https://github.com/datamole-ai/pysparkdt
An open-source Python library for simplifying local testing of Databricks workflows that use PySpark and Delta tables.
https://github.com/datamole-ai/pysparkdt
databricks delta delta-tables pipelines pyspark pytest python testing workflows
Last synced: 9 months ago
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An open-source Python library for simplifying local testing of Databricks workflows that use PySpark and Delta tables.
- Host: GitHub
- URL: https://github.com/datamole-ai/pysparkdt
- Owner: datamole-ai
- License: mit
- Created: 2024-03-04T11:13:56.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-11T07:46:59.000Z (about 1 year ago)
- Last Synced: 2025-08-21T16:09:08.261Z (10 months ago)
- Topics: databricks, delta, delta-tables, pipelines, pyspark, pytest, python, testing, workflows
- Language: Python
- Homepage:
- Size: 48.8 KB
- Stars: 38
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# pysparkdt (PySpark Delta Testing)
**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.
# Guideline
## Table of Contents
- [Overview](#overview)
- [Scope](#scope)
- [Prerequisites](#prerequisites)
- [Setup](#setup)
1. [Installation](#1-installation)
2. [Testable Code](#2-testable-code)
3. [File Structure](#3-file-structure)
4. [Tests](#4-tests)
- [Advanced](#advanced)
- [Testing Stream Processing](#testing-stream-processing)
- [Mocking Inside RDD and UDF Operations](#mocking-inside-rdd-and-udf-operations)
- [Limitations](#limitations)
- [Map Key Type Must Be String](#map-key-type-must-be-string)
## Overview
### Scope
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.
### Key Points
- **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.
## Setup
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.
### 1. Installation
**Install pysparkdt**
- Get this package from the pypi. It's only needed in your test environment.
```bash
pip install pysparkdt
```
### 2. Testable code
- **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. `dbutils` usage)
entrypoint.py (Databricks Notebook)
```python
# 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(
spark=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`
### 3. File structure
```
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](https://spark.apache.org/docs/latest/api/python/_modules/pyspark/sql/types.html#StructType.fromJson).
example_input.ndjson
```json lines
{"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}
```
example_input.json
```json
{
"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:
```python
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.
### 4. Tests
**Constants:** Define paths for test data and the temporary metastore.
```python
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.
```python
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`). You can also choose to enable or disable
deletion vectors for Delta tables (default: enabled). 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.*
```python
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, deletion_vectors=True)
process_data(
spark=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).
**ℹ️ For complete example, please look at [example](https://github.com/datamole-ai/pysparkdt/blob/main/example).**
**⚠️ Manual deletion of local metastore**
Deleting the local metastore manually invalidates any Spark session configured
for that location. You would need to start a new Spark session because
the original session’s state is no longer valid. Avoid manual deletion —
use `reinit_local_metastore` for reinitialization instead.
**⚠️ Note on running tests in parallel**
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.
## Advanced
### Testing Stream Processing
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.
myjobpackage/processing.py
```python
def process_data(
spark: SparkSession,
input_table: str,
output_table: str,
checkpoint_location: str,
) -> StreamingQuery:
load_query = spark.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()
)
```
myjobpackage/tests/test_processing.py
```python
def test_process_data(spark: SparkSession):
...
spark_processing = process_data(
spark=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')
...
```
### Mocking Inside RDD and UDF Operations
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
```python
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`.
```python
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_client
```
As `ApiSessionClient` is created inside `rdd.mapPartitions` we need to also
mock `call_api`.
```python
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.
```python
def test_process_data(
spark: SparkSession,
call_api_mock,
):
...
```
## Limitations
### Map Key Type Must Be String
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.
## License
pysparkdt is licensed under the [MIT
license](https://opensource.org/license/mit/). See the
[LICENSE file](https://github.com/datamole-ai/pysparkdt/blob/main/LICENSE) for more details.
## How to Contribute
See [CONTRIBUTING.md](https://github.com/datamole-ai/pysparkdt/blob/main/CONTRIBUTING.md).