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Migration Guide: Structured Streaming
Migration Guide: Structured Streaming
Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
  • 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.

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.