From 7508571241a9f618d1713dd6e4130550d8d850da Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Mon, 8 Jul 2024 10:42:36 -0600 Subject: [PATCH 01/12] Test commit --- README.md | 5 + airflow_variables_dev.json | 2 + airflow_variables_prod.json | 2 + dags/history_tables_dag.py | 318 ++++++++---------- .../build_del_ins_from_gcs_to_bq_task.py | 185 ++++++++++ documentation/images/history_table_export.png | Bin 74820 -> 114655 bytes 6 files changed, 342 insertions(+), 170 deletions(-) create mode 100644 dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py diff --git a/README.md b/README.md index c125e85d..c0b2f452 100644 --- a/README.md +++ b/README.md @@ -548,6 +548,7 @@ This section contains information about the Airflow setup. It includes our DAG d - [build_delete_data_task](#build_delete_data_task) - [build_dbt_task](#build_dbt_task) - [build_elementary_slack_alert_task](#build_elementary_slack_alert_task) + - [build_del_ins_from_gcs_to_bq_task](#build_del_ins_from_gcs_to_bq_task) --- @@ -668,6 +669,10 @@ This section contains information about the Airflow setup. It includes our DAG d [This file](https://github.com/stellar/stellar-etl-airflow/blob/master/dags/stellar_etl_airflow/build_gcs_to_bq_task.py) contains methods for creating tasks that appends information from a Google Cloud Storage file to a BigQuery table. These tasks will create a new table if one does not exist. These tasks are used for history archive data structures, as Stellar wants to keep a complete record of the ledger's entire history. +### **build_del_ins_from_gcs_to_bq_task** + +[This file](https://github.com/stellar/stellar-etl-airflow/blob/master/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py) contains methods for deleting data from old partitions if the table exists and also import fresh data from gcs to the corresponding Big Query table. These tasks will create a new table if one does not exist. These tasks are used for history archive data structures, as Stellar wants to keep a complete record of the ledger's entire history. + ### **build_apply_gcs_changes_to_bq_task** [This file](https://github.com/stellar/stellar-etl-airflow/blob/master/dags/stellar_etl_airflow/build_apply_gcs_changes_to_bq_task.py) contains methods for creating apply tasks. Apply tasks are used to merge a file from Google Cloud Storage into a BigQuery table. Apply tasks differ from the other task that appends in that they apply changes. This means that they update, delete, and insert rows. These tasks are used for accounts, offers, and trustlines, as the BigQuery table represents the point in time state of these data structures. This means that, for example, a merge task could alter the account balance field in the table if a user performed a transaction, delete a row in the table if a user deleted their account, or add a new row if a new account was created. diff --git a/airflow_variables_dev.json b/airflow_variables_dev.json index 54cbf4c7..da53b6f5 100644 --- a/airflow_variables_dev.json +++ b/airflow_variables_dev.json @@ -337,6 +337,7 @@ "build_delete_data_task": 1020, "build_export_task": 840, "build_gcs_to_bq_task": 960, + "build_del_ins_from_gcs_to_bq_task" : 2000, "build_time_task": 480, "cleanup_metadata": 60, "create_sandbox": 2400, @@ -368,6 +369,7 @@ "build_copy_table": 180, "build_dbt_task": 960, "build_delete_data_task": 180, + "build_del_ins_from_gcs_to_bq_task": 400, "build_export_task": 420, "build_gcs_to_bq_task": 300, "build_time_task": 480 diff --git a/airflow_variables_prod.json b/airflow_variables_prod.json index 72299422..d0ea35af 100644 --- a/airflow_variables_prod.json +++ b/airflow_variables_prod.json @@ -335,6 +335,7 @@ "build_delete_data_task": 780, "build_export_task": 600, "build_gcs_to_bq_task": 660, + "build_del_ins_from_gcs_to_bq_task" : 2000, "build_time_task": 300, "cleanup_metadata": 60, "create_sandbox": 1020, @@ -366,6 +367,7 @@ "build_copy_table": 180, "build_dbt_task": 1800, "build_delete_data_task": 180, + "build_del_ins_from_gcs_to_bq_task": 400, "build_export_task": 300, "build_gcs_to_bq_task": 300, "build_time_task": 360 diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index a6368c86..58c2e58b 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -1,8 +1,7 @@ """ -The history_archive_export DAG exports operations and trades from the history archives. +The history_archive_export DAG exports trades, asstes, ledgers, operations, transactions, effects from the history archives. It is scheduled to export information to BigQuery at regular intervals. """ - from ast import literal_eval from datetime import datetime from json import loads @@ -11,6 +10,8 @@ from airflow.models.variable import Variable from kubernetes.client import models as k8s from stellar_etl_airflow import macros +from airflow.operators.python import PythonOperator + from stellar_etl_airflow.build_batch_stats import build_batch_stats from stellar_etl_airflow.build_bq_insert_job_task import build_bq_insert_job from stellar_etl_airflow.build_cross_dependency_task import build_cross_deps @@ -18,6 +19,10 @@ from stellar_etl_airflow.build_export_task import build_export_task from stellar_etl_airflow.build_gcs_to_bq_task import build_gcs_to_bq_task from stellar_etl_airflow.build_time_task import build_time_task +from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import build_del_ins_from_gcs_to_bq_task + +from stellar_etl_airflow.default import alert_after_max_retries + from stellar_etl_airflow.default import ( alert_sla_miss, get_default_dag_args, @@ -26,13 +31,13 @@ init_sentry() - +# Initialize the DAG dag = DAG( - "history_table_export", + "history_tables_export", default_args=get_default_dag_args(), - start_date=datetime(2024, 6, 11, 17, 30), + start_date=datetime(2024, 7, 8, 16, 00), catchup=True, - description="This DAG exports trades and operations from the history archive using CaptiveCore. This supports parsing sponsorship and AMMs.", + description="This DAG exports trades, asstes, ledgers, operations, transactions, effects from the history archive using CaptiveCore. This supports parsing sponsorship and AMMs.", schedule_interval="*/10 * * * *", params={ "alias": "cc", @@ -47,10 +52,16 @@ "subtract_data_interval": macros.subtract_data_interval, "batch_run_date_as_datetime_string": macros.batch_run_date_as_datetime_string, }, - # sla_miss_callback=alert_sla_miss, + #sla_miss_callback=alert_sla_miss, ) +#fetch and set batch metadata variables +batch_id = macros.get_batch_id() +batch_date = "{{ batch_run_date_as_datetime_string(dag, data_interval_start) }}" + +source_object_suffix="" +# Fetch necessary variables table_names = Variable.get("table_ids", deserialize_json=True) public_project = "{{ var.value.public_project }}" public_dataset = "{{ var.value.public_dataset }}" @@ -59,14 +70,12 @@ use_captive_core = literal_eval(Variable.get("use_captive_core")) txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" - """ The time task reads in the execution time of the current run, as well as the next execution time. It converts these two times into ledger ranges. """ time_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) - """ The write batch stats task will take a snapshot of the DAG run_id, execution date, start and end ledgers so that reconciliation and data validation are easier. The @@ -80,6 +89,7 @@ write_asset_stats = build_batch_stats(dag, table_names["assets"]) write_contract_events_stats = build_batch_stats(dag, "contract_events") + """ The export tasks call export commands on the Stellar ETL using the ledger range from the time task. The results of the command are stored in a file. There is one task for each of the data types that @@ -90,6 +100,7 @@ script time to copy the file over to the correct directory. If there is no sleep, the load task starts prematurely and will not load data. """ + op_export_task = build_export_task( dag, "archive", @@ -169,225 +180,192 @@ ) +#define the variables needed for making the call to the Operator that does the Delete from BQ Table and Inserts data from GCS to BigQuery + +ops_export_task_id = op_export_task.task_id +trades_export_task_id = trade_export_task.task_id +effects_export_task_id = effects_export_task.task_id +txns_export_task_id = tx_export_task.task_id +ledgers_export_task_id = ledger_export_task.task_id +assets_export_task_id = asset_export_task.task_id +cont_evnts_export_task_id = contract_events_export_task.task_id + +ops_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + ops_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + +trade_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + trades_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + +effects_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + effects_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + +txns_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + txns_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + +ledger_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + ledgers_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + +asset_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + assets_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + +cont_evnts_source_objects=[ + "{{ task_instance.xcom_pull(task_ids='" + + cont_evnts_export_task_id + + '\')["output"] }}' + + source_object_suffix +] + """ -The delete partition task checks to see if the given partition/batch id exists in -Bigquery. If it does, the records are deleted prior to reinserting the batch. +The task calls the Custom Python Task that does the following operations in sequence + 1. The delete operation in the task checks to see if the given partition/batch id exists in + Bigquery. If it does, the records are deleted prior to reinserting the batch. + 2. The Insert operation in the tasks receive the location of the file in Google Cloud storage + through Airflow's XCOM system and merges the unique entries in the file into the corresponding table in BigQuery. """ - -delete_old_op_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["operations"], "pub" +del_ins_ops_task = PythonOperator( + task_id=f"del_ins_ops_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["operations"], ops_export_task_id, source_object_suffix, True, True, batch_id, batch_date, ops_source_objects], + provide_context=True, + dag=dag, ) - -delete_old_trade_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["trades"], "pub" +del_ins_trades_task = PythonOperator( + task_id=f"del_ins_trades_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["trades"], trades_export_task_id, source_object_suffix, True, True, batch_id, batch_date, trade_source_objects], + provide_context=True, + dag=dag, ) - -delete_enrich_op_pub_task = build_delete_data_task( - dag, - public_project, - public_dataset, - "enriched_history_operations", - "pub", +del_ins_effects_task = PythonOperator( + task_id=f"del_ins_effects_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["effects"], effects_export_task_id, source_object_suffix, True, True, batch_id, batch_date, effects_source_objects], + provide_context=True, + dag=dag, ) - -delete_old_effects_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["effects"], "pub" +del_ins_txns_task = PythonOperator( + task_id=f"del_ins_txns_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["transactions"], txns_export_task_id, source_object_suffix, True, True, batch_id, batch_date, txns_source_objects], + provide_context=True, + dag=dag, ) - -delete_old_tx_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["transactions"], "pub" +del_ins_ledgers_task = PythonOperator( + task_id=f"del_ins_ledgers_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["ledgers"], ledgers_export_task_id, source_object_suffix, True, True, batch_id, batch_date, ledger_source_objects], + provide_context=True, + dag=dag, ) - -delete_old_ledger_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["ledgers"], "pub" +del_ins_assets_task = PythonOperator( + task_id=f"del_ins_assets_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["assets"], assets_export_task_id, source_object_suffix, True, True, batch_id, batch_date, asset_source_objects], + provide_context=True, + dag=dag, ) -delete_old_asset_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["assets"], "pub" +del_ins_cont_events_task = PythonOperator( + task_id=f"del_ins_cont_evnts_task", + python_callable=build_del_ins_from_gcs_to_bq_task, + op_args=[public_project, public_dataset, table_names["contract_events"], cont_evnts_export_task_id, source_object_suffix, True, True, batch_id, batch_date, cont_evnts_source_objects], + provide_context=True, + dag=dag, ) -delete_contract_events_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["contract_events"], "pub" -) - - """ -The send tasks receive the location of the file in Google Cloud storage through Airflow's XCOM system. -Then, the task merges the unique entries in the file into the corresponding table in BigQuery. -""" - -""" -Load final public dataset, crypto-stellar +The delete partition task checks to see if the given partition/batch id exists in +Bigquery. If it does, the records are deleted prior to reinserting the batch. """ -send_ops_to_pub_task = build_gcs_to_bq_task( - dag, - op_export_task.task_id, - public_project, - public_dataset, - table_names["operations"], - "", - partition=True, - cluster=True, - dataset_type="pub", -) -send_trades_to_pub_task = build_gcs_to_bq_task( - dag, - trade_export_task.task_id, - public_project, - public_dataset, - table_names["trades"], - "", - partition=True, - cluster=True, - dataset_type="pub", -) -send_effects_to_pub_task = build_gcs_to_bq_task( - dag, - effects_export_task.task_id, - public_project, - public_dataset, - table_names["effects"], - "", - partition=True, - cluster=True, - dataset_type="pub", -) -send_txs_to_pub_task = build_gcs_to_bq_task( - dag, - tx_export_task.task_id, - public_project, - public_dataset, - table_names["transactions"], - "", - partition=True, - cluster=True, - dataset_type="pub", -) -send_ledgers_to_pub_task = build_gcs_to_bq_task( - dag, - ledger_export_task.task_id, - public_project, - public_dataset, - table_names["ledgers"], - "", - partition=True, - cluster=True, - dataset_type="pub", -) - - -send_assets_to_pub_task = build_gcs_to_bq_task( +delete_enrich_op_pub_task = build_delete_data_task( dag, - asset_export_task.task_id, public_project, public_dataset, - table_names["assets"], - "", - partition=True, - cluster=True, - dataset_type="pub", + "enriched_history_operations", + "pub", ) +""" +The task handles the Insert operation into BQ for EHO and assets tables +""" -send_contract_events_to_pub_task = build_gcs_to_bq_task( +insert_enriched_hist_pub_task = build_bq_insert_job( dag, - contract_events_export_task.task_id, public_project, public_dataset, - table_names["contract_events"], - "", + "enriched_history_operations", partition=True, cluster=True, dataset_type="pub", ) - -insert_enriched_hist_pub_task = build_bq_insert_job( +dedup_assets_pub_task = build_bq_insert_job( dag, public_project, public_dataset, - "enriched_history_operations", + table_names["assets"], partition=True, cluster=True, + create=True, dataset_type="pub", ) +# Set Task dependencies ( - time_task - >> write_op_stats - >> op_export_task - >> delete_old_op_pub_task - >> send_ops_to_pub_task - >> delete_enrich_op_pub_task - >> insert_enriched_hist_pub_task + time_task >> write_op_stats >> op_export_task >> del_ins_ops_task >> delete_enrich_op_pub_task >> insert_enriched_hist_pub_task ) - ( - time_task - >> write_trade_stats - >> trade_export_task - >> delete_old_trade_pub_task - >> send_trades_to_pub_task + time_task >> write_trade_stats >> trade_export_task >> del_ins_trades_task ) - ( - time_task - >> write_effects_stats - >> effects_export_task - >> delete_old_effects_pub_task - >> send_effects_to_pub_task + time_task >> write_effects_stats >> effects_export_task >> del_ins_effects_task ) - ( - time_task - >> write_tx_stats - >> tx_export_task - >> delete_old_tx_pub_task - >> send_txs_to_pub_task - >> delete_enrich_op_pub_task + time_task >> write_tx_stats >> tx_export_task >> del_ins_txns_task >> delete_enrich_op_pub_task ) -dedup_assets_pub_task = build_bq_insert_job( - dag, - public_project, - public_dataset, - table_names["assets"], - partition=True, - cluster=True, - create=True, - dataset_type="pub", -) ( - time_task - >> write_ledger_stats - >> ledger_export_task - >> delete_old_ledger_pub_task - >> send_ledgers_to_pub_task - >> delete_enrich_op_pub_task + time_task >> write_ledger_stats >> ledger_export_task >> del_ins_ledgers_task >> delete_enrich_op_pub_task ) + ( - time_task - >> write_asset_stats - >> asset_export_task - >> delete_old_asset_pub_task - >> send_assets_to_pub_task - >> dedup_assets_pub_task + time_task >> write_asset_stats >> asset_export_task >> del_ins_assets_task >> dedup_assets_pub_task ) ( - time_task - >> write_contract_events_stats - >> contract_events_export_task - >> delete_contract_events_task - >> send_contract_events_to_pub_task + time_task >> write_contract_events_stats >> contract_events_export_task >> del_ins_cont_events_task ) diff --git a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py new file mode 100644 index 00000000..32af986e --- /dev/null +++ b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py @@ -0,0 +1,185 @@ +from datetime import timedelta +from airflow.models import Variable +from airflow.operators.python import PythonOperator +from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator +from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator +from stellar_etl_airflow import macros +from stellar_etl_airflow.build_apply_gcs_changes_to_bq_task import read_local_schema +from stellar_etl_airflow.default import alert_after_max_retries +from sentry_sdk import capture_message, push_scope + +from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator +from sentry_sdk import capture_message, push_scope + +class CustomGCSToBigQueryOperator(GCSToBigQueryOperator): + template_fields = list(GCSToBigQueryOperator.template_fields) + [ + "failed_transforms", + "max_failed_transforms", + "export_task_id", + ] + + def __init__(self, failed_transforms, max_failed_transforms, export_task_id, **kwargs): + self.failed_transforms = failed_transforms + self.max_failed_transforms = max_failed_transforms + self.export_task_id = export_task_id + super().__init__(**kwargs) + + def pre_execute(self, context, **kwargs): + # Expand the template fields using Jinja2 templating from the context + self.failed_transforms = self.render_template(self.failed_transforms, context) + self.max_failed_transforms = self.render_template(self.max_failed_transforms, context) + self.export_task_id = self.render_template(self.export_task_id, context) + + if int(self.failed_transforms) > self.max_failed_transforms: + with push_scope() as scope: + scope.set_tag("data-quality", "max-failed-transforms") + scope.set_extra("failed_transforms", self.failed_transforms) + scope.set_extra("file", f"gs://{self.bucket}/{self.source_objects[0]}") + scope.set_extra("task_id", self.task_id) + scope.set_extra("export_task_id", self.export_task_id) + scope.set_extra("destination_table", self.destination_project_dataset_table) + capture_message( + f"failed_transforms ({self.failed_transforms}) has exceeded the max value ({self.max_failed_transforms})", + "fatal", + ) + super().pre_execute(context, **kwargs) + + + +def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_id, source_object_suffix, partition, cluster, batch_id ,batch_date, source_objects, **context): + dag = context['dag'] + + # Delete operation + + DELETE_ROWS_QUERY = ( + f"DELETE FROM {dataset}.{data_type} " + f"WHERE batch_run_date = '{batch_date}'" + f"AND batch_id = '{batch_id}';" + ) + delete_task = BigQueryInsertJobOperator( + project_id=project, + task_id=f"delete_old_partition_{data_type}_bq", + execution_timeout=timedelta( + seconds=Variable.get("task_timeout", deserialize_json=True)[ + build_del_ins_from_gcs_to_bq_task.__name__ + ] + ), + on_failure_callback=alert_after_max_retries, + sla=timedelta( + seconds=Variable.get("task_sla", deserialize_json=True)[ + build_del_ins_from_gcs_to_bq_task.__name__ + ] + ), + configuration={ + "query": { + "query": DELETE_ROWS_QUERY, + "useLegacySql": False, + } + }, + ) + delete_task.execute(context) + + # Insert operation + + bucket_name = Variable.get("gcs_exported_data_bucket_name") + history_tables = [ + "ledgers", + "assets", + "transactions", + "operations", + "trades", + "effects", + "contract_events", + ] + + if cluster: + cluster_fields = Variable.get("cluster_fields", deserialize_json=True) + cluster_fields = ( + cluster_fields[f"{data_type}"] + if data_type in history_tables + else cluster_fields[data_type] + ) + else: + cluster_fields = None + + time_partition = {} + if partition: + partition_fields = Variable.get("partition_fields", deserialize_json=True) + partition_fields = ( + partition_fields[f"{data_type}"] + if data_type in history_tables + else partition_fields[data_type] + ) + time_partition["type"] = partition_fields["type"] + time_partition["field"] = partition_fields["field"] + + staging_table_suffix = "" + if data_type == "history_assets": + staging_table_suffix = "_staging" + + if data_type in history_tables: + schema_fields = read_local_schema(f"history_{data_type}") + gcs_to_bq_operator = CustomGCSToBigQueryOperator( + task_id=f"send_{data_type}_to_bq", + execution_timeout=timedelta( + seconds=Variable.get("task_timeout", deserialize_json=True)[ + build_del_ins_from_gcs_to_bq_task.__name__ + ] + ), + bucket=bucket_name, + schema_fields=schema_fields, + schema_update_options=["ALLOW_FIELD_ADDITION"], + autodetect=False, + source_format="NEWLINE_DELIMITED_JSON", + source_objects = source_objects, + destination_project_dataset_table=f"{project}.{dataset}.{data_type}{staging_table_suffix}", + write_disposition="WRITE_APPEND", + create_disposition="CREATE_IF_NEEDED", + #schema_update_option="ALLOW_FIELD_ADDITION", + max_bad_records=0, + time_partitioning=time_partition, + cluster_fields=cluster_fields, + export_task_id=export_task_id, + failed_transforms="{{ task_instance.xcom_pull(task_ids='" + + export_task_id + + '\')["failed_transforms"] }}', + max_failed_transforms=0, + on_failure_callback=alert_after_max_retries, + sla=timedelta( + seconds=Variable.get("task_sla", deserialize_json=True)[ + build_del_ins_from_gcs_to_bq_task.__name__ + ] + ), + dag=dag, + ) + else: + schema_fields = read_local_schema(f"{data_type}") + gcs_to_bq_operator = GCSToBigQueryOperator( + task_id=f"send_{data_type}_to_bq", + execution_timeout=timedelta( + seconds=Variable.get("task_timeout", deserialize_json=True)[ + build_del_ins_from_gcs_to_bq_task.__name__ + ] + ), + bucket=bucket_name, + schema_fields=schema_fields, + schema_update_options=["ALLOW_FIELD_ADDITION"], + autodetect=False, + source_format="NEWLINE_DELIMITED_JSON", + source_objects = source_objects, + destination_project_dataset_table=f"{project}.{dataset}.{data_type}{staging_table_suffix}", + write_disposition="WRITE_APPEND", + create_disposition="CREATE_IF_NEEDED", + max_bad_records=0, + time_partitioning=time_partition, + cluster_fields=cluster_fields, + on_failure_callback=alert_after_max_retries, + sla=timedelta( + seconds=Variable.get("task_sla", deserialize_json=True)[ + build_del_ins_from_gcs_to_bq_task.__name__ + ] + ), + dag=dag, + ) + + gcs_to_bq_operator.execute(context) \ No newline at end of file diff --git a/documentation/images/history_table_export.png b/documentation/images/history_table_export.png index e02ce0ca66129c775c263e721ad42fbcdb445322..bdda7c27ff5b416753d6f6f533df8e59f4b5073e 100644 GIT binary patch literal 114655 zcmeFZWmuG5*Dy>YB7&mMMb6Nogwh?-AfTk=sB{cScZmpsbT^U$0@BTZiu6#z&_g#2 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zhvG8P7kAcffA{C)r7iQ1=Ck~se*U}p8=1)tyBmU5);50*khzDqG%@3$UG8$|L@DAsdA`a%0 zP%H(?T+mw(l68N5)AQO?N3$hb6!;jRaU zy|_K>wUlAkL=1b&fad&Wd#a8e5G*WHyckQAG3mdKI;Vst0N!S-rvLx| From c524e462bb7375fb41b3e91fb6695b503824b3fd Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Mon, 8 Jul 2024 11:08:37 -0600 Subject: [PATCH 02/12] Addressed the PR Pre-Commit Comments from GH Actions --- dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py index 32af986e..ad9176bb 100644 --- a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py +++ b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py @@ -182,4 +182,5 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i dag=dag, ) - gcs_to_bq_operator.execute(context) \ No newline at end of file + gcs_to_bq_operator.execute(context) + \ No newline at end of file From aa97c035db8aae8016ff0f28c56f856f97ae21c6 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Mon, 8 Jul 2024 11:13:28 -0600 Subject: [PATCH 03/12] Addressed the pre-commit review comments --- dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py | 1 - 1 file changed, 1 deletion(-) diff --git a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py index ad9176bb..5da85428 100644 --- a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py +++ b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py @@ -183,4 +183,3 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i ) gcs_to_bq_operator.execute(context) - \ No newline at end of file From 3efd5c7edb8268efaed4d677672638da43a18344 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Mon, 8 Jul 2024 11:26:34 -0600 Subject: [PATCH 04/12] Modified the code to account for the pre-commit changes --- airflow_variables_dev.json | 4 +- airflow_variables_prod.json | 4 +- dags/history_tables_dag.py | 165 ++++++++++++++---- .../build_del_ins_from_gcs_to_bq_task.py | 50 ++++-- 4 files changed, 167 insertions(+), 56 deletions(-) diff --git a/airflow_variables_dev.json b/airflow_variables_dev.json index da53b6f5..98c873b0 100644 --- a/airflow_variables_dev.json +++ b/airflow_variables_dev.json @@ -334,10 +334,10 @@ "asset_stats": 720, "build_batch_stats": 840, "build_bq_insert_job": 1080, + "build_del_ins_from_gcs_to_bq_task": 2000, "build_delete_data_task": 1020, "build_export_task": 840, "build_gcs_to_bq_task": 960, - "build_del_ins_from_gcs_to_bq_task" : 2000, "build_time_task": 480, "cleanup_metadata": 60, "create_sandbox": 2400, @@ -368,8 +368,8 @@ "build_bq_insert_job": 180, "build_copy_table": 180, "build_dbt_task": 960, - "build_delete_data_task": 180, "build_del_ins_from_gcs_to_bq_task": 400, + "build_delete_data_task": 180, "build_export_task": 420, "build_gcs_to_bq_task": 300, "build_time_task": 480 diff --git a/airflow_variables_prod.json b/airflow_variables_prod.json index d0ea35af..c24d9942 100644 --- a/airflow_variables_prod.json +++ b/airflow_variables_prod.json @@ -332,10 +332,10 @@ "asset_stats": 420, "build_batch_stats": 600, "build_bq_insert_job": 840, + "build_del_ins_from_gcs_to_bq_task": 2000, "build_delete_data_task": 780, "build_export_task": 600, "build_gcs_to_bq_task": 660, - "build_del_ins_from_gcs_to_bq_task" : 2000, "build_time_task": 300, "cleanup_metadata": 60, "create_sandbox": 1020, @@ -366,8 +366,8 @@ "build_bq_insert_job": 180, "build_copy_table": 180, "build_dbt_task": 1800, + "build_del_ins_from_gcs_to_bq_task": 400, "build_delete_data_task": 180, - "build_del_ins_from_gcs_to_bq_task": 400, "build_export_task": 300, "build_gcs_to_bq_task": 300, "build_time_task": 360 diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index 58c2e58b..f39db08e 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -2,28 +2,28 @@ The history_archive_export DAG exports trades, asstes, ledgers, operations, transactions, effects from the history archives. It is scheduled to export information to BigQuery at regular intervals. """ + from ast import literal_eval from datetime import datetime from json import loads from airflow import DAG from airflow.models.variable import Variable +from airflow.operators.python import PythonOperator from kubernetes.client import models as k8s from stellar_etl_airflow import macros -from airflow.operators.python import PythonOperator - from stellar_etl_airflow.build_batch_stats import build_batch_stats from stellar_etl_airflow.build_bq_insert_job_task import build_bq_insert_job from stellar_etl_airflow.build_cross_dependency_task import build_cross_deps +from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import ( + build_del_ins_from_gcs_to_bq_task, +) from stellar_etl_airflow.build_delete_data_task import build_delete_data_task from stellar_etl_airflow.build_export_task import build_export_task from stellar_etl_airflow.build_gcs_to_bq_task import build_gcs_to_bq_task from stellar_etl_airflow.build_time_task import build_time_task -from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import build_del_ins_from_gcs_to_bq_task - -from stellar_etl_airflow.default import alert_after_max_retries - from stellar_etl_airflow.default import ( + alert_after_max_retries, alert_sla_miss, get_default_dag_args, init_sentry, @@ -52,14 +52,14 @@ "subtract_data_interval": macros.subtract_data_interval, "batch_run_date_as_datetime_string": macros.batch_run_date_as_datetime_string, }, - #sla_miss_callback=alert_sla_miss, + # sla_miss_callback=alert_sla_miss, ) -#fetch and set batch metadata variables +# fetch and set batch metadata variables batch_id = macros.get_batch_id() batch_date = "{{ batch_run_date_as_datetime_string(dag, data_interval_start) }}" -source_object_suffix="" +source_object_suffix = "" # Fetch necessary variables table_names = Variable.get("table_ids", deserialize_json=True) @@ -180,7 +180,7 @@ ) -#define the variables needed for making the call to the Operator that does the Delete from BQ Table and Inserts data from GCS to BigQuery +# define the variables needed for making the call to the Operator that does the Delete from BQ Table and Inserts data from GCS to BigQuery ops_export_task_id = op_export_task.task_id trades_export_task_id = trade_export_task.task_id @@ -190,49 +190,49 @@ assets_export_task_id = asset_export_task.task_id cont_evnts_export_task_id = contract_events_export_task.task_id -ops_source_objects=[ +ops_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + ops_export_task_id + '\')["output"] }}' + source_object_suffix ] -trade_source_objects=[ +trade_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + trades_export_task_id + '\')["output"] }}' + source_object_suffix ] -effects_source_objects=[ +effects_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + effects_export_task_id + '\')["output"] }}' + source_object_suffix ] -txns_source_objects=[ +txns_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + txns_export_task_id + '\')["output"] }}' + source_object_suffix ] -ledger_source_objects=[ +ledger_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + ledgers_export_task_id + '\')["output"] }}' + source_object_suffix ] -asset_source_objects=[ +asset_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + assets_export_task_id + '\')["output"] }}' + source_object_suffix ] -cont_evnts_source_objects=[ +cont_evnts_source_objects = [ "{{ task_instance.xcom_pull(task_ids='" + cont_evnts_export_task_id + '\')["output"] }}' @@ -243,14 +243,25 @@ The task calls the Custom Python Task that does the following operations in sequence 1. The delete operation in the task checks to see if the given partition/batch id exists in Bigquery. If it does, the records are deleted prior to reinserting the batch. - 2. The Insert operation in the tasks receive the location of the file in Google Cloud storage + 2. The Insert operation in the tasks receive the location of the file in Google Cloud storage through Airflow's XCOM system and merges the unique entries in the file into the corresponding table in BigQuery. """ del_ins_ops_task = PythonOperator( task_id=f"del_ins_ops_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["operations"], ops_export_task_id, source_object_suffix, True, True, batch_id, batch_date, ops_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["operations"], + ops_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + ops_source_objects, + ], provide_context=True, dag=dag, ) @@ -258,7 +269,18 @@ del_ins_trades_task = PythonOperator( task_id=f"del_ins_trades_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["trades"], trades_export_task_id, source_object_suffix, True, True, batch_id, batch_date, trade_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["trades"], + trades_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + trade_source_objects, + ], provide_context=True, dag=dag, ) @@ -266,7 +288,18 @@ del_ins_effects_task = PythonOperator( task_id=f"del_ins_effects_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["effects"], effects_export_task_id, source_object_suffix, True, True, batch_id, batch_date, effects_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["effects"], + effects_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + effects_source_objects, + ], provide_context=True, dag=dag, ) @@ -274,7 +307,18 @@ del_ins_txns_task = PythonOperator( task_id=f"del_ins_txns_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["transactions"], txns_export_task_id, source_object_suffix, True, True, batch_id, batch_date, txns_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["transactions"], + txns_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + txns_source_objects, + ], provide_context=True, dag=dag, ) @@ -282,7 +326,18 @@ del_ins_ledgers_task = PythonOperator( task_id=f"del_ins_ledgers_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["ledgers"], ledgers_export_task_id, source_object_suffix, True, True, batch_id, batch_date, ledger_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["ledgers"], + ledgers_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + ledger_source_objects, + ], provide_context=True, dag=dag, ) @@ -290,7 +345,18 @@ del_ins_assets_task = PythonOperator( task_id=f"del_ins_assets_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["assets"], assets_export_task_id, source_object_suffix, True, True, batch_id, batch_date, asset_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["assets"], + assets_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + asset_source_objects, + ], provide_context=True, dag=dag, ) @@ -298,7 +364,18 @@ del_ins_cont_events_task = PythonOperator( task_id=f"del_ins_cont_evnts_task", python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[public_project, public_dataset, table_names["contract_events"], cont_evnts_export_task_id, source_object_suffix, True, True, batch_id, batch_date, cont_evnts_source_objects], + op_args=[ + public_project, + public_dataset, + table_names["contract_events"], + cont_evnts_export_task_id, + source_object_suffix, + True, + True, + batch_id, + batch_date, + cont_evnts_source_objects, + ], provide_context=True, dag=dag, ) @@ -343,29 +420,45 @@ # Set Task dependencies ( - time_task >> write_op_stats >> op_export_task >> del_ins_ops_task >> delete_enrich_op_pub_task >> insert_enriched_hist_pub_task + time_task + >> write_op_stats + >> op_export_task + >> del_ins_ops_task + >> delete_enrich_op_pub_task + >> insert_enriched_hist_pub_task ) -( - time_task >> write_trade_stats >> trade_export_task >> del_ins_trades_task -) +(time_task >> write_trade_stats >> trade_export_task >> del_ins_trades_task) -( - time_task >> write_effects_stats >> effects_export_task >> del_ins_effects_task -) +(time_task >> write_effects_stats >> effects_export_task >> del_ins_effects_task) ( - time_task >> write_tx_stats >> tx_export_task >> del_ins_txns_task >> delete_enrich_op_pub_task + time_task + >> write_tx_stats + >> tx_export_task + >> del_ins_txns_task + >> delete_enrich_op_pub_task ) ( - time_task >> write_ledger_stats >> ledger_export_task >> del_ins_ledgers_task >> delete_enrich_op_pub_task + time_task + >> write_ledger_stats + >> ledger_export_task + >> del_ins_ledgers_task + >> delete_enrich_op_pub_task ) ( - time_task >> write_asset_stats >> asset_export_task >> del_ins_assets_task >> dedup_assets_pub_task + time_task + >> write_asset_stats + >> asset_export_task + >> del_ins_assets_task + >> dedup_assets_pub_task ) ( - time_task >> write_contract_events_stats >> contract_events_export_task >> del_ins_cont_events_task + time_task + >> write_contract_events_stats + >> contract_events_export_task + >> del_ins_cont_events_task ) diff --git a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py index 5da85428..3b8325a6 100644 --- a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py +++ b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py @@ -1,15 +1,16 @@ from datetime import timedelta + from airflow.models import Variable from airflow.operators.python import PythonOperator from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator -from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator +from airflow.providers.google.cloud.transfers.gcs_to_bigquery import ( + GCSToBigQueryOperator, +) +from sentry_sdk import capture_message, push_scope from stellar_etl_airflow import macros from stellar_etl_airflow.build_apply_gcs_changes_to_bq_task import read_local_schema from stellar_etl_airflow.default import alert_after_max_retries -from sentry_sdk import capture_message, push_scope -from airflow.providers.google.cloud.transfers.gcs_to_bigquery import GCSToBigQueryOperator -from sentry_sdk import capture_message, push_scope class CustomGCSToBigQueryOperator(GCSToBigQueryOperator): template_fields = list(GCSToBigQueryOperator.template_fields) + [ @@ -18,7 +19,9 @@ class CustomGCSToBigQueryOperator(GCSToBigQueryOperator): "export_task_id", ] - def __init__(self, failed_transforms, max_failed_transforms, export_task_id, **kwargs): + def __init__( + self, failed_transforms, max_failed_transforms, export_task_id, **kwargs + ): self.failed_transforms = failed_transforms self.max_failed_transforms = max_failed_transforms self.export_task_id = export_task_id @@ -27,7 +30,9 @@ def __init__(self, failed_transforms, max_failed_transforms, export_task_id, **k def pre_execute(self, context, **kwargs): # Expand the template fields using Jinja2 templating from the context self.failed_transforms = self.render_template(self.failed_transforms, context) - self.max_failed_transforms = self.render_template(self.max_failed_transforms, context) + self.max_failed_transforms = self.render_template( + self.max_failed_transforms, context + ) self.export_task_id = self.render_template(self.export_task_id, context) if int(self.failed_transforms) > self.max_failed_transforms: @@ -37,7 +42,9 @@ def pre_execute(self, context, **kwargs): scope.set_extra("file", f"gs://{self.bucket}/{self.source_objects[0]}") scope.set_extra("task_id", self.task_id) scope.set_extra("export_task_id", self.export_task_id) - scope.set_extra("destination_table", self.destination_project_dataset_table) + scope.set_extra( + "destination_table", self.destination_project_dataset_table + ) capture_message( f"failed_transforms ({self.failed_transforms}) has exceeded the max value ({self.max_failed_transforms})", "fatal", @@ -45,9 +52,20 @@ def pre_execute(self, context, **kwargs): super().pre_execute(context, **kwargs) - -def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_id, source_object_suffix, partition, cluster, batch_id ,batch_date, source_objects, **context): - dag = context['dag'] +def build_del_ins_from_gcs_to_bq_task( + project, + dataset, + data_type, + export_task_id, + source_object_suffix, + partition, + cluster, + batch_id, + batch_date, + source_objects, + **context, +): + dag = context["dag"] # Delete operation @@ -61,7 +79,7 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i task_id=f"delete_old_partition_{data_type}_bq", execution_timeout=timedelta( seconds=Variable.get("task_timeout", deserialize_json=True)[ - build_del_ins_from_gcs_to_bq_task.__name__ + build_del_ins_from_gcs_to_bq_task.__name__ ] ), on_failure_callback=alert_after_max_retries, @@ -80,7 +98,7 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i delete_task.execute(context) # Insert operation - + bucket_name = Variable.get("gcs_exported_data_bucket_name") history_tables = [ "ledgers", @@ -90,7 +108,7 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i "trades", "effects", "contract_events", - ] + ] if cluster: cluster_fields = Variable.get("cluster_fields", deserialize_json=True) @@ -131,11 +149,11 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i schema_update_options=["ALLOW_FIELD_ADDITION"], autodetect=False, source_format="NEWLINE_DELIMITED_JSON", - source_objects = source_objects, + source_objects=source_objects, destination_project_dataset_table=f"{project}.{dataset}.{data_type}{staging_table_suffix}", write_disposition="WRITE_APPEND", create_disposition="CREATE_IF_NEEDED", - #schema_update_option="ALLOW_FIELD_ADDITION", + # schema_update_option="ALLOW_FIELD_ADDITION", max_bad_records=0, time_partitioning=time_partition, cluster_fields=cluster_fields, @@ -166,7 +184,7 @@ def build_del_ins_from_gcs_to_bq_task(project, dataset, data_type, export_task_i schema_update_options=["ALLOW_FIELD_ADDITION"], autodetect=False, source_format="NEWLINE_DELIMITED_JSON", - source_objects = source_objects, + source_objects=source_objects, destination_project_dataset_table=f"{project}.{dataset}.{data_type}{staging_table_suffix}", write_disposition="WRITE_APPEND", create_disposition="CREATE_IF_NEEDED", From 3f31dcae810bda3e436b08d000f02ac592f16d5b Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Wed, 10 Jul 2024 09:25:31 -0600 Subject: [PATCH 05/12] Added Utilities.py for reusable templates to serve both history_tables and state_tabe DAGS --- README.md | 5 +- dags/history_tables_dag.py | 315 +++++--------------------- dags/state_table_dag_upgraded.py | 159 +++++++++++++ dags/stellar_etl_airflow/utilities.py | 84 +++++++ 4 files changed, 303 insertions(+), 260 deletions(-) create mode 100644 dags/state_table_dag_upgraded.py create mode 100644 dags/stellar_etl_airflow/utilities.py diff --git a/README.md b/README.md index c0b2f452..8698fc2d 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,7 @@ This repository contains the Airflow DAGs for the [Stellar ETL](https://github.c - [build_time_task](#build_time_task) - [build_export_task](#build_export_task) - [build_gcs_to_bq_task](#build_gcs_to_bq_task) + - [build_del_ins_from_gcs_to_bq_task](#build_del_ins_from_gcs_to_bq_task) - [build_apply_gcs_changes_to_bq_task](#build_apply_gcs_changes_to_bq_task) - [build_batch_stats](#build_batch_stats) - [bq_insert_job_task](#bq_insert_job_task) @@ -542,13 +543,13 @@ This section contains information about the Airflow setup. It includes our DAG d - [build_export_task](#build_export_task) - [build_gcs_to_bq_task](#build_gcs_to_bq_task) - [build_apply_gcs_changes_to_bq_task](#build_apply_gcs_changes_to_bq_task) + - [build_del_ins_from_gcs_to_bq_task](#build_del_ins_from_gcs_to_bq_task) - [build_batch_stats](#build_batch_stats) - [bq_insert_job_task](#bq_insert_job_task) - [cross_dependency_task](#cross_dependency_task) - [build_delete_data_task](#build_delete_data_task) - [build_dbt_task](#build_dbt_task) - [build_elementary_slack_alert_task](#build_elementary_slack_alert_task) - - [build_del_ins_from_gcs_to_bq_task](#build_del_ins_from_gcs_to_bq_task) --- @@ -671,7 +672,7 @@ This section contains information about the Airflow setup. It includes our DAG d ### **build_del_ins_from_gcs_to_bq_task** -[This file](https://github.com/stellar/stellar-etl-airflow/blob/master/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py) contains methods for deleting data from old partitions if the table exists and also import fresh data from gcs to the corresponding Big Query table. These tasks will create a new table if one does not exist. These tasks are used for history archive data structures, as Stellar wants to keep a complete record of the ledger's entire history. +[This file](https://github.com/stellar/stellar-etl-airflow/blob/master/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py) contains methods for deleting data from a specified BigQuery table according to the batch interval and also imports data from gcs to the corresponding BigQuery table. These tasks will create a new table if one does not exist. These tasks are used for history and state data structures, as Stellar wants to keep a complete record of the ledger's entire history. ### **build_apply_gcs_changes_to_bq_task** diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index f39db08e..afb5102e 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -1,5 +1,7 @@ """ -The history_archive_export DAG exports trades, asstes, ledgers, operations, transactions, effects from the history archives. +The history_table_export DAG exports trades, assets, ledgers, operations, transactions, effects, and contract events +from the history archives and loads the data into the corresponding BigQuery tables. It also performs a Delete operation +based on the batch interval value to perform clean up before the Insert to avoid any potential duplication issues. It is scheduled to export information to BigQuery at regular intervals. """ @@ -9,7 +11,6 @@ from airflow import DAG from airflow.models.variable import Variable -from airflow.operators.python import PythonOperator from kubernetes.client import models as k8s from stellar_etl_airflow import macros from stellar_etl_airflow.build_batch_stats import build_batch_stats @@ -28,16 +29,17 @@ get_default_dag_args, init_sentry, ) +from stellar_etl_airflow.utilities import create_del_ins_task, initialize_task_vars init_sentry() # Initialize the DAG dag = DAG( - "history_tables_export", + "history_table_export", default_args=get_default_dag_args(), - start_date=datetime(2024, 7, 8, 16, 00), + start_date=datetime(2024, 7, 10, 14, 30), catchup=True, - description="This DAG exports trades, asstes, ledgers, operations, transactions, effects from the history archive using CaptiveCore. This supports parsing sponsorship and AMMs.", + description="This DAG exports information for the trades, assets, ledgers, operations, transactions, effects and contract events history tables.", schedule_interval="*/10 * * * *", params={ "alias": "cc", @@ -52,15 +54,13 @@ "subtract_data_interval": macros.subtract_data_interval, "batch_run_date_as_datetime_string": macros.batch_run_date_as_datetime_string, }, - # sla_miss_callback=alert_sla_miss, + sla_miss_callback=alert_sla_miss, ) -# fetch and set batch metadata variables +# Initialize batch metadata variables batch_id = macros.get_batch_id() batch_date = "{{ batch_run_date_as_datetime_string(dag, data_interval_start) }}" -source_object_suffix = "" - # Fetch necessary variables table_names = Variable.get("table_ids", deserialize_json=True) public_project = "{{ var.value.public_project }}" @@ -70,37 +70,7 @@ use_captive_core = literal_eval(Variable.get("use_captive_core")) txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" -""" -The time task reads in the execution time of the current run, as well as the next -execution time. It converts these two times into ledger ranges. -""" -time_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) - -""" -The write batch stats task will take a snapshot of the DAG run_id, execution date, -start and end ledgers so that reconciliation and data validation are easier. The -record is written to an internal dataset for data eng use only. -""" -write_op_stats = build_batch_stats(dag, table_names["operations"]) -write_trade_stats = build_batch_stats(dag, table_names["trades"]) -write_effects_stats = build_batch_stats(dag, table_names["effects"]) -write_tx_stats = build_batch_stats(dag, table_names["transactions"]) -write_ledger_stats = build_batch_stats(dag, table_names["ledgers"]) -write_asset_stats = build_batch_stats(dag, table_names["assets"]) -write_contract_events_stats = build_batch_stats(dag, "contract_events") - - -""" -The export tasks call export commands on the Stellar ETL using the ledger range from the time task. -The results of the command are stored in a file. There is one task for each of the data types that -can be exported from the history archives. - - -The DAG sleeps for 30 seconds after the export_task writes to the file to give the poststart.sh -script time to copy the file over to the correct directory. If there is no sleep, the load task -starts prematurely and will not load data. -""" - +# Define export tasks op_export_task = build_export_task( dag, "archive", @@ -112,6 +82,7 @@ use_captive_core=use_captive_core, txmeta_datastore_path=txmeta_datastore_path, ) + trade_export_task = build_export_task( dag, "archive", @@ -123,6 +94,7 @@ use_captive_core=use_captive_core, txmeta_datastore_path=txmeta_datastore_path, ) + effects_export_task = build_export_task( dag, "archive", @@ -134,6 +106,7 @@ use_captive_core=use_captive_core, txmeta_datastore_path=txmeta_datastore_path, ) + tx_export_task = build_export_task( dag, "archive", @@ -145,6 +118,7 @@ use_captive_core=use_captive_core, txmeta_datastore_path=txmeta_datastore_path, ) + ledger_export_task = build_export_task( dag, "archive", @@ -156,6 +130,7 @@ use_captive_core=use_captive_core, txmeta_datastore_path=txmeta_datastore_path, ) + asset_export_task = build_export_task( dag, "archive", @@ -167,6 +142,7 @@ use_captive_core=use_captive_core, txmeta_datastore_path=txmeta_datastore_path, ) + contract_events_export_task = build_export_task( dag, "archive", @@ -179,211 +155,44 @@ txmeta_datastore_path=txmeta_datastore_path, ) - -# define the variables needed for making the call to the Operator that does the Delete from BQ Table and Inserts data from GCS to BigQuery - -ops_export_task_id = op_export_task.task_id -trades_export_task_id = trade_export_task.task_id -effects_export_task_id = effects_export_task.task_id -txns_export_task_id = tx_export_task.task_id -ledgers_export_task_id = ledger_export_task.task_id -assets_export_task_id = asset_export_task.task_id -cont_evnts_export_task_id = contract_events_export_task.task_id - -ops_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + ops_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -trade_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + trades_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -effects_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + effects_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -txns_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + txns_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -ledger_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + ledgers_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -asset_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + assets_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -cont_evnts_source_objects = [ - "{{ task_instance.xcom_pull(task_ids='" - + cont_evnts_export_task_id - + '\')["output"] }}' - + source_object_suffix -] - -""" -The task calls the Custom Python Task that does the following operations in sequence - 1. The delete operation in the task checks to see if the given partition/batch id exists in - Bigquery. If it does, the records are deleted prior to reinserting the batch. - 2. The Insert operation in the tasks receive the location of the file in Google Cloud storage - through Airflow's XCOM system and merges the unique entries in the file into the corresponding table in BigQuery. -""" - -del_ins_ops_task = PythonOperator( - task_id=f"del_ins_ops_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ - public_project, - public_dataset, - table_names["operations"], - ops_export_task_id, - source_object_suffix, - True, - True, - batch_id, - batch_date, - ops_source_objects, - ], - provide_context=True, - dag=dag, -) - -del_ins_trades_task = PythonOperator( - task_id=f"del_ins_trades_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ - public_project, - public_dataset, - table_names["trades"], - trades_export_task_id, - source_object_suffix, - True, - True, - batch_id, - batch_date, - trade_source_objects, - ], - provide_context=True, - dag=dag, -) - -del_ins_effects_task = PythonOperator( - task_id=f"del_ins_effects_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ - public_project, - public_dataset, - table_names["effects"], - effects_export_task_id, - source_object_suffix, - True, - True, - batch_id, - batch_date, - effects_source_objects, - ], - provide_context=True, - dag=dag, -) - -del_ins_txns_task = PythonOperator( - task_id=f"del_ins_txns_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ - public_project, - public_dataset, - table_names["transactions"], - txns_export_task_id, - source_object_suffix, - True, - True, +# Define del_ins tasks +del_ins_tasks = {} +export_tasks = { + "operations": op_export_task, + "trades": trade_export_task, + "effects": effects_export_task, + "transactions": tx_export_task, + "ledgers": ledger_export_task, + "assets": asset_export_task, + "contract_events": contract_events_export_task, +} + +for data_type, export_task in export_tasks.items(): + task_vars = initialize_task_vars( + data_type, + export_task.task_id, batch_id, batch_date, - txns_source_objects, - ], - provide_context=True, - dag=dag, -) - -del_ins_ledgers_task = PythonOperator( - task_id=f"del_ins_ledgers_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ + table_names, public_project, public_dataset, - table_names["ledgers"], - ledgers_export_task_id, - source_object_suffix, - True, - True, - batch_id, - batch_date, - ledger_source_objects, - ], - provide_context=True, - dag=dag, -) + ) + del_ins_tasks[data_type] = create_del_ins_task( + dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task + ) -del_ins_assets_task = PythonOperator( - task_id=f"del_ins_assets_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ - public_project, - public_dataset, - table_names["assets"], - assets_export_task_id, - source_object_suffix, - True, - True, - batch_id, - batch_date, - asset_source_objects, - ], - provide_context=True, - dag=dag, -) +# Define time task +time_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) -del_ins_cont_events_task = PythonOperator( - task_id=f"del_ins_cont_evnts_task", - python_callable=build_del_ins_from_gcs_to_bq_task, - op_args=[ - public_project, - public_dataset, - table_names["contract_events"], - cont_evnts_export_task_id, - source_object_suffix, - True, - True, - batch_id, - batch_date, - cont_evnts_source_objects, - ], - provide_context=True, - dag=dag, -) +# Define other tasks +write_op_stats = build_batch_stats(dag, table_names["operations"]) +write_trade_stats = build_batch_stats(dag, table_names["trades"]) +write_effects_stats = build_batch_stats(dag, table_names["effects"]) +write_tx_stats = build_batch_stats(dag, table_names["transactions"]) +write_ledger_stats = build_batch_stats(dag, table_names["ledgers"]) +write_asset_stats = build_batch_stats(dag, table_names["assets"]) +write_contract_events_stats = build_batch_stats(dag, "contract_events") -""" -The delete partition task checks to see if the given partition/batch id exists in -Bigquery. If it does, the records are deleted prior to reinserting the batch. -""" delete_enrich_op_pub_task = build_delete_data_task( dag, public_project, @@ -392,11 +201,7 @@ "pub", ) -""" -The task handles the Insert operation into BQ for EHO and assets tables -""" - -insert_enriched_hist_pub_task = build_bq_insert_job( +insert_enrich_op_pub_task = build_bq_insert_job( dag, public_project, public_dataset, @@ -417,48 +222,42 @@ dataset_type="pub", ) -# Set Task dependencies - +# Set dependencies ( time_task >> write_op_stats >> op_export_task - >> del_ins_ops_task + >> del_ins_tasks["operations"] >> delete_enrich_op_pub_task - >> insert_enriched_hist_pub_task + >> insert_enrich_op_pub_task ) -(time_task >> write_trade_stats >> trade_export_task >> del_ins_trades_task) - -(time_task >> write_effects_stats >> effects_export_task >> del_ins_effects_task) - +(time_task >> write_trade_stats >> trade_export_task >> del_ins_tasks["trades"]) +(time_task >> write_effects_stats >> effects_export_task >> del_ins_tasks["effects"]) ( time_task >> write_tx_stats >> tx_export_task - >> del_ins_txns_task + >> del_ins_tasks["transactions"] >> delete_enrich_op_pub_task ) - ( time_task >> write_ledger_stats >> ledger_export_task - >> del_ins_ledgers_task + >> del_ins_tasks["ledgers"] >> delete_enrich_op_pub_task ) - ( time_task >> write_asset_stats >> asset_export_task - >> del_ins_assets_task + >> del_ins_tasks["assets"] >> dedup_assets_pub_task ) - ( time_task >> write_contract_events_stats >> contract_events_export_task - >> del_ins_cont_events_task + >> del_ins_tasks["contract_events"] ) diff --git a/dags/state_table_dag_upgraded.py b/dags/state_table_dag_upgraded.py new file mode 100644 index 00000000..bd7ebb15 --- /dev/null +++ b/dags/state_table_dag_upgraded.py @@ -0,0 +1,159 @@ +""" +The state_table_export DAG exports ledger entry changes (accounts, offers, and trustlines) within a bounded range using stellar-core. +This DAG should be triggered manually if it is required to export entry changes within a specified time range. +""" + +from ast import literal_eval +from datetime import datetime +from json import loads + +from airflow import DAG +from airflow.models import Variable +from kubernetes.client import models as k8s +from stellar_etl_airflow import macros +from stellar_etl_airflow.build_batch_stats import build_batch_stats +from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import ( + build_del_ins_from_gcs_to_bq_task, +) +from stellar_etl_airflow.build_export_task import build_export_task +from stellar_etl_airflow.build_time_task import build_time_task +from stellar_etl_airflow.default import ( + alert_sla_miss, + get_default_dag_args, + init_sentry, +) +from stellar_etl_airflow.utilities import create_del_ins_task, initialize_task_vars + +init_sentry() + +# Initialize the DAG +dag = DAG( + "state_table_export_upgraded", + default_args=get_default_dag_args(), + start_date=datetime(2024, 7, 10, 14, 30), + description="This DAG runs a bounded stellar-core instance, which allows it to export accounts, offers, liquidity pools, and trustlines to BigQuery.", + schedule_interval="*/10 * * * *", + params={ + "alias": "state", + }, + render_template_as_native_obj=True, + user_defined_filters={ + "fromjson": lambda s: loads(s), + "container_resources": lambda s: k8s.V1ResourceRequirements(requests=s), + "literal_eval": lambda e: literal_eval(e), + }, + user_defined_macros={ + "subtract_data_interval": macros.subtract_data_interval, + "batch_run_date_as_datetime_string": macros.batch_run_date_as_datetime_string, + }, + catchup=True, + # sla_miss_callback=alert_sla_miss, +) + +# Initialize batch metadata variables +batch_id = macros.get_batch_id() +batch_date = "{{ batch_run_date_as_datetime_string(dag, data_interval_start) }}" + +# Fetch necessary variables +table_names = Variable.get("table_ids", deserialize_json=True) +internal_project = "{{ var.value.bq_project }}" +internal_dataset = "{{ var.value.bq_dataset }}" +public_project = "{{ var.value.public_project }}" +public_dataset = "{{ var.value.public_dataset }}" +use_testnet = literal_eval(Variable.get("use_testnet")) +use_futurenet = literal_eval(Variable.get("use_futurenet")) +use_captive_core = literal_eval(Variable.get("use_captive_core")) +txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" + +# Define tasks +date_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) + +changes_task = build_export_task( + dag, + "bounded-core", + "export_ledger_entry_changes", + "{{ var.json.output_file_names.changes }}", + use_testnet=use_testnet, + use_futurenet=use_futurenet, + use_gcs=True, + use_captive_core=use_captive_core, + txmeta_datastore_path=txmeta_datastore_path, +) + +# Define del_ins tasks +del_ins_tasks = {} +data_types_and_suffixes = { + "accounts": "/*-accounts.txt", + "claimable_balances": "/*-claimable_balances.txt", + "offers": "/*-offers.txt", + "liquidity_pools": "/*-liquidity_pools.txt", + "signers": "/*-signers.txt", + "trustlines": "/*-trustlines.txt", + "contract_data": "/*-contract_data.txt", + "contract_code": "/*-contract_code.txt", + "config_settings": "/*-config_settings.txt", + "ttl": "/*-ttl.txt", +} + +for data_type, source_object_suffix in data_types_and_suffixes.items(): + task_vars = initialize_task_vars( + data_type, + changes_task.task_id, + batch_id, + batch_date, + table_names, + public_project, + public_dataset, + source_object_suffix=source_object_suffix, + ) + del_ins_tasks[data_type] = create_del_ins_task( + dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task + ) + +# Define other tasks +write_acc_stats = build_batch_stats(dag, table_names["accounts"]) +write_bal_stats = build_batch_stats(dag, table_names["claimable_balances"]) +write_off_stats = build_batch_stats(dag, table_names["offers"]) +write_pool_stats = build_batch_stats(dag, table_names["liquidity_pools"]) +write_sign_stats = build_batch_stats(dag, table_names["signers"]) +write_trust_stats = build_batch_stats(dag, table_names["trustlines"]) +write_contract_data_stats = build_batch_stats(dag, table_names["contract_data"]) +write_contract_code_stats = build_batch_stats(dag, table_names["contract_code"]) +write_config_settings_stats = build_batch_stats(dag, table_names["config_settings"]) +write_ttl_stats = build_batch_stats(dag, table_names["ttl"]) + +# Set dependencies +(date_task >> changes_task >> write_acc_stats >> del_ins_tasks["accounts"]) + +(date_task >> changes_task >> write_bal_stats >> del_ins_tasks["claimable_balances"]) + +(date_task >> changes_task >> write_off_stats >> del_ins_tasks["offers"]) + +(date_task >> changes_task >> write_pool_stats >> del_ins_tasks["liquidity_pools"]) + +(date_task >> changes_task >> write_sign_stats >> del_ins_tasks["signers"]) + +(date_task >> changes_task >> write_trust_stats >> del_ins_tasks["trustlines"]) + +( + date_task + >> changes_task + >> write_contract_data_stats + >> del_ins_tasks["contract_data"] +) + +( + date_task + >> changes_task + >> write_contract_code_stats + >> del_ins_tasks["contract_code"] +) + +( + date_task + >> changes_task + >> write_config_settings_stats + >> del_ins_tasks["config_settings"] +) + +(date_task >> changes_task >> write_ttl_stats >> del_ins_tasks["ttl"]) diff --git a/dags/stellar_etl_airflow/utilities.py b/dags/stellar_etl_airflow/utilities.py new file mode 100644 index 00000000..51c9ee38 --- /dev/null +++ b/dags/stellar_etl_airflow/utilities.py @@ -0,0 +1,84 @@ +from airflow.operators.python import PythonOperator + + +def initialize_task_vars( + data_type, + export_task_id, + batch_id, + batch_date, + table_names, + public_project, + public_dataset, + source_object_suffix="", + source_objects=None, +): + """ + Initialize task variables for data export and import. + + Args: + data_type (str): Type of data (e.g., operations, trades). + export_task_id (str): Task ID of the export task. + batch_id (str): Batch ID. + batch_date (str): Batch date. + table_names (dict): Dictionary of table names. + public_project (str): Public project name. + public_dataset (str): Public dataset name. + source_object_suffix (str): Suffix for source objects. + source_objects (list): List of source objects. + + Returns: + dict: Task variables. + """ + table_name = table_names[data_type] if data_type in table_names else data_type + if source_objects is None: + source_objects = [ + "{{ task_instance.xcom_pull(task_ids='" + + export_task_id + + '\')["output"] }}' + + source_object_suffix + ] + return { + "project": public_project, + "dataset": public_dataset, + "table_name": table_name, + "export_task_id": export_task_id, + "source_object_suffix": source_object_suffix, + "partition": True, + "cluster": True, + "batch_id": batch_id, + "batch_date": batch_date, + "source_objects": source_objects, + } + + +def create_del_ins_task(dag, task_id, task_vars, del_ins_callable): + """ + Create a PythonOperator for delete and insert tasks. + + Args: + dag (DAG): The DAG to which the task belongs. + task_id (str): Task ID. + task_vars (dict): Task variables. + del_ins_callable (callable): The callable function to be used in the PythonOperator. + + Returns: + PythonOperator: The created PythonOperator. + """ + return PythonOperator( + task_id=task_id, + python_callable=del_ins_callable, + op_args=[ + task_vars["project"], + task_vars["dataset"], + task_vars["table_name"], + task_vars["export_task_id"], + task_vars["source_object_suffix"], + task_vars["partition"], + task_vars["cluster"], + task_vars["batch_id"], + task_vars["batch_date"], + task_vars["source_objects"], + ], + provide_context=True, + dag=dag, + ) From 5bfd4074b62c65f5ec70267691576578c92483d7 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Wed, 10 Jul 2024 09:58:38 -0600 Subject: [PATCH 06/12] Mofified the reusable script name and updated the documentation image for state_table_export --- dags/history_tables_dag.py | 5 +- dags/state_table_dag.py | 287 ++++-------------- dags/state_table_dag_upgraded.py | 159 ---------- ...utilities.py => build_del_ins_operator.py} | 0 documentation/images/state_table_export.png | Bin 222043 -> 112735 bytes 5 files changed, 64 insertions(+), 387 deletions(-) delete mode 100644 dags/state_table_dag_upgraded.py rename dags/stellar_etl_airflow/{utilities.py => build_del_ins_operator.py} (100%) diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index afb5102e..3c88adcf 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -19,6 +19,10 @@ from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import ( build_del_ins_from_gcs_to_bq_task, ) +from stellar_etl_airflow.build_del_ins_operator import ( + create_del_ins_task, + initialize_task_vars, +) from stellar_etl_airflow.build_delete_data_task import build_delete_data_task from stellar_etl_airflow.build_export_task import build_export_task from stellar_etl_airflow.build_gcs_to_bq_task import build_gcs_to_bq_task @@ -29,7 +33,6 @@ get_default_dag_args, init_sentry, ) -from stellar_etl_airflow.utilities import create_del_ins_task, initialize_task_vars init_sentry() diff --git a/dags/state_table_dag.py b/dags/state_table_dag.py index 06395991..94d3c4c6 100644 --- a/dags/state_table_dag.py +++ b/dags/state_table_dag.py @@ -12,9 +12,14 @@ from kubernetes.client import models as k8s from stellar_etl_airflow import macros from stellar_etl_airflow.build_batch_stats import build_batch_stats -from stellar_etl_airflow.build_delete_data_task import build_delete_data_task +from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import ( + build_del_ins_from_gcs_to_bq_task, +) +from stellar_etl_airflow.build_del_ins_operator import ( + create_del_ins_task, + initialize_task_vars, +) from stellar_etl_airflow.build_export_task import build_export_task -from stellar_etl_airflow.build_gcs_to_bq_task import build_gcs_to_bq_task from stellar_etl_airflow.build_time_task import build_time_task from stellar_etl_airflow.default import ( alert_sla_miss, @@ -24,11 +29,11 @@ init_sentry() - +# Initialize the DAG dag = DAG( "state_table_export", default_args=get_default_dag_args(), - start_date=datetime(2024, 6, 11, 17, 30), + start_date=datetime(2024, 7, 10, 14, 30), description="This DAG runs a bounded stellar-core instance, which allows it to export accounts, offers, liquidity pools, and trustlines to BigQuery.", schedule_interval="*/10 * * * *", params={ @@ -48,7 +53,11 @@ # sla_miss_callback=alert_sla_miss, ) +# Initialize batch metadata variables +batch_id = macros.get_batch_id() +batch_date = "{{ batch_run_date_as_datetime_string(dag, data_interval_start) }}" +# Fetch necessary variables table_names = Variable.get("table_ids", deserialize_json=True) internal_project = "{{ var.value.bq_project }}" internal_dataset = "{{ var.value.bq_dataset }}" @@ -59,8 +68,9 @@ use_captive_core = literal_eval(Variable.get("use_captive_core")) txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" - +# Define tasks date_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) + changes_task = build_export_task( dag, "bounded-core", @@ -73,12 +83,37 @@ txmeta_datastore_path=txmeta_datastore_path, ) - -""" -The write batch stats task will take a snapshot of the DAG run_id, execution date, -start and end ledgers so that reconciliation and data validation are easier. The -record is written to an internal dataset for data eng use only. -""" +# Define del_ins tasks +del_ins_tasks = {} +data_types_and_suffixes = { + "accounts": "/*-accounts.txt", + "claimable_balances": "/*-claimable_balances.txt", + "offers": "/*-offers.txt", + "liquidity_pools": "/*-liquidity_pools.txt", + "signers": "/*-signers.txt", + "trustlines": "/*-trustlines.txt", + "contract_data": "/*-contract_data.txt", + "contract_code": "/*-contract_code.txt", + "config_settings": "/*-config_settings.txt", + "ttl": "/*-ttl.txt", +} + +for data_type, source_object_suffix in data_types_and_suffixes.items(): + task_vars = initialize_task_vars( + data_type, + changes_task.task_id, + batch_id, + batch_date, + table_names, + public_project, + public_dataset, + source_object_suffix=source_object_suffix, + ) + del_ins_tasks[data_type] = create_del_ins_task( + dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task + ) + +# Define other tasks write_acc_stats = build_batch_stats(dag, table_names["accounts"]) write_bal_stats = build_batch_stats(dag, table_names["claimable_balances"]) write_off_stats = build_batch_stats(dag, table_names["offers"]) @@ -90,240 +125,38 @@ write_config_settings_stats = build_batch_stats(dag, table_names["config_settings"]) write_ttl_stats = build_batch_stats(dag, table_names["ttl"]) +# Set dependencies +(date_task >> changes_task >> write_acc_stats >> del_ins_tasks["accounts"]) -""" -The delete partition task checks to see if the given partition/batch id exists in -Bigquery. If it does, the records are deleted prior to reinserting the batch. -""" -delete_acc_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["accounts"], "pub" -) -delete_bal_pub_task = build_delete_data_task( - dag, - public_project, - public_dataset, - table_names["claimable_balances"], - "pub", -) -delete_off_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["offers"], "pub" -) -delete_pool_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["liquidity_pools"], "pub" -) -delete_sign_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["signers"], "pub" -) -delete_trust_pub_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["trustlines"], "pub" -) -delete_contract_data_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["contract_data"], "pub" -) -delete_contract_code_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["contract_code"], "pub" -) -delete_config_settings_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["config_settings"], "pub" -) -delete_ttl_task = build_delete_data_task( - dag, public_project, public_dataset, table_names["ttl"], "pub" -) - - -""" -The apply tasks receive the location of the file in Google Cloud storage through Airflow's XCOM system. -Then, the task merges the entries in the file with the entries in the corresponding table in the public dataset. -Entries are updated, deleted, or inserted as needed. -""" -send_acc_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["accounts"], - "/*-accounts.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_bal_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["claimable_balances"], - "/*-claimable_balances.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_off_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["offers"], - "/*-offers.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_pool_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["liquidity_pools"], - "/*-liquidity_pools.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_sign_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["signers"], - "/*-signers.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_trust_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["trustlines"], - "/*-trustlines.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_contract_data_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["contract_data"], - "/*-contract_data.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_contract_code_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["contract_code"], - "/*-contract_code.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_config_settings_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["config_settings"], - "/*-config_settings.txt", - partition=True, - cluster=True, - dataset_type="pub", -) -send_ttl_to_pub_task = build_gcs_to_bq_task( - dag, - changes_task.task_id, - public_project, - public_dataset, - table_names["ttl"], - "/*-ttl.txt", - partition=True, - cluster=True, - dataset_type="pub", -) - -( - date_task - >> changes_task - >> write_acc_stats - >> delete_acc_pub_task - >> send_acc_to_pub_task -) - +(date_task >> changes_task >> write_bal_stats >> del_ins_tasks["claimable_balances"]) -( - date_task - >> changes_task - >> write_bal_stats - >> delete_bal_pub_task - >> send_bal_to_pub_task -) +(date_task >> changes_task >> write_off_stats >> del_ins_tasks["offers"]) +(date_task >> changes_task >> write_pool_stats >> del_ins_tasks["liquidity_pools"]) -( - date_task - >> changes_task - >> write_off_stats - >> delete_off_pub_task - >> send_off_to_pub_task -) +(date_task >> changes_task >> write_sign_stats >> del_ins_tasks["signers"]) +(date_task >> changes_task >> write_trust_stats >> del_ins_tasks["trustlines"]) -( - date_task - >> changes_task - >> write_pool_stats - >> delete_pool_pub_task - >> send_pool_to_pub_task -) - - -( - date_task - >> changes_task - >> write_sign_stats - >> delete_sign_pub_task - >> send_sign_to_pub_task -) - - -( - date_task - >> changes_task - >> write_trust_stats - >> delete_trust_pub_task - >> send_trust_to_pub_task -) ( date_task >> changes_task >> write_contract_data_stats - >> delete_contract_data_task - >> send_contract_data_to_pub_task + >> del_ins_tasks["contract_data"] ) + ( date_task >> changes_task >> write_contract_code_stats - >> delete_contract_code_task - >> send_contract_code_to_pub_task + >> del_ins_tasks["contract_code"] ) + ( date_task >> changes_task >> write_config_settings_stats - >> delete_config_settings_task - >> send_config_settings_to_pub_task -) -( - date_task - >> changes_task - >> write_ttl_stats - >> delete_ttl_task - >> send_ttl_to_pub_task + >> del_ins_tasks["config_settings"] ) + +(date_task >> changes_task >> write_ttl_stats >> del_ins_tasks["ttl"]) diff --git a/dags/state_table_dag_upgraded.py b/dags/state_table_dag_upgraded.py deleted file mode 100644 index bd7ebb15..00000000 --- a/dags/state_table_dag_upgraded.py +++ /dev/null @@ -1,159 +0,0 @@ -""" -The state_table_export DAG exports ledger entry changes (accounts, offers, and trustlines) within a bounded range using stellar-core. -This DAG should be triggered manually if it is required to export entry changes within a specified time range. -""" - -from ast import literal_eval -from datetime import datetime -from json import loads - -from airflow import DAG -from airflow.models import Variable -from kubernetes.client import models as k8s -from stellar_etl_airflow import macros -from stellar_etl_airflow.build_batch_stats import build_batch_stats -from stellar_etl_airflow.build_del_ins_from_gcs_to_bq_task import ( - build_del_ins_from_gcs_to_bq_task, -) -from stellar_etl_airflow.build_export_task import build_export_task -from stellar_etl_airflow.build_time_task import build_time_task -from stellar_etl_airflow.default import ( - alert_sla_miss, - get_default_dag_args, - init_sentry, -) -from stellar_etl_airflow.utilities import create_del_ins_task, initialize_task_vars - -init_sentry() - -# Initialize the DAG -dag = DAG( - "state_table_export_upgraded", - default_args=get_default_dag_args(), - start_date=datetime(2024, 7, 10, 14, 30), - description="This DAG runs a bounded stellar-core instance, which allows it to export accounts, offers, liquidity pools, and trustlines to BigQuery.", - schedule_interval="*/10 * * * *", - params={ - "alias": "state", - }, - render_template_as_native_obj=True, - user_defined_filters={ - "fromjson": lambda s: loads(s), - "container_resources": lambda s: k8s.V1ResourceRequirements(requests=s), - "literal_eval": lambda e: literal_eval(e), - }, - user_defined_macros={ - "subtract_data_interval": macros.subtract_data_interval, - "batch_run_date_as_datetime_string": macros.batch_run_date_as_datetime_string, - }, - catchup=True, - # sla_miss_callback=alert_sla_miss, -) - -# Initialize batch metadata variables -batch_id = macros.get_batch_id() -batch_date = "{{ batch_run_date_as_datetime_string(dag, data_interval_start) }}" - -# Fetch necessary variables -table_names = Variable.get("table_ids", deserialize_json=True) -internal_project = "{{ var.value.bq_project }}" -internal_dataset = "{{ var.value.bq_dataset }}" -public_project = "{{ var.value.public_project }}" -public_dataset = "{{ var.value.public_dataset }}" -use_testnet = literal_eval(Variable.get("use_testnet")) -use_futurenet = literal_eval(Variable.get("use_futurenet")) -use_captive_core = literal_eval(Variable.get("use_captive_core")) -txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" - -# Define tasks -date_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) - -changes_task = build_export_task( - dag, - "bounded-core", - "export_ledger_entry_changes", - "{{ var.json.output_file_names.changes }}", - use_testnet=use_testnet, - use_futurenet=use_futurenet, - use_gcs=True, - use_captive_core=use_captive_core, - txmeta_datastore_path=txmeta_datastore_path, -) - -# Define del_ins tasks -del_ins_tasks = {} -data_types_and_suffixes = { - "accounts": "/*-accounts.txt", - "claimable_balances": "/*-claimable_balances.txt", - "offers": "/*-offers.txt", - "liquidity_pools": "/*-liquidity_pools.txt", - "signers": "/*-signers.txt", - "trustlines": "/*-trustlines.txt", - "contract_data": "/*-contract_data.txt", - "contract_code": "/*-contract_code.txt", - "config_settings": "/*-config_settings.txt", - "ttl": "/*-ttl.txt", -} - -for data_type, source_object_suffix in data_types_and_suffixes.items(): - task_vars = initialize_task_vars( - data_type, - changes_task.task_id, - batch_id, - batch_date, - table_names, - public_project, - public_dataset, - source_object_suffix=source_object_suffix, - ) - del_ins_tasks[data_type] = create_del_ins_task( - dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task - ) - -# Define other tasks -write_acc_stats = build_batch_stats(dag, table_names["accounts"]) -write_bal_stats = build_batch_stats(dag, table_names["claimable_balances"]) -write_off_stats = build_batch_stats(dag, table_names["offers"]) -write_pool_stats = build_batch_stats(dag, table_names["liquidity_pools"]) -write_sign_stats = build_batch_stats(dag, table_names["signers"]) -write_trust_stats = build_batch_stats(dag, table_names["trustlines"]) -write_contract_data_stats = build_batch_stats(dag, table_names["contract_data"]) -write_contract_code_stats = build_batch_stats(dag, table_names["contract_code"]) -write_config_settings_stats = build_batch_stats(dag, table_names["config_settings"]) -write_ttl_stats = build_batch_stats(dag, table_names["ttl"]) - -# Set dependencies -(date_task >> changes_task >> write_acc_stats >> del_ins_tasks["accounts"]) - -(date_task >> changes_task >> write_bal_stats >> del_ins_tasks["claimable_balances"]) - -(date_task >> changes_task >> write_off_stats >> del_ins_tasks["offers"]) - -(date_task >> changes_task >> write_pool_stats >> del_ins_tasks["liquidity_pools"]) - -(date_task >> changes_task >> write_sign_stats >> del_ins_tasks["signers"]) - -(date_task >> 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zCjj{y*dikSdt6#zrc780sD(s$x6(n z^A4A#Uu~w7l1@^lh1q$r?jngY9E!!}=Mf^^rl7C*SYiNjznyPP(cUJu|cyDZ8+*Hxuf(M}+a z=X!l-hy3#(t?E;Nt%p?yF4&VX~KxLxUYoK15 zZI)(6Hb!#OHad>d%&uf1Pr$T(53G2)6m*Ye0a%?gX2zcro1E!H=ML>gpXl|mT8W?T z7^D+C*eiY$wB~d}vTCELlz|Ffo*q$I6VdoZMrlNb)SnOYV%H>N}vbIcJy zcdTQW)`8U{6JvOM6xV(2*a8+{)-+v3X$s3MLtsn4eY-vyN-9bz#fTX{jpyS+Hpi{* z@1Tc`xAg*@_FTx+Z)|rb+et_DUe>IzQS!6cNnCTTuLuC$gZ{4i8S45csTF%W+!OjM z4xA6aJqB|8LX>s2Ib6 zqT2j*(b&OXF8$vJ;2iz0S0S=EVXotqyTpHu=U;c|Ixafwh@2Nbi)eu_`IIRmD~sn- zKk2lXQo(y(&uEaSSmL3isOPDd$7UvXVpD&cO3Z!}J<~dl;;Oe19fWTyN={E)Sv2W7OdRd+|t*I59AiPk_7N%XN&Jj z_U04fsi_GZki5AjDyr)TalGsP%h}1$UN&(GmntD6Q)u0_$OS%BZGI6Y!^=f`tn^v48i9iEyQNo219DinfQnb@eTLem0C!C8%BTNcfi@`t$Qz_IPAsieKXhp-Oc(O_P8s%ZJyQf7XTmdT4OP z-%m97<~()o+{B$Wf0q3J$Ej)g;;O@^l0QiQ%FS?JbjB{zWFVJJHuF0tcqsnuJpUTB cZ@?+ZmO9l0eHn!99Pp?3P*tw@zDeN!0qWU!Q~&?~ From fb019a0d52d08079f58916f01aaa7943aea59d50 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Wed, 10 Jul 2024 17:13:46 -0600 Subject: [PATCH 07/12] Updated the code to use Kwargs, Added Comments --- dags/history_tables_dag.py | 58 ++++++++++++++----- dags/state_table_dag.py | 44 +++++++++----- .../build_del_ins_operator.py | 13 +---- 3 files changed, 73 insertions(+), 42 deletions(-) diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index 3c88adcf..4a62d675 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -57,7 +57,7 @@ "subtract_data_interval": macros.subtract_data_interval, "batch_run_date_as_datetime_string": macros.batch_run_date_as_datetime_string, }, - sla_miss_callback=alert_sla_miss, + # sla_miss_callback=alert_sla_miss, ) # Initialize batch metadata variables @@ -73,7 +73,35 @@ use_captive_core = literal_eval(Variable.get("use_captive_core")) txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" -# Define export tasks +""" +The time task reads in the execution time of the current run, as well as the next +execution time. It converts these two times into ledger ranges. +""" +time_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) + +""" +The build batch stats task will take a snapshot of the DAG run_id, execution date, +start and end ledgers so that reconciliation and data validation are easier. The +record is written to an internal dataset for data eng use only. +""" +write_op_stats = build_batch_stats(dag, table_names["operations"]) +write_trade_stats = build_batch_stats(dag, table_names["trades"]) +write_effects_stats = build_batch_stats(dag, table_names["effects"]) +write_tx_stats = build_batch_stats(dag, table_names["transactions"]) +write_ledger_stats = build_batch_stats(dag, table_names["ledgers"]) +write_asset_stats = build_batch_stats(dag, table_names["assets"]) +write_contract_events_stats = build_batch_stats(dag, "contract_events") + +""" +The export tasks call export commands on the Stellar ETL using the ledger range from the time task. +The results of the command are stored in a file. There is one task for each of the data types that +can be exported from the history archives. + +The DAG sleeps for 30 seconds after the export_task writes to the file to give the poststart.sh +script time to copy the file over to the correct directory. If there is no sleep, the load task +starts prematurely and will not load data. +""" + op_export_task = build_export_task( dag, "archive", @@ -158,7 +186,14 @@ txmeta_datastore_path=txmeta_datastore_path, ) -# Define del_ins tasks +""" +The delete part of the task checks to see if the given partition/batch id exists in +Bigquery. If it does, the records are deleted prior to reinserting the batch. + +The Insert part of the task receive the location of the file in Google Cloud storage through Airflow's XCOM system. +Then, the task merges the unique entries in the file into the corresponding table in BigQuery. + +""" del_ins_tasks = {} export_tasks = { "operations": op_export_task, @@ -184,17 +219,9 @@ dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task ) -# Define time task -time_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) - -# Define other tasks -write_op_stats = build_batch_stats(dag, table_names["operations"]) -write_trade_stats = build_batch_stats(dag, table_names["trades"]) -write_effects_stats = build_batch_stats(dag, table_names["effects"]) -write_tx_stats = build_batch_stats(dag, table_names["transactions"]) -write_ledger_stats = build_batch_stats(dag, table_names["ledgers"]) -write_asset_stats = build_batch_stats(dag, table_names["assets"]) -write_contract_events_stats = build_batch_stats(dag, "contract_events") +""" +Delete and Insert operations for the Enriched History Operations table +""" delete_enrich_op_pub_task = build_delete_data_task( dag, @@ -225,7 +252,8 @@ dataset_type="pub", ) -# Set dependencies +# Set Task dependencies + ( time_task >> write_op_stats diff --git a/dags/state_table_dag.py b/dags/state_table_dag.py index 94d3c4c6..f2d2d46f 100644 --- a/dags/state_table_dag.py +++ b/dags/state_table_dag.py @@ -68,7 +68,10 @@ use_captive_core = literal_eval(Variable.get("use_captive_core")) txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" -# Define tasks +""" +The date task reads in the execution time of the current run, as well as the next +execution time. It converts these two times into ledger ranges. +""" date_task = build_time_task(dag, use_testnet=use_testnet, use_futurenet=use_futurenet) changes_task = build_export_task( @@ -83,7 +86,30 @@ txmeta_datastore_path=txmeta_datastore_path, ) -# Define del_ins tasks +""" +The write batch stats task will take a snapshot of the DAG run_id, execution date, +start and end ledgers so that reconciliation and data validation are easier. The +record is written to an internal dataset for data eng use only. +""" +write_acc_stats = build_batch_stats(dag, table_names["accounts"]) +write_bal_stats = build_batch_stats(dag, table_names["claimable_balances"]) +write_off_stats = build_batch_stats(dag, table_names["offers"]) +write_pool_stats = build_batch_stats(dag, table_names["liquidity_pools"]) +write_sign_stats = build_batch_stats(dag, table_names["signers"]) +write_trust_stats = build_batch_stats(dag, table_names["trustlines"]) +write_contract_data_stats = build_batch_stats(dag, table_names["contract_data"]) +write_contract_code_stats = build_batch_stats(dag, table_names["contract_code"]) +write_config_settings_stats = build_batch_stats(dag, table_names["config_settings"]) +write_ttl_stats = build_batch_stats(dag, table_names["ttl"]) + +""" +The delete part of the task checks to see if the given partition/batch id exists in +Bigquery. If it does, the records are deleted prior to reinserting the batch. + +The Insert part of the task receives the location of the file in Google Cloud storage through Airflow's XCOM system. +Then, the task merges the entries in the file with the entries in the corresponding table in the public dataset. +Entries are updated, deleted, or inserted as needed. +""" del_ins_tasks = {} data_types_and_suffixes = { "accounts": "/*-accounts.txt", @@ -113,19 +139,7 @@ dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task ) -# Define other tasks -write_acc_stats = build_batch_stats(dag, table_names["accounts"]) -write_bal_stats = build_batch_stats(dag, table_names["claimable_balances"]) -write_off_stats = build_batch_stats(dag, table_names["offers"]) -write_pool_stats = build_batch_stats(dag, table_names["liquidity_pools"]) -write_sign_stats = build_batch_stats(dag, table_names["signers"]) -write_trust_stats = build_batch_stats(dag, table_names["trustlines"]) -write_contract_data_stats = build_batch_stats(dag, table_names["contract_data"]) -write_contract_code_stats = build_batch_stats(dag, table_names["contract_code"]) -write_config_settings_stats = build_batch_stats(dag, table_names["config_settings"]) -write_ttl_stats = build_batch_stats(dag, table_names["ttl"]) - -# Set dependencies +# Set task dependencies (date_task >> changes_task >> write_acc_stats >> del_ins_tasks["accounts"]) (date_task >> changes_task >> write_bal_stats >> del_ins_tasks["claimable_balances"]) diff --git a/dags/stellar_etl_airflow/build_del_ins_operator.py b/dags/stellar_etl_airflow/build_del_ins_operator.py index 51c9ee38..842836d1 100644 --- a/dags/stellar_etl_airflow/build_del_ins_operator.py +++ b/dags/stellar_etl_airflow/build_del_ins_operator.py @@ -67,18 +67,7 @@ def create_del_ins_task(dag, task_id, task_vars, del_ins_callable): return PythonOperator( task_id=task_id, python_callable=del_ins_callable, - op_args=[ - task_vars["project"], - task_vars["dataset"], - task_vars["table_name"], - task_vars["export_task_id"], - task_vars["source_object_suffix"], - task_vars["partition"], - task_vars["cluster"], - task_vars["batch_id"], - task_vars["batch_date"], - task_vars["source_objects"], - ], + op_kwargs=task_vars, provide_context=True, dag=dag, ) From faeee880104989023ca8fe320893817039e7daf7 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Wed, 10 Jul 2024 17:41:58 -0600 Subject: [PATCH 08/12] Updated formatting to match the linting standards as well as moved task_id definition to resuable function --- dags/history_tables_dag.py | 2 +- dags/state_table_dag.py | 11 +---------- dags/stellar_etl_airflow/build_del_ins_operator.py | 5 ++++- 3 files changed, 6 insertions(+), 12 deletions(-) diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index 4a62d675..b44357e9 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -216,7 +216,7 @@ public_dataset, ) del_ins_tasks[data_type] = create_del_ins_task( - dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task + dag, task_vars, build_del_ins_from_gcs_to_bq_task ) """ diff --git a/dags/state_table_dag.py b/dags/state_table_dag.py index f2d2d46f..52ad8811 100644 --- a/dags/state_table_dag.py +++ b/dags/state_table_dag.py @@ -136,41 +136,32 @@ source_object_suffix=source_object_suffix, ) del_ins_tasks[data_type] = create_del_ins_task( - dag, f"del_ins_{data_type}_task", task_vars, build_del_ins_from_gcs_to_bq_task + dag, task_vars, build_del_ins_from_gcs_to_bq_task ) # Set task dependencies (date_task >> changes_task >> write_acc_stats >> del_ins_tasks["accounts"]) - (date_task >> changes_task >> write_bal_stats >> del_ins_tasks["claimable_balances"]) - (date_task >> changes_task >> write_off_stats >> del_ins_tasks["offers"]) - (date_task >> changes_task >> write_pool_stats >> del_ins_tasks["liquidity_pools"]) - (date_task >> changes_task >> write_sign_stats >> del_ins_tasks["signers"]) - (date_task >> changes_task >> write_trust_stats >> del_ins_tasks["trustlines"]) - ( date_task >> changes_task >> write_contract_data_stats >> del_ins_tasks["contract_data"] ) - ( date_task >> changes_task >> write_contract_code_stats >> del_ins_tasks["contract_code"] ) - ( date_task >> changes_task >> write_config_settings_stats >> del_ins_tasks["config_settings"] ) - (date_task >> changes_task >> write_ttl_stats >> del_ins_tasks["ttl"]) diff --git a/dags/stellar_etl_airflow/build_del_ins_operator.py b/dags/stellar_etl_airflow/build_del_ins_operator.py index 842836d1..c9bdfe0e 100644 --- a/dags/stellar_etl_airflow/build_del_ins_operator.py +++ b/dags/stellar_etl_airflow/build_del_ins_operator.py @@ -37,7 +37,9 @@ def initialize_task_vars( + '\')["output"] }}' + source_object_suffix ] + task_id = f"del_ins_{data_type}_task" return { + "task_id": task_id, "project": public_project, "dataset": public_dataset, "table_name": table_name, @@ -48,6 +50,7 @@ def initialize_task_vars( "batch_id": batch_id, "batch_date": batch_date, "source_objects": source_objects, + "data_type": data_type, } @@ -65,7 +68,7 @@ def create_del_ins_task(dag, task_id, task_vars, del_ins_callable): PythonOperator: The created PythonOperator. """ return PythonOperator( - task_id=task_id, + task_id=task_vars["task_id"], python_callable=del_ins_callable, op_kwargs=task_vars, provide_context=True, From aadd04a9e007d119a99f09ff80e75810010b63b2 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Wed, 10 Jul 2024 17:50:52 -0600 Subject: [PATCH 09/12] Removed task_id param and fetching it directly from task_vars --- dags/stellar_etl_airflow/build_del_ins_operator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/dags/stellar_etl_airflow/build_del_ins_operator.py b/dags/stellar_etl_airflow/build_del_ins_operator.py index c9bdfe0e..a838df80 100644 --- a/dags/stellar_etl_airflow/build_del_ins_operator.py +++ b/dags/stellar_etl_airflow/build_del_ins_operator.py @@ -54,13 +54,12 @@ def initialize_task_vars( } -def create_del_ins_task(dag, task_id, task_vars, del_ins_callable): +def create_del_ins_task(dag, task_vars, del_ins_callable): """ Create a PythonOperator for delete and insert tasks. Args: dag (DAG): The DAG to which the task belongs. - task_id (str): Task ID. task_vars (dict): Task variables. del_ins_callable (callable): The callable function to be used in the PythonOperator. From 4f492898e9b45164b67e2782d86d90fcb24ec7e4 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Wed, 10 Jul 2024 20:50:27 -0600 Subject: [PATCH 10/12] updated table names parameter to get values from table_ids airflow variable --- dags/history_tables_dag.py | 15 ++++++++------- dags/state_table_dag.py | 20 ++++++++++---------- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index b44357e9..fadfb26c 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -196,15 +196,16 @@ """ del_ins_tasks = {} export_tasks = { - "operations": op_export_task, - "trades": trade_export_task, - "effects": effects_export_task, - "transactions": tx_export_task, - "ledgers": ledger_export_task, - "assets": asset_export_task, - "contract_events": contract_events_export_task, + table_names["operations"]: op_export_task, + table_names["trades"]: trade_export_task, + table_names["effects"]: effects_export_task, + table_names["transactions"]: tx_export_task, + table_names["ledgers"]: ledger_export_task, + table_names["assets"]: asset_export_task, + table_names["contract_events"]: contract_events_export_task, } + for data_type, export_task in export_tasks.items(): task_vars = initialize_task_vars( data_type, diff --git a/dags/state_table_dag.py b/dags/state_table_dag.py index 52ad8811..a4440ef9 100644 --- a/dags/state_table_dag.py +++ b/dags/state_table_dag.py @@ -112,16 +112,16 @@ """ del_ins_tasks = {} data_types_and_suffixes = { - "accounts": "/*-accounts.txt", - "claimable_balances": "/*-claimable_balances.txt", - "offers": "/*-offers.txt", - "liquidity_pools": "/*-liquidity_pools.txt", - "signers": "/*-signers.txt", - "trustlines": "/*-trustlines.txt", - "contract_data": "/*-contract_data.txt", - "contract_code": "/*-contract_code.txt", - "config_settings": "/*-config_settings.txt", - "ttl": "/*-ttl.txt", + table_names["accounts"]: "/*-accounts.txt", + table_names["claimable_balances"]: "/*-claimable_balances.txt", + table_names["offers"]: "/*-offers.txt", + table_names["liquidity_pools"]: "/*-liquidity_pools.txt", + table_names["signers"]: "/*-signers.txt", + table_names["trustlines"]: "/*-trustlines.txt", + table_names["contract_data"]: "/*-contract_data.txt", + table_names["contract_code"]: "/*-contract_code.txt", + table_names["config_settings"]: "/*-config_settings.txt", + table_names["ttl"]: "/*-ttl.txt", } for data_type, source_object_suffix in data_types_and_suffixes.items(): From 956c05230da178edeb6596ae62482a880621f6ed Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Thu, 11 Jul 2024 12:14:06 -0600 Subject: [PATCH 11/12] - Modified the DAG scripts to account for the naming convention differences between history and state tables. - Modified the resuable python functions to account for the naming convention chnages in schema files between history and state DAGs - Updated the code to use table_id and table_name as parameters instead of data_type which is confusing --- dags/history_tables_dag.py | 24 ++++++----- dags/state_table_dag.py | 40 ++++++++++++------- .../build_del_ins_from_gcs_to_bq_task.py | 35 ++++++++-------- .../build_del_ins_operator.py | 13 +++--- 4 files changed, 62 insertions(+), 50 deletions(-) diff --git a/dags/history_tables_dag.py b/dags/history_tables_dag.py index fadfb26c..68b7cbbd 100644 --- a/dags/history_tables_dag.py +++ b/dags/history_tables_dag.py @@ -195,28 +195,30 @@ """ del_ins_tasks = {} + export_tasks = { - table_names["operations"]: op_export_task, - table_names["trades"]: trade_export_task, - table_names["effects"]: effects_export_task, - table_names["transactions"]: tx_export_task, - table_names["ledgers"]: ledger_export_task, - table_names["assets"]: asset_export_task, - table_names["contract_events"]: contract_events_export_task, + "operations": op_export_task, + "trades": trade_export_task, + "effects": effects_export_task, + "transactions": tx_export_task, + "ledgers": ledger_export_task, + "assets": asset_export_task, + "contract_events": contract_events_export_task, } -for data_type, export_task in export_tasks.items(): +for table_id, export_task in export_tasks.items(): + table_name = table_names[table_id] task_vars = initialize_task_vars( - data_type, + table_id, + table_name, export_task.task_id, batch_id, batch_date, - table_names, public_project, public_dataset, ) - del_ins_tasks[data_type] = create_del_ins_task( + del_ins_tasks[table_id] = create_del_ins_task( dag, task_vars, build_del_ins_from_gcs_to_bq_task ) diff --git a/dags/state_table_dag.py b/dags/state_table_dag.py index a4440ef9..3a39a863 100644 --- a/dags/state_table_dag.py +++ b/dags/state_table_dag.py @@ -111,31 +111,41 @@ Entries are updated, deleted, or inserted as needed. """ del_ins_tasks = {} -data_types_and_suffixes = { - table_names["accounts"]: "/*-accounts.txt", - table_names["claimable_balances"]: "/*-claimable_balances.txt", - table_names["offers"]: "/*-offers.txt", - table_names["liquidity_pools"]: "/*-liquidity_pools.txt", - table_names["signers"]: "/*-signers.txt", - table_names["trustlines"]: "/*-trustlines.txt", - table_names["contract_data"]: "/*-contract_data.txt", - table_names["contract_code"]: "/*-contract_code.txt", - table_names["config_settings"]: "/*-config_settings.txt", - table_names["ttl"]: "/*-ttl.txt", + +# Define the suffixes for the DAG related tables +source_object_suffix_mapping = { + "accounts": "/*-accounts.txt", + "claimable_balances": "/*-claimable_balances.txt", + "offers": "/*-offers.txt", + "liquidity_pools": "/*-liquidity_pools.txt", + "signers": "/*-signers.txt", + "trustlines": "/*-trustlines.txt", + "contract_data": "/*-contract_data.txt", + "contract_code": "/*-contract_code.txt", + "config_settings": "/*-config_settings.txt", + "ttl": "/*-ttl.txt", +} + +# filter for only the required tables pertaining to the DAG +table_id_and_suffixes = { + key: suffix + for key, suffix in source_object_suffix_mapping.items() + if key in table_names } -for data_type, source_object_suffix in data_types_and_suffixes.items(): +for table_id, source_object_suffix in table_id_and_suffixes.items(): + table_name = table_names[table_id] # Get the expanded table name task_vars = initialize_task_vars( - data_type, + table_id, + table_name, changes_task.task_id, batch_id, batch_date, - table_names, public_project, public_dataset, source_object_suffix=source_object_suffix, ) - del_ins_tasks[data_type] = create_del_ins_task( + del_ins_tasks[table_id] = create_del_ins_task( dag, task_vars, build_del_ins_from_gcs_to_bq_task ) diff --git a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py index 3b8325a6..fe9dec8f 100644 --- a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py +++ b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py @@ -55,7 +55,8 @@ def pre_execute(self, context, **kwargs): def build_del_ins_from_gcs_to_bq_task( project, dataset, - data_type, + table_id, + table_name, export_task_id, source_object_suffix, partition, @@ -70,13 +71,13 @@ def build_del_ins_from_gcs_to_bq_task( # Delete operation DELETE_ROWS_QUERY = ( - f"DELETE FROM {dataset}.{data_type} " + f"DELETE FROM {dataset}.{table_name} " f"WHERE batch_run_date = '{batch_date}'" f"AND batch_id = '{batch_id}';" ) delete_task = BigQueryInsertJobOperator( project_id=project, - task_id=f"delete_old_partition_{data_type}_bq", + task_id=f"delete_old_partition_{table_name}_bq", execution_timeout=timedelta( seconds=Variable.get("task_timeout", deserialize_json=True)[ build_del_ins_from_gcs_to_bq_task.__name__ @@ -113,9 +114,9 @@ def build_del_ins_from_gcs_to_bq_task( if cluster: cluster_fields = Variable.get("cluster_fields", deserialize_json=True) cluster_fields = ( - cluster_fields[f"{data_type}"] - if data_type in history_tables - else cluster_fields[data_type] + cluster_fields[f"{table_name}"] + if table_id in history_tables + else cluster_fields[table_name] ) else: cluster_fields = None @@ -124,21 +125,21 @@ def build_del_ins_from_gcs_to_bq_task( if partition: partition_fields = Variable.get("partition_fields", deserialize_json=True) partition_fields = ( - partition_fields[f"{data_type}"] - if data_type in history_tables - else partition_fields[data_type] + partition_fields[f"{table_name}"] + if table_id in history_tables + else partition_fields[table_name] ) time_partition["type"] = partition_fields["type"] time_partition["field"] = partition_fields["field"] staging_table_suffix = "" - if data_type == "history_assets": + if table_name == "history_assets": staging_table_suffix = "_staging" - if data_type in history_tables: - schema_fields = read_local_schema(f"history_{data_type}") + if table_id in history_tables: + schema_fields = read_local_schema(f"history_{table_id}") gcs_to_bq_operator = CustomGCSToBigQueryOperator( - task_id=f"send_{data_type}_to_bq", + task_id=f"send_{table_name}_to_bq", execution_timeout=timedelta( seconds=Variable.get("task_timeout", deserialize_json=True)[ build_del_ins_from_gcs_to_bq_task.__name__ @@ -150,7 +151,7 @@ def build_del_ins_from_gcs_to_bq_task( autodetect=False, source_format="NEWLINE_DELIMITED_JSON", source_objects=source_objects, - destination_project_dataset_table=f"{project}.{dataset}.{data_type}{staging_table_suffix}", + destination_project_dataset_table=f"{project}.{dataset}.{table_name}{staging_table_suffix}", write_disposition="WRITE_APPEND", create_disposition="CREATE_IF_NEEDED", # schema_update_option="ALLOW_FIELD_ADDITION", @@ -171,9 +172,9 @@ def build_del_ins_from_gcs_to_bq_task( dag=dag, ) else: - schema_fields = read_local_schema(f"{data_type}") + schema_fields = read_local_schema(f"{table_name}") gcs_to_bq_operator = GCSToBigQueryOperator( - task_id=f"send_{data_type}_to_bq", + task_id=f"send_{table_name}_to_bq", execution_timeout=timedelta( seconds=Variable.get("task_timeout", deserialize_json=True)[ build_del_ins_from_gcs_to_bq_task.__name__ @@ -185,7 +186,7 @@ def build_del_ins_from_gcs_to_bq_task( autodetect=False, source_format="NEWLINE_DELIMITED_JSON", source_objects=source_objects, - destination_project_dataset_table=f"{project}.{dataset}.{data_type}{staging_table_suffix}", + destination_project_dataset_table=f"{project}.{dataset}.{table_name}{staging_table_suffix}", write_disposition="WRITE_APPEND", create_disposition="CREATE_IF_NEEDED", max_bad_records=0, diff --git a/dags/stellar_etl_airflow/build_del_ins_operator.py b/dags/stellar_etl_airflow/build_del_ins_operator.py index a838df80..2e1b7156 100644 --- a/dags/stellar_etl_airflow/build_del_ins_operator.py +++ b/dags/stellar_etl_airflow/build_del_ins_operator.py @@ -2,11 +2,11 @@ def initialize_task_vars( - data_type, + table_id, + table_name, export_task_id, batch_id, batch_date, - table_names, public_project, public_dataset, source_object_suffix="", @@ -16,11 +16,11 @@ def initialize_task_vars( Initialize task variables for data export and import. Args: - data_type (str): Type of data (e.g., operations, trades). + table_id (str): The ID of the table (json key). + table_name (str): The name of the table (json value). export_task_id (str): Task ID of the export task. batch_id (str): Batch ID. batch_date (str): Batch date. - table_names (dict): Dictionary of table names. public_project (str): Public project name. public_dataset (str): Public dataset name. source_object_suffix (str): Suffix for source objects. @@ -29,7 +29,6 @@ def initialize_task_vars( Returns: dict: Task variables. """ - table_name = table_names[data_type] if data_type in table_names else data_type if source_objects is None: source_objects = [ "{{ task_instance.xcom_pull(task_ids='" @@ -37,7 +36,7 @@ def initialize_task_vars( + '\')["output"] }}' + source_object_suffix ] - task_id = f"del_ins_{data_type}_task" + task_id = f"del_ins_{table_name}_task" return { "task_id": task_id, "project": public_project, @@ -50,7 +49,7 @@ def initialize_task_vars( "batch_id": batch_id, "batch_date": batch_date, "source_objects": source_objects, - "data_type": data_type, + "table_id": table_id, } From f30f9ce0f75f11568c12e5a23ebf6cdd00f299d4 Mon Sep 17 00:00:00 2001 From: harsha-stellar-data Date: Thu, 11 Jul 2024 13:41:01 -0600 Subject: [PATCH 12/12] Updated the Documentation images Removed redundant code added checks for missing key values --- dags/state_table_dag.py | 27 ++++++++++++------ .../build_del_ins_from_gcs_to_bq_task.py | 4 +-- documentation/images/history_table_export.png | Bin 114655 -> 159638 bytes documentation/images/state_table_export.png | Bin 112735 -> 94423 bytes 4 files changed, 21 insertions(+), 10 deletions(-) diff --git a/dags/state_table_dag.py b/dags/state_table_dag.py index 3a39a863..236027f1 100644 --- a/dags/state_table_dag.py +++ b/dags/state_table_dag.py @@ -68,6 +68,24 @@ use_captive_core = literal_eval(Variable.get("use_captive_core")) txmeta_datastore_path = "{{ var.value.txmeta_datastore_path }}" +# Ensure all required keys are present in table_names +required_keys = [ + "accounts", + "claimable_balances", + "offers", + "liquidity_pools", + "signers", + "trustlines", + "contract_data", + "contract_code", + "config_settings", + "ttl", +] + +missing_keys = [key for key in required_keys if key not in table_names] +if missing_keys: + raise KeyError(f"Missing Id in the table_ids Airflow Variable: {missing_keys}") + """ The date task reads in the execution time of the current run, as well as the next execution time. It converts these two times into ledger ranges. @@ -126,14 +144,7 @@ "ttl": "/*-ttl.txt", } -# filter for only the required tables pertaining to the DAG -table_id_and_suffixes = { - key: suffix - for key, suffix in source_object_suffix_mapping.items() - if key in table_names -} - -for table_id, source_object_suffix in table_id_and_suffixes.items(): +for table_id, source_object_suffix in source_object_suffix_mapping.items(): table_name = table_names[table_id] # Get the expanded table name task_vars = initialize_task_vars( table_id, diff --git a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py index fe9dec8f..4d76a2ee 100644 --- a/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py +++ b/dags/stellar_etl_airflow/build_del_ins_from_gcs_to_bq_task.py @@ -136,8 +136,9 @@ def build_del_ins_from_gcs_to_bq_task( if table_name == "history_assets": staging_table_suffix = "_staging" + schema_fields = read_local_schema(f"{table_name}") + if table_id in history_tables: - schema_fields = read_local_schema(f"history_{table_id}") gcs_to_bq_operator = CustomGCSToBigQueryOperator( task_id=f"send_{table_name}_to_bq", execution_timeout=timedelta( @@ -172,7 +173,6 @@ def build_del_ins_from_gcs_to_bq_task( dag=dag, ) else: - schema_fields = read_local_schema(f"{table_name}") gcs_to_bq_operator = GCSToBigQueryOperator( task_id=f"send_{table_name}_to_bq", execution_timeout=timedelta( diff --git a/documentation/images/history_table_export.png b/documentation/images/history_table_export.png index bdda7c27ff5b416753d6f6f533df8e59f4b5073e..16c2952ba4bcf8014b5a161ac4a1dab90457a127 100644 GIT binary patch literal 159638 zcmeFZXIN9))-XyhDqu%IiXtMQpdcWj1V!lt1qA6Ty#|5MLMXBor3C3x0w^WY5_*S# zi1c1U51ZaX=mA2)jpv;AJKKG~`<(aR{d2;@%F3F3%rV9sb$xsaQe!*CeTs&LhD}5L zu^tT#^BxTi-3$v8^@$DpjV0K!fhk0#WC zhT$LgG&Izns2_%y_cYAZ@AK4uvkcmQ*UX^%pPI~j8T9{iPd7uYNApl$MMH!7t#9RS 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