d99d0d27be
This changeset is published into the public domain. ### What changes were proposed in this pull request? Some typos and syntax issues in docstrings and the output of `dev/lint-python` have been fixed. ### Why are the changes needed? In some places, the documentation did not refer to parameters or classes by the full and correct name, potentially causing uncertainty in the reader or rendering issues in Sphinx. Also, a typo in the standard output of `dev/lint-python` was fixed. ### Does this PR introduce _any_ user-facing change? Slight improvements in documentation, and in standard output of `dev/lint-python`. ### How was this patch tested? Manual testing and `dev/lint-python` run. No new Sphinx warnings arise due to this change. Closes #31401 from DavidToneian/SPARK-34300. Authored-by: David Toneian <david@toneian.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
164 lines
5.9 KiB
Python
164 lines
5.9 KiB
Python
#
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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 SparkContext
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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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def from_avro(data, jsonFormatSchema, options=None):
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"""
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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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.. versionadded:: 3.0.0
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Parameters
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----------
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data : :class:`~pyspark.sql.Column` or str
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the binary column.
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jsonFormatSchema : str
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the avro schema in JSON string format.
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options : dict, optional
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options to control how the Avro record is parsed.
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Notes
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-----
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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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Examples
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--------
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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(age=2, name='Alice'))]
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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='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 or {})
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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 to_avro(data, jsonFormatSchema=""):
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"""
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Converts a column into binary of avro format.
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.. versionadded:: 3.0.0
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Parameters
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----------
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data : :class:`~pyspark.sql.Column` or str
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the data column.
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jsonFormatSchema : str, optional
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user-specified output avro schema in JSON string format.
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Notes
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-----
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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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Examples
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--------
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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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>>> 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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"""
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sc = SparkContext._active_spark_context
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try:
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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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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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avro_jar = search_jar("external/avro", "spark-avro", "spark-avro")
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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 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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