0001 ---
0002 layout: global
0003 title: "Migration Guide: Structured Streaming"
0004 displayTitle: "Migration Guide: Structured Streaming"
0005 license: |
0006 Licensed to the Apache Software Foundation (ASF) under one or more
0007 contributor license agreements. See the NOTICE file distributed with
0008 this work for additional information regarding copyright ownership.
0009 The ASF licenses this file to You under the Apache License, Version 2.0
0010 (the "License"); you may not use this file except in compliance with
0011 the License. You may obtain a copy of the License at
0012
0013 http://www.apache.org/licenses/LICENSE-2.0
0014
0015 Unless required by applicable law or agreed to in writing, software
0016 distributed under the License is distributed on an "AS IS" BASIS,
0017 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0018 See the License for the specific language governing permissions and
0019 limitations under the License.
0020 ---
0021
0022 * Table of contents
0023 {:toc}
0024
0025 Note that this migration guide describes the items specific to Structured Streaming.
0026 Many items of SQL migration can be applied when migrating Structured Streaming to higher versions.
0027 Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.html).
0028
0029 ## Upgrading from Structured Streaming 2.4 to 3.0
0030
0031 - 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`.
0032
0033 - 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.
