spark-python-data-source

name	spark-python-data-source
description	Use when building custom Spark data source connectors for external systems (databases, APIs, message queues), implementing batch/streaming readers/writers, or creating data source plugins for systems without native Spark support. Triggers - "build Spark data source", "create Spark connector", "implement Spark reader/writer", "connect Spark to [system]", "streaming data source"

spark-python-data-source

Build custom Python data sources for Apache Spark 4.0+ to read from and write to external systems in batch and streaming modes.

When to use

Use when building Spark connectors for external systems that lack native support:

• External databases, APIs, message queues
• Custom file formats or protocols
• Real-time streaming data sources
• Systems requiring specialized authentication or protocols

Triggers: "build Spark data source", "create Spark connector", "implement Spark reader/writer", "connect Spark to [system]", "streaming data source"

Instructions

You are an experienced Spark developer building custom Python data sources following the PySpark DataSource API. Follow these principles and patterns:

Core Architecture

Each data source follows a flat, single-level inheritance structure:

1. DataSource class - Entry point returning readers/writers
2. Base Reader/Writer classes - Shared logic for options and data processing
3. Batch classes - Inherit from base + DataSourceReader/DataSourceWriter
4. Stream classes - Inherit from base + DataSourceStreamReader/DataSourceStreamWriter

Critical Design Principles

SIMPLE over CLEVER - These are non-negotiable:

✅ REQUIRED:

• Flat single-level inheritance only
• Direct implementations, no abstractions
• Explicit imports, explicit control flow
• Standard library first, minimal dependencies
• Simple classes with single responsibilities

❌ FORBIDDEN:

• Abstract base classes or complex inheritance
• Factory patterns or dependency injection
• Decorators for cross-cutting concerns
• Complex configuration classes
• Async/await (unless absolutely necessary)
• Connection pooling or caching (unless critical)
• Generic "framework" code
• Premature optimization

Implementation Pattern

from pyspark.sql.datasource import (
    DataSource, DataSourceReader, DataSourceWriter,
    DataSourceStreamReader, DataSourceStreamWriter
)

# 1. DataSource class
class YourDataSource(DataSource):
    @classmethod
    def name(cls):
        return "your-format"

    def __init__(self, options):
        self.options = options

    def schema(self):
        return self._infer_or_return_schema()

    def reader(self, schema):
        return YourBatchReader(self.options, schema)

    def streamReader(self, schema):
        return YourStreamReader(self.options, schema)

    def writer(self, schema, overwrite):
        return YourBatchWriter(self.options, schema)

    def streamWriter(self, schema, overwrite):
        return YourStreamWriter(self.options, schema)

# 2. Base Writer with shared logic
class YourWriter:
    def __init__(self, options, schema=None):
        # Validate required options
        self.url = options.get("url")
        assert self.url, "url is required"
        self.batch_size = int(options.get("batch_size", "50"))
        self.schema = schema

    def write(self, iterator):
        # Import libraries here for partition execution
        import requests
        from pyspark import TaskContext

        context = TaskContext.get()
        partition_id = context.partitionId()

        msgs = []
        cnt = 0

        for row in iterator:
            cnt += 1
            msgs.append(row.asDict())

            if len(msgs) >= self.batch_size:
                self._send_batch(msgs)
                msgs = []

        if msgs:
            self._send_batch(msgs)

        return SimpleCommitMessage(partition_id=partition_id, count=cnt)

    def _send_batch(self, msgs):
        # Implement send logic
        pass

# 3. Batch Writer
class YourBatchWriter(YourWriter, DataSourceWriter):
    pass

# 4. Stream Writer
class YourStreamWriter(YourWriter, DataSourceStreamWriter):
    def commit(self, messages, batchId):
        pass

    def abort(self, messages, batchId):
        pass

# 5. Base Reader with partitioning
class YourReader:
    def __init__(self, options, schema):
        self.url = options.get("url")
        assert self.url, "url is required"
        self.schema = schema

    def partitions(self):
        # Return list of partitions for parallel reading
        return [YourPartition(0, start, end)]

    def read(self, partition):
        # Import here for executor execution
        import requests

        response = requests.get(f"{self.url}?start={partition.start}")
        for item in response.json():
            yield tuple(item.values())

# 6. Batch Reader
class YourBatchReader(YourReader, DataSourceReader):
    pass

# 7. Stream Reader
class YourStreamReader(YourReader, DataSourceStreamReader):
    def initialOffset(self):
        return {"offset": "0"}

    def latestOffset(self):
        return {"offset": str(self._get_latest())}

    def partitions(self, start, end):
        return [YourPartition(0, start["offset"], end["offset"])]

    def commit(self, end):
        pass

Project Setup

# Create project
poetry new your-datasource
cd your-datasource
poetry add pyspark pytest pytest-spark

# Development commands - CRITICAL: Always use 'poetry run'
poetry run pytest                    # Run tests
poetry run ruff check src/          # Lint
poetry run ruff format src/         # Format
poetry build                        # Build wheel

Registration and Usage

# Register
from your_package import YourDataSource
spark.dataSource.register(YourDataSource)

# Batch read
df = spark.read.format("your-format").option("url", "...").load()

# Batch write
df.write.format("your-format").option("url", "...").save()

# Streaming read
df = spark.readStream.format("your-format").option("url", "...").load()

# Streaming write
df.writeStream.format("your-format").option("url", "...").start()

Key Implementation Decisions

Partitioning Strategy: Choose based on data source characteristics

• Time-based: For APIs with temporal data (see partitioning-patterns.md)
• Token-range: For distributed databases (see partitioning-patterns.md)
• ID-range: For paginated APIs

Authentication: Support multiple methods in priority order

• Databricks Unity Catalog credentials
• Cloud default credentials (managed identity)
• Explicit credentials (service principal, API key, username/password)
• See authentication-patterns.md

Type Conversion: Map between Spark and external types

• Handle nulls, timestamps, UUIDs, collections
• See type-conversion.md

Streaming Offsets: Design for exactly-once semantics

• JSON-serializable offset class
• Non-overlapping partition boundaries
• See streaming-patterns.md

Error Handling: Implement retries and resilience

• Exponential backoff for retryable errors
• Circuit breakers for cascading failures
• See error-handling.md

Testing Approach

import pytest
from unittest.mock import patch, Mock

@pytest.fixture
def spark():
    from pyspark.sql import SparkSession
    return SparkSession.builder.master("local[2]").getOrCreate()

def test_data_source_name():
    assert YourDataSource.name() == "your-format"

def test_writer_sends_data(spark):
    with patch('requests.post') as mock_post:
        mock_post.return_value = Mock(status_code=200)

        df = spark.createDataFrame([(1, "test")], ["id", "value"])
        df.write.format("your-format").option("url", "http://api").save()

        assert mock_post.called

Code Review Checklist

Before implementing, ask:

1. Is this the simplest way to solve this problem?
2. Would a new developer understand this immediately?
3. Am I adding abstraction for real needs vs hypothetical flexibility?
4. Can I solve this with standard library?
5. Does this follow the established flat pattern?

Common Mistakes to Avoid

• Creating abstract base classes for "reusability"
• Adding configuration frameworks or dependency injection
• Premature optimization before measuring performance
• Complex error handling hierarchies
• Importing heavy libraries at module level (import in methods)
• Using python command directly (always use poetry run)

Reference Implementations

Study these for real-world patterns:

• cyber-spark-data-connectors - Sentinel, Splunk, REST
• spark-cassandra-data-source - Token-range partitioning
• pyspark-hubspot - REST API pagination
• pyspark-mqtt - Streaming with TLS

Usage

Create a Spark data source for reading from MongoDB with sharding support
Build a streaming connector for RabbitMQ with at-least-once delivery
Implement a batch writer for Snowflake with staged uploads
Write a data source for REST API with OAuth2 authentication and pagination

Related

• databricks-testing: Test data sources on Databricks clusters
• databricks-spark-declarative-pipelines: Use custom sources in DLT pipelines
• python-dev: Python development best practices

References

• partitioning-patterns.md - Parallel reading strategies
• authentication-patterns.md - Multi-method auth implementations
• type-conversion.md - Bidirectional type mapping
• streaming-patterns.md - Offset management and watermarking
• error-handling.md - Retries, circuit breakers, resilience
• testing-patterns.md - Unit and integration testing
• production-patterns.md - Observability, security, validation
• Official Databricks Documentation
• Apache Spark Python DataSource Tutorial
• awesome-python-datasources - directory of available implementations.