databricks-spark-structured-streaming

name	databricks-spark-structured-streaming
description	Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
compatibility	Requires databricks CLI (>= v1.0.0)
metadata	{"version":"0.1.0","upstream":{"parent":"databricks-core"},"category":"data","source":{"repository":"https://github.com/databricks/databricks-agent-skills","path":"experimental/databricks-spark-structured-streaming","license_path":"LICENSE","commit":"3985599b8efaf0bb155be7e60847a3975bf45331"}}

Spark Structured Streaming

Production-ready streaming pipelines with Spark Structured Streaming. This skill provides navigation to detailed patterns and best practices.

Quick Start

from pyspark.sql.functions import col, from_json

# Basic Kafka to Delta streaming
df = (spark
    .readStream
    .format("kafka")
    .option("kafka.bootstrap.servers", "broker:9092")
    .option("subscribe", "topic")
    .load()
    .select(from_json(col("value").cast("string"), schema).alias("data"))
    .select("data.*")
)

df.writeStream \
    .format("delta") \
    .outputMode("append") \
    .option("checkpointLocation", "/Volumes/catalog/checkpoints/stream") \
    .trigger(processingTime="30 seconds") \
    .start("/delta/target_table")

Core Patterns

Pattern	Description	Reference
Kafka Streaming	Kafka to Delta, Kafka to Kafka, Real-Time Mode	See references/kafka-streaming.md
Stream Joins	Stream-stream joins, stream-static joins	See references/stream-stream-joins.md, references/stream-static-joins.md
Multi-Sink Writes	Write to multiple tables, parallel merges	See references/multi-sink-writes.md
Merge Operations	MERGE performance, parallel merges, optimizations	See references/merge-operations.md

Configuration

Topic	Description	Reference
Checkpoints	Checkpoint management and best practices	See references/checkpoint-best-practices.md
Stateful Operations	Watermarks, state stores, RocksDB configuration	See references/stateful-operations.md
Trigger & Cost	Trigger selection, cost optimization, RTM	See references/trigger-and-cost-optimization.md

Best Practices

Topic	Description	Reference
Production Checklist	Comprehensive best practices	See references/streaming-best-practices.md

Production Checklist

• Checkpoint location is persistent (UC volumes, not DBFS)
• Unique checkpoint per stream
• Fixed-size cluster (no autoscaling for streaming)
• Monitoring configured (input rate, lag, batch duration)
• Exactly-once verified (txnVersion/txnAppId)
• Watermark configured for stateful operations
• Left joins for stream-static (not inner)