Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: sql-ref-functions-udf-hive.md

layout: global title: Integration with Hive UDFs/UDAFs/UDTFs displayTitle: Integration with Hive UDFs/UDAFs/UDTFs 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. Spark SQL supports integration of Hive UDFs, UDAFs and UDTFs. Similar to Spark UDFs and UDAFs, Hive UDFs work on a single row as input and generate a single row as output, while Hive UDAFs operate on multiple rows and return a single aggregated row as a result. In addition, Hive also supports UDTFs (User Defined Tabular Functions) that act on one row as input and return multiple rows as output. To use Hive UDFs/UDAFs/UTFs, the user should register them in Spark, and then use them in Spark SQL queries. Hive has two UDF interfaces: and . An example below uses derived from `GenericUDF`. ```sql -- Register `GenericUDFAbs` and use it in Spark SQL. -- Note that, if you use your own programmed one, you need to add a JAR containing it -- into a classpath, -- e.g., ADD JAR yourHiveUDF.jar; CREATE TEMPORARY FUNCTION testUDF AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDFAbs'; SELECT * FROM t; +--+ |value| +--+ | -1.0| | 2.0| | -3.0| +--+ SELECT testUDF(value) FROM t; +--+ |testUDF(value)| +--+ | 1.0| | 2.0| | 3.0| +--+ -- Register `UDFSubstr` and use it in Spark SQL. -- Note that, it can achieve better performance if the return types and method parameters use Java primitives. -- e.g., UDFSubstr. The data processing method is UTF8String <-> Text <-> String. we can avoid UTF8String <-> Text. CREATE TEMPORARY FUNCTION hive_substr AS 'org.apache.hadoop.hive.ql.udf.UDFSubstr'; select hive_substr('Spark SQL', 1, 5) as value; +--+ |value| +--+ |Spark| +--+ ``` An example below uses derived from . ```sql -- Register `GenericUDTFExplode` and use it in Spark SQL CREATE TEMPORARY FUNCTION hiveUDTF AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDTFExplode'; SELECT * FROM t; ++ | value| ++ |[1, 2]| |[3, 4]| ++ SELECT hiveUDTF(value) FROM t; ++ |col| ++ | 1| | 2| | 3| | 4| ++ ``` Hive has two UDAF interfaces: and . An example below uses derived from `GenericUDAFResolver`. ```sql -- Register `GenericUDAFSum` and use it in Spark SQL CREATE TEMPORARY FUNCTION hiveUDAF AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum'; SELECT * FROM t; ++--+ |key|value| ++--+ | a| 1| | a| 2| | b| 3| ++--+ SELECT key, hiveUDAF(value) FROM t GROUP BY key; +++ |key|hiveUDAF(value)| +++ | b| 3| | a| 3| +++ ```