databricks-spark-structured-streaming

Production-grade Spark Structured Streaming pipelines require reliable checkpoint management, optimized triggers like processingTime or availableNow, and stateful processing with watermarks. Implementing standardized patterns for Kafka ingestion and stream joins ensures robust real-time data processing.

Stateful processing in Spark Structured Streaming uses watermarks to handle late data and manage state size. Defining watermarks on event time allows the engine to discard old state, preventing unbounded memory growth during stream-stream or stream-static joins.

To ingest Kafka data into Delta Lake, configure a Spark Structured Streaming read stream from Kafka topics, apply transformations, and use a write stream with a Delta sink and checkpoint location. This pattern enables fault-tolerant, exactly-once real-time processing.

The processingTime trigger processes data at fixed intervals, while availableNow processes all available data incrementally and stops upon completion. availableNow is ideal for batch-like, cost-effective streaming jobs, whereas processingTime suits continuous low-latency real-time processing.

Stream-stream joins in Spark Structured Streaming allow merging two real-time data sources by time range and watermark. This enables event-driven analytics and IoT stream processing by matching concurrent events within a defined temporal window.

Checkpoint management is essential for Spark Structured Streaming jobs to ensure fault tolerance and exactly-once processing guarantees. It stores metadata and state to Delta locations, allowing pipelines to recover seamlessly from failures and continue processing accurately.