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gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Databricks Spark Structured Streaming by gabrielmoreira

Name: Databricks Spark Structured Streaming
Rating: 4.5 (4 reviews)
Author: gabrielmoreira

Databricks Spark Structured Streaming

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.

gabrielmoreira Updated Sep 9, 2026 17 repo stars

File contents

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)

--- name: databricks-spark-structured-streaming 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." compatibility: Requires databricks CLI (>= v1.0.0) metadata: version: "0.1.0" parent: databricks-core --- # Spark Structured Streaming Production-ready streaming pipelines with Spark Structured Streaming. This skill provides navigation to detailed patterns and best practices. ## Quick Start ```python 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) | | **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](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](references/lakebase-sink-python.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)

Frequently asked questions