An open-source Python library for simplifying local testing of Databricks workflows using PySpark and Delta tables.
This library enables seamless testing of PySpark processing logic outside Databricks by emulating Unity Catalog behavior. It dynamically generates a local metastore to mimic Unity Catalog and supports simplified handling of Delta tables for both batch and streaming workloads.
This guideline helps you test Databricks Python pipelines with a focus on PySpark code. While basic unit testing knowledge with pytest is helpful, it's not the central focus.
-
Standalone Testing: The setup allows you to test code without Databricks access, enabling easy CI integration.
-
Local Metastore: Mimic the Databricks Unity Catalog using a dynamically generated local metastore with local Delta tables.
-
Code Testability: Move core processing logic from notebooks to Python modules. Notebooks then serve as entrypoints.
In the following section we will assume that you are creating tests for a job which has one delta table on input and produces one delta table on output. It utilizes PySpark for its processing.
Install pysparkdt
- Get this package from the pypi. It's only needed in your test environment.
pip install pysparkdt-
Modularization: Move processing logic from notebooks to modules.
-
Notebook Role: Notebooks primarily handle initialization and triggering processing. They should contain all the code specific to Databricks (e.g.
dbutilsusage)
# Databricks notebook source
import sys
from pathlib import Path
MODULE_DIR = Path.cwd().parent
sys.path.append(MODULE_DIR.as_posix())
# COMMAND ----------
from myjobpackage.processing import process_data
# COMMAND ----------
input_table = dbutils.widgets.get('input_table')
output_table = dbutils.widgets.get('output_table')
# COMMAND ----------
process_data(
session=spark,
input_table=input_table,
output_table=output_table,
)myjobpackage.processing
- Contains the core logic to test
- Our test focuses on the core function
myjobpackage.processing.process_data
myjobpackage
├── __init__.py
├── entrypoint.py # Databricks Notebook
├── processing.py
└── tests
├── __init__.py
├── test_processing.py
└── data
├── tables
├── example_input.ndjson
└── expected_output.ndjson
└── schema
├── example_input.json
└── expected_output.json
Data Format
- Test Data: Newline-delimited JSON (
.ndjson) - Optional Schema: JSON
- If present, full schema must be provided (all columns included).
- The format of the schema file is defined by PySpark StructType JSON representation.
{"id": 0, "time_utc": "2024-01-08T11:00:00", "name": "Jorge", "feature": 0.5876}
{"id": 1, "time_utc": "2024-01-11T14:28:00", "name": "Ricardo", "feature": 0.42}{
"type": "struct",
"fields":
[
{
"name": "id",
"type": "long",
"nullable": false,
"metadata": {}
},
{
"name": "time_utc",
"type": "timestamp",
"nullable": false,
"metadata": {}
},
{
"name": "name",
"type": "string",
"nullable": true,
"metadata": {}
},
{
"name": "feature",
"type": "double",
"nullable": true,
"metadata": {}
}
]
}Tip: A schema file for a loaded PySpark DataFrame df can be created using:
with(open('example_input.json', 'w')) as file:
file.write(json.dumps(df.schema.jsonValue(), indent=4))Thus, you can first load a table without a schema, then create schema file from it and modify the types to the desired one.
Constants: Define paths for test data and the temporary metastore.
DATA_DIR = f'{os.path.dirname(__file__)}/data'
JSON_TABLES_DIR = f'{DATA_DIR}/tables'
TMP_DIR = f'{DATA_DIR}/tmp'
METASTORE_DIR = f'{TMP_DIR}/metastore'Spark Fixture: Define fixture for the local spark session using
spark_base function from the testing package. Specify the temporal metastore
location.
from pytest import fixture
from pysparkdt import spark_base
@fixture(scope='module')
def spark():
yield from spark_base(METASTORE_DIR)Metastore Initialization: Use reinit_local_metastore
At the beginning of your test method call reinit_local_metastore function
from the testing package to initialize the metastore with the tables from
your json folder (JSON_TABLES_DIR). If the method is called while the
metastore already exists, it will delete all the existing tables before
initializing the new ones.
Alternatively, you can call this method only once per testing module, but then individual testing methods might affect each other by modifying metastore tables.
from myjobpackage.processing import process_data
from pysparkdt import reinit_local_metastore
from pyspark.testing import assertDataFrameEqual
def test_process_data(
spark: SparkSession,
):
reinit_local_metastore(spark, JSON_TABLES_DIR)
process_data(
session=spark,
input_table='example_input',
output_table='output',
)
output = spark.read.format('delta').table('output')
expected = spark.read.format('delta').table('expected_output')
assertDataFrameEqual(
actual=output.select(sorted(output.columns)),
expected=expected.select(sorted(expected.columns)),
)In the example above, we use assertDataFrameEqual to compare PySpark
DataFrames. We ensure the columns are ordered so that the order of result
columns does not matter. By default, the order of rows does not matter in
assertDataFrameEqual (this can be adjusted using the checkRowOrder
parameter).
With the setup above, the metastore is shared on the module scope. Therefore, if tests defined in the same module are run in parallel, race conditions can occur if multiple test functions use the same tables.
To mitigate this, make sure each test in the module uses its own set of tables.
Let's now focus on a case where a job is reading input delta table using PySpark streaming, performing some computation on the data and saving it to the output delta table.
In order to be able to test the processing we need to explicitly wait for its completion. The best way to do it is to await the streaming function performing the processing.
To be able to await the streaming function, the test function needs to have access to it. Thus, we need to make sure the streaming function (query in Databricks terms) is accessible - for example by returning it by the processing function.
def process_data(
input_table: str,
output_table: str,
checkpoint_location: str,
) -> StreamingQuery
load_query = session.readStream.format('delta').table(input_table)
def process_batch(df: pyspark.sql.DataFrame, _) -> None:
... process df ...
df.write.mode('append').format('delta').saveAsTable(output_table)
return (
load_query.writeStream.format('delta')
.foreachBatch(process_batch)
.trigger(availableNow=True)
.option('checkpointLocation', checkpoint_location)
.start()
)def test_process_data(spark: SparkSession):
...
spark_processing = process_data(
session=spark,
input_table_name='example_input',
output_table='output',
checkpoint_location=f'{TMP_DIR}/_checkpoint/output',
)
spark_processing.awaitTermination(60)
output = spark.read.format('delta').table('output')
expected = spark.read.format('delta').table('expected_output')
...If we are testing whole job’s processing code and inside it we have functions
executed through rdd.mapPartitions, rdd.map, or UDFs, we need to add
special handling for mocking as regular patching does not propagate to worker
nodes.
myjobpackage/processing.py
def call_api(
data_df: pyspark.sql.DataFrame,
) -> pyspark.sql.DataFrame:
# Call API in parallel (session per partition)
result = data_df.rdd.mapPartitions(_partition_run).toDF()
return result
def _partition_run(
iterable: Iterable[Row],
) -> Iterable[dict[str, Any]]:
with ApiSessionClient() as client:
for row in iterable:
...
output = client.post(prepared_data)
...
yield output
def process_data(
data_df: pyspark.sql.DataFrame,
) -> pyspark.sql.DataFrame:
...
... = call_api(...)
...In this example we have a code that calls external API in _partition_run,
we do not want to call the actual API in our test, thus we want to mock
ApiSessionClient.
from pytest import fixture
def _mocked_session_post(json_data: dict):
...
return output
@fixture
def api_session_client(mocker):
api_session_client_mock = mocker.patch.object(
myjobpackage.processing,
'ApiSessionClient',
)
api_session_client_mock.return_value = session_client = mocker.Mock()
session_client.__enter__ = mocker.Mock()
session_client.__enter__.return_value = session_client_ctx = mocker.Mock()
session_client.__exit__ = mocker.Mock()
session_client_ctx.post = mocker.Mock(side_effect=_mocked_session_post)
return session_clientAs ApiSessionClient is created inside rdd.mapPartitions we need to also
mock call_api.
def _mocked_call_api(
data_df: pyspark.sql.DataFrame,
) -> pyspark.sql.DataFrame:
results = list(_partition_run(data_df.collect()))
spark = SparkSession.builder.getOrCreate()
pandas_df = pd.DataFrame(results)
return spark.createDataFrame(pandas_df)
@fixture
def call_api_mock(mocker, api_session_client):
mocker.patch.object(
myjobpackage.processing, 'call_api', _mocked_call_api
)Then we can run the test with the mocked API.
def test_process_data(
spark: SparkSession,
call_api_mock,
):
...Although Spark supports non-string key types in map fields, the JSON format
itself does not support non-string keys. In JSON, all keys are inherently
interpreted as strings, regardless of their declared type in the schema.
This discrepancy becomes problematic when testing with .ndjson files.
Specifically, if the schema defines a map key type as anything other than
string (such as long or integer), the reinitialization of the metastore
will result in None values for all fields in the Delta table when the data
is loaded. This happens because the keys in the JSON data are read as strings,
but the schema expects another type, leading to a silent failure where no
exception or warning is raised. This makes the issue difficult to detect
and debug.
pysparkdt is licensed under the MIT license. See the LICENSE file for more details.
See CONTRIBUTING.md.