#
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# 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.
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#
"""
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
[docs]@ignore_unicode_prefix
@since(3.0)
def from_avro(data, jsonFormatSchema, options={}):
"""
Converts a binary column of Avro format into its corresponding catalyst value. If a schema is
provided via the option actualSchema, a different (but compatible) schema can be used for
reading. If no actualSchema option is provided, 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)
[docs]@ignore_unicode_prefix
@since(3.0)
def to_avro(data, jsonFormatSchema=""):
"""
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.
:param jsonFormatSchema: user-specified output avro schema in JSON string format.
>>> from pyspark.sql import Row
>>> from pyspark.sql.avro.functions import to_avro
>>> data = ['SPADES']
>>> df = spark.createDataFrame(data, "string")
>>> df.select(to_avro(df.value).alias("suite")).collect()
[Row(suite=bytearray(b'\\x00\\x0cSPADES'))]
>>> jsonFormatSchema = '''["null", {"type": "enum", "name": "value",
... "symbols": ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"]}]'''
>>> df.select(to_avro(df.value, jsonFormatSchema).alias("suite")).collect()
[Row(suite=bytearray(b'\\x02\\x00'))]
"""
sc = SparkContext._active_spark_context
try:
if jsonFormatSchema == "":
jc = sc._jvm.org.apache.spark.sql.avro.functions.to_avro(_to_java_column(data))
else:
jc = sc._jvm.org.apache.spark.sql.avro.functions.to_avro(
_to_java_column(data), jsonFormatSchema)
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", "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()
globs['spark'] = spark
(failure_count, test_count) = doctest.testmod(
pyspark.sql.avro.functions, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()
