spark-instrumented-optimizer/docs/ss-migration-guide.md

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title: "Migration Guide: Structured Streaming"
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* Table of contents
{:toc}

Note that this migration guide describes the items specific to Structured Streaming.
Many items of SQL migration can be applied when migrating Structured Streaming to higher versions.
Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.html).

## Upgrading from Structured Streaming 3.0 to 3.1

- In Spark 3.0 and before, for the queries that have stateful operation which can emit rows older than the current watermark plus allowed late record delay, which are "late rows" in downstream stateful operations and these rows can be discarded, Spark only prints a warning message. Since Spark 3.1, Spark will check for such queries with possible correctness issue and throw AnalysisException for it by default. For the users who understand the possible risk of correctness issue and still decide to run the query, please disable this check by setting the config `spark.sql.streaming.statefulOperator.checkCorrectness.enabled` to false.

- In Spark 3.0 and before Spark uses `KafkaConsumer` for offset fetching which could cause infinite wait in the driver.
  In Spark 3.1 a new configuration option added `spark.sql.streaming.kafka.useDeprecatedOffsetFetching` (default: `true`)
  which could be set to `false` allowing Spark to use new offset fetching mechanism using `AdminClient`.
  For further details please see [Structured Streaming Kafka Integration](structured-streaming-kafka-integration.html#offset-fetching).

## Upgrading from Structured Streaming 2.4 to 3.0

- In Spark 3.0, Structured Streaming forces the source schema into nullable when file-based datasources such as text, json, csv, parquet and orc are used via `spark.readStream(...)`. Previously, it respected the nullability in source schema; however, it caused issues tricky to debug with NPE. To restore the previous behavior, set `spark.sql.streaming.fileSource.schema.forceNullable` to `false`.

- Spark 3.0 fixes the correctness issue on Stream-stream outer join, which changes the schema of state. (See [SPARK-26154](https://issues.apache.org/jira/browse/SPARK-26154) for more details). If you start your query from checkpoint constructed from Spark 2.x which uses stream-stream outer join, Spark 3.0 fails the query. To recalculate outputs, discard the checkpoint and replay previous inputs.

- In Spark 3.0, the deprecated class `org.apache.spark.sql.streaming.ProcessingTime` has been removed. Use `org.apache.spark.sql.streaming.Trigger.ProcessingTime` instead. Likewise, `org.apache.spark.sql.execution.streaming.continuous.ContinuousTrigger` has been removed in favor of `Trigger.Continuous`, and `org.apache.spark.sql.execution.streaming.OneTimeTrigger` has been hidden in favor of `Trigger.Once`.