Migration Guide: Structured Streaming

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.

Upgrading from Structured Streaming 3.5 to 4.0

• Since Spark 4.0, Spark falls back to single batch execution if any source in the query does not support Trigger.AvailableNow. This is to avoid any possible correctness, duplication, and dataloss issue due to incompatibility between source and wrapper implementation. (See SPARK-45178 for more details.)
• Since Spark 4.0, new configuration spark.sql.streaming.ratioExtraSpaceAllowedInCheckpoint (default: 0.3) controls the amount of additional space allowed in the checkpoint directory to store stale version files for batch deletion inside maintenance task. This is to amortize the cost of listing in cloud store. Setting this to 0 defaults to the old behavior. (See SPARK-48931 for more details.)

Upgrading from Structured Streaming 3.3 to 3.4

• Since Spark 3.4, Trigger.Once is deprecated, and users are encouraged to migrate from Trigger.Once to Trigger.AvailableNow. Please refer SPARK-39805 for more details.

• Since Spark 3.4, the default value of configuration for Kafka offset fetching (spark.sql.streaming.kafka.useDeprecatedOffsetFetching) is changed from true to false. The default no longer relies consumer group based scheduling, which affect the required ACL. For further details please see Structured Streaming Kafka Integration.

Upgrading from Structured Streaming 3.2 to 3.3

• Since Spark 3.3, all stateful operators require hash partitioning with exact grouping keys. In previous versions, all stateful operators except stream-stream join require loose partitioning criteria which opens the possibility on correctness issue. (See SPARK-38204 for more details.) To ensure backward compatibility, we retain the old behavior with the checkpoint built from older versions.

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.

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 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.