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### What changes were proposed in this pull request? Fixed typo in `docs` directory and in `project/MimaExcludes.scala` ### Why are the changes needed? Better readability of documents ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? No test needed Closes #28447 from kiszk/typo_20200504. Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
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| layout | title | displayTitle | license |
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| global | Functions | Functions | 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 provides two function features to meet a wide range of user needs: built-in functions and user-defined functions (UDFs). Built-in functions are commonly used routines that Spark SQL predefines and a complete list of the functions can be found in the Built-in Functions API document. UDFs allow users to define their own functions when the system’s built-in functions are not enough to perform the desired task.
Built-in Functions
Spark SQL has some categories of frequently-used built-in functions for aggregation, arrays/maps, date/timestamp, and JSON data. This subsection presents the usages and descriptions of these functions.
Scalar Functions
Aggregate-like Functions
UDFs (User-Defined Functions)
User-Defined Functions (UDFs) are a feature of Spark SQL that allows users to define their own functions when the system's built-in functions are not enough to perform the desired task. To use UDFs in Spark SQL, users must first define the function, then register the function with Spark, and finally call the registered function. The User-Defined Functions can act on a single row or act on multiple rows at once. Spark SQL also supports integration of existing Hive implementations of UDFs, UDAFs and UDTFs.