2c64b731ae
### What changes were proposed in this pull request? This patch proposes to disable the streaming query with possible correctness issue in chained stateful operators. The behavior can be controlled by a SQL config, so if users understand the risk and still want to run the query, they can disable the check. ### Why are the changes needed? The possible correctness in chained stateful operators in streaming query is not straightforward for users. From users perspective, it will be considered as a Spark bug. It is also possible the worse case, users are not aware of the correctness issue and use wrong results. A better approach should be to disable such queries and let users choose to run the query if they understand there is such risk, instead of implicitly running the query and let users to find out correctness issue by themselves and report this known to Spark community. ### Does this PR introduce _any_ user-facing change? Yes. Streaming query with possible correctness issue will be blocked to run, except for users explicitly disable the SQL config. ### How was this patch tested? Unit test. Closes #30210 from viirya/SPARK-33259. Authored-by: Liang-Chi Hsieh <viirya@gmail.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
40 lines
3.1 KiB
Markdown
40 lines
3.1 KiB
Markdown
---
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layout: global
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title: "Migration Guide: Structured Streaming"
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displayTitle: "Migration Guide: Structured Streaming"
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license: |
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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---
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* Table of contents
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{:toc}
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Note that this migration guide describes the items specific to Structured Streaming.
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Many items of SQL migration can be applied when migrating Structured Streaming to higher versions.
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Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.html).
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## Upgrading from Structured Streaming 3.0 to 3.1
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- 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.
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## Upgrading from Structured Streaming 2.4 to 3.0
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- 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`.
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- 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.
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- 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`.
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