spark-python-data-source

To build a custom Spark data source for a REST API, you implement Python DataSource, Reader, and Writer classes using the PySpark DataSource API to handle batch and streaming workloads. This approach enables Spark to read from and write to external systems without native connectors.

Yes, the PySpark DataSource API supports both batch and streaming workloads through single-level inheritance DataSource, Reader/Writer, and Batch/Stream classes. This unified architecture ensures easy serialization across Spark driver and executors for custom data integration.

When creating a Python Spark data source, you can implement time-based, token-range, and ID-range partitioning strategies. These patterns enable parallel reads and writes to optimize data ingestion from external APIs, databases, or custom protocols into Spark.

You handle authentication in a custom PySpark data source connector by supporting Unity Catalog, cloud default credentials, service principals, API keys, and basic auth. This flexibility ensures secure access when reading from and writing to external systems.

The best way to manage error handling in Spark data source connectors is to implement exponential backoff, circuit breakers, dead-letter queues, and timeout handling. These robust error handling mechanisms ensure reliable streaming and batch processing.

Yes, you need to implement separate Reader and Writer classes with flat inheritance for your PySpark data source. This structure, alongside Batch and Stream classes, allows Spark to correctly interface with external systems for reading and writing operations.