allisonwang-db commented on code in PR #42272: URL: https://github.com/apache/spark/pull/42272#discussion_r1310942775
########## examples/src/main/python/sql/udtf.py: ########## @@ -0,0 +1,230 @@ +# +# 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. +# + +""" +A simple example demonstrating Python UDTFs in Spark +Run with: + ./bin/spark-submit examples/src/main/python/sql/udtf.py +""" + +# NOTE that this file is imported in the User Guides in PySpark documentation. +# The codes are referred via line numbers. See also `literalinclude` directive in Sphinx. +from pyspark.sql import SparkSession +from pyspark.sql.pandas.utils import require_minimum_pandas_version, require_minimum_pyarrow_version + +# Python UDTFs use Arrow by default. +require_minimum_pandas_version() +require_minimum_pyarrow_version() + + +def python_udtf_simple_example(spark: SparkSession) -> None: + + # Define the UDTF class and implement the required `eval` method. + class SquareNumbers: + def eval(self, start: int, end: int): + for num in range(start, end + 1): + yield (num, num * num) + + from pyspark.sql.functions import lit, udtf + + # Create a UDTF using the class definition and the `udtf` function. + square_num = udtf(SquareNumbers, returnType="num: int, squared: int") + + # Invoke the UDTF in PySpark. + square_num(lit(1), lit(3)).show() + # +---+-------+ + # |num|squared| + # +---+-------+ + # | 1| 1| + # | 2| 4| + # | 3| 9| + # +---+-------+ + + +def python_udtf_decorator_example(spark: SparkSession) -> None: + + from pyspark.sql.functions import lit, udtf + + # Define a UDTF using the `udtf` decorator directly on the class. + @udtf(returnType="num: int, squared: int") + class SquareNumbers: + def eval(self, start: int, end: int): + for num in range(start, end + 1): + yield (num, num * num) + + # Invoke the UDTF in PySpark using the SquareNumbers class directly. + SquareNumbers(lit(1), lit(3)).show() + # +---+-------+ + # |num|squared| + # +---+-------+ + # | 1| 1| + # | 2| 4| + # | 3| 9| + # +---+-------+ + + +def python_udtf_registration(spark: SparkSession) -> None: + + from pyspark.sql.functions import udtf + + @udtf(returnType="word: string") + class WordSplitter: + def eval(self, text: str): + for word in text.split(" "): + yield (word.strip(),) + + # Register the UDTF for use in Spark SQL. + spark.udtf.register("split_words", WordSplitter) + + # Example: Using the UDTF in SQL. + spark.sql("SELECT * FROM split_words('hello world')").show() + # +-----+ + # | word| + # +-----+ + # |hello| + # |world| + # +-----+ + + # Example: Using the UDTF with a lateral join in SQL. + # The lateral join allows us to reference the columns and aliases + # in the previous FROM clause items as inputs to the UDTF. + spark.sql( + "SELECT * FROM VALUES ('Hello World'), ('Apache Spark') t(text), " + "LATERAL split_words(text)" + ).show() + # +------------+------+ + # | text| word| + # +------------+------+ + # | Hello World| Hello| + # | Hello World| World| + # |Apache Spark|Apache| + # |Apache Spark| Spark| + # +------------+------+ + + +def python_udtf_arrow_example(spark: SparkSession) -> None: + + from pyspark.sql.functions import udtf + + @udtf(returnType="c1: int, c2: int", useArrow=True) + class PlusOne: + def eval(self, x: int): + yield x, x + 1 + + +def python_udtf_date_expander_example(spark: SparkSession) -> None: + + from datetime import datetime, timedelta + from pyspark.sql.functions import lit, udtf + + @udtf(returnType="date: string") + class DateExpander: + def eval(self, start_date: str, end_date: str): + current = datetime.strptime(start_date, '%Y-%m-%d') + end = datetime.strptime(end_date, '%Y-%m-%d') + while current <= end: + yield (current.strftime('%Y-%m-%d'),) + current += timedelta(days=1) + + DateExpander(lit("2023-02-25"), lit("2023-03-01")).show() + # +----------+ + # | date| + # +----------+ + # |2023-02-25| + # |2023-02-26| + # |2023-02-27| + # |2023-02-28| + # |2023-03-01| + # +----------+ + + +def python_udtf_terminate_example(spark: SparkSession) -> None: + + from pyspark.sql.functions import udtf + + @udtf(returnType="cnt: int") + class CountUDTF: + def __init__(self): + self.count = 0 + + def eval(self, x: int): + self.count += 1 + + def terminate(self): + yield self.count, + + spark.udtf.register("count_udtf", CountUDTF) + spark.sql("SELECT * FROM range(0, 10, 1, 1), LATERAL count_udtf(id)").show() + # +---+---+ + # | id|cnt| + # +---+---+ + # | 9| 10| + # +---+---+ + + +def python_udtf_table_argument(spark: SparkSession) -> None: + + from pyspark.sql.functions import udtf + from pyspark.sql.types import Row + + @udtf(returnType="id: int") + class FilterUDTF: + def eval(self, row: Row): + if row["id"] > 5: + yield row["id"], + + spark.udtf.register("filter_udtf", FilterUDTF) + + spark.sql("SELECT * FROM filter_udtf(TABLE(SELECT * FROM range(10)))").show() Review Comment: We can follow up in [SPARK-44746](https://issues.apache.org/jira/browse/SPARK-44746) -- This is an automated message from the Apache Git Service. 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