databricks-spark-structured-streaming - SKILL.md Agent Skill

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."

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 kafka-streaming.md
Stream Joins	Stream-stream joins, stream-static joins	See stream-stream-joins.md, stream-static-joins.md
Multi-Sink Writes	Write to multiple tables, parallel merges	See multi-sink-writes.md
Merge Operations	MERGE performance, parallel merges, optimizations	See merge-operations.md

Configuration

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

Best Practices

Topic	Description	Reference
Production Checklist	Comprehensive best practices	See 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)