| # | |
| # 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. | |
| # | |
| """ | |
| pyspark.inprocess -- In-process Python UDF framework for Apache Spark. | |
| Provides zero-copy Arrow-based UDF execution via jep (Java Embedded Python), | |
| eliminating the socket IPC overhead of standard Python/pandas UDFs. | |
| Public API: | |
| inprocess_udf - decorator to create an in-process Python UDF | |
| Example:: | |
| import pyarrow.compute as pc | |
| from pyspark.inprocess import inprocess_udf | |
| from pyspark.sql.types import LongType | |
| @inprocess_udf(returnType=LongType()) | |
| def double(x): | |
| return pc.multiply(x, 2) | |
| df.select(double(df.value)).show() | |
| """ | |
| from pyspark.inprocess.udf import inprocess_udf | |
| __all__ = ["inprocess_udf"] |