Databricks adds dynamic state repartitioning for streaming
Databricks announced the public preview of on-demand state repartitioning for Apache Spark Structured Streaming in Databricks Runtime 18 and up. This feature lets users adjust the number of state partitions in running stateful streaming queries while preserving checkpoint state. Previously, changing partitions required restarting queries and losing accumulated state. Early customer Coveo reports that this new ability let them reduce S3 API costs by 40%.
- Feature in public preview for Databricks Runtime 18+
- Lets users resize partitions in running queries
- Checkpoint state is maintained during repartitioning
- Previously, resizing required losing existing state
- Coveo says S3 API costs dropped by 40%
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