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databricks-spark-structured-streaming

Um guia completo para Spark Structured Streaming em cargas de trabalho de produção, abordando a construção de pipelines de streaming, ingestão de Kafka, operações com estado com watermarks e otimização de desempenho.

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Atualizado há 2 meses

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

Configuration

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

Best Practices

| Topic | Description | Reference | |-------|-------------|-----------| | Production Checklist | Comprehensive best practices | See [references/streaming-best-practices.md](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)
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