hequn8128 commented on a change in pull request #11724: [FLINK-16486][python][docs] Add documentation for vectorized Python UDF URL: https://github.com/apache/flink/pull/11724#discussion_r408698992
########## File path: docs/dev/table/python/vectorized_python_udfs.md ########## @@ -0,0 +1,65 @@ +--- +title: "Vectorized User-defined Functions" +nav-parent_id: python_tableapi +nav-pos: 30 +--- +<!-- +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. +--> + +Vectorized Python user-defined functions are functions which are executed by transferring a batch of elements between JVM and Python VM in Arrow columnar format. +The performance of vectorized Python user-defined functions are usually much higher than non-vectorized Python user-defined functions as the serialization/deserialization +overhead is much reduced. Besides, users could leverage the popular Python libraries such as Pandas, Numpy, etc for the vectorized Python user-defined functions implementation. +These Python libraries are highly optimized and provide high-performance data structures and functions. It shares the similar way as the +[non-vectorized user-defined functions]({{ site.baseurl }}/dev/table/python/python_udfs.html) on how to define vectorized user-defined functions. +Users only need to add an extra parameter `udf_type="pandas"` in the decorator `udf` to mark it as a vectorized user-defined function. + +**NOTE:** Python UDF execution requires Python version (3.5, 3.6 or 3.7) with PyFlink installed. It's required on both the client side and the cluster side. + +* This will be replaced by the TOC +{:toc} + +## Vectorized Scalar Functions + +Vectorized Python scalar functions take pandas.Series as the inputs and return a pandas.Series of the same length as the output. Review comment: to \`pandas.Series\`. Also note for other places. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
