gaborgsomogyi commented on a change in pull request #23797: [SPARK-26856][PYSPARK] Python support for from_avro and to_avro APIs URL: https://github.com/apache/spark/pull/23797#discussion_r263707603
########## File path: python/pyspark/sql/avro/functions.py ########## @@ -0,0 +1,137 @@ +# +# 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 collections of builtin avro functions +""" + + +from pyspark import since, SparkContext +from pyspark.rdd import ignore_unicode_prefix +from pyspark.sql.column import Column, _to_java_column +from pyspark.util import _print_missing_jar + + +@ignore_unicode_prefix +@since(3.0) +def from_avro(data, jsonFormatSchema, options={}): + """ + Converts a binary column of avro format into its corresponding catalyst value. The specified + schema must match the read data, otherwise the behavior is undefined: it may fail or return + arbitrary result. + + Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the + application as per the deployment section of "Apache Avro Data Source Guide". + + :param data: the binary column. + :param jsonFormatSchema: the avro schema in JSON string format. + :param options: options to control how the Avro record is parsed. + + >>> from pyspark.sql import Row + >>> from pyspark.sql.avro.functions import from_avro, to_avro + >>> data = [(1, Row(name='Alice', age=2))] + >>> df = spark.createDataFrame(data, ("key", "value")) + >>> avroDf = df.select(to_avro(df.value).alias("avro")) + >>> avroDf.collect() + [Row(avro=bytearray(b'\\x00\\x00\\x04\\x00\\nAlice'))] + >>> jsonFormatSchema = '''{"type":"record","name":"topLevelRecord","fields": + ... [{"name":"avro","type":[{"type":"record","name":"value","namespace":"topLevelRecord", + ... "fields":[{"name":"age","type":["long","null"]}, + ... {"name":"name","type":["string","null"]}]},"null"]}]}''' + >>> avroDf.select(from_avro(avroDf.avro, jsonFormatSchema).alias("value")).collect() + [Row(value=Row(avro=Row(age=2, name=u'Alice')))] + """ + + sc = SparkContext._active_spark_context + try: + jc = sc._jvm.org.apache.spark.sql.avro.functions.from_avro( + _to_java_column(data), jsonFormatSchema, options) + except TypeError as e: + if str(e) == "'JavaPackage' object is not callable": + _print_missing_jar("Avro", "avro", "avro", sc.version) + raise + return Column(jc) + + +@ignore_unicode_prefix +@since(3.0) +def to_avro(data): + """ + Converts a column into binary of avro format. + + Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the + application as per the deployment section of "Apache Avro Data Source Guide". + + :param data: the data column. + + >>> from pyspark.sql import Row + >>> from pyspark.sql.avro.functions import to_avro + >>> data = [(1, Row(name='Alice', age=2))] + >>> df = spark.createDataFrame(data, ("key", "value")) + >>> df.select(to_avro(df.value).alias("avro")).collect() + [Row(avro=bytearray(b'\\x00\\x00\\x04\\x00\\nAlice'))] + """ + + sc = SparkContext._active_spark_context + try: + jc = sc._jvm.org.apache.spark.sql.avro.functions.to_avro(_to_java_column(data)) + except TypeError as e: + if str(e) == "'JavaPackage' object is not callable": + _print_missing_jar("Avro", "avro", "avro", sc.version) + raise + return Column(jc) + + +def _test(): + import os + import sys + from pyspark.testing.utils import search_jar + avro_jar = search_jar("external/avro", "spark-avro") + if avro_jar is None: + print( + "Skipping all Avro Python tests as the optional Avro project was " + "not compiled into a JAR. To run these tests, " + "you need to build Spark with 'build/sbt -Pavro package' or " + "'build/mvn -Pavro package' before running this test.") + sys.exit(0) + else: + existing_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell") + jars_args = "--jars %s" % avro_jar + os.environ["PYSPARK_SUBMIT_ARGS"] = " ".join([jars_args, existing_args]) + + import doctest + from pyspark.sql import Row, SparkSession + import pyspark.sql.avro.functions + globs = pyspark.sql.avro.functions.__dict__.copy() + spark = SparkSession.builder\ + .master("local[4]")\ + .appName("sql.avro.functions tests")\ + .getOrCreate() + sc = spark.sparkContext + globs['sc'] = sc + globs['spark'] = spark + globs['df'] = spark.createDataFrame([Row(name='Alice', age=2), Row(name='Bob', age=5)]) Review comment: Removed. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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