Databricks Spark Structured Streaming

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)