Skill

databricks-spark-structured-streaming

Name: databricks-spark-structured-streaming
Author: Databricks

build Spark Structured Streaming pipelines

Published by Databricks Updated Jul 12

Covers Data Engineering Data Pipeline Databricks Spark

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.

SKILL.md

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
Real-Time Mode (RTM)	Sub-second E2E latency — cluster setup, slot math, supported ops (incl. stream-stream inner join on DBR 18+), `transformWithState`, observability, error classes, delivery semantics	See references/real-time-mode.md
Lakebase Sink	Write streaming records into Lakebase Postgres with transactional upserts. Native `format("postgresql")` sink (DBR 18.3+) and manual `foreach` sink as a fallback	See references/lakebase-sink-python.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)

More skills from the databricks-agent-skills repository

View all 32 skills

SkillTagsUpdated

More from Databricks

View publisher

SkillTagsUpdated