2019-03-10 21:15:07 -04:00
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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A collections of builtin avro functions
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"""
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from pyspark import since, SparkContext
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from pyspark.rdd import ignore_unicode_prefix
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from pyspark.sql.column import Column, _to_java_column
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from pyspark.util import _print_missing_jar
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@ignore_unicode_prefix
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@since(3.0)
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def from_avro(data, jsonFormatSchema, options={}):
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"""
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2019-12-30 04:14:21 -05:00
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Converts a binary column of Avro format into its corresponding catalyst value.
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The specified schema must match the read data, otherwise the behavior is undefined:
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it may fail or return arbitrary result.
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To deserialize the data with a compatible and evolved schema, the expected Avro schema can be
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set via the option avroSchema.
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2019-03-10 21:15:07 -04:00
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Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the
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application as per the deployment section of "Apache Avro Data Source Guide".
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:param data: the binary column.
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:param jsonFormatSchema: the avro schema in JSON string format.
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:param options: options to control how the Avro record is parsed.
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>>> from pyspark.sql import Row
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>>> from pyspark.sql.avro.functions import from_avro, to_avro
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>>> data = [(1, Row(name='Alice', age=2))]
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>>> df = spark.createDataFrame(data, ("key", "value"))
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>>> avroDf = df.select(to_avro(df.value).alias("avro"))
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>>> avroDf.collect()
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[Row(avro=bytearray(b'\\x00\\x00\\x04\\x00\\nAlice'))]
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>>> jsonFormatSchema = '''{"type":"record","name":"topLevelRecord","fields":
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... [{"name":"avro","type":[{"type":"record","name":"value","namespace":"topLevelRecord",
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... "fields":[{"name":"age","type":["long","null"]},
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... {"name":"name","type":["string","null"]}]},"null"]}]}'''
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>>> avroDf.select(from_avro(avroDf.avro, jsonFormatSchema).alias("value")).collect()
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[Row(value=Row(avro=Row(age=2, name=u'Alice')))]
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"""
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sc = SparkContext._active_spark_context
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try:
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jc = sc._jvm.org.apache.spark.sql.avro.functions.from_avro(
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_to_java_column(data), jsonFormatSchema, options)
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except TypeError as e:
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if str(e) == "'JavaPackage' object is not callable":
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_print_missing_jar("Avro", "avro", "avro", sc.version)
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raise
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return Column(jc)
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@ignore_unicode_prefix
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@since(3.0)
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2019-08-13 08:52:16 -04:00
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def to_avro(data, jsonFormatSchema=""):
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2019-03-10 21:15:07 -04:00
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"""
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Converts a column into binary of avro format.
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Note: Avro is built-in but external data source module since Spark 2.4. Please deploy the
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application as per the deployment section of "Apache Avro Data Source Guide".
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:param data: the data column.
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2019-08-13 08:52:16 -04:00
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:param jsonFormatSchema: user-specified output avro schema in JSON string format.
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2019-03-10 21:15:07 -04:00
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>>> from pyspark.sql import Row
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>>> from pyspark.sql.avro.functions import to_avro
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2019-08-13 08:52:16 -04:00
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>>> data = ['SPADES']
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>>> df = spark.createDataFrame(data, "string")
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>>> df.select(to_avro(df.value).alias("suite")).collect()
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[Row(suite=bytearray(b'\\x00\\x0cSPADES'))]
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>>> jsonFormatSchema = '''["null", {"type": "enum", "name": "value",
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... "symbols": ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"]}]'''
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>>> df.select(to_avro(df.value, jsonFormatSchema).alias("suite")).collect()
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[Row(suite=bytearray(b'\\x02\\x00'))]
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2019-03-10 21:15:07 -04:00
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"""
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sc = SparkContext._active_spark_context
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try:
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2019-08-13 08:52:16 -04:00
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if jsonFormatSchema == "":
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jc = sc._jvm.org.apache.spark.sql.avro.functions.to_avro(_to_java_column(data))
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else:
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jc = sc._jvm.org.apache.spark.sql.avro.functions.to_avro(
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_to_java_column(data), jsonFormatSchema)
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2019-03-10 21:15:07 -04:00
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except TypeError as e:
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if str(e) == "'JavaPackage' object is not callable":
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_print_missing_jar("Avro", "avro", "avro", sc.version)
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raise
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return Column(jc)
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def _test():
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import os
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import sys
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from pyspark.testing.utils import search_jar
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2019-04-02 01:52:56 -04:00
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avro_jar = search_jar("external/avro", "spark-avro", "spark-avro")
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2019-03-10 21:15:07 -04:00
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if avro_jar is None:
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print(
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"Skipping all Avro Python tests as the optional Avro project was "
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"not compiled into a JAR. To run these tests, "
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"you need to build Spark with 'build/sbt -Pavro package' or "
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"'build/mvn -Pavro package' before running this test.")
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sys.exit(0)
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else:
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existing_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell")
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jars_args = "--jars %s" % avro_jar
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os.environ["PYSPARK_SUBMIT_ARGS"] = " ".join([jars_args, existing_args])
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import doctest
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from pyspark.sql import Row, SparkSession
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import pyspark.sql.avro.functions
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globs = pyspark.sql.avro.functions.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[4]")\
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.appName("sql.avro.functions tests")\
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.getOrCreate()
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globs['spark'] = spark
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(failure_count, test_count) = doctest.testmod(
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pyspark.sql.avro.functions, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
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spark.stop()
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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