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)