Structured Streaming Programming Guide

Miscellaneous Notes

• Several configurations are not modifiable after the query has run. To change them, discard the checkpoint and start a new query. These configurations include:
  • spark.sql.shuffle.partitions
    • This is due to the physical partitioning of state: state is partitioned via applying hash function to key, hence the number of partitions for state should be unchanged.
    • If you want to run fewer tasks for stateful operations, coalesce would help with avoiding unnecessary repartitioning.
      • After coalesce, the number of (reduced) tasks will be kept unless another shuffle happens.
  • spark.sql.streaming.stateStore.providerClass: To read the previous state of the query properly, the class of state store provider should be unchanged.
  • spark.sql.streaming.multipleWatermarkPolicy: Modification of this would lead inconsistent watermark value when query contains multiple watermarks, hence the policy should be unchanged.

Related Resources

Further Reading

• See and run the Python/Scala/Java/R examples.
  • Instructions on how to run Spark examples
• Read about integrating with Kafka in the Structured Streaming Kafka Integration Guide
• Read more details about using DataFrames/Datasets in the Spark SQL Programming Guide
• Third-party Blog Posts
  • Real-time Streaming ETL with Structured Streaming in Apache Spark 2.1 (Databricks Blog)
  • Real-Time End-to-End Integration with Apache Kafka in Apache Spark’s Structured Streaming (Databricks Blog)
  • Event-time Aggregation and Watermarking in Apache Spark’s Structured Streaming (Databricks Blog)

Talks

• Spark Summit Europe 2017
  • Easy, Scalable, Fault-tolerant Stream Processing with Structured Streaming in Apache Spark - Part 1 slides/video, Part 2 slides/video
  • Deep Dive into Stateful Stream Processing in Structured Streaming - slides/video
• Spark Summit 2016
  • A Deep Dive into Structured Streaming - slides/video

Migration Guide

The migration guide is now archived on this page.