sdp-test 0.1.3

Declarative unit testing for Spark Declarative Pipelines (SDP) and Lakeflow Declarative Pipelines (LDP)

sdp-test

Declarative unit testing for Spark Declarative Pipelines (SDP) and Lakeflow Declarative Pipelines (LDP).

Write pipeline tests as YAML and run them locally with pytest — no boilerplate Python test code, no remote cluster or Databricks workspace required.

# stg_customers.unit_tests.yml
tests:
  - name: maps_customer_fields
    model: stg_customers.sql
    given:
      - table: ${bronze_schema}.raw_customers
        rows:
          - id: "1"
            first_name: John
            last_name: Doe
    expect:
      rows:
        - customer_id: "1"
          customer_name: John Doe

$ pytest
jaffle_shop_sql::stg_customers::maps_customer_fields  PASSED

Installation

pip install sdp-test

PySpark is not bundled — install it separately or use the extra:

pip install "sdp-test[spark]"           # includes pyspark[pipelines]>=4.1
pip install "pyspark>=4.1"              # or install open source PySpark yourself

Quick start

1. Place a *.unit_tests.yml file next to the model it tests:

src/transformations/silver/
    stg_customers.sql
    stg_customers.unit_tests.yml   # <-- test file

2. Run pytest:

pytest

sdp-test automatically discovers your pipeline definition (databricks.yml or spark-pipeline.yml), resolves configuration variables, finds all *.unit_tests.yml files, and runs them.

How it works

sdp-test reads your pipeline definition to learn two things:

1. Where models live — the libraries paths
2. What variables to use — the configuration section (schema names, etc.)

It scans those paths for *.unit_tests.yml files, substitutes variables, and runs each test case against a local SparkSession. For SQL models it strips the DDL preamble (CREATE OR REFRESH MATERIALIZED VIEW … AS) and executes only the SELECT query. For Python models it shims the pipeline decorators (@dp.table, @dp.view, etc.) so your model functions run without a live pipeline or remote connection.

Writing tests

Unit test format

tests:
  - name: descriptive_test_name
    model: stg_customers.sql          # model file (.sql or .py)
    given:                            # input fixtures
      - table: ${bronze_schema}.raw_customers
        rows:
          - id: "1"
            first_name: John
    expect:                           # expected output
      rows:
        - customer_id: "1"
          customer_name: John

Key rules

• table must be schema-qualified — e.g. ${bronze_schema}.raw_customers
• model is relative to the test file — stg_customers.sql resolves from the same directory
• Only listed columns are checked — you don't need to specify every output column
• One model per .sql file — the SDP convention

SQL models

Given a SQL model:

CREATE OR REFRESH MATERIALIZED VIEW ${silver_schema}.stg_products AS
SELECT
    CAST(sku AS STRING) AS product_id,
    CAST(ROUND(CAST(price AS DOUBLE) / 100, 2) AS DECIMAL(16, 2)) AS product_price,
    CAST(COALESCE(type = 'beverage', FALSE) AS BOOLEAN) AS is_drink_item
FROM ${bronze_schema}.raw_products;

Test it:

tests:
  - name: maps_product_type_flags_and_price
    model: stg_products.sql
    given:
      - table: ${bronze_schema}.raw_products
        rows:
          - sku: "1"
            type: beverage
            price: 350
    expect:
      rows:
        - product_id: "1"
          product_price: 3.50
          is_drink_item: true

Python models

Given a Python model:

from pyspark import pipelines as dp
from pyspark.sql import SparkSession, functions as F

spark = SparkSession.getActiveSession() or SparkSession.builder.getOrCreate()

@dp.table(name="stg_products")
def stg_products():
    return spark.read.table(f"{spark.conf.get('bronze_schema')}.raw_products").select(
        F.col("sku").cast("string").alias("product_id"),
        F.round(F.col("price").cast("double") / 100, 2).cast("decimal(16,2)").alias("product_price"),
    )

Test it:

tests:
  - name: maps_price_correctly
    model: stg_products.py
    given:
      - table: ${bronze_schema}.raw_products
        rows:
          - sku: "1"
            price: 350
    expect:
      rows:
        - product_id: "1"
          product_price: 3.50

For files with multiple functions, use callable to select which one to run:

tests:
  - name: test_customers
    model: models.py
    callable: stg_customers

Models with joins

Provide all source tables in given:

tests:
  - name: supply_costs_sum_correctly
    model: order_items.sql
    given:
      - table: ${silver_schema}.stg_order_items
        rows:
          - order_id: 1
            product_id: "1"
      - table: ${silver_schema}.stg_supplies
        rows:
          - product_id: "1"
            supply_cost: 4.50
    expect:
      rows:
        - order_id: 1
          supply_cost: 4.50

Auto column repair

If your model references a column not in the test fixtures, sdp-test adds it automatically with an inferred type:

Column pattern	Inferred type
Used in CASE WHEN <col>	BOOLEAN
Used in SUM(<col>)	DOUBLE
is_*	BOOLEAN
Everything else	STRING

Type coercion

YAML values are coerced to match the Spark schema:

YAML	Spark type	Result
3.50	DecimalType	Decimal("3.50")
"2024-01-15"	DateType	date(2024, 1, 15)
"2024-01-15T10:30:00"	TimestampType	datetime(2024, 1, 15, 10, 30)

Pipeline definitions

sdp-test supports two pipeline formats. Both are auto-discovered from the project root.

Databricks bundle (databricks.yml)

# resources/my_pipeline.pipeline.yml
resources:
  pipelines:
    my_pipeline:
      name: my_pipeline
      catalog: my_catalog
      schema: gold
      configuration:
        bronze_schema: bronze
        silver_schema: silver
        gold_schema: gold
      libraries:
        - file:
            path: ../src/transformations/**/*.sql

Open source SDP (spark-pipeline.yml)

name: my_pipeline
catalog: my_catalog
database: gold
configuration:
  bronze_schema: bronze
  silver_schema: silver
  gold_schema: gold
libraries:
  - transformations/**

Configuration

pyproject.toml

[tool.sdp-test]
auto_discover = true    # default: true

[tool.pytest.ini_options]
testpaths = [
    "tests",
    "spark-pipeline.yml",              # collect a specific pipeline file
    "resources/my_pipeline.pipeline.yml",
]

Option	Default	Description
auto_discover	true	Auto-discover databricks.yml and spark-pipeline.yml from the project root

To collect specific pipeline files, add them to testpaths in [tool.pytest.ini_options] — or pass them as CLI arguments (pytest spark-pipeline.yml).

Logging

sdp-test uses Python's logging module under the sdp_test logger. Control verbosity with pytest's standard settings:

[tool.pytest.ini_options]
log_cli = true
log_cli_level = "DEBUG"    # shows detailed test execution info

Disabling the plugin

pytest -p no:sdp_test

Advanced: pipeline test specs

For full control over bundle resolution and variable overrides, create a *_pipeline_tests.yml file:

bundle:
  file: ../databricks.yml
  target: dev
  variables:
    catalog: test_catalog

pipeline: pipelines.my_pipeline

defaults:
  bronze_schema: ${bronze_schema}
  silver_schema: ${silver_schema}

tests:
  - name: inline_test
    model: path/to/model.sql
    given:
      - table: ${bronze_schema}.raw_data
        rows:
          - col1: value1
    expect:
      rows:
        - out_col: expected

The pipeline field supports several forms:

# String reference to a bundle pipeline
pipeline: pipelines.my_pipeline

# Object reference
pipeline:
  ref: pipelines.my_pipeline

# Databricks resource file
pipeline:
  file: ../resources/my_pipeline.yml
  key: my_pipeline

# Open source pipeline file
pipeline:
  file: ../spark-pipeline.yml

Template variables

Variables use ${...} syntax and resolve from the pipeline configuration and bundle context:

Variable	Source
${bronze_schema}	Pipeline configuration
${var.catalog}	Bundle variables
${bundle.target}	Bundle metadata

Project structure

my-project/
    databricks.yml
    resources/
        my_pipeline.pipeline.yml
    src/
        transformations/
            silver/
                stg_customers.sql
                stg_customers.unit_tests.yml
                stg_products.py
                stg_products.unit_tests.yml
            gold/
                orders.sql
                orders.unit_tests.yml

Development

git clone https://github.com/godatadriven/sdp-test.git
cd sdp-test
uv sync --dev
uv run pytest
uv run ruff check src/ tests/

License

Apache License 2.0