--- layout: global title: "Migration Guide: PySpark (Python on Spark)" displayTitle: "Migration Guide: PySpark (Python on Spark)" license: | 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 PySpark. Many items of SQL migration can be applied when migrating PySpark to higher versions. Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.html). ## Upgrading from PySpark 2.4 to 3.0 - Since Spark 3.0, PySpark requires a Pandas version of 0.23.2 or higher to use Pandas related functionality, such as `toPandas`, `createDataFrame` from Pandas DataFrame, etc. - Since Spark 3.0, PySpark requires a PyArrow version of 0.12.1 or higher to use PyArrow related functionality, such as `pandas_udf`, `toPandas` and `createDataFrame` with "spark.sql.execution.arrow.enabled=true", etc. - In PySpark, when creating a `SparkSession` with `SparkSession.builder.getOrCreate()`, if there is an existing `SparkContext`, the builder was trying to update the `SparkConf` of the existing `SparkContext` with configurations specified to the builder, but the `SparkContext` is shared by all `SparkSession`s, so we should not update them. Since 3.0, the builder comes to not update the configurations. This is the same behavior as Java/Scala API in 2.3 and above. If you want to update them, you need to update them prior to creating a `SparkSession`. - In PySpark, when Arrow optimization is enabled, if Arrow version is higher than 0.11.0, Arrow can perform safe type conversion when converting Pandas.Series to Arrow array during serialization. Arrow will raise errors when detecting unsafe type conversion like overflow. Setting `spark.sql.execution.pandas.arrowSafeTypeConversion` to true can enable it. The default setting is false. PySpark's behavior for Arrow versions is illustrated in the table below:
PyArrow version | Integer Overflow | Floating Point Truncation |
---|---|---|
version < 0.11.0 | Raise error | Silently allows |
version > 0.11.0, arrowSafeTypeConversion=false | Silent overflow | Silently allows |
version > 0.11.0, arrowSafeTypeConversion=true | Raise error | Raise error |