spark-python-data-source

Build a custom Python DataSource connector for Apache Spark using templates for DataSource, reader/writer, and batch and stream modes that ensure execution safety and handle partitioning. These patterns codify production-ready connectors to read from and write to external systems reliably in Python.

Handle authentication in PySpark external data source connectors by applying a fallback authentication chain. This approach ensures secure access to external systems by sequentially attempting multiple authentication methods, such as OAuth2, if the primary method fails.

Yes, Apache Spark 4.0 supports Python DataSource connectors for both batch and streaming scenarios. These connectors handle continuous reads and writes from message queues or REST APIs using JSON-serializable offsets and robust retry patterns for operational resilience.

PySpark DataSource imports fail on worker executors because the distributed environment lacks the required local dependencies. You must use executor-safe imports and flat single-level inheritance for serializable readers and writers to ensure proper execution across distributed Spark nodes.

Read a paginated REST API in parallel using Spark by implementing a custom Python DataSource with time-based partitioning. This approach applies partitioning strategies to distribute API requests across executors, converting types and writing results directly into a Spark DataFrame.

Manage streaming offsets and retries in a Spark Python connector by utilizing JSON-serializable offsets for checkpointing and applying robust retry and error-handling patterns. This includes circuit breakers and dead-letter queues to handle operational failures without data loss.