalse
app-20220209235114-009724Exchangenull4.225398true35:40nullnullfalse
app-20220209235114-009724BroadcastHashJoinnull5.09012true35nullnullfalse
app-20220209235114-009724ColumnarToRownull1.0019false35nullnulltrue
app-20220209235114-009724Scan parquet null3.029832true32nullnullfalse
app-20220209235114-009724BroadcastExchangenull3.0149021truenullnullnullfalse
app-20220209235114-009711Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-00970Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724BroadcastExchangenull3.0117527truenullnullnullfalse
app-20220209235114-009724ColumnarToRownull1.0037false33nullnulltrue
app-20220209235114-009724Projectnull3.0051truenullnullnullfalse
app-20220209235114-009724ColumnarToRownull1.0053false36nullnulltrue
app-20220209235114-009724Scan parquet null3.051226true31nullnullfalse
app-20220209235114-009724ColumnarToRownull1.0059false34nullnulltrue
app-20220209235114-009724WholeStageCodegen (4)WholeStageCodegen (4)2.953256true34Project:Filter:ColumnarToRow57:58:59false
app-20220209235114-009718Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009710Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Filternull2.8030true32nullnullfalse
app-20220209235114-00972Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724Scan parquet null3.018709354true36nullnullfalse
app-20220209235114-009724BroadcastExchangenull3.031539truenullnullnullfalse
app-20220209235114-00976Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235114-009724WholeStageCodegen (2)WholeStageCodegen (2)2.931728true32Project:Filter:ColumnarToRow29:30:31false
app-20220209235114-009724Filternull2.8018true35nullnullfalse
app-20220209235114-009724ColumnarToRownull1.0025false31nullnulltrue
app-20220209235114-009724HashAggregatenull4.5010true35nullnullfalse
app-20220209230506-003124BroadcastExchangenull3.0170449truenullnullnullfalse
app-20220209230506-003124Exchangenull4.294true39:40nullnullfalse
app-20220209230506-003124WholeStageCodegen (10)WholeStageCodegen (10)2.963570true31Project:Filter:ColumnarToRow71:72:73false
app-20220209230506-003124WholeStageCodegen (13)WholeStageCodegen (13)2.8451381true32Filter:ColumnarToRow82:83false
app-20220209230506-003111Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Projectnull3.009truenullnullnullfalse
app-20220209230506-003124BroadcastExchangenull3.0170469truenullnullnullfalse
app-20220209230506-003124Projectnull3.0023truenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (8)WholeStageCodegen (8)8.03521355true39Sort56false
app-20220209230506-003124WholeStageCodegen (7)WholeStageCodegen (7)2.888475758true37Filter:ColumnarToRow59:60false
app-20220209230506-003118Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Exchangenull4.2611942true36:38nullnullfalse
app-20220209230506-003113Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (12)WholeStageCodegen (12)2.881776true33Filter:ColumnarToRow77:78false
app-20220209230506-003124Filternull2.8052true31nullnullfalse
app-20220209230506-003124WholeStageCodegen (15)WholeStageCodegen (15)4.5334762true40HashAggregate3false
app-20220209230506-003124WholeStageCodegen (3)WholeStageCodegen (3)2.814564643true36Filter:ColumnarToRow44:45false
app-20220209230506-003124HashAggregatenull4.503true40nullnullfalse
app-20220209230506-003124Filternull2.8077true33nullnullfalse
app-20220209230506-003124Sortnull8.0020true39nullnullfalse
app-20220209230506-003124Projectnull3.0036truenullnullnullfalse
app-20220209230506-003124Sortnull8.0026true38nullnullfalse
app-20220209230506-003122Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124ColumnarToRownull1.0053false31nullnulltrue
app-20220209230506-003124Projectnull3.0015truenullnullnullfalse
app-20220209230506-003120Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Scan parquet null3.05912746true36nullnullfalse
app-20220209230506-003124SortMergeJoinnull22.7018true39nullnullfalse
app-20220209230506-003124Filternull2.8029true35nullnullfalse
app-20220209230506-003124ColumnarToRownull1.0073false31nullnulltrue
app-20220209230506-003112Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124ColumnarToRownull1.0083false32nullnulltrue
app-20220209230506-00319Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (6)WholeStageCodegen (6)8.0167505819true39Sort20false
app-20220209230506-003124BroadcastHashJoinnull5.09016true39nullnullfalse
app-20220209230506-00316Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003117Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-00318Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003121Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209230506-003124Projectnull3.0051truenullnullnullfalse
app-20220209230506-003124BroadcastHashJoinnull5.0908true39nullnullfalse
app-20220209230506-003124BroadcastHashJoinnull5.09010true39nullnullfalse
app-20220209230506-003115Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Filternull2.8037true34nullnullfalse
app-20220209230506-003124Scan parquet null3.061074true31nullnullfalse
app-20220209230506-003116Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Scan parquet null3.0426884true32nullnullfalse
app-20220209230506-003110Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003119Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Scan parquet null3.061054true31nullnullfalse
app-20220209230506-003124Scan parquet null3.055986561true37nullnullfalse
app-20220209230506-003124Exchangenull4.2769727true35:38nullnullfalse
app-20220209230506-003124Projectnull3.0011truenullnullnullfalse
app-20220209230506-003124Sortnull8.0056true39nullnullfalse
app-20220209230506-00312Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124BroadcastHashJoinnull5.09014true39nullnullfalse
app-20220209230506-00311Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (9)WholeStageCodegen (9)2.963764true34Project:Filter:ColumnarToRow65:66:67false
app-20220209230506-003124ColumnarToRownull1.0038false34nullnulltrue
app-20220209230506-003124ColumnarToRownull1.0030false35nullnulltrue
app-20220209230506-003124Exchangenull4.2496057true37:39nullnullfalse
app-20220209230506-003124SortMergeJoinnull22.7024true38nullnullfalse
app-20220209230506-003124BroadcastExchangenull3.0246280truenullnullnullfalse
app-20220209230506-003124Filternull2.8059true37nullnullfalse
app-20220209230506-003124WholeStageCodegen (4)WholeStageCodegen (4)8.096863440true38Sort41false
app-20220209230506-003124BroadcastHashJoinnull5.09012true39nullnullfalse
app-20220209230506-00310Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Filternull2.8044true36nullnullfalse
app-20220209230506-00317Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124HashAggregatenull4.506true39nullnullfalse
app-20220209230506-00314Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Projectnull3.0017truenullnullnullfalse
app-20220209230506-003124ColumnarToRownull1.0067false34nullnulltrue
app-20220209230506-003124Scan parquet null3.061868true34nullnullfalse
app-20220209230506-003124WholeStageCodegen (5)WholeStageCodegen (5)12.85158104722true38Project:SortMergeJoin23:24false
app-20220209230506-003124Scan parquet null3.02823131true35nullnullfalse
app-20220209230506-003124Filternull2.8072true31nullnullfalse
app-20220209230506-003124ColumnarToRownull1.0045false36nullnulltrue
app-20220209230506-00313Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Projectnull3.0071truenullnullnullfalse
app-20220209230506-003124Projectnull3.007truenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (14)WholeStageCodegen (14)5.4412225455true39HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:SortMergeJoin6:7:8:9:10:11:12:13:14:15:16:17:18false
app-20220209230506-003124WholeStageCodegen (10)WholeStageCodegen (10)2.963550true31Project:Filter:ColumnarToRow51:52:53false
app-20220209230506-003124Scan parquet null3.061839true34nullnullfalse
app-20220209230506-003124Filternull2.8082true32nullnullfalse
app-20220209230506-003124BroadcastExchangenull3.0169675truenullnullnullfalse
app-20220209230506-003124Scan parquet null3.080779true33nullnullfalse
app-20220209230506-003124SubqueryBroadcastnull3.0171047truenullnullnullfalse
app-20220209230506-003124Projectnull3.0013truenullnullnullfalse
app-20220209230506-003124BroadcastExchangenull3.0166134truenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (2)WholeStageCodegen (2)8.0104414825true38Sort26false
app-20220209230506-003124BroadcastExchangenull3.0166163truenullnullnullfalse
app-20220209230506-003123Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (9)WholeStageCodegen (9)2.963735true34Project:Filter:ColumnarToRow36:37:38false
app-20220209230506-003124SubqueryBroadcastnull3.0171062truenullnullnullfalse
app-20220209230506-003124Projectnull3.0065truenullnullnullfalse
app-20220209230506-003124TakeOrderedAndProjectnull3.001true40:41nullnullfalse
app-20220209230506-003124Filternull2.8066true34nullnullfalse
app-20220209230506-003124ColumnarToRownull1.0060false37nullnulltrue
app-20220209230506-003124ReusedExchangenull1.0033falsenullnullnullfalse
app-20220209230506-003124Exchangenull4.2997321true38:39nullnullfalse
app-20220209230506-003124ColumnarToRownull1.0078false33nullnulltrue
app-20220209230506-003124SubqueryBroadcastnull3.0168032truenullnullnullfalse
app-20220209230506-003124WholeStageCodegen (1)WholeStageCodegen (1)2.86176328true35Filter:ColumnarToRow29:30false
app-20220209230506-003114Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124Sortnull8.0041true38nullnullfalse
app-20220209230506-00315Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230506-003124ReusedExchangenull1.0048falsenullnullnullfalse
app-20220209232634-007024Windownull3.005truenullnullnullfalse
app-20220209232634-007024Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209232634-007024HashAggregatenull4.5010true35nullnullfalse
app-20220209232634-00705Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024Projectnull3.0022truenullnullnullfalse
app-20220209232634-00707Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-00702Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024Scan parquet null3.029742true33nullnullfalse
app-20220209232634-007024Scan parquet null3.049647true32nullnullfalse
app-20220209232634-007014Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-00701Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-00703Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024WholeStageCodegen (5)WholeStageCodegen (5)4.51701719true35HashAggregate10false
app-20220209232634-007024Filternull2.8026true34nullnullfalse
app-20220209232634-007024Sortnull8.007true36nullnullfalse
app-20220209232634-007024ColumnarToRownull1.0035false33nullnulltrue
app-20220209232634-007020Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024Projectnull3.0039truenullnullnullfalse
app-20220209232634-007024Projectnull3.0033truenullnullnullfalse
app-20220209232634-007016Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024ColumnarToRownull1.0046false32nullnulltrue
app-20220209232634-007024WholeStageCodegen (4)WholeStageCodegen (4)3.95123396612true34HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Filter:ColumnarToRow13:14:15:16:17:18:19:26:27false
app-20220209232634-007024Exchangenull4.28426111true34:35nullnullfalse
app-20220209232634-00708Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024WholeStageCodegen (1)WholeStageCodegen (1)2.9304421true31Project:Filter:ColumnarToRow22:23:24false
app-20220209232634-007024BroadcastExchangenull3.0146043truenullnullnullfalse
app-20220209232634-007024SubqueryBroadcastnull3.0112129truenullnullnullfalse
app-20220209232634-007024WholeStageCodegen (3)WholeStageCodegen (3)2.850244true32Filter:ColumnarToRow45:46false
app-20220209232634-007024WholeStageCodegen (6)WholeStageCodegen (6)8.01345746true36Sort7false
app-20220209232634-007010Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024BroadcastExchangenull3.0111031truenullnullnullfalse
app-20220209232634-007024BroadcastHashJoinnull5.09017true34nullnullfalse
app-20220209232634-007012Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007015Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007019Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024ColumnarToRownull1.0041false33nullnulltrue
app-20220209232634-007024TakeOrderedAndProjectnull3.001true36:37nullnullfalse
app-20220209232634-007024Filternull2.8040true33nullnullfalse
app-20220209232634-00704Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024Exchangenull4.2328true35:36nullnullfalse
app-20220209232634-007024Projectnull3.003truenullnullnullfalse
app-20220209232634-007021Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-00709Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024WholeStageCodegen (2)WholeStageCodegen (2)2.931638true33Project:Filter:ColumnarToRow39:40:41false
app-20220209232634-007024ReusedExchangenull1.0030falsenullnullnullfalse
app-20220209232634-007024BroadcastHashJoinnull5.09015true34nullnullfalse
app-20220209232634-007024BroadcastHashJoinnull5.09019true34nullnullfalse
app-20220209232634-007024WholeStageCodegen (2)WholeStageCodegen (2)2.931632true33Project:Filter:ColumnarToRow33:34:35false
app-20220209232634-007024Filternull2.8023true31nullnullfalse
app-20220209232634-007022Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024ColumnarToRownull1.0027false34nullnulltrue
app-20220209232634-007024Scan parquet null3.029736true33nullnullfalse
app-20220209232634-007024Filternull2.8045true32nullnullfalse
app-20220209232634-007024Projectnull3.0016truenullnullnullfalse
app-20220209232634-007017Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024BroadcastExchangenull3.0172620truenullnullnullfalse
app-20220209232634-007018Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007013Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024ColumnarToRownull1.0024false31nullnulltrue
app-20220209232634-007024Projectnull3.0014truenullnullnullfalse
app-20220209232634-00706Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024Projectnull3.0018truenullnullnullfalse
app-20220209232634-007024Filternull1.004false36nullnullfalse
app-20220209232634-007024Scan parquet null3.065125328true34nullnullfalse
app-20220209232634-007011Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-00700Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209232634-007024HashAggregatenull4.5013true34nullnullfalse
app-20220209232634-007024WholeStageCodegen (7)WholeStageCodegen (7)2.056212true36Project:Filter3:4false
app-20220209232634-007024BroadcastExchangenull3.0111037truenullnullnullfalse
app-20220209232634-007024Scan parquet null3.0291325true31nullnullfalse
app-20220209232634-007024Filternull2.8034true33nullnullfalse
app-20220209232634-007023Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-00262Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624WholeStageCodegen (3)WholeStageCodegen (3)2.846537true33Filter:ColumnarToRow38:39false
app-20220209225845-002624SubqueryBroadcastnull3.0160817truenullnullnullfalse
app-20220209225845-002624BroadcastHashJoinnull5.09013true34nullnullfalse
app-20220209225845-002617Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Exchangenull4.242414true34:35nullnullfalse
app-20220209225845-002624ColumnarToRownull1.0039false33nullnulltrue
app-20220209225845-002614Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Scan parquet null3.068724true31nullnullfalse
app-20220209225845-002624BroadcastHashJoinnull5.09011true34nullnullfalse
app-20220209225845-002613Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002622Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Scan parquet null3.07024716true34nullnullfalse
app-20220209225845-002624Projectnull3.008truenullnullnullfalse
app-20220209225845-00268Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002610Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624WholeStageCodegen (4)WholeStageCodegen (4)3.844430465true34HashAggregate:Expand:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Filter:ColumnarToRow6:7:8:9:10:11:12:13:14:15false
app-20220209225845-002624Projectnull3.0010truenullnullnullfalse
app-20220209225845-002624HashAggregatenull4.506true34nullnullfalse
app-20220209225845-002624Filternull2.8028true31nullnullfalse
app-20220209225845-00266Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Projectnull3.0012truenullnullnullfalse
app-20220209225845-00263Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002616Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Filternull2.8038true33nullnullfalse
app-20220209225845-002624Projectnull3.0021truenullnullnullfalse
app-20220209225845-002624WholeStageCodegen (2)WholeStageCodegen (2)2.8254432true32Filter:ColumnarToRow33:34false
app-20220209225845-002624Filternull2.8014true34nullnullfalse
app-20220209225845-00267Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-00265Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-00269Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624BroadcastHashJoinnull5.0909true34nullnullfalse
app-20220209225845-002623Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624ColumnarToRownull1.0029false31nullnulltrue
app-20220209225845-002619Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Scan parquet null3.068730true31nullnullfalse
app-20220209225845-002624Scan parquet null3.0232935true32nullnullfalse
app-20220209225845-002624ReusedExchangenull1.0018falsenullnullnullfalse
app-20220209225845-002618Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624Expandnull3.007true34nullnullfalse
app-20220209225845-002624BroadcastExchangenull3.0158719truenullnullnullfalse
app-20220209225845-002620Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624BroadcastExchangenull3.0132636truenullnullnullfalse
app-20220209225845-002624Filternull2.8022true31nullnullfalse
app-20220209225845-002624Projectnull3.0027truenullnullnullfalse
app-20220209225845-002624ColumnarToRownull1.0023false31nullnulltrue
app-20220209225845-002624Filternull2.8033true32nullnullfalse
app-20220209225845-002624BroadcastExchangenull3.0158725truenullnullnullfalse
app-20220209225845-002624TakeOrderedAndProjectnull3.001true35:36nullnullfalse
app-20220209225845-002624HashAggregatenull4.503true35nullnullfalse
app-20220209225845-002621Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624ColumnarToRownull1.0034false32nullnulltrue
app-20220209225845-00261Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002611Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624WholeStageCodegen (5)WholeStageCodegen (5)4.54723102true35HashAggregate3false
app-20220209225845-002624BroadcastExchangenull3.0190531truenullnullnullfalse
app-20220209225845-002615Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002612Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-00260Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209225845-002624WholeStageCodegen (1)WholeStageCodegen (1)2.970726true31Project:Filter:ColumnarToRow27:28:29false
app-20220209225845-002624ColumnarToRownull1.0015false34nullnulltrue
app-20220209225845-002624WholeStageCodegen (1)WholeStageCodegen (1)2.970720true31Project:Filter:ColumnarToRow21:22:23false
app-20220209225845-002624Scan parquet null3.046240true33nullnullfalse
app-20220209225845-002624Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209225845-00264Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-01031Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010314Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010313Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324WholeStageCodegen (5)WholeStageCodegen (5)5.172152true36Project:Sort:HashAggregate3:4:5false
app-20220209235526-010319Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324BroadcastHashJoinnull5.09010true34nullnullfalse
app-20220209235526-010310Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-01039Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-01030Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-01033Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Projectnull3.0011truenullnullnullfalse
app-20220209235526-01036Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Projectnull3.009truenullnullnullfalse
app-20220209235526-01037Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324BroadcastHashJoinnull5.09012true34nullnullfalse
app-20220209235526-010324ColumnarToRownull1.0035false31nullnulltrue
app-20220209235526-010324BroadcastExchangenull3.0130719truenullnullnullfalse
app-20220209235526-010324ColumnarToRownull1.0023false33nullnulltrue
app-20220209235526-01034Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324ColumnarToRownull1.0017false34nullnulltrue
app-20220209235526-010322Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010312Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Scan parquet null3.0105143618true34nullnullfalse
app-20220209235526-010315Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Projectnull3.0027truenullnullnullfalse
app-20220209235526-010324HashAggregatenull4.508true34nullnullfalse
app-20220209235526-010324BroadcastExchangenull3.0147031truenullnullnullfalse
app-20220209235526-010316Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Scan parquet null3.060536true31nullnullfalse
app-20220209235526-010324Filternull2.8016true34nullnullfalse
app-20220209235526-010324BroadcastExchangenull3.0114625truenullnullnullfalse
app-20220209235526-01032Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Scan parquet null3.028330true32nullnullfalse
app-20220209235526-010321Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Projectnull3.0021truenullnullnullfalse
app-20220209235526-010324Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209235526-010324ColumnarToRownull1.0029false32nullnulltrue
app-20220209235526-010318Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Projectnull3.0015truenullnullnullfalse
app-20220209235526-010324Filternull2.8034true31nullnullfalse
app-20220209235526-010324Filternull2.8028true32nullnullfalse
app-20220209235526-010311Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010323Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324AdaptiveSparkPlannull1.001falsenullnullnullfalse
app-20220209235526-010324Sortnull8.004true36nullnullfalse
app-20220209235526-010317Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010320Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324BroadcastHashJoinnull5.09014true34nullnullfalse
app-20220209235526-010324Scan parquet null3.049224true33nullnullfalse
app-20220209235526-010324WholeStageCodegen (1)WholeStageCodegen (1)2.949820true33Project:Filter:ColumnarToRow21:22:23false
app-20220209235526-010324WholeStageCodegen (4)WholeStageCodegen (4)3.8415773287true34HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:Filter:ColumnarToRow8:9:10:11:12:13:14:15:16:17false
app-20220209235526-010324Projectnull3.0013truenullnullnullfalse
app-20220209235526-010324Projectnull3.0033truenullnullnullfalse
app-20220209235526-010324WholeStageCodegen (2)WholeStageCodegen (2)2.930326true32Project:Filter:ColumnarToRow27:28:29false
app-20220209235526-01035Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Filternull2.8022true33nullnullfalse
app-20220209235526-010324HashAggregatenull4.505true36nullnullfalse
app-20220209235526-010324WholeStageCodegen (3)WholeStageCodegen (3)2.961032true31Project:Filter:ColumnarToRow33:34:35false
app-20220209235526-01038Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209235526-010324Exchangenull4.222986true34:36nullnullfalse
app-20220209235526-010324Projectnull3.003truenullnullnullfalse
app-20220209231919-005716Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-00579Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209231919-005724Exchangenull4.251124true34:36nullnullfalse
app-20220209231919-005714Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Scan parquet null3.029836true32nullnullfalse
app-20220209231919-005724Projectnull3.0033truenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (4)WholeStageCodegen (4)8.01353893522true36Sort23false
app-20220209231919-00570Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Filternull2.8019true35nullnullfalse
app-20220209231919-005724WholeStageCodegen (7)WholeStageCodegen (7)2.931648true32Project:Filter:ColumnarToRow49:50:51false
app-20220209231919-00574Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Filternull2.8044true31nullnullfalse
app-20220209231919-005724Scan parquet null3.028546true31nullnullfalse
app-20220209231919-005724ColumnarToRownull1.0045false31nullnulltrue
app-20220209231919-005724BroadcastExchangenull3.0120831truenullnullnullfalse
app-20220209231919-00573Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724SortMergeJoinnull22.7014true36nullnullfalse
app-20220209231919-005724Projectnull3.0013truenullnullnullfalse
app-20220209231919-005717Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005715Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Filternull2.8034true32nullnullfalse
app-20220209231919-005724ColumnarToRownull1.0040false33nullnulltrue
app-20220209231919-005724Exchangenull4.214647617true35:36nullnullfalse
app-20220209231919-005724ColumnarToRownull1.0027false34nullnulltrue
app-20220209231919-005724Scan parquet null3.029852true32nullnullfalse
app-20220209231919-005724SubqueryBroadcastnull3.0123429truenullnullnullfalse
app-20220209231919-00571Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005712Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (8)WholeStageCodegen (8)6.0675557345true36HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Project:SortMergeJoin6:7:8:9:10:11:12:13:14false
app-20220209231919-005721Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Projectnull3.009truenullnullnullfalse
app-20220209231919-005724Filternull2.8026true34nullnullfalse
app-20220209231919-005713Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (3)WholeStageCodegen (3)2.87635225true34Filter:ColumnarToRow26:27false
app-20220209231919-00577Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724TakeOrderedAndProjectnull3.001true37:38nullnullfalse
app-20220209231919-005724Filternull2.8039true33nullnullfalse
app-20220209231919-005724Projectnull3.0011truenullnullnullfalse
app-20220209231919-005724ColumnarToRownull1.0035false32nullnulltrue
app-20220209231919-005724Scan parquet null3.028941true33nullnullfalse
app-20220209231919-005724Exchangenull4.214924true36:37nullnullfalse
app-20220209231919-005718Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724HashAggregatenull4.503true37nullnullfalse
app-20220209231919-00575Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (6)WholeStageCodegen (6)2.831343true31Filter:ColumnarToRow44:45false
app-20220209231919-005724WholeStageCodegen (2)WholeStageCodegen (2)8.02180315true36Sort16false
app-20220209231919-005711Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (5)WholeStageCodegen (5)2.830238true33Filter:ColumnarToRow39:40false
app-20220209231919-005724ColumnarToRownull1.0020false35nullnulltrue
app-20220209231919-005724Sortnull8.0016true36nullnullfalse
app-20220209231919-005724BroadcastHashJoinnull5.09010true36nullnullfalse
app-20220209231919-005723Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Scan parquet null3.04765328true34nullnullfalse
app-20220209231919-005724BroadcastExchangenull3.0120847truenullnullnullfalse
app-20220209231919-005724HashAggregatenull4.506true36nullnullfalse
app-20220209231919-005724BroadcastExchangenull3.0121437truenullnullnullfalse
app-20220209231919-005724Scan parquet null3.0147268121true35nullnullfalse
app-20220209231919-005724Sortnull8.0023true36nullnullfalse
app-20220209231919-005724ReusedExchangenull1.0030falsenullnullnullfalse
app-20220209231919-005724BroadcastHashJoinnull5.09012true36nullnullfalse
app-20220209231919-005724WholeStageCodegen (7)WholeStageCodegen (7)2.931632true32Project:Filter:ColumnarToRow33:34:35false
app-20220209231919-00576Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005710Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724ColumnarToRownull1.0051false32nullnulltrue
app-20220209231919-005724BroadcastHashJoinnull5.0908true36nullnullfalse
app-20220209231919-005724Projectnull3.0049truenullnullnullfalse
app-20220209231919-005722Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-00572Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724BroadcastExchangenull3.0125842truenullnullnullfalse
app-20220209231919-005724Projectnull3.007truenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (9)WholeStageCodegen (9)4.5841222true37HashAggregate3false
app-20220209231919-00578Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005719Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724Filternull2.8050true32nullnullfalse
app-20220209231919-005720Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231919-005724WholeStageCodegen (1)WholeStageCodegen (1)2.8441400118true35Filter:ColumnarToRow19:20false
app-20220209231533-00509Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Filternull2.8014true32nullnullfalse
app-20220209231533-005024WholeStageCodegen (4)WholeStageCodegen (4)4.5804502true34HashAggregate3false
app-20220209231533-005024BroadcastHashJoinnull5.09010true33nullnullfalse
app-20220209231533-005024BroadcastExchangenull3.0143211truenullnullnullfalse
app-20220209231533-005024SubqueryBroadcastnull3.0144220truenullnullnullfalse
app-20220209231533-005018Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024BroadcastExchangenull3.0146121truenullnullnullfalse
app-20220209231533-005024Projectnull3.0023truenullnullnullfalse
app-20220209231533-00507Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005023Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Filternull2.8017true33nullnullfalse
app-20220209231533-005014Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Filternull2.8024true31nullnullfalse
app-20220209231533-005024HashAggregatenull4.506true33nullnullfalse
app-20220209231533-00502Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024ColumnarToRownull1.0025false31nullnulltrue
app-20220209231533-00506Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Scan parquet null3.054616true32nullnullfalse
app-20220209231533-005010Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Scan parquet null3.072029019true33nullnullfalse
app-20220209231533-005024WholeStageCodegen (2)WholeStageCodegen (2)2.966122true31Project:Filter:ColumnarToRow23:24:25false
app-20220209231533-005017Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024HashAggregatenull4.503true34nullnullfalse
app-20220209231533-005024TakeOrderedAndProjectnull3.001true34:35nullnullfalse
app-20220209231533-005019Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024ColumnarToRownull1.0018false33nullnulltrue
app-20220209231533-005024BroadcastHashJoinnull5.0908true33nullnullfalse
app-20220209231533-005013Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005012Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Projectnull3.0013truenullnullnullfalse
app-20220209231533-005021Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024ColumnarToRownull1.0015false32nullnulltrue
app-20220209231533-00508Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Scan parquet null3.065226true31nullnullfalse
app-20220209231533-005024WholeStageCodegen (3)WholeStageCodegen (3)3.9214097295true33HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Filter:ColumnarToRow6:7:8:9:10:17:18false
app-20220209231533-005024Projectnull3.009truenullnullnullfalse
app-20220209231533-00504Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005016Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005022Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005015Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005020Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-00501Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-00500Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-00505Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024WholeStageCodegen (1)WholeStageCodegen (1)2.956612true32Project:Filter:ColumnarToRow13:14:15false
app-20220209231533-00503Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209231533-005024Execute InsertIntoHadoopFsRelationCommand parquetnull3.000truenullnullnullfalse
app-20220209231533-005024Projectnull3.007truenullnullnullfalse
app-20220209231533-005024Exchangenull4.2115424true33:34nullnullfalse
app-20220209231533-005011Execute CreateViewCommandnull1.000falsenullnullnullfalse
application_1665146417445_00023WholeStageCodegenWholeStageCodegen3.7503true6HashAggregate:Project4:5false
application_1665146417445_00020CollectLimitnull1.000falsenullnullnullfalse
application_1665146417445_00023WholeStageCodegenWholeStageCodegen4.500true7HashAggregate1false
application_1665146417445_00021Projectnull3.001truenullnullnullfalse
application_1665146417445_00020WholeStageCodegenWholeStageCodegen3.001truenullProject2false
application_1665146417445_00021WholeStageCodegenWholeStageCodegen3.000true1Project1false
application_1665146417445_00023Exchangenull4.206true5nullnullfalse
application_1665146417445_00022HashAggregatenull4.501true4nullnullfalse
application_1665146417445_00022HashAggregatenull4.506true3nullnullfalse
application_1665146417445_00022HashAggregatenull4.504true3nullnullfalse
application_1665146417445_00021Scan hive ecomm_ops_tables.s0s0jzb_browse_shelf_cat_mapnull1.002false1nullnullfalse
application_1665146417445_00022Scan hive ecomm_ops_tables.zzz_dart_browse_ads_prod_modnull1.008false2nullnullfalse
application_1665146417445_00020Projectnull3.002truenullnullnullfalse
application_1665146417445_00023HashAggregatenull4.504true6nullnullfalse
application_1665146417445_00023HashAggregatenull4.501true7nullnullfalse
application_1665146417445_00023Scan hive ecomm_ops_tables.zzz_dart_browse_ads_prod_modnull1.007false5nullnullfalse
application_1665146417445_00022HashAggregatenull4.505true3nullnullfalse
application_1665146417445_00020Scan hive ecomm_ops_tables.zzz_dart_browse_ads_prod_modnull1.003false0nullnullfalse
application_1665146417445_00022WholeStageCodegenWholeStageCodegen4.500true4HashAggregate1false
application_1665146417445_00023Projectnull3.005truenullnullnullfalse
application_1665146417445_00022Exchangenull4.202true3nullnullfalse
application_1665146417445_00022WholeStageCodegenWholeStageCodegen4.503true3HashAggregate:HashAggregate:HashAggregate4:5:6false
application_1665146417445_00022Exchangenull4.207true2nullnullfalse
application_1665146417445_00023Exchangenull4.202true6nullnullfalse
app-20220209230053-002824Exchangenull4.23630436true49:50nullnullfalse
app-20220209230053-002824Exchangenull4.2164896true37:38nullnullfalse
app-20220209230053-002824WholeStageCodegen (35)WholeStageCodegen (35)8.09740356227true52Sort228false
app-20220209230053-002824Projectnull3.00155truenullnullnullfalse
app-20220209230053-00284Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824Projectnull3.00204truenullnullnullfalse
app-20220209230053-002824Projectnull3.00143truenullnullnullfalse
app-20220209230053-002824Projectnull3.0072truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (6)WholeStageCodegen (6)3.4333255637true49Project:Filter:HashAggregate38:39:40false
app-20220209230053-002824WholeStageCodegen (32)WholeStageCodegen (32)3.0334418172true44Project173false
app-20220209230053-002824SubqueryBroadcastnull3.0160551truenullnullnullfalse
app-20220209230053-002824HashAggregatenull4.5043true47nullnullfalse
app-20220209230053-002824Projectnull3.0030truenullnullnullfalse
app-20220209230053-00287Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824Projectnull3.0017truenullnullnullfalse
app-20220209230053-002824BroadcastExchangenull3.0108202truenullnullnullfalse
app-20220209230053-002815Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-00283Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824Scan parquet null3.08779181true41nullnullfalse
app-20220209230053-002824Filternull2.8062true33nullnullfalse
app-20220209230053-002824HashAggregatenull1.00224false52nullnullfalse
app-20220209230053-002824WholeStageCodegen (2)WholeStageCodegen (2)8.057073819true50Sort20false
app-20220209230053-002824Projectnull3.00225truenullnullnullfalse
app-20220209230053-002824HashAggregatenull1.00223false52nullnullfalse
app-20220209230053-002824Exchangenull4.2309177true41:44nullnullfalse
app-20220209230053-002824TakeOrderedAndProjectnull3.001true53:54nullnullfalse
app-20220209230053-002824WholeStageCodegen (15)WholeStageCodegen (15)2.821724137true45Filter:ColumnarToRow138:139false
app-20220209230053-002824SortMergeJoinnull22.7012true51nullnullfalse
app-20220209230053-002824WholeStageCodegen (23)WholeStageCodegen (23)6.96293476true51HashAggregate:Project:BroadcastHashJoin:Project:SortMergeJoin7:8:9:10:11false
app-20220209230053-002824WholeStageCodegen (4)WholeStageCodegen (4)2.82258215true32Filter:ColumnarToRow216:217false
app-20220209230053-002824SortMergeJoinnull22.70241true52nullnullfalse
app-20220209230053-002824Sortnull8.00228true52nullnullfalse
app-20220209230053-002824Projectnull3.00236truenullnullnullfalse
app-20220209230053-002824Filternull2.80189true42nullnullfalse
app-20220209230053-002824Scan parquet null3.028733true31nullnullfalse
app-20220209230053-002824ColumnarToRownull1.00157false31nullnulltrue
app-20220209230053-002824HashAggregatenull4.50193true40nullnullfalse
app-20220209230053-002824Projectnull3.0046truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (1)WholeStageCodegen (1)2.81533322true48Filter:ColumnarToRow23:24false
app-20220209230053-00286Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002812Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (42)WholeStageCodegen (42)8.07107509242true52Sort243false
app-20220209230053-002824Sortnull8.00233true52nullnullfalse
app-20220209230053-002824ColumnarToRownull1.0049false47nullnulltrue
app-20220209230053-002824WholeStageCodegen (4)WholeStageCodegen (4)8.07271900120true37Sort121false
app-20220209230053-002824Filternull2.8048true47nullnullfalse
app-20220209230053-002824Exchangenull4.2836815true50:51nullnullfalse
app-20220209230053-002824BroadcastExchangenull3.01206127truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (26)WholeStageCodegen (26)8.0616179175true44Sort176false
app-20220209230053-002824HashAggregatenull1.00163false52nullnullfalse
app-20220209230053-002824HashAggregatenull1.00239false52nullnullfalse
app-20220209230053-002824Sortnull8.00235true52nullnullfalse
app-20220209230053-002824WholeStageCodegen (21)WholeStageCodegen (21)5.671192790141true51Sort:Project:Filter:HashAggregate:HashAggregate:Project:SortMergeJoin142:143:144:145:146:147:148false
app-20220209230053-002824SortMergeJoinnull22.70226true52nullnullfalse
app-20220209230053-002824SortMergeJoinnull22.70104true37nullnullfalse
app-20220209230053-002824ColumnarToRownull1.00118false34nullnulltrue
app-20220209230053-002824WholeStageCodegen (3)WholeStageCodegen (3)2.954860true33Project:Filter:ColumnarToRow61:62:63false
app-20220209230053-002824BroadcastHashJoinnull5.090165true52nullnullfalse
app-20220209230053-002824Filternull2.8067true32nullnullfalse
app-20220209230053-002824BroadcastExchangenull3.0115228truenullnullnullfalse
app-20220209230053-002824HashAggregatenull1.00146false51nullnullfalse
app-20220209230053-002824Sortnull8.0020true50nullnullfalse
app-20220209230053-002824WholeStageCodegen (1)WholeStageCodegen (1)2.948203true39Project:Filter:ColumnarToRow204:205:206false
app-20220209230053-002824Exchangenull4.21065true51:53nullnullfalse
app-20220209230053-002824Exchangenull4.26954171true44:52nullnullfalse
app-20220209230053-002824Scan parquet null3.0203869true32nullnullfalse
app-20220209230053-002824BroadcastExchangenull3.01925214truenullnullnullfalse
app-20220209230053-002824Projectnull3.00173truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (47)WholeStageCodegen (47)6.9658082162true52HashAggregate:Project:BroadcastHashJoin:Project:SortMergeJoin163:164:165:166:167false
app-20220209230053-002824WholeStageCodegen (13)WholeStageCodegen (13)8.0768468986true51Sort87false
app-20220209230053-002824Sortnull8.00176true44nullnullfalse
app-20220209230053-002811Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824HashAggregatenull4.50190true42nullnullfalse
app-20220209230053-002824Sortnull8.00152true51nullnullfalse
app-20220209230053-002824BroadcastExchangenull3.0159653truenullnullnullfalse
app-20220209230053-002824Projectnull3.0055truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (5)WholeStageCodegen (5)3.924787686192true40HashAggregate:Project:BroadcastHashJoin:Project:BroadcastHashJoin:Filter:ColumnarToRow193:194:195:196:197:198:199false
app-20220209230053-002824WholeStageCodegen (5)WholeStageCodegen (5)2.9341115true34Project:Filter:ColumnarToRow116:117:118false
app-20220209230053-002824Projectnull3.00221truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (38)WholeStageCodegen (38)5.6715748975219true52Sort:Project:Filter:HashAggregate:HashAggregate:Project:SortMergeJoin220:221:222:223:224:225:226false
app-20220209230053-00282Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-00289Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824SortMergeJoinnull22.70167true52nullnullfalse
app-20220209230053-002824WholeStageCodegen (10)WholeStageCodegen (10)2.9162909081true46Project:Filter:ColumnarToRow82:83:84false
app-20220209230053-002824Filternull2.8031true31nullnullfalse
app-20220209230053-002824SortMergeJoinnull22.70133true51nullnullfalse
app-20220209230053-00288Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824Scan parquet null3.02038218true32nullnullfalse
app-20220209230053-002824WholeStageCodegen (8)WholeStageCodegen (8)3.031701316true50Project17false
app-20220209230053-002810Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824BroadcastHashJoinnull5.090102true37nullnullfalse
app-20220209230053-002824Unionnull3.002truenullnullnullfalse
app-20220209230053-002824WholeStageCodegen (45)WholeStageCodegen (45)5.67579620234true52Sort:Project:Filter:HashAggregate:HashAggregate:Project:SortMergeJoin235:236:237:238:239:240:241false
app-20220209230053-002824HashAggregatenull4.5095true38nullnullfalse
app-20220209230053-002824Sortnull8.00230true52nullnullfalse
app-20220209230053-002824Exchangenull4.257306107true35:37nullnullfalse
app-20220209230053-00281Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824BroadcastExchangenull3.01152153truenullnullnullfalse
app-20220209230053-002824HashAggregatenull1.0075false51nullnullfalse
app-20220209230053-002824Scan parquet null3.052564true33nullnullfalse
app-20220209230053-002824SortMergeJoinnull22.70231true52nullnullfalse
app-20220209230053-002824Sortnull8.00142true51nullnullfalse
app-20220209230053-002824ColumnarToRownull1.00212false33nullnulltrue
app-20220209230053-002824BroadcastExchangenull3.01596208truenullnullnullfalse
app-20220209230053-002824Projectnull3.00210truenullnullnullfalse
app-20220209230053-002824ColumnarToRownull1.0091false43nullnulltrue
app-20220209230053-002824WholeStageCodegen (4)WholeStageCodegen (4)2.8225866true32Filter:ColumnarToRow67:68false
app-20220209230053-002824Filternull2.80179true41nullnullfalse
app-20220209230053-002824Sortnull8.0079true51nullnullfalse
app-20220209230053-002824Scan parquet null3.01128383111true35nullnullfalse
app-20220209230053-002824Filternull1.0073false51nullnullfalse
app-20220209230053-002824WholeStageCodegen (5)WholeStageCodegen (5)2.9341128true34Project:Filter:ColumnarToRow129:130:131false
app-20220209230053-002824Sortnull8.00245true52nullnullfalse
app-20220209230053-002824ColumnarToRownull1.0024false48nullnulltrue
app-20220209230053-002824BroadcastHashJoinnull5.090195true40nullnullfalse
app-20220209230053-002824SortMergeJoinnull22.7018true50nullnullfalse
app-20220209230053-002824HashAggregatenull1.004false53nullnullfalse
app-20220209230053-002824Projectnull3.00196truenullnullnullfalse
app-20220209230053-002824SubqueryBroadcastnull3.01212112truenullnullnullfalse
app-20220209230053-002824Filternull1.00144false51nullnullfalse
app-20220209230053-002823Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824Exchangenull4.2374136true45:52:51nullnullfalse
app-20220209230053-002817Execute CreateViewCommandnull1.000falsenullnullnullfalse
app-20220209230053-002824HashAggregatenull4.5098true37nullnullfalse
app-20220209230053-002824Filternull2.80216true32nullnullfalse
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Showing the first 1000 rows.
"]}}],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"[RAPIDS Accelerator for Apache Spark] Qualification Tool Notebook Template","dashboards":[{"elements":[],"guid":"0ed3c80b-b2f6-4c89-9a92-1af2f168d5ea","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"91c1bfb2-695a-4e5c-8a25-848a433108dc","origId":2721260844584915,"title":"Executive View","width":1600,"globalVars":{}},{"elements":[],"guid":"ab4cecf9-0471-4fee-aa33-8927bb7e1bb1","layoutOption":{"stack":true,"grid":true},"version":"DashboardViewV1","nuid":"62243296-4562-4f06-90ac-d7a609f19c16","origId":2721260844584916,"title":"App View","width":1920,"globalVars":{}}],"notebookMetadata":{"pythonIndentUnit":2,"widgetLayout":[{"name":"log_path","width":551,"breakBefore":false},{"name":"output_path","width":551,"breakBefore":false}]},"language":"python","widgets":{"log_path":{"nuid":"88986aa6-6e67-4d09-aeeb-7c96ea1ea8f1","currentValue":"/dbfs/user1/qualification_logs","widgetInfo":{"widgetType":"text","name":"log_path","defaultValue":"","label":null,"options":{"widgetType":"text","validationRegex":null}}},"output_path":{"nuid":"0b9846bd-5c3e-4ae2-ba00-80bb9da1ee32","currentValue":"/tmp","widgetInfo":{"widgetType":"text","name":"output_path","defaultValue":"","label":null,"options":{"widgetType":"text","validationRegex":null}}}},"notebookOrigID":2721260844584890}},"nbformat":4,"nbformat_minor":0} From 032b385005eeb004be4a0499c52f6216bab27344 Mon Sep 17 00:00:00 2001 From: Chong Gao Date: Fri, 6 Jan 2023 18:17:44 +0800 Subject: [PATCH 03/10] Fix UDF test cases failed on Databricks 3.1.2 (#269) Signed-off-by: Chong Gao --- .../RAPIDS-accelerated-UDFs/src/main/python/asserts.py | 10 +++++----- .../src/main/python/spark_init_internal.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/asserts.py b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/asserts.py index b112d1e9b..5aa3485e9 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/asserts.py +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/asserts.py @@ -287,12 +287,12 @@ def assert_gpu_fallback_write(write_func, cpu_end = time.time() print('### GPU RUN ###') jvm = spark_jvm() - jvm.com.nvidia.spark.rapids.ExecutionPlanCaptureCallback.startCapture() + jvm.org.apache.spark.sql.rapids.ExecutionPlanCaptureCallback.startCapture() gpu_start = time.time() gpu_path = base_path + '/GPU' with_gpu_session(lambda spark : write_func(spark, gpu_path), conf=conf) gpu_end = time.time() - jvm.com.nvidia.spark.rapids.ExecutionPlanCaptureCallback.assertCapturedAndGpuFellBack(cpu_fallback_class_name, 2000) + jvm.org.apache.spark.sql.rapids.ExecutionPlanCaptureCallback.assertCapturedAndGpuFellBack(cpu_fallback_class_name, 10000) print('### WRITE: GPU TOOK {} CPU TOOK {} ###'.format( gpu_end - gpu_start, cpu_end - cpu_start)) @@ -328,10 +328,10 @@ def assert_cpu_and_gpu_are_equal_collect_with_capture(func, jvm = spark_jvm() if exist_classes: for clz in exist_classes.split(','): - jvm.com.nvidia.spark.rapids.ExecutionPlanCaptureCallback.assertContains(gpu_df._jdf, clz) + jvm.org.apache.spark.sql.rapids.ExecutionPlanCaptureCallback.assertContains(gpu_df._jdf, clz) if non_exist_classes: for clz in non_exist_classes.split(','): - jvm.com.nvidia.spark.rapids.ExecutionPlanCaptureCallback.assertNotContain(gpu_df._jdf, clz) + jvm.org.apache.spark.sql.rapids.ExecutionPlanCaptureCallback.assertNotContain(gpu_df._jdf, clz) print('### {}: GPU TOOK {} CPU TOOK {} ###'.format(collect_type, gpu_end - gpu_start, cpu_end - cpu_start)) if should_sort_locally(): @@ -374,7 +374,7 @@ def assert_gpu_fallback_collect(func, from_gpu, gpu_df = with_gpu_session(bring_back, conf=conf) gpu_end = time.time() jvm = spark_jvm() - jvm.com.nvidia.spark.rapids.ExecutionPlanCaptureCallback.assertDidFallBack(gpu_df._jdf, cpu_fallback_class_name) + jvm.org.apache.spark.sql.rapids.ExecutionPlanCaptureCallback.assertDidFallBack(gpu_df._jdf, cpu_fallback_class_name) print('### {}: GPU TOOK {} CPU TOOK {} ###'.format(collect_type, gpu_end - gpu_start, cpu_end - cpu_start)) if should_sort_locally(): diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/spark_init_internal.py b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/spark_init_internal.py index 26a687024..c557c1be0 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/spark_init_internal.py +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/spark_init_internal.py @@ -31,7 +31,7 @@ def _spark__init(): _sb = pyspark.sql.SparkSession.builder _sb.config('spark.plugins', 'com.nvidia.spark.SQLPlugin') \ .config("spark.sql.adaptive.enabled", "false") \ - .config('spark.sql.queryExecutionListeners', 'com.nvidia.spark.rapids.ExecutionPlanCaptureCallback') + .config('spark.sql.queryExecutionListeners', 'org.apache.spark.sql.rapids.ExecutionPlanCaptureCallback') for key, value in os.environ.items(): if key.startswith('PYSP_TEST_') and key != _DRIVER_ENV: From 2b49483df3287712fc4b1d40419419863efa31ed Mon Sep 17 00:00:00 2001 From: Gary Shen Date: Mon, 9 Jan 2023 09:21:05 +0800 Subject: [PATCH 04/10] Change the new interface of RapidsUDF (#259) RapidsUDF.evaluateColumnar has a new parameter of numRows Signed-off-by: Gary Shen Signed-off-by: Gary Shen --- .../com/nvidia/spark/rapids/udf/hive/DecimalFraction.java | 5 ++++- .../com/nvidia/spark/rapids/udf/hive/StringWordCount.java | 5 ++++- .../java/com/nvidia/spark/rapids/udf/hive/URLDecode.java | 5 ++++- .../java/com/nvidia/spark/rapids/udf/hive/URLEncode.java | 5 ++++- .../com/nvidia/spark/rapids/udf/java/CosineSimilarity.java | 2 +- .../com/nvidia/spark/rapids/udf/java/DecimalFraction.java | 2 +- .../java/com/nvidia/spark/rapids/udf/java/URLDecode.java | 5 ++++- .../java/com/nvidia/spark/rapids/udf/java/URLEncode.java | 5 ++++- .../scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala | 3 ++- .../scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala | 3 ++- .../java/com/nvidia/spark/rapids/udf/PointInPolygon.java | 2 +- 11 files changed, 31 insertions(+), 11 deletions(-) diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/DecimalFraction.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/DecimalFraction.java index 37c749faf..501983147 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/DecimalFraction.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/DecimalFraction.java @@ -81,11 +81,14 @@ public Object evaluate(GenericUDF.DeferredObject[] arguments) throws HiveExcepti } @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } ColumnVector input = args[0]; + if (numRows != input.getRowCount()) { + throw new IllegalArgumentException("Expected " + numRows + " rows, received " + input.getRowCount()); + } if (!input.getType().isDecimalType()) { throw new IllegalArgumentException("Argument type is not a decimal column: " + input.getType()); diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java index 43555c003..f3fb2e244 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/StringWordCount.java @@ -59,13 +59,16 @@ public Integer evaluate(String str) { /** Columnar implementation that runs on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } ColumnVector strs = args[0]; + if (numRows != strs.getRowCount()) { + throw new IllegalArgumentException("Expected " + numRows + " rows, received " + strs.getRowCount()); + } if (!strs.getType().equals(DType.STRING)) { throw new IllegalArgumentException("type mismatch, expected strings but found " + strs.getType()); diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java index d89f634b8..e97f0a21c 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLDecode.java @@ -51,13 +51,16 @@ public String evaluate(String s) { /** Columnar implementation that runs on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } ColumnVector input = args[0]; + if (numRows != input.getRowCount()) { + throw new IllegalArgumentException("Expected " + numRows + " rows, received " + input.getRowCount()); + } if (!input.getType().equals(DType.STRING)) { throw new IllegalArgumentException("Argument type is not a string column: " + input.getType()); diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java index c7569a235..98bcb73eb 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/hive/URLEncode.java @@ -93,13 +93,16 @@ public Object evaluate(GenericUDF.DeferredObject[] arguments) throws HiveExcepti /** Columnar implementation that runs on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } ColumnVector input = args[0]; + if (numRows != input.getRowCount()) { + throw new IllegalArgumentException("Expected " + numRows + " rows, received " + input.getRowCount()); + } if (!input.getType().equals(DType.STRING)) { throw new IllegalArgumentException("Argument type is not a string column: " + input.getType()); diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java index 57f2f4925..e85774777 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/CosineSimilarity.java @@ -61,7 +61,7 @@ private double magnitude(WrappedArray v) { /** Columnar implementation that processes data on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { if (args.length != 2) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/DecimalFraction.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/DecimalFraction.java index 0b132d12b..6c3126f29 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/DecimalFraction.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/DecimalFraction.java @@ -40,7 +40,7 @@ public BigDecimal call(BigDecimal dec) throws Exception { } @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java index df4466aa5..96d07384f 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLDecode.java @@ -51,13 +51,16 @@ public String call(String s) { /** Columnar implementation that runs on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } ColumnVector input = args[0]; + if (numRows != input.getRowCount()) { + throw new IllegalArgumentException("Expected " + numRows + " rows, received " + input.getRowCount()); + } if (!input.getType().equals(DType.STRING)) { throw new IllegalArgumentException("Argument type is not a string column: " + input.getType()); diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java index 398f25f55..13bdfff55 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/java/com/nvidia/spark/rapids/udf/java/URLEncode.java @@ -50,13 +50,16 @@ public String call(String s) { /** Columnar implementation that runs on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. if (args.length != 1) { throw new IllegalArgumentException("Unexpected argument count: " + args.length); } ColumnVector input = args[0]; + if (numRows != input.getRowCount()) { + throw new IllegalArgumentException("Expected " + numRows + " rows, received " + input.getRowCount()); + } if (!input.getType().equals(DType.STRING)) { throw new IllegalArgumentException("Argument type is not a string column: " + input.getType()); diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala index f3a202c97..5cf8da123 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLDecode.scala @@ -40,11 +40,12 @@ class URLDecode extends Function[String, String] with RapidsUDF with Serializabl } /** Columnar implementation that runs on the GPU */ - override def evaluateColumnar(args: ColumnVector*): ColumnVector = { + override def evaluateColumnar(numRows: Int, args: ColumnVector*): ColumnVector = { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. require(args.length == 1, s"Unexpected argument count: ${args.length}") val input = args.head + require(numRows == input.getRowCount, s"Expected $numRows rows, received ${input.getRowCount}") require(input.getType == DType.STRING, s"Argument type is not a string: ${input.getType}") // The cudf urlDecode does not convert '+' to a space, so do that as a pre-pass first. diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala index d01cb1312..6facee3b9 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/scala/com/nvidia/spark/rapids/udf/scala/URLEncode.scala @@ -39,11 +39,12 @@ class URLEncode extends Function[String, String] with RapidsUDF with Serializabl } /** Columnar implementation that runs on the GPU */ - override def evaluateColumnar(args: ColumnVector*): ColumnVector = { + override def evaluateColumnar(numRows: Int, args: ColumnVector*): ColumnVector = { // The CPU implementation takes a single string argument, so similarly // there should only be one column argument of type STRING. require(args.length == 1, s"Unexpected argument count: ${args.length}") val input = args.head + require(numRows == input.getRowCount, s"Expected $numRows rows, received ${input.getRowCount}") require(input.getType == DType.STRING, s"Argument type is not a string: ${input.getType}") input.urlEncode() } diff --git a/examples/UDF-Examples/Spark-cuSpatial/src/main/java/com/nvidia/spark/rapids/udf/PointInPolygon.java b/examples/UDF-Examples/Spark-cuSpatial/src/main/java/com/nvidia/spark/rapids/udf/PointInPolygon.java index 8d5b368ec..9ae2a4b97 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/src/main/java/com/nvidia/spark/rapids/udf/PointInPolygon.java +++ b/examples/UDF-Examples/Spark-cuSpatial/src/main/java/com/nvidia/spark/rapids/udf/PointInPolygon.java @@ -116,7 +116,7 @@ private void releaseShapeData() { /** Columnar implementation that processes data on the GPU */ @Override - public ColumnVector evaluateColumnar(ColumnVector... args) { + public ColumnVector evaluateColumnar(int numRows, ColumnVector... args) { if (args.length != 2) { throw new IllegalArgumentException("Unexpected argument count: " + args.length + ", expects 2 for (x, y)"); From 9809e8530fb171ce7eb882900d43b1e57987ca5d Mon Sep 17 00:00:00 2001 From: Peixin Date: Mon, 30 Jan 2023 09:21:34 +0800 Subject: [PATCH 05/10] Enable automerge from 23.02 to 23.04 (#272) Signed-off-by: Peixin Li --- .github/workflows/auto-merge.yml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/auto-merge.yml b/.github/workflows/auto-merge.yml index 035c57a9c..c9832fa53 100644 --- a/.github/workflows/auto-merge.yml +++ b/.github/workflows/auto-merge.yml @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -18,7 +18,7 @@ name: auto-merge HEAD to BASE on: pull_request_target: branches: - - branch-22.12 + - branch-23.02 types: [closed] jobs: @@ -29,13 +29,14 @@ jobs: steps: - uses: actions/checkout@v3 with: - ref: branch-22.12 # force to fetch from latest upstream instead of PR ref + ref: branch-23.02 # force to fetch from latest upstream instead of PR ref - name: auto-merge job uses: ./.github/workflows/auto-merge env: OWNER: NVIDIA REPO_NAME: spark-rapids-examples - HEAD: branch-22.12 - BASE: branch-23.02 + HEAD: branch-23.02 + BASE: branch-23.04 AUTOMERGE_TOKEN: ${{ secrets.AUTOMERGE_TOKEN }} # use to merge PR + From ab100b79f0d14d0f88800f8d51e47cb9b8e10e0d Mon Sep 17 00:00:00 2001 From: liyuan <84758614+nvliyuan@users.noreply.github.com> Date: Tue, 14 Feb 2023 11:08:13 +0800 Subject: [PATCH 06/10] update legacy docs (#274) * 1:update python packages build steps 2:incorporate maxRecordsPerBatch to all scripts 3:remove deprecated api docs 4: update xgboost repo to source version Signed-off-by: liyuan * since we have consistence api for pyspasrk+xgboost, so udpate gpu_main or cpu_main to main Signed-off-by: liyuan * since we have consistence api for pyspasrk+xgboost, so udpate gpu_main or cpu_main to main Signed-off-by: liyuan * add aws emr demo Signed-off-by: liyuan * add emr doc links Signed-off-by: liyuan * add getting start pyspark+xgboost on dataproc Signed-off-by: liyuan * remove aws-emr demo due to nccl issue Signed-off-by: liyuan --------- Signed-off-by: liyuan --- .../xgboost-examples-api-docs/python.md | 64 ------ .../xgboost-examples-api-docs/scala.md | 64 ------ .../building-sample-apps/scala.md | 2 +- .../csp/databricks/databricks.md | 44 ++-- .../xgboost-examples/csp/dataproc/gcp.md | 190 ++++++++++++++++++ .../notebook/python-notebook.md | 15 +- .../on-prem-cluster/kubernetes-scala.md | 2 - .../on-prem-cluster/standalone-python.md | 10 +- .../on-prem-cluster/standalone-scala.md | 2 - .../on-prem-cluster/yarn-python.md | 14 +- .../on-prem-cluster/yarn-scala.md | 2 - .../preparation-python.md | 8 +- docs/img/AWS-EMR/RAPIDS_EMR_GUI_1.png | Bin 0 -> 123442 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_2.png | Bin 0 -> 148994 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_2b.png | Bin 0 -> 77707 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_3.png | Bin 0 -> 132071 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_4.png | Bin 0 -> 101190 bytes docs/img/AWS-EMR/RAPIDS_EMR_GUI_5.png | Bin 0 -> 170569 bytes docs/img/GCP/dataproc-cluster.png | Bin 0 -> 36132 bytes docs/img/GCP/dataproc-img2.png | Bin 0 -> 105120 bytes docs/img/GCP/dataproc-img3.png | Bin 0 -> 87085 bytes docs/img/GCP/dataproc-img4.png | Bin 0 -> 182559 bytes docs/img/GCP/dataproc-img5.png | Bin 0 -> 93715 bytes docs/img/GCP/dataproc-img6.png | Bin 0 -> 192933 bytes .../src/main/python/data_gen.py | 2 - examples/XGBoost-Examples/README.md | 3 +- .../notebooks/python/agaricus-gpu.ipynb | 1 + .../supported_xgboost_parameters_python.md | 12 +- .../python/MortgageETL+XGBoost.ipynb | 4 +- .../notebooks/python/MortgageETL.ipynb | 1 - .../notebooks/python/cv-mortgage-gpu.ipynb | 1 + .../notebooks/python/mortgage-gpu.ipynb | 1 + .../notebooks/scala/mortgage-ETL.ipynb | 1 - .../notebooks/scala/mortgage-gpu.ipynb | 1 - .../taxi/notebooks/python/cv-taxi-gpu.ipynb | 1 + .../taxi/notebooks/python/taxi-gpu.ipynb | 1 + 36 files changed, 245 insertions(+), 201 deletions(-) delete mode 100644 docs/api-docs/xgboost-examples-api-docs/python.md delete mode 100644 docs/api-docs/xgboost-examples-api-docs/scala.md create mode 100644 docs/get-started/xgboost-examples/csp/dataproc/gcp.md create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_1.png create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_2.png create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_2b.png create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_3.png create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_4.png create mode 100644 docs/img/AWS-EMR/RAPIDS_EMR_GUI_5.png create mode 100644 docs/img/GCP/dataproc-cluster.png create mode 100644 docs/img/GCP/dataproc-img2.png create mode 100644 docs/img/GCP/dataproc-img3.png create mode 100644 docs/img/GCP/dataproc-img4.png create mode 100644 docs/img/GCP/dataproc-img5.png create mode 100644 docs/img/GCP/dataproc-img6.png diff --git a/docs/api-docs/xgboost-examples-api-docs/python.md b/docs/api-docs/xgboost-examples-api-docs/python.md deleted file mode 100644 index 4575384d5..000000000 --- a/docs/api-docs/xgboost-examples-api-docs/python.md +++ /dev/null @@ -1,64 +0,0 @@ -# Python API for XGBoost-Spark - -This doc focuses on GPU related Python API interfaces. Four new classes are introduced to support ML things on spark. - -- [XGBoostClassifier](#xgboostclassifier) -- [XGBoostClassificationModel](#xgboostclassificationmodel) -- [XGBoostRegressor](#xgboostregressor) -- [XGBoostRegressionModel](#xgboostregressionmodel) - - -### XGBoostClassifier - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier. It is a wrapper around [Scala XGBoostClassifier](scala.md#xgboostclassifier). - -##### Constructors - -+ XGBoostClassifier(\*\*params) - + all [standard xgboost parameters](https://xgboost.readthedocs.io/en/latest/parameter.html) are supported, but please note a few differences: - + only camelCase is supported when specifying parameter names, e.g., *maxDepth* - + parameter *lambda* is renamed to *lambda_*, because *lambda* is a keyword in Python - -##### Methods - -*Note: Only GPU related methods are listed below.* - -+ setFeaturesCols(features_cols). This method sets the feature columns for training. - + features_cols: a list of feature column names in String format to set - + returns the classifier itself - -### XGBoostClassificationModel - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel. It is a wrapper around [Scala XGBoostClassificationModel](scala.md#xgboostclassificationmodel). - -##### Methods - -*No GPU specific methods, use it as a normal spark model.* - -### XGBoostRegressor - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostRegressor. It is a wrapper around [Scala XGBoostRegressor](scala.md#xgboostregressor). - -##### Constructors - -+ XGBoostRegressor(\*\*params) - + all [standard xgboost parameters](https://xgboost.readthedocs.io/en/latest/parameter.html) are supported, but please note a few differences: - + only camelCase is supported when specifying parameter names, e.g., *maxDepth* - + parameter *lambda* is renamed to *lambda_*, because *lambda* is a keyword in Python - -##### Methods - -*Note: Only GPU related methods are listed below.* - -+ setFeaturesCols(features_cols). This method sets the feature columns for training. - + features_cols: a list of feature column names in String format to set - + returns the regressor itself - -### XGBoostRegressionModel - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostRegressionModel. It is a wrapper around [Scala XGBoostRegressionModel](scala.md#xgboostregressionmodel). - -##### Methods - -*No GPU specific methods, use it as a normal spark model.* - diff --git a/docs/api-docs/xgboost-examples-api-docs/scala.md b/docs/api-docs/xgboost-examples-api-docs/scala.md deleted file mode 100644 index 8ad403ba3..000000000 --- a/docs/api-docs/xgboost-examples-api-docs/scala.md +++ /dev/null @@ -1,64 +0,0 @@ -# Scala API for XGBoost-Spark3.0 - -This doc focuses on GPU related Scala API interfaces, and fortunately only one new API is introduced to support training on GPU. - -XGBoost-Spark3.0 provides four classes as below to support ML things on spark: - -- [XGBoostClassifier](#xgboostclassifier) -- [XGBoostClassificationModel](#xgboostclassificationmodel) -- [XGBoostRegressor](#xgboostregressor) -- [XGBoostRegressionModel](#xgboostregressionmodel) - -### XGBoostClassifier - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier. It extends [ProbabilisticClassifier](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.classification.ProbabilisticClassifier)[[Vector](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.linalg.Vector), [XGBoostClassifier](#xgboostclassifier), [XGBoostClassificationModel](#xgboostclassificationmodel)]. - -##### Constructors - -+ XGBoostClassifier(xgboostParams: Map[String, Any]) - + all [standard xgboost parameters](https://xgboost.readthedocs.io/en/latest/parameter.html) are supported - + eval_sets: Map[String,DataFrame] is used to set the named evaluation dataset(s) for training. - -##### Methods - -*Note: Only GPU related methods are listed below.* - -+ setFeaturesCols(value: Seq[String]): [XGBoostClassifier](#xgboostclassifier). This method sets the feature columns for training. - + value: a sequence of feature column name - + returns the classifier itself - -### XGBoostClassificationModel - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel. It extends [ProbabilisticClassificationModel](https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.classification.ProbabilisticClassificationModel)[[Vector](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.linalg.Vector), [XGBoostClassificationModel](#xgboostclassificationmodel)]. - -##### Methods - -*No GPU specific methods, use it as a normal spark model.* - -### XGBoostRegressor - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostRegressor. It extends [Predictor](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.Predictor)[[Vector](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.linalg.Vector), [XGBoostRegressor](#xgboostregressor), [XGBoostRegressionModel](#xgboostregressionmodel)]. - -##### Constructors - -+ XGBoostRegressor(xgboostParams: Map[String, Any]) - + all [standard xgboost parameters](https://xgboost.readthedocs.io/en/latest/parameter.html) are supported - + eval_sets: Map[String,DataFrame] is used to set the named evaluation dataset(s) for training. - -##### Methods - -*Note: Only GPU related methods are listed below.* - -+ setFeaturesCols(value: Seq[String]): [XGBoostRegressor](#xgboostregressor). This method sets the feature columns for training. - + value: a sequence of feature column names to set - + returns the regressor itself - -### XGBoostRegressionModel - -The full name is ml.dmlc.xgboost4j.scala.spark.XGBoostRegressionModel. It extends [PredictionModel](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.PredictionModel)[[Vector](https://spark.apache.org/docs/latest/api/scala/#org.apache.spark.ml.linalg.Vector), [XGBoostRegressionModel](#xgboostregressionmodel)]. - -##### Methods - -*No GPU specific methods, use it as a normal spark model.* - - diff --git a/docs/get-started/xgboost-examples/building-sample-apps/scala.md b/docs/get-started/xgboost-examples/building-sample-apps/scala.md index dc8327c21..fd91415b1 100644 --- a/docs/get-started/xgboost-examples/building-sample-apps/scala.md +++ b/docs/get-started/xgboost-examples/building-sample-apps/scala.md @@ -1,6 +1,6 @@ # Build XGBoost Scala Examples -The examples rely on [XGBoost](https://github.com/nvidia/spark-xgboost). +The examples rely on [XGBoost](https://github.com/dmlc/xgboost). ## Build diff --git a/docs/get-started/xgboost-examples/csp/databricks/databricks.md b/docs/get-started/xgboost-examples/csp/databricks/databricks.md index c09576d0e..61891bf45 100644 --- a/docs/get-started/xgboost-examples/csp/databricks/databricks.md +++ b/docs/get-started/xgboost-examples/csp/databricks/databricks.md @@ -159,35 +159,33 @@ Accuracy is 0.9980699597729774 Limitations ------------- -1. Adaptive query execution(AQE) and Delta optimization write do not work. These should be disabled -when using the plugin. Queries may still see significant speedups even with AQE disabled. +1. When selecting GPU nodes, Databricks UI requires the driver node to be a GPU node. However you + can use Databricks API to create a cluster with CPU driver node. + Outside of Databricks the plugin can operate with the driver as a CPU node and workers as GPU nodes. - ```bash - spark.databricks.delta.optimizeWrite.enabled false - spark.sql.adaptive.enabled false - ``` - - See [issue-1059](https://github.com/NVIDIA/spark-rapids/issues/1059) for more detail. - -2. Dynamic partition pruning(DPP) does not work. This results in poor performance for queries which - would normally benefit from DPP. See - [issue-3143](https://github.com/NVIDIA/spark-rapids/issues/3143) for more detail. +2. Cannot spin off multiple executors on a multi-GPU node. -3. When selecting GPU nodes, Databricks requires the driver node to be a GPU node. Outside of - Databricks the plugin can operate with the driver as a CPU node and workers as GPU nodes. + Even though it is possible to set `spark.executor.resource.gpu.amount=1` in the in Spark + Configuration tab, Databricks overrides this to `spark.executor.resource.gpu.amount=N` + (where N is the number of GPUs per node). This will result in failed executors when starting the + cluster. -4. Cannot spin off multiple executors on a multi-GPU node. +3. Parquet rebase mode is set to "LEGACY" by default. - Even though it is possible to set `spark.executor.resource.gpu.amount=N` (where N is the number - of GPUs per node) in the in Spark Configuration tab, Databricks overrides this to - `spark.executor.resource.gpu.amount=1`. This will result in failed executors when starting the - cluster. + The following Spark configurations are set to `LEGACY` by default on Databricks: + + ``` + spark.sql.legacy.parquet.datetimeRebaseModeInWrite + spark.sql.legacy.parquet.int96RebaseModeInWrite + ``` + + These settings will cause a CPU fallback for Parquet writes involving dates and timestamps. + If you do not need `LEGACY` write semantics, set these configs to `EXCEPTION` which is + the default value in Apache Spark 3.0 and higher. -5. Databricks makes changes to the runtime without notification. +4. Databricks makes changes to the runtime without notification. Databricks makes changes to existing runtimes, applying patches, without notification. [Issue-3098](https://github.com/NVIDIA/spark-rapids/issues/3098) is one example of this. We run regular integration tests on the Databricks environment to catch these issues and fix them once - detected. - -* The timings in this Getting Started guide are only illustrative. Please see our [release announcement](https://medium.com/rapids-ai/nvidia-gpus-and-apache-spark-one-step-closer-2d99e37ac8fd) for official benchmarks. + detected. \ No newline at end of file diff --git a/docs/get-started/xgboost-examples/csp/dataproc/gcp.md b/docs/get-started/xgboost-examples/csp/dataproc/gcp.md new file mode 100644 index 000000000..bfbb362b0 --- /dev/null +++ b/docs/get-started/xgboost-examples/csp/dataproc/gcp.md @@ -0,0 +1,190 @@ +# Getting started pyspark+xgboost with RAPIDS Accelerator on GCP Dataproc + [Google Cloud Dataproc](https://cloud.google.com/dataproc) is Google Cloud's fully managed Apache + Spark and Hadoop service. Please make sure to install gcloud CLI by following + this [guide](https://cloud.google.com/sdk/docs/install) before getting started. + +## Create a Dataproc Cluster using T4's +* One 16-core master node and 2 32-core worker nodes +* Two NVIDIA T4 for each worker node + +```bash + export REGION=[Your Preferred GCP Region] + export GCS_BUCKET=[Your GCS Bucket] + export CLUSTER_NAME=[Your Cluster Name] + export NUM_GPUS=2 + export NUM_WORKERS=2 + +gcloud dataproc clusters create $CLUSTER_NAME \ + --region=$REGION \ + --image-version=2.0-ubuntu18 \ + --master-machine-type=n1-standard-16 \ + --num-workers=$NUM_WORKERS \ + --worker-accelerator=type=nvidia-tesla-t4,count=$NUM_GPUS \ + --worker-machine-type=n1-highmem-32\ + --num-worker-local-ssds=4 \ + --initialization-actions=gs://goog-dataproc-initialization-actions-${REGION}/spark-rapids/spark-rapids.sh \ + --optional-components=JUPYTER,ZEPPELIN \ + --metadata=rapids-runtime=SPARK \ + --bucket=$GCS_BUCKET \ + --enable-component-gateway \ + --subnet=default +``` + +Explanation of parameters: +* NUM_GPUS = number of GPUs to attach to each worker node in the cluster +* NUM_WORKERS = number of Spark worker nodes in the cluster + +This takes around 10-15 minutes to complete. You can navigate to the Dataproc clusters tab in the +Google Cloud Console to see the progress. + +![Dataproc Cluster](../../../../img/GCP/dataproc-cluster.png) + +If you'd like to further accelerate init time to 4-5 minutes, create a custom Dataproc image using +[this](#build-custom-dataproc-image-to-accelerate-cluster-init-time) guide. + + +## Get Application Files, Jar and Dataset + +Bash into the master node and make sure you have prepared the necessary packages and dataset by following this [guide](/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md). + +Note: Since there is no maven CLI in master node, so we need to manually install. +``` bash +gcloud compute ssh your-name@your-cluster-m --zone your-zone +sudo apt-get install maven -y +``` + +Then create a directory in HDFS, and run below commands, + +``` bash +[xgboost4j_spark_python]$ hadoop fs -mkdir /tmp/xgboost4j_spark_python +[xgboost4j_spark_python]$ hadoop fs -copyFromLocal ${SPARK_XGBOOST_DIR}/mortgage/* /tmp/xgboost4j_spark_python +``` + +## Preparing libraries +Please make sure to install the XGBoost, cudf-cu11, numpy libraries on all nodes before running XGBoost application. +``` bash +pip install xgboost +pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com +pip install numpy +pip install scikit-learn +``` +You can also create an isolated python environment by using (Virtualenv)[https://virtualenv.pypa.io/en/latest/], +and then directly pass/unpack the archive file and enable the environment on executors +by leveraging the --archives option or spark.archives configuration. +``` bash +# create an isolated python environment and install libraries +python -m venv pyspark_venv +source pyspark_venv/bin/activate +pip install xgboost +pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com +pip install numpy +pip install scikit-learn +pip install venv-pack +venv-pack -o pyspark_venv.tar.gz + +# enable archive python environment on executors +export PYSPARK_DRIVER_PYTHON=python # Do not set in cluster modes. +export PYSPARK_PYTHON=./environment/bin/python +spark-submit --archives pyspark_venv.tar.gz#environment app.py +``` +## Run jupyter notebooks on Dataproc + +Bash into the master node and start up the notebook. +``` +jupyter notebook --ip=0.0.0.0 --port=8124 --no-browser +``` + +If you want to remote access the notebook from local, please reserve an external static IP address first: +1. Access the IP addresses page through the navigation menu: `VPC network` -> `IP addresses` +![dataproc img2](../../../../img/GCP/dataproc-img2.png) +2. Click the `RESERVE EXTERNAL STATIC ADDRESS` button +![dataproc img3](../../../../img/GCP/dataproc-img3.png) +3. Attached the static address to the master node of your cluster +![dataproc img4](../../../../img/GCP/dataproc-img4.png) +4. Then you can access and run the notebook from the browser in local using the reserved address. +![dataproc img5](../../../../img/GCP/dataproc-img5.png) + +Then you can run the [notebook](../../../../../examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb) and get the benchmark results. +![dataproc img6](../../../../img/GCP/dataproc-img6.png) + +## Build custom dataproc image to accelerate cluster init time +In order to accelerate cluster init time to 3-4 minutes, we need to build a custom Dataproc image +that already has NVIDIA drivers and CUDA toolkit installed, with RAPIDS deployed. The custom image +could also be used in an air gap environment. In this section, we will be using [these instructions +from GCP](https://cloud.google.com/dataproc/docs/guides/dataproc-images) to create a custom image. + +Currently, we can directly download the [spark-rapids.sh](https://github.com/GoogleCloudDataproc/initialization-actions/tree/master/spark-rapids) +script to create the Dataproc image: + +Google provides a `generate_custom_image.py` script that: +- Launches a temporary Compute Engine VM instance with the specified Dataproc base image. +- Then runs the customization script inside the VM instance to install custom packages and/or +update configurations. +- After the customization script finishes, it shuts down the VM instance and creates a Dataproc + custom image from the disk of the VM instance. +- The temporary VM is deleted after the custom image is created. +- The custom image is saved and can be used to create Dataproc clusters. + +Download `spark-rapids.sh` in this repo. The script uses +Google's `generate_custom_image.py` script. This step may take 20-25 minutes to complete. + +```bash +git clone https://github.com/GoogleCloudDataproc/custom-images +cd custom-images + +export CUSTOMIZATION_SCRIPT=/path/to/spark-rapids.sh +export ZONE=[Your Preferred GCP Zone] +export GCS_BUCKET=[Your GCS Bucket] +export IMAGE_NAME=sample-20-ubuntu18-gpu-t4 +export DATAPROC_VERSION=2.0-ubuntu18 +export GPU_NAME=nvidia-tesla-t4 +export GPU_COUNT=1 + +python generate_custom_image.py \ + --image-name $IMAGE_NAME \ + --dataproc-version $DATAPROC_VERSION \ + --customization-script $CUSTOMIZATION_SCRIPT \ + --no-smoke-test \ + --zone $ZONE \ + --gcs-bucket $GCS_BUCKET \ + --machine-type n1-standard-4 \ + --accelerator type=$GPU_NAME,count=$GPU_COUNT \ + --disk-size 200 \ + --subnet default +``` + +See [here](https://cloud.google.com/dataproc/docs/guides/dataproc-images#running_the_code) for more +details on `generate_custom_image.py` script arguments and +[here](https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions) for dataproc +version description. + +The image `sample-20-ubuntu18-gpu-t4` is now ready and can be viewed in the GCP console under +`Compute Engine > Storage > Images`. The next step is to launch the cluster using this new image +and new initialization actions (that do not install NVIDIA drivers since we are already past that +step). + +Move this to your own bucket. Let's launch the cluster: + +```bash +export REGION=[Your Preferred GCP Region] +export GCS_BUCKET=[Your GCS Bucket] +export CLUSTER_NAME=[Your Cluster Name] +export NUM_GPUS=1 +export NUM_WORKERS=2 + +gcloud dataproc clusters create $CLUSTER_NAME \ + --region=$REGION \ + --image=sample-20-ubuntu18-gpu-t4 \ + --master-machine-type=n1-standard-4 \ + --num-workers=$NUM_WORKERS \ + --worker-accelerator=type=nvidia-tesla-t4,count=$NUM_GPUS \ + --worker-machine-type=n1-standard-4 \ + --num-worker-local-ssds=1 \ + --optional-components=JUPYTER,ZEPPELIN \ + --metadata=rapids-runtime=SPARK \ + --bucket=$GCS_BUCKET \ + --enable-component-gateway \ + --subnet=default +``` + +The new cluster should be up and running within 3-4 minutes! \ No newline at end of file diff --git a/docs/get-started/xgboost-examples/notebook/python-notebook.md b/docs/get-started/xgboost-examples/notebook/python-notebook.md index c8cf57c3c..e00d3622f 100644 --- a/docs/get-started/xgboost-examples/notebook/python-notebook.md +++ b/docs/get-started/xgboost-examples/notebook/python-notebook.md @@ -1,4 +1,4 @@ -Get Started with XGBoost4J-Spark with Jupyter Notebook +Get Started with pyspark+XGBoost with Jupyter Notebook =================================================================== This is a getting started guide to XGBoost4J-Spark using an [Jupyter notebook](https://jupyter.org/). @@ -29,14 +29,13 @@ and the home directory for Apache Spark respectively. PYSPARK_DRIVER_PYTHON_OPTS=notebook \ pyspark \ --master ${SPARK_MASTER} \ - --jars ${RAPIDS_JAR},${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}\ - --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ + --jars ${RAPIDS_JAR}\ + --py-files ${SAMPLE_ZIP} \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.executor.cores=10 \ --conf spark.task.resource.gpu.amount=0.1 \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ - --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh ``` @@ -48,20 +47,18 @@ and the home directory for Apache Spark respectively. PYSPARK_DRIVER_PYTHON_OPTS=notebook \ pyspark \ --master ${SPARK_MASTER} \ - --jars ${RAPIDS_JAR},${XGBOOST4J_JAR},${XGBOOST4J_SPARK_JAR}\ - --py-files ${XGBOOST4J_SPARK_JAR},${SAMPLE_ZIP} \ + --jars ${RAPIDS_JAR}\ + --py-files ${SAMPLE_ZIP} \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.executor.cores=10 \ --conf spark.task.resource.gpu.amount=1 \ - --conf spark.rapids.sql.hasNans=false \ + --conf spark.sql.execution.arrow.maxRecordsPerBatch=200000 \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh ``` - - 4. Launch ETL Part - Mortgage ETL Notebook: [Python](../../../../examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb) diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md index 4c823e7d7..ed7394d9b 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/kubernetes-scala.md @@ -106,7 +106,6 @@ ${SPARK_HOME}/bin/spark-submit \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ - --conf spark.rapids.sql.hasNans=false \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ --master \ @@ -186,7 +185,6 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ - --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md index b41824fe2..5e24f3983 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md @@ -115,7 +115,6 @@ ${SPARK_HOME}/bin/spark-submit \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ - --conf spark.rapids.sql.hasNans=false \ --py-files ${SAMPLE_ZIP} \ main.py \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ @@ -186,9 +185,9 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.gpu_main -# or change to com.nvidia.spark.examples.taxi.gpu_main to run Taxi Xgboost benchmark -# or change to com.nvidia.spark.examples.agaricus.gpu_main to run Agaricus Xgboost benchmark +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.main +# or change to com.nvidia.spark.examples.taxi.main to run Taxi Xgboost benchmark +# or change to com.nvidia.spark.examples.agaricus.main to run Agaricus Xgboost benchmark # tree construction algorithm export TREE_METHOD=gpu_hist @@ -206,7 +205,6 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ - --conf spark.rapids.sql.hasNans=false \ --master ${SPARK_MASTER} \ --driver-memory ${SPARK_DRIVER_MEMORY} \ --executor-memory ${SPARK_EXECUTOR_MEMORY} \ @@ -271,7 +269,7 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.cpu_main +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.main # Please make sure to change the class while running Taxi or Agaricus benchmark # tree construction algorithm diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md index c1b512b07..bbb688bef 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/standalone-scala.md @@ -109,7 +109,6 @@ ${SPARK_HOME}/bin/spark-submit \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ - --conf spark.rapids.sql.hasNans=false \ --class com.nvidia.spark.examples.mortgage.ETLMain \ $SAMPLE_JAR \ -format=csv \ @@ -194,7 +193,6 @@ Run spark-submit: ${SPARK_HOME}/bin/spark-submit \ --conf spark.plugins=com.nvidia.spark.SQLPlugin \ --conf spark.rapids.memory.gpu.pool=NONE \ - --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ --master ${SPARK_MASTER} \ diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md index f2bff0fdd..3565d6267 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-python.md @@ -91,7 +91,6 @@ ${SPARK_HOME}/bin/spark-submit \ --conf spark.rapids.sql.incompatibleDateFormats.enabled=true \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ - --conf spark.rapids.sql.hasNans=false \ --jars ${RAPIDS_JAR}\ ${MAIN_PY} \ --mainClass='com.nvidia.spark.examples.mortgage.etl_main' \ @@ -136,9 +135,9 @@ export SPARK_EXECUTOR_MEMORY=8g export SPARK_PYTHON_ENTRYPOINT=${LIBS_PATH}/main.py # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.gpu_main -# or change to com.nvidia.spark.examples.taxi.gpu_main to run Taxi Xgboost benchmark -# or change to com.nvidia.spark.examples.agaricus.gpu_main to run Agaricus Xgboost benchmark +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.main +# or change to com.nvidia.spark.examples.taxi.main to run Taxi Xgboost benchmark +# or change to com.nvidia.spark.examples.agaricus.main to run Agaricus Xgboost benchmark # tree construction algorithm export TREE_METHOD=gpu_hist @@ -156,7 +155,6 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ - --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files ${SPARK_HOME}/examples/src/main/scripts/getGpusResources.sh \ --master yarn \ @@ -217,9 +215,9 @@ export SPARK_DRIVER_MEMORY=4g export SPARK_EXECUTOR_MEMORY=8g # example class to use -export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.cpu_main -# or change to com.nvidia.spark.examples.taxi.cpu_main to run Taxi Xgboost benchmark -# or change to com.nvidia.spark.examples.agaricus.cpu_main to run Agaricus Xgboost benchmark +export EXAMPLE_CLASS=com.nvidia.spark.examples.mortgage.main +# or change to com.nvidia.spark.examples.taxi.main to run Taxi Xgboost benchmark +# or change to com.nvidia.spark.examples.agaricus.main to run Agaricus Xgboost benchmark # tree construction algorithm export TREE_METHOD=hist diff --git a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md index dc6918ee4..244a1ebf2 100644 --- a/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md +++ b/docs/get-started/xgboost-examples/on-prem-cluster/yarn-scala.md @@ -67,7 +67,6 @@ ${SPARK_HOME}/bin/spark-submit \ --conf spark.rapids.sql.csv.read.double.enabled=true \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --conf spark.sql.cache.serializer=com.nvidia.spark.ParquetCachedBatchSerializer \ - --conf spark.rapids.sql.hasNans=false \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ --master yarn \ @@ -131,7 +130,6 @@ ${SPARK_HOME}/bin/spark-submit --conf spark.rapids.memory.gpu.pool=NONE \ --conf spark.executor.resource.gpu.amount=1 \ --conf spark.task.resource.gpu.amount=1 \ - --conf spark.rapids.sql.hasNans=false \ --conf spark.executor.resource.gpu.discoveryScript=./getGpusResources.sh \ --files $SPARK_HOME/examples/src/main/scripts/getGpusResources.sh \ --jars ${RAPIDS_JAR} \ diff --git a/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md b/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md index 2178d6d75..2a43add8d 100644 --- a/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md +++ b/docs/get-started/xgboost-examples/prepare-package-data/preparation-python.md @@ -4,12 +4,8 @@ For simplicity export the location to these jars. All examples assume the packag ### Download the jars -1. Download the XGBoost for Apache Spark jars - * [XGBoost4j Package](https://repo1.maven.org/maven2/com/nvidia/xgboost4j_3.0/1.4.2-0.3.0/) - * [XGBoost4j-Spark Package](https://repo1.maven.org/maven2/com/nvidia/xgboost4j-spark_3.0/1.4.2-0.3.0/) - -2. 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znXB!BSf`v(utqmKAF|t=@5qqELaWqW$`w7#o-t#Mmnv);da=^cyFB)0OxYvVe*sYa zZWvK_he~7~P|B;_#4i6XV7uguZo9}Bv!>d{$!Nn)74I1L8;ISh-5xMm$z+7@_fvN9e0)W8<0vf z&UAMHzGD+ssyv{|dBYbIxL#aVf}ks0zJ8OCAaiYJS0m=pg2rK>;Wd^-Z=UOLaA1b zpm^#>x}I35?7uQ+m$5)h0Ah}!8yM){>nVpDxs}v*bXBAYrfKnieC&D{AHi#_o+|VQ z#dDH?cqt$}mDfbzKv(?`E^gm)B&Nd11aA$vxAH73Cc>DChy0_qAC{Id20?Q?g@Z8> zSaf|pprr~~RB->O`{zk6>$sb%?TaFJJeaV%>b`AG|JDEXAAd&m$Od=ktjYXelmLIZ ze?QkZFpQn^WYjdv@#)`R=)eD=zX(vqnTOaXrvH$vs9|*y_ z2G-rA|Cdzi$=$G=S2YWSRx7W6zK5+po7W zhr1OfEOP5t+VMYLd|@ms=j852&!0DZwNL;5@UQw3|Nq0k>Pz{5|KVQ}R>_Y?wRo>f RS_6MH)lVPKJ9_TV{{cAkq}%`i literal 0 HcmV?d00001 diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/data_gen.py b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/data_gen.py index 6d637b2f8..6267058e7 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/data_gen.py +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/src/main/python/data_gen.py @@ -958,8 +958,6 @@ def gen_scalars_for_sql(data_gen, count, seed=0, force_no_nulls=False): allow_negative_scale_of_decimal_conf = {'spark.sql.legacy.allowNegativeScaleOfDecimal': 'true'} -no_nans_conf = {'spark.rapids.sql.hasNans': 'false'} - def copy_and_update(conf, *more_confs): local_conf = conf.copy() for more in more_confs: diff --git a/examples/XGBoost-Examples/README.md b/examples/XGBoost-Examples/README.md index 5d38f816f..7ec7dda9e 100644 --- a/examples/XGBoost-Examples/README.md +++ b/examples/XGBoost-Examples/README.md @@ -84,7 +84,8 @@ Please follow below steps to run the example Spark jobs in different Spark envir - Amazon AWS - [EC2](/docs/get-started/xgboost-examples/csp/aws/ec2.md) - [Databricks](/docs/get-started/xgboost-examples/csp/databricks/databricks.md) - + - [GCP](/docs/get-started/xgboost-examples/csp/dataproc/gcp.md) + Please follow below steps to run the example notebooks in different notebook environments: - Getting started for Jupyter Notebook applications diff --git a/examples/XGBoost-Examples/agaricus/notebooks/python/agaricus-gpu.ipynb b/examples/XGBoost-Examples/agaricus/notebooks/python/agaricus-gpu.ipynb index a49b8eca0..d41cc0b81 100644 --- a/examples/XGBoost-Examples/agaricus/notebooks/python/agaricus-gpu.ipynb +++ b/examples/XGBoost-Examples/agaricus/notebooks/python/agaricus-gpu.ipynb @@ -116,6 +116,7 @@ "conf.set(\"spark.rapids.sql.enabled\", \"true\") \n", "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", "conf.set(\"spark.sql.cache.serializer\",\"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", + "conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\", 200000) \n", "conf.set(\"spark.driver.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.executor.extraClassPath\", RAPIDS_JAR)\n", "\n", diff --git a/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_python.md b/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_python.md index b3cf72ebf..ad9190ff4 100644 --- a/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_python.md +++ b/examples/XGBoost-Examples/app-parameters/supported_xgboost_parameters_python.md @@ -7,12 +7,12 @@ This is a description of all the parameters available when you are running examp * Please use the `camelCase`, e.g., `--treeMethod=gpu_hist`. * `lambda` is replaced with `lambda_`, because `lambda` is a keyword in Python. 2. `--mainClass=[app class]`: The entry class of the application to be started. Available value is one of the below classes. - * com.nvidia.spark.examples.agaricus.cpu_main - * com.nvidia.spark.examples.agaricus.gpu_main - * com.nvidia.spark.examples.mortgage.cpu_main - * com.nvidia.spark.examples.mortgage.gpu_main - * com.nvidia.spark.examples.taxi.cpu_main - * com.nvidia.spark.examples.taxi.gpu_main + * com.nvidia.spark.examples.agaricus.main + * com.nvidia.spark.examples.agaricus.main + * com.nvidia.spark.examples.mortgage.main + * com.nvidia.spark.examples.mortgage.main + * com.nvidia.spark.examples.taxi.main + * com.nvidia.spark.examples.taxi.main * com.nvidia.spark.examples.mortgage.etl_main * com.nvidia.spark.examples.taxi.etl_main 3. `--format=[csv|parquet|orc]`: The format of the data for training/transforming, now only supports 'csv', 'parquet' and 'orc'. *Required*. diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb index 490dc08f2..538a6f3fd 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL+XGBoost.ipynb @@ -95,6 +95,7 @@ "conf.set(\"spark.rapids.sql.enabled\", \"true\") \n", "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", "conf.set(\"spark.sql.cache.serializer\",\"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", + "conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\", 200000) \n", "conf.set(\"spark.driver.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.executor.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.jars\", RAPIDS_JAR)\n", @@ -773,8 +774,7 @@ "spark.conf.set(\"spark.sql.files.maxPartitionBytes\", \"1G\")\n", "spark.conf.set(\"spark.rapids.sql.explain\", \"ALL\")\n", "spark.conf.set(\"spark.rapids.sql.batchSizeBytes\", \"512M\")\n", - "spark.conf.set(\"spark.rapids.sql.reader.batchSizeBytes\", \"768M\")\n", - "spark.conf.set(\"spark.rapids.sql.hasNans\", \"false\")" + "spark.conf.set(\"spark.rapids.sql.reader.batchSizeBytes\", \"768M\")\n" ] }, { diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb index f0f5bd7b4..bb1249d61 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/MortgageETL.ipynb @@ -822,7 +822,6 @@ "# CPU run, set to false\n", "# spark.conf.set('spark.rapids.sql.enabled', 'false')\n", "spark.conf.set('spark.sql.files.maxPartitionBytes', '1G')\n", - "spark.conf.set(\"spark.rapids.sql.hasNans\", \"false\")\n", "# use GPU to read CSV\n", "spark.conf.set(\"spark.rapids.sql.csv.read.double.enabled\", \"true\")" ] diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb index 336595d59..971ffdc6a 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb @@ -102,6 +102,7 @@ "conf.set(\"spark.rapids.sql.enabled\", \"true\") \n", "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", "conf.set(\"spark.sql.cache.serializer\",\"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", + "conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\", 200000) \n", "conf.set(\"spark.driver.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.executor.extraClassPath\", RAPIDS_JAR)\n", "# if you pass/unpack the archive file and enable the environment\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb index e103567b4..a5e9e174f 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/mortgage-gpu.ipynb @@ -124,6 +124,7 @@ "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", "conf.set(\"spark.sql.cache.serializer\",\"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", "conf.set(\"spark.driver.extraClassPath\", RAPIDS_JAR)\n", + "conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\", 200000) \n", "conf.set(\"spark.executor.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.jars\", RAPIDS_JAR)\n", "\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index 75319f42e..d1b8e5f7e 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -732,7 +732,6 @@ "// remove config(\"spark.sql.cache.serializer\", \"com.nvidia.spark.ParquetCachedBatchSerializer\") for CPU\n", "sparkSession.conf.set(\"spark.sql.files.maxPartitionBytes\", \"1G\")\n", "sparkSession.conf.set(\"spark.sql.broadcastTimeout\", 700)\n", - "sparkSession.conf.set(\"spark.rapids.sql.hasNans\", false)\n", "// use GPU to read CSV\n", "sparkSession.conf.set(\"spark.rapids.sql.csv.read.double.enabled\", true)\n", "\n", diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb index 872e09c2d..cf61395c9 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-gpu.ipynb @@ -184,7 +184,6 @@ "source": [ "// Build the spark session and data reader as usual\n", "val sparkSession = SparkSession.builder.appName(\"mortgage-gpu\").getOrCreate\n", - "sparkSession.conf.set(\"spark.rapids.sql.hasNans\", false)\n", "val reader = sparkSession.read" ] }, diff --git a/examples/XGBoost-Examples/taxi/notebooks/python/cv-taxi-gpu.ipynb b/examples/XGBoost-Examples/taxi/notebooks/python/cv-taxi-gpu.ipynb index 829d3c541..a2d5b7c60 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/python/cv-taxi-gpu.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/python/cv-taxi-gpu.ipynb @@ -103,6 +103,7 @@ "conf.set(\"spark.rapids.sql.enabled\", \"true\") \n", "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", "conf.set(\"spark.sql.cache.serializer\",\"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", + "conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\", 200000) \n", "conf.set(\"spark.driver.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.executor.extraClassPath\", RAPIDS_JAR)\n", "# if you pass/unpack the archive file and enable the environment\n", diff --git a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-gpu.ipynb b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-gpu.ipynb index 593d381d2..5cb8c5c0e 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-gpu.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-gpu.ipynb @@ -115,6 +115,7 @@ "conf.set(\"spark.rapids.sql.enabled\", \"true\") \n", "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", "conf.set(\"spark.sql.cache.serializer\",\"com.nvidia.spark.ParquetCachedBatchSerializer\")\n", + "conf.set(\"spark.sql.execution.arrow.maxRecordsPerBatch\", 200000) \n", "conf.set(\"spark.driver.extraClassPath\", RAPIDS_JAR)\n", "conf.set(\"spark.executor.extraClassPath\", RAPIDS_JAR)\n", "\n", From e8d7108f54cce8b5ddb063bf1656d0c278494ff7 Mon Sep 17 00:00:00 2001 From: Saurav Date: Wed, 15 Feb 2023 18:47:25 +0530 Subject: [PATCH 07/10] Retail analytics (#275) * merge dev-2210 branch to Main branch (#237) * Init 22.10.0-SNAPSHOT (#214) Signed-off-by: Peixin Li Signed-off-by: Peixin Li * update version and fix some document error, add more comments for running xgboost notebooks on GCP (#215) (#222) Signed-off-by: liyuan Signed-off-by: liyuan Signed-off-by: liyuan * update version and fix some document error, add more comments for running xgboost notebooks on GCP (#215) (#224) Signed-off-by: liyuan Signed-off-by: liyuan Signed-off-by: liyuan * Update default cmake to 3.23.X in udf exmaple dockerfile (#227) Signed-off-by: Peixin Li Signed-off-by: Peixin Li * [xgboost] Remove default parameters (#226) * remove the default parameters for xgboost examples * remove the default parameters Signed-off-by: Bobby Wang * remove unused variables for mortgage-ETL Signed-off-by: Bobby Wang * add more details/notes for the mortgage perforamcne tests (#229) * add more details/notes for the mortgage perforamcne tests Signed-off-by: liyuan * Update examples/XGBoost-Examples/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/XGBoost-Examples/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Update examples/XGBoost-Examples/README.md Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> Signed-off-by: liyuan Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Enable automerge from 22.10 to 22.12 (#230) Signed-off-by: Peixin Li Signed-off-by: Peixin Li * update versions for v22.10 release (#235) Signed-off-by: liyuan Signed-off-by: liyuan Signed-off-by: Peixin Li Signed-off-by: liyuan Signed-off-by: Bobby Wang Co-authored-by: Jenkins Automation <70000568+nvauto@users.noreply.github.com> Co-authored-by: Peixin Co-authored-by: Bobby Wang Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> * Adding Databricks tool demo notebooks for qualification and profiling (#249) * Adding Databricks tool demo notebooks for basic qualification and profiling Signed-off-by: Matt Ahrens * Adding README updates for the databricks tool notebooks Signed-off-by: Matt Ahrens * Fixing typo in OUTPUT_DIR in qual notebook Signed-off-by: Matt Ahrens Signed-off-by: Matt Ahrens * Revert "Adding Databricks tool demo notebooks for qualification and profiling (#249)" (#251) This reverts commit 5f7707079e47db921d0c874d820b473f64253e18. * Updated code on Retail Analytics Spark RAPIDS * Update DataGen.ipynb * Update DataGen.ipynb * Updated code * Update Retail Analytics.ipynb * Update README.md --------- Signed-off-by: Peixin Li Signed-off-by: liyuan Signed-off-by: Bobby Wang Signed-off-by: Matt Ahrens Co-authored-by: liyuan <84758614+nvliyuan@users.noreply.github.com> Co-authored-by: Jenkins Automation <70000568+nvauto@users.noreply.github.com> Co-authored-by: Peixin Co-authored-by: Bobby Wang Co-authored-by: Hao Zhu <9665750+viadea@users.noreply.github.com> Co-authored-by: Matt Ahrens Co-authored-by: liyuan --- .../retail-analytics/README.md | 7 + .../notebooks/python/Data Generator.ipynb | 188 ++++++++++++ .../notebooks/python/Retail Analytics.ipynb | 282 ++++++++++++++++++ 3 files changed, 477 insertions(+) create mode 100644 examples/SQL+DF-Examples/retail-analytics/README.md create mode 100644 examples/SQL+DF-Examples/retail-analytics/notebooks/python/Data Generator.ipynb create mode 100644 examples/SQL+DF-Examples/retail-analytics/notebooks/python/Retail Analytics.ipynb diff --git a/examples/SQL+DF-Examples/retail-analytics/README.md b/examples/SQL+DF-Examples/retail-analytics/README.md new file mode 100644 index 000000000..d65dd2931 --- /dev/null +++ b/examples/SQL+DF-Examples/retail-analytics/README.md @@ -0,0 +1,7 @@ + +# Overview Retail Analytics +This repository contains two Jupyter notebooks: + +Data Generation: This notebook generates sample data that can be used for analysis. It demonstrates how to use various Python libraries to create synthetic data sets that can be used for testing and experimentation. This notebook can be run in GCP n1-standard-32 instance type + +Data Cleaning and Analysis: This notebook takes the generated data and performs a series of cleaning and analysis tasks. It demonstrates how to use Spark RAPIDS library to manipulate and analyze data sets. diff --git a/examples/SQL+DF-Examples/retail-analytics/notebooks/python/Data Generator.ipynb b/examples/SQL+DF-Examples/retail-analytics/notebooks/python/Data Generator.ipynb new file mode 100644 index 000000000..dc2b2b4bb --- /dev/null +++ b/examples/SQL+DF-Examples/retail-analytics/notebooks/python/Data Generator.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating and Writing Data to GCS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import multiprocessing as mp\n", + "import random\n", + "\n", + "# You need to update these to your real paths!\n", + "dataRoot = os.getenv(\"DATA_ROOT\", 'gs://bucket-name/data/raw/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#We define the generate_data function which takes an integer i as input and generates sales data using random numbers. The generated data includes sales ID, product name, price, quantity sold, date of sale, and customer ID. The function returns a tuple of the generated data.\n", + "def generate_data(i):\n", + " sales_id = \"s_{}\".format(i)\n", + " product_name = \"Product_{}\".format(i)\n", + " price = random.uniform(1,100)\n", + " quantity_sold = random.randint(1,100)\n", + " date_of_sale = \"2022-{}-{}\".format(random.randint(1,12), random.randint(1,28))\n", + " customer_id = \"c_{}\".format(random.randint(1,1000000))\n", + " return (sales_id, product_name, price, quantity_sold, date_of_sale, customer_id)\n", + "\n", + "with mp.Pool(mp.cpu_count()) as p:\n", + " sales_data = p.map(generate_data, range(100000000))\n", + " sales_data = list(sales_data)\n", + " \n", + "print(\"write to gcs started\")\n", + "sales_df = pd.DataFrame(sales_data, columns=[\"sales_id\", \"product_name\", \"price\", \"quantity_sold\", \"date_of_sale\", \"customer_id\"])\n", + "sales_df.to_csv(dataRoot+\"sales/data.csv\", index=False, header=True)\n", + "print(\"Write to gcs completed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(i):\n", + " product_name = \"Product_{}\".format(i)\n", + " shelf_life = random.randint(1,365)\n", + " contains_promotion = \"{} % off\".format(random.randint(0,10))\n", + " quantity_in_stock = random.randint(1,1000)\n", + " location = \"Location_{}\".format(random.randint(1,100))\n", + " date_received = \"2022-{}-{}\".format(random.randint(1,12), random.randint(1,28))\n", + " return (product_name,shelf_life,contains_promotion,quantity_in_stock, location, date_received)\n", + "\n", + "with mp.Pool(mp.cpu_count()) as p:\n", + " stock_data = p.map(generate_data, range(5000000))\n", + " stock_data = list(stock_data)\n", + " \n", + "stock_df = pd.DataFrame(stock_data, columns=[\"product_name\",\"shelf_life\",\"contains_promotion\",\"quantity_in_stock\", \"location\", \"date_received\"])\n", + "stock_df.to_json(dataRoot+\"stock/stock.json\", orient='records')\n", + "print(\"Write to gcs completed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(i):\n", + " sup_id = \"s_{}\".format(i)\n", + " product_name = \"Product_{}\".format(i)\n", + " quantity_ordered = random.randint(1,1000)\n", + " price = random.uniform(1,100)\n", + " date_ordered = \"2022-{}-{}\".format(random.randint(1,12), random.randint(1,28))\n", + " return (sup_id,product_name, quantity_ordered, price, date_ordered)\n", + "\n", + "with mp.Pool(mp.cpu_count()) as p:\n", + " supplier_data = p.map(generate_data, range(5000000))\n", + " supplier_data = list(supplier_data)\n", + " \n", + "supplier_df = pd.DataFrame(supplier_data, columns=[\"sup_id\",\"product_name\", \"quantity_ordered\", \"price\", \"date_ordered\"])\n", + "supplier_df.to_json(dataRoot+\"supplier/supplier.json\", orient='records')\n", + "print(\"Write to gcs completed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(i):\n", + " customer_id = \"c_{}\".format(i)\n", + " customer_name = \"Customer_{}\".format(i)\n", + " age = random.randint(20,70)\n", + " gender = random.choice([\"male\", \"female\"])\n", + " purchase_history = random.randint(1,100)\n", + " contact_info = \"email_{}@gmail.com\".format(i)\n", + " return (customer_id,customer_name, age, gender, purchase_history, contact_info)\n", + "\n", + "with mp.Pool(mp.cpu_count()) as p:\n", + " customer_data = p.map(generate_data, range(100000))\n", + " customer_data = list(customer_data)\n", + " \n", + "customer_df = pd.DataFrame(customer_data, columns=[\"customer_id\",\"customer_name\", \"age\", \"gender\", \"purchase_history\", \"contact_info\"])\n", + "customer_df.to_csv(dataRoot+\"customer/customer.csv\", index=False,header=True)\n", + "print(\"Write to gcs completed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(i):\n", + " product_name = \"Product_{}\".format(i)\n", + " competitor_price = random.uniform(1,100)\n", + " sales_trend = random.randint(1,100)\n", + " demand_forecast = random.randint(1,100)\n", + " return (product_name, competitor_price, sales_trend, demand_forecast)\n", + "\n", + "with mp.Pool(mp.cpu_count()) as p:\n", + " market_data = p.map(generate_data, range(50000000))\n", + " market_data = list(market_data)\n", + " \n", + "market_df = pd.DataFrame(market_data, columns=[\"product_name\", \"competitor_price\", \"sales_trend\", \"demand_forecast\"])\n", + "market_df.to_csv(dataRoot+\"market/market.csv\", index=False,header=True)\n", + "print(\"Write to gcs completed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data(i):\n", + " product_name = \"Product_{}\".format(i)\n", + " shipping_cost = random.uniform(1,100)\n", + " transportation_cost = random.uniform(1,100)\n", + " warehouse_cost = random.uniform(1,100)\n", + " return (product_name, shipping_cost, transportation_cost, warehouse_cost)\n", + "\n", + "with mp.Pool(mp.cpu_count()) as p:\n", + " logistic_data = p.map(generate_data, range(50000000))\n", + " logistic_data = list(logistic_data)\n", + " \n", + "logistic_df = pd.DataFrame(logistic_data, columns=[\"product_name\", \"shipping_cost\", \"transportation_cost\", \"warehouse_cost\"])\n", + "logistic_df.to_csv(dataRoot+\"logistic/logistic.csv\", index=False,header=True)\n", + "print(\"Write to gcs completed\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/SQL+DF-Examples/retail-analytics/notebooks/python/Retail Analytics.ipynb b/examples/SQL+DF-Examples/retail-analytics/notebooks/python/Retail Analytics.ipynb new file mode 100644 index 000000000..4142b96a0 --- /dev/null +++ b/examples/SQL+DF-Examples/retail-analytics/notebooks/python/Retail Analytics.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from pyspark.sql import SparkSession\n", + "from pyspark import broadcast, SparkConf\n", + "import time\n", + "\n", + "if \"sc\" in globals():\n", + " sc.stop()\n", + "\n", + "### Configure the parameters based on your dataproc cluster ###\n", + "conf = SparkConf().setAppName(\"Retail Analytics\")\n", + "conf.set(\"spark.plugins\", \"com.nvidia.spark.SQLPlugin\")\n", + "conf.set(\"spark.executor.instances\", \"8\")\n", + "conf.set(\"spark.executor.cores\", \"4\")\n", + "conf.set(\"spark.task.resource.gpu.amount\", \"0.25\")\n", + "conf.set(\"spark.rapids.sql.concurrentGpuTasks\", \"2\")\n", + "conf.set(\"spark.executor.memory\", \"8192m\")\n", + "conf.set(\"spark.sql.files.maxPartitionBytes\", \"512m\")\n", + "conf.set(\"spark.executor.resource.gpu.amount\", \"1\")\n", + "conf.set(\"spark.rapids.memory.pinnedPool.size\", \"4096m\")\n", + "conf.set(\"spark.executor.memoryOverhead\", \"4915m\")\n", + "conf.set(\"spark.sql.broadcastTimeout\", \"700\")\n", + "conf.set(\"spark.sql.shuffle.partitions\", \"500\")\n", + "conf.set(\"spark.driver.maxResultSize\", \"8g\")\n", + "conf.set(\"spark.driver.memory\", \"10g\")\n", + "conf.set(\"spark.dynamicAllocation.enabled\", \"false\")\n", + "conf.set(\"spark.sql.adaptive.enabled\", \"true\")\n", + "conf.set(\"spark.sql.autoBroadcastJoinThreshold\", \"300M\")\n", + "conf.set(\"spark.rapids.memory.host.spillStorageSize\", \"4g\")\n", + "conf.set(\"spark.rapids.sql.multiThreadedRead.numThreads\", \"40\")\n", + "conf.set(\"spark.rapids.sql.castDecimalToString.enabled\",True)\n", + "conf.set(\"spark.rapids.sql.castStringToTimestamp.enabled\",True)\n", + "conf.set(\"spark.rapids.sql.expression.PercentRank\",False)\n", + "conf.set(\"spark.rapids.sql.castDecimalToString.enabled\",True)\n", + "conf.set(\"spark.scheduler.minRegisteredResourcesRatio\", \"0.0\")\n", + "conf.set(\"spark.sql.adaptive.advisoryPartitionSizeInBytes\", \"128M\")\n", + "conf.set(\"spark.sql.adaptive.coalescePartitions.minPartitionNum\", \"1\")\n", + "conf.set(\"spark.yarn.executor.launch.excludeOnFailure.enabled\",True)\n", + "conf.set(\"spark.rapids.sql.format.json.read.enabled\",True)\n", + "conf.set(\"spark.rapids.sql.explain\",None)\n", + "conf.set(\"spark.rapids.sql.enabled\",True)\n", + " \n", + "\n", + "spark = SparkSession.builder \\\n", + " .config(conf=conf) \\\n", + " .getOrCreate()\n", + "# create a SparkSession\n", + "spark = SparkSession.builder.appName(\"RetailInvMgmt\").getOrCreate()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "# You need to update these to your real paths!\n", + "dataRoot = os.getenv(\"DATA_ROOT\", 'gs:///data')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import *\n", + "from pyspark.sql.types import *\n", + "from pyspark.sql.window import Window\n", + "\n", + "start = time.time()\n", + "\n", + "def clean_data(df):\n", + " # remove missing values\n", + " df = df.dropna()\n", + " # remove duplicate data\n", + " df = df.dropDuplicates()\n", + " return df\n", + "\n", + "\n", + "def read_data(spark, format, file_path):\n", + " if format==\"csv\":\n", + " return spark.read.format(format).load(file_path,header=True)\n", + " else:\n", + " return spark.read.format(format).load(file_path)\n", + "\n", + "# read sales data\n", + "sales_df = read_data(spark, \"csv\", dataRoot+\"/raw/sales/\")\n", + "\n", + "# read stock data\n", + "stock_df = read_data(spark, \"json\", dataRoot+\"/raw/stock/\")\n", + "\n", + "# read supplier data\n", + "supplier_df = read_data(spark, \"json\", dataRoot+\"/raw/supplier/\")\n", + "\n", + "# read customer data\n", + "customer_df = read_data(spark, \"csv\", dataRoot+\"/raw/customer/\")\n", + "\n", + "# read market data\n", + "market_df = read_data(spark, \"csv\", dataRoot+\"/raw/market/\")\n", + "\n", + "# read logistic data\n", + "logistic_df = read_data(spark, \"csv\", dataRoot+\"/raw/logistic/\")\n", + "\n", + "\n", + "# data cleaning\n", + "sales_df = clean_data(sales_df)\n", + "stock_df = clean_data(stock_df)\n", + "supplier_df = clean_data(supplier_df)\n", + "customer_df = clean_data(customer_df)\n", + "market_df = clean_data(market_df)\n", + "logistic_df = clean_data(logistic_df)\n", + "\n", + "\n", + "# convert date columns to date type\n", + "sales_df = sales_df.withColumn(\"date_of_sale\", to_date(col(\"date_of_sale\")))\n", + "stock_df = stock_df.withColumn(\"date_received\", to_date(col(\"date_received\")))\n", + "supplier_df = supplier_df.withColumn(\"date_ordered\", to_date(col(\"date_ordered\")))\n", + "\n", + "# standardize case of string columns\n", + "sales_df = sales_df.withColumn(\"product_name\", upper(col(\"product_name\")))\n", + "stock_df = stock_df.withColumn(\"product_name\", upper(col(\"product_name\")))\n", + "stock_df = stock_df.withColumn(\"location\", upper(col(\"location\")))\n", + "supplier_df = supplier_df.withColumn(\"product_name\", upper(col(\"product_name\")))\n", + "customer_df = customer_df.withColumn(\"customer_name\", upper(col(\"customer_name\")))\n", + "market_df = market_df.withColumn(\"product_name\", upper(col(\"product_name\")))\n", + "logistic_df = logistic_df.withColumn(\"product_name\", upper(col(\"product_name\")))\n", + "\n", + "# remove leading and trailing whitespaces\n", + "sales_df = sales_df.withColumn(\"product_name\", trim(col(\"product_name\")))\n", + "stock_df = stock_df.withColumn(\"location\", trim(col(\"location\")))\n", + "\n", + "supplier_df = supplier_df.withColumn(\"product_name\", trim(col(\"product_name\")))\n", + "customer_df = customer_df.withColumn(\"customer_name\", trim(col(\"customer_name\")))\n", + "market_df = market_df.withColumn(\"product_name\", trim(col(\"product_name\")))\n", + "logistic_df = logistic_df.withColumn(\"product_name\", trim(col(\"product_name\")))\n", + "\n", + "# check for invalid values\n", + "sales_df = sales_df.filter(col(\"product_name\").isNotNull())\n", + "stock_df = stock_df.filter(col(\"location\").isNotNull())\n", + "customer_df = customer_df.filter(col(\"gender\").isin(\"male\",\"female\"))\n", + "market_df = market_df.filter(col(\"product_name\").isNotNull())\n", + "logistic_df = logistic_df.filter(col(\"product_name\").isNotNull())\n", + "\n", + "#drop extra columns\n", + "market_df = market_df.drop(\"price\")\n", + "supplier_df = supplier_df.drop(\"price\")\n", + "\n", + "# join all data\n", + "data_int = sales_df.join(stock_df, \"product_name\",\"leftouter\").join(supplier_df, \"product_name\",\"leftouter\").join(market_df, \"product_name\",\"leftouter\").join(logistic_df, \"product_name\",\"leftouter\").join(customer_df, \"customer_id\",\"leftouter\") \n", + "\n", + "# write the cleaned data\n", + "data_int.write.format(\"parquet\").save(dataRoot+\"/cleaned/\")\n", + "\n", + "end = time.time()\n", + "\n", + "print(\"Time taken on GPU for Data Cleaning: \", end - start)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.functions import *\n", + "from pyspark.sql.types import *\n", + "from pyspark.sql.window import Window\n", + "\n", + "#DO VARIOUS RETAIL DATA ANALYTICS \n", + "\n", + "start = time.time()\n", + "\n", + "# read cleaned data\n", + "\n", + "data = spark.read.format(\"parquet\").load(dataRoot+\"/cleaned/\")\n", + "\n", + "#Case when statement to create a new column to indicate whether the product is perishable or not:\n", + "\n", + "data = data.withColumn(\"perishable\", when(col(\"shelf_life\") <= 30, \"yes\").otherwise(\"no\"))\n", + "\n", + "# You can use the when() and otherwise() functions to create new columns based on certain conditions:\n", + "\n", + "data = data.withColumn(\"sales_status\", when(col(\"quantity_sold\") > 50, \"good\").otherwise(\"bad\"))\n", + "\n", + "# create a window to perform time series analysis\n", + "window = Window.partitionBy(\"product_name\").orderBy(\"date_of_sale\")\n", + "\n", + "# calculate the rolling average of sales for each product\n", + "time_series_df = data.withColumn(\"rolling_avg_sales\", avg(\"quantity_sold\").over(window))\n", + "\n", + "# use window function for forecasting\n", + "\n", + "forecast_df = time_series_df.withColumn(\"prev_sales\", lag(\"rolling_avg_sales\").over(window))\\\n", + " .withColumn(\"next_sales\", lead(\"rolling_avg_sales\").over(window))\n", + "\n", + "\n", + "# Calculate the average price of a product, grouped by supplier\n", + "forecast_df.groupBy(\"sup_id\").agg({\"price\": \"avg\"}).show()\n", + "\n", + "\n", + "# Calculate the total quantity in stock and total sales by supplier\n", + "forecast_df.groupBy(\"sup_id\").agg({\"quantity_in_stock\": \"sum\", \"price\": \"sum\"}).show()\n", + "\n", + "#Calculate the number of perishable v/s non-perishable product per location\n", + "forecast_df.groupBy(\"perishable\").agg({\"perishable\": \"count\"}).show()\n", + "\n", + "\n", + "#Calculate number of good v/s bad sales status per location\n", + "forecast_df.groupBy(\"sales_status\").agg({\"sales_status\": \"count\"}).show()\n", + "\n", + "# Count the number of sales that contain a 10% off promotion\n", + "countt = forecast_df.filter(forecast_df[\"contains_promotion\"].contains(\"10% off\")).count()\n", + "print(countt)\n", + "# Perform some complex analysis on the DataFrame\n", + "\n", + "# Calculate the total sales, quantity sold by product and location\n", + "total_sales_by_product_location = forecast_df.groupBy(\"product_name\", \"location\").agg(sum(\"price\").alias(\"total_price\"),sum(\"quantity_ordered\").alias(\"total_quantity_sold\"),avg(\"quantity_sold\").alias(\"avg_quantity_sold\")).sort(desc(\"total_price\"))\n", + "\n", + "# Group the data by product_name\n", + "grouped_df = forecast_df.groupBy(\"product_name\")\n", + "\n", + "#Sum the quantity_in_stock, quantity_ordered, quantity_sold, and (price * quantity_sold) for each group\n", + "aggregated_df = grouped_df.agg(sum(\"quantity_in_stock\").alias(\"total_quantity_in_stock\"),avg(\"price\").alias(\"average_price\"),sum(\"quantity_ordered\").alias(\"total_quantity_ordered\"),sum(\"quantity_sold\").alias(\"total_quantity_sold\"),sum(col(\"price\") * col(\"quantity_sold\")).alias(\"total_sales\"),sum(\"prev_sales\").alias(\"total_prev_sales\"),sum(\"next_sales\").alias(\"total_next_sales\"),).sort(desc(\"total_sales\"))\n", + "\n", + "#WRITE THE AGGREGATES TO DISK\n", + "aggregated_df.write.format(\"parquet\").save(dataRoot+\"/app/data.parquet\")\n", + "total_sales_by_product_location.write.format(\"parquet\").save(dataRoot+\"/app1/data.parquet\")\n", + "\n", + "end = time.time()\n", + "\n", + "print(\"Time taken on GPU for Data Analysis: \", end - start)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "spark.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 79c0601a6eac1b60aea8604c1efadd61b7b3a2f8 Mon Sep 17 00:00:00 2001 From: liyuan Date: Mon, 20 Feb 2023 10:29:02 +0800 Subject: [PATCH 08/10] remove snapshot Signed-off-by: liyuan --- examples/ML+DL-Examples/Spark-cuML/pca/README.md | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/pom.xml | 4 ++-- examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh | 6 +++--- .../micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb | 2 +- examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml | 4 ++-- .../RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh | 2 +- examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh | 2 +- .../notebooks/cuspatial_sample_standalone.ipynb | 2 +- examples/UDF-Examples/Spark-cuSpatial/pom.xml | 4 ++-- examples/XGBoost-Examples/agaricus/pom.xml | 2 +- examples/XGBoost-Examples/aggregator/pom.xml | 2 +- .../mortgage/notebooks/python/cv-mortgage-gpu.ipynb | 2 +- .../mortgage/notebooks/scala/mortgage-ETL.ipynb | 2 +- examples/XGBoost-Examples/mortgage/pom.xml | 2 +- examples/XGBoost-Examples/pom.xml | 2 +- .../XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb | 2 +- .../XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb | 2 +- examples/XGBoost-Examples/taxi/pom.xml | 2 +- examples/XGBoost-Examples/utility/pom.xml | 2 +- 19 files changed, 24 insertions(+), 24 deletions(-) diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/README.md b/examples/ML+DL-Examples/Spark-cuML/pca/README.md index d0185f7d0..e83e40683 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/README.md +++ b/examples/ML+DL-Examples/Spark-cuML/pca/README.md @@ -48,7 +48,7 @@ It is assumed that a Standalone Spark cluster has been set up, the `SPARK_MASTER ``` bash RAPIDS_ML_JAR=PATH_TO_rapids-4-spark-ml_2.12-22.02.0-cuda11.jar - PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar + PLUGIN_JAR=PATH_TO_rapids-4-spark_2.12-23.02.0.jar jupyter toree install \ --spark_home=${SPARK_HOME} \ diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml index bcdc8192f..a1182028d 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml +++ b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml @@ -21,7 +21,7 @@ com.nvidia PCAExample jar - 23.02.0-SNAPSHOT + 23.02.0 8 @@ -51,7 +51,7 @@ com.nvidia rapids-4-spark-ml_2.12 - 23.02.0-SNAPSHOT + 23.02.0 diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh index e0991aa36..9d5b25a9f 100755 --- a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh +++ b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh @@ -15,8 +15,8 @@ # limitations under the License. # -ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/23.02.0-SNAPSHOT/rapids-4-spark-ml_2.12-23.02.0-SNAPSHOT.jar -PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.02.0-SNAPSHOT/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar +ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/23.02.0/rapids-4-spark-ml_2.12-23.02.0.jar +PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar $SPARK_HOME/bin/spark-submit \ --master spark://127.0.0.1:7077 \ @@ -38,4 +38,4 @@ $SPARK_HOME/bin/spark-submit \ --conf spark.network.timeout=1000s \ --jars $ML_JAR,$PLUGIN_JAR \ --class com.nvidia.spark.examples.pca.Main \ -/workspace/target/PCAExample-23.02.0-SNAPSHOT.jar +/workspace/target/PCAExample-23.02.0.jar diff --git a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb index d900a2ec3..0a672cc09 100644 --- a/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb +++ b/examples/SQL+DF-Examples/micro-benchmarks/notebooks/micro-benchmarks-gpu.ipynb @@ -22,7 +22,7 @@ "import os\n", "# Change to your cluster ip:port and directories\n", "SPARK_MASTER_URL = os.getenv(\"SPARK_MASTER_URL\", \"spark:your-ip:port\")\n", - "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\")\n" + "RAPIDS_JAR = os.getenv(\"RAPIDS_JAR\", \"/your-path/rapids-4-spark_2.12-23.02.0.jar\")\n" ] }, { diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml index ea9a25dac..92593984e 100644 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/pom.xml @@ -25,7 +25,7 @@ user defined functions for use with the RAPIDS Accelerator for Apache Spark - 23.02.0-SNAPSHOT + 23.02.0 1.8 @@ -37,7 +37,7 @@ cuda11 2.12 - 23.02.0-SNAPSHOT + 23.02.0 3.1.1 2.12.15 ${project.build.directory}/cpp-build diff --git a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh index 6cf4f5c70..a802066df 100755 --- a/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh +++ b/examples/UDF-Examples/RAPIDS-accelerated-UDFs/run_pyspark_from_build.sh @@ -33,7 +33,7 @@ else # more lineant configuration, else it will set them to 1 as spurious task failures are not expected # for Spark 3.1.1+ VERSION_STRING=`$SPARK_HOME/bin/pyspark --version 2>&1|grep -v Scala|awk '/version\ [0-9.]+/{print $NF}'` - VERSION_STRING="${VERSION_STRING/-SNAPSHOT/}" + VERSION_STRING="${VERSION_STRING//}" [[ -z $VERSION_STRING ]] && { echo "Unable to detect the Spark version at $SPARK_HOME"; exit 1; } [[ -z $SPARK_SHIM_VER ]] && { SPARK_SHIM_VER="spark${VERSION_STRING//./}"; } diff --git a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh index bd675849d..c79947967 100755 --- a/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh +++ b/examples/UDF-Examples/Spark-cuSpatial/gpu-run.sh @@ -31,7 +31,7 @@ rm -rf $DATA_OUT_PATH # the path to keep the jars of spark-rapids & spark-cuspatial JARS=$ROOT_PATH/jars -JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar,$JARS/spark-cuspatial-23.02.0-SNAPSHOT.jar} +JARS_PATH=${JARS_PATH:-$JARS/rapids-4-spark_2.12-23.02.0.jar,$JARS/spark-cuspatial-23.02.0.jar} $SPARK_HOME/bin/spark-submit --master spark://$HOSTNAME:7077 \ --name "Gpu Spatial Join UDF" \ diff --git a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb index 1fd1c4855..7c2a13477 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb +++ b/examples/UDF-Examples/Spark-cuSpatial/notebooks/cuspatial_sample_standalone.ipynb @@ -9,7 +9,7 @@ "source": [ "from pyspark.sql import SparkSession\n", "import os\n", - "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar,/data/cuspatial_data/jars/spark-cuspatial-23.02.0-SNAPSHOT.jar\")\n", + "jarsPath = os.getenv(\"JARS_PATH\", \"/data/cuspatial_data/jars/rapids-4-spark_2.12-23.02.0.jar,/data/cuspatial_data/jars/spark-cuspatial-23.02.0.jar\")\n", "spark = SparkSession.builder \\\n", " .config(\"spark.jars\", jarsPath) \\\n", " .config(\"spark.sql.adaptive.enabled\", \"false\") \\\n", diff --git a/examples/UDF-Examples/Spark-cuSpatial/pom.xml b/examples/UDF-Examples/Spark-cuSpatial/pom.xml index dd81a615a..324236e27 100644 --- a/examples/UDF-Examples/Spark-cuSpatial/pom.xml +++ b/examples/UDF-Examples/Spark-cuSpatial/pom.xml @@ -24,13 +24,13 @@ UDF of the cuSpatial case for the RAPIDS Accelerator The RAPIDS accelerated user defined function of the cuSpatial case for use with the RAPIDS Accelerator for Apache Spark - 23.02.0-SNAPSHOT + 23.02.0 1.8 1.8 8 - 23.02.0-SNAPSHOT + 23.02.0 2.12 3.2.0 ${project.build.directory}/cpp-build diff --git a/examples/XGBoost-Examples/agaricus/pom.xml b/examples/XGBoost-Examples/agaricus/pom.xml index ee30ec285..6b4bab9a8 100644 --- a/examples/XGBoost-Examples/agaricus/pom.xml +++ b/examples/XGBoost-Examples/agaricus/pom.xml @@ -21,7 +21,7 @@ sample_xgboost_examples com.nvidia - 0.2.3-SNAPSHOT + 0.2.3 4.0.0 diff --git a/examples/XGBoost-Examples/aggregator/pom.xml b/examples/XGBoost-Examples/aggregator/pom.xml index 5fb27d872..3485b8d08 100644 --- a/examples/XGBoost-Examples/aggregator/pom.xml +++ b/examples/XGBoost-Examples/aggregator/pom.xml @@ -21,7 +21,7 @@ sample_xgboost_examples com.nvidia - 0.2.3-SNAPSHOT + 0.2.3 4.0.0 diff --git a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb index 971ffdc6a..f2675ac93 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/python/cv-mortgage-gpu.ipynb @@ -63,7 +63,7 @@ "Setting default log level to \"WARN\".\n", "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n", "2022-11-25 09:34:43,952 WARN resource.ResourceUtils: The configuration of cores (exec = 4 task = 1, runnable tasks = 4) will result in wasted resources due to resource gpu limiting the number of runnable tasks per executor to: 1. Please adjust your configuration.\n", - "2022-11-25 09:34:58,155 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator 23.02.0-SNAPSHOT using cudf 23.02.0-SNAPSHOT.\n", + "2022-11-25 09:34:58,155 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator 23.02.0 using cudf 23.02.0.\n", "2022-11-25 09:34:58,171 WARN rapids.RapidsPluginUtils: spark.rapids.sql.multiThreadedRead.numThreads is set to 20.\n", "2022-11-25 09:34:58,175 WARN rapids.RapidsPluginUtils: RAPIDS Accelerator is enabled, to disable GPU support set `spark.rapids.sql.enabled` to false.\n", "2022-11-25 09:34:58,175 WARN rapids.RapidsPluginUtils: spark.rapids.sql.explain is set to `NOT_ON_GPU`. Set it to 'NONE' to suppress the diagnostics logging about the query placement on the GPU.\n" diff --git a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb index d1b8e5f7e..faaf3245b 100644 --- a/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb +++ b/examples/XGBoost-Examples/mortgage/notebooks/scala/mortgage-ETL.ipynb @@ -27,7 +27,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0.jar\n", "\n", "```\n", "\n", diff --git a/examples/XGBoost-Examples/mortgage/pom.xml b/examples/XGBoost-Examples/mortgage/pom.xml index 1eb2ffa32..ffef6cfd5 100644 --- a/examples/XGBoost-Examples/mortgage/pom.xml +++ b/examples/XGBoost-Examples/mortgage/pom.xml @@ -21,7 +21,7 @@ sample_xgboost_examples com.nvidia - 0.2.3-SNAPSHOT + 0.2.3 4.0.0 diff --git a/examples/XGBoost-Examples/pom.xml b/examples/XGBoost-Examples/pom.xml index d6977f8c5..a0537bf0d 100644 --- a/examples/XGBoost-Examples/pom.xml +++ b/examples/XGBoost-Examples/pom.xml @@ -33,7 +33,7 @@ aggregator - 0.2.3-SNAPSHOT + 0.2.3 sample_xgboost_apps diff --git a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb index ab334a6bf..774ef7185 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/python/taxi-ETL.ipynb @@ -26,7 +26,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0.jar\n", "$ export PYSPARK_DRIVER_PYTHON=jupyter \n", "$ export PYSPARK_DRIVER_PYTHON_OPTS=notebook\n", "```\n", diff --git a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb index d8395e439..cf47acfd7 100644 --- a/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb +++ b/examples/XGBoost-Examples/taxi/notebooks/scala/taxi-ETL.ipynb @@ -26,7 +26,7 @@ "\n", "### 4. Add ENV\n", "```\n", - "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0-SNAPSHOT.jar\n", + "$ export SPARK_JARS=rapids-4-spark_2.12-23.02.0.jar\n", "\n", "```\n", "\n", diff --git a/examples/XGBoost-Examples/taxi/pom.xml b/examples/XGBoost-Examples/taxi/pom.xml index e4cbff02d..5fcb7796b 100644 --- a/examples/XGBoost-Examples/taxi/pom.xml +++ b/examples/XGBoost-Examples/taxi/pom.xml @@ -21,7 +21,7 @@ sample_xgboost_examples com.nvidia - 0.2.3-SNAPSHOT + 0.2.3 4.0.0 diff --git a/examples/XGBoost-Examples/utility/pom.xml b/examples/XGBoost-Examples/utility/pom.xml index ca26ca70d..174b509de 100644 --- a/examples/XGBoost-Examples/utility/pom.xml +++ b/examples/XGBoost-Examples/utility/pom.xml @@ -21,7 +21,7 @@ sample_xgboost_examples com.nvidia - 0.2.3-SNAPSHOT + 0.2.3 4.0.0 From 99d0060871579caf578eea40b68ae61bc34ff04a Mon Sep 17 00:00:00 2001 From: liyuan Date: Thu, 23 Feb 2023 11:14:36 +0800 Subject: [PATCH 09/10] we do not release rapids-4-spark-ml_2.12, submit as snapshot Signed-off-by: liyuan --- examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh index 9d5b25a9f..c77f80930 100755 --- a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh +++ b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh @@ -15,7 +15,7 @@ # limitations under the License. # -ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/23.02.0/rapids-4-spark-ml_2.12-23.02.0.jar +ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/23.02.0/rapids-4-spark-ml_2.12-23.02.0-SNAPSHOT.jar PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar $SPARK_HOME/bin/spark-submit \ From 6213dadc97a218cf12a62dfe0d31edf9fbbd6fa1 Mon Sep 17 00:00:00 2001 From: liyuan Date: Thu, 23 Feb 2023 11:19:25 +0800 Subject: [PATCH 10/10] updated version to v22.02 Signed-off-by: liyuan --- examples/ML+DL-Examples/Spark-cuML/pca/pom.xml | 2 +- examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml index a1182028d..a33bfefce 100644 --- a/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml +++ b/examples/ML+DL-Examples/Spark-cuML/pca/pom.xml @@ -51,7 +51,7 @@ com.nvidia rapids-4-spark-ml_2.12 - 23.02.0 + 22.02.0 diff --git a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh index c77f80930..10ca8f4ed 100755 --- a/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh +++ b/examples/ML+DL-Examples/Spark-cuML/pca/spark-submit.sh @@ -15,7 +15,7 @@ # limitations under the License. # -ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/23.02.0/rapids-4-spark-ml_2.12-23.02.0-SNAPSHOT.jar +ML_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark-ml_2.12/22.02.0/rapids-4-spark-ml_2.12-22.02.0.jar PLUGIN_JAR=/root/.m2/repository/com/nvidia/rapids-4-spark_2.12/23.02.0/rapids-4-spark_2.12-23.02.0.jar $SPARK_HOME/bin/spark-submit \

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