Spark Structured Streaming Skill

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)

Agent Skills: Spark Structured Streaming

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