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[SPARK-2736] PySpark converter and example script for reading Avro files
JIRA: https://issues.apache.org/jira/browse/SPARK-2736 This patch includes: 1. An Avro converter that converts Avro data types to Python. It handles all 3 Avro data mappings (Generic, Specific and Reflect). 2. An example Python script for reading Avro files using AvroKeyInputFormat and the converter. 3. Fixing a classloading issue. cc @MLnick @JoshRosen @mateiz Author: Kan Zhang <kzhang@apache.org> Closes apache#1916 from kanzhang/SPARK-2736 and squashes the following commits: 02443f8 [Kan Zhang] [SPARK-2736] Adding .avsc files to .rat-excludes f74e9a9 [Kan Zhang] [SPARK-2736] nit: clazz -> className 82cc505 [Kan Zhang] [SPARK-2736] Update data sample 0be7761 [Kan Zhang] [SPARK-2736] Example pyspark script and data files c8e5881 [Kan Zhang] [SPARK-2736] Trying to work with all 3 Avro data models 2271a5b [Kan Zhang] [SPARK-2736] Using the right class loader to find Avro classes 536876b [Kan Zhang] [SPARK-2736] Adding Avro to Java converter
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| # | ||
| # 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. | ||
| # | ||
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| import sys | ||
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| from pyspark import SparkContext | ||
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| """ | ||
| Read data file users.avro in local Spark distro: | ||
| $ cd $SPARK_HOME | ||
| $ ./bin/spark-submit --driver-class-path /path/to/example/jar ./examples/src/main/python/avro_inputformat.py \ | ||
| > examples/src/main/resources/users.avro | ||
| {u'favorite_color': None, u'name': u'Alyssa', u'favorite_numbers': [3, 9, 15, 20]} | ||
| {u'favorite_color': u'red', u'name': u'Ben', u'favorite_numbers': []} | ||
| To read name and favorite_color fields only, specify the following reader schema: | ||
| $ cat examples/src/main/resources/user.avsc | ||
| {"namespace": "example.avro", | ||
| "type": "record", | ||
| "name": "User", | ||
| "fields": [ | ||
| {"name": "name", "type": "string"}, | ||
| {"name": "favorite_color", "type": ["string", "null"]} | ||
| ] | ||
| } | ||
| $ ./bin/spark-submit --driver-class-path /path/to/example/jar ./examples/src/main/python/avro_inputformat.py \ | ||
| > examples/src/main/resources/users.avro examples/src/main/resources/user.avsc | ||
| {u'favorite_color': None, u'name': u'Alyssa'} | ||
| {u'favorite_color': u'red', u'name': u'Ben'} | ||
| """ | ||
| if __name__ == "__main__": | ||
| if len(sys.argv) != 2 and len(sys.argv) != 3: | ||
| print >> sys.stderr, """ | ||
| Usage: avro_inputformat <data_file> [reader_schema_file] | ||
| Run with example jar: | ||
| ./bin/spark-submit --driver-class-path /path/to/example/jar /path/to/examples/avro_inputformat.py <data_file> [reader_schema_file] | ||
| Assumes you have Avro data stored in <data_file>. Reader schema can be optionally specified in [reader_schema_file]. | ||
| """ | ||
| exit(-1) | ||
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| path = sys.argv[1] | ||
| sc = SparkContext(appName="AvroKeyInputFormat") | ||
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| conf = None | ||
| if len(sys.argv) == 3: | ||
| schema_rdd = sc.textFile(sys.argv[2], 1).collect() | ||
| conf = {"avro.schema.input.key" : reduce(lambda x, y: x+y, schema_rdd)} | ||
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| avro_rdd = sc.newAPIHadoopFile(path, | ||
| "org.apache.avro.mapreduce.AvroKeyInputFormat", | ||
| "org.apache.avro.mapred.AvroKey", | ||
| "org.apache.hadoop.io.NullWritable", | ||
| keyConverter="org.apache.spark.examples.pythonconverters.AvroWrapperToJavaConverter", | ||
| conf=conf) | ||
| output = avro_rdd.map(lambda x: x[0]).collect() | ||
| for k in output: | ||
| print k |
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| {"namespace": "example.avro", | ||
| "type": "record", | ||
| "name": "User", | ||
| "fields": [ | ||
| {"name": "name", "type": "string"}, | ||
| {"name": "favorite_color", "type": ["string", "null"]} | ||
| ] | ||
| } |
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examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.examples.pythonconverters | ||
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| import java.util.{Collection => JCollection, Map => JMap} | ||
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| import scala.collection.JavaConversions._ | ||
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| import org.apache.avro.generic.{GenericFixed, IndexedRecord} | ||
| import org.apache.avro.mapred.AvroWrapper | ||
| import org.apache.avro.Schema | ||
| import org.apache.avro.Schema.Type._ | ||
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| import org.apache.spark.api.python.Converter | ||
| import org.apache.spark.SparkException | ||
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| /** | ||
| * Implementation of [[org.apache.spark.api.python.Converter]] that converts | ||
| * an Avro Record wrapped in an AvroKey (or AvroValue) to a Java Map. It tries | ||
| * to work with all 3 Avro data mappings (Generic, Specific and Reflect). | ||
| */ | ||
| class AvroWrapperToJavaConverter extends Converter[Any, Any] { | ||
| override def convert(obj: Any): Any = { | ||
| if (obj == null) { | ||
| return null | ||
| } | ||
| obj.asInstanceOf[AvroWrapper[_]].datum() match { | ||
| case null => null | ||
| case record: IndexedRecord => unpackRecord(record) | ||
| case other => throw new SparkException( | ||
| s"Unsupported top-level Avro data type ${other.getClass.getName}") | ||
| } | ||
| } | ||
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| def unpackRecord(obj: Any): JMap[String, Any] = { | ||
| val map = new java.util.HashMap[String, Any] | ||
| obj match { | ||
| case record: IndexedRecord => | ||
| record.getSchema.getFields.zipWithIndex.foreach { case (f, i) => | ||
| map.put(f.name, fromAvro(record.get(i), f.schema)) | ||
| } | ||
| case other => throw new SparkException( | ||
| s"Unsupported RECORD type ${other.getClass.getName}") | ||
| } | ||
| map | ||
| } | ||
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| def unpackMap(obj: Any, schema: Schema): JMap[String, Any] = { | ||
| obj.asInstanceOf[JMap[_, _]].map { case (key, value) => | ||
| (key.toString, fromAvro(value, schema.getValueType)) | ||
| } | ||
| } | ||
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| def unpackFixed(obj: Any, schema: Schema): Array[Byte] = { | ||
| unpackBytes(obj.asInstanceOf[GenericFixed].bytes()) | ||
| } | ||
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| def unpackBytes(obj: Any): Array[Byte] = { | ||
| val bytes: Array[Byte] = obj match { | ||
| case buf: java.nio.ByteBuffer => buf.array() | ||
| case arr: Array[Byte] => arr | ||
| case other => throw new SparkException( | ||
| s"Unknown BYTES type ${other.getClass.getName}") | ||
| } | ||
| val bytearray = new Array[Byte](bytes.length) | ||
| System.arraycopy(bytes, 0, bytearray, 0, bytes.length) | ||
| bytearray | ||
| } | ||
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| def unpackArray(obj: Any, schema: Schema): JCollection[Any] = obj match { | ||
| case c: JCollection[_] => | ||
| c.map(fromAvro(_, schema.getElementType)) | ||
| case arr: Array[_] if arr.getClass.getComponentType.isPrimitive => | ||
| arr.toSeq | ||
| case arr: Array[_] => | ||
| arr.map(fromAvro(_, schema.getElementType)).toSeq | ||
| case other => throw new SparkException( | ||
| s"Unknown ARRAY type ${other.getClass.getName}") | ||
| } | ||
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| def unpackUnion(obj: Any, schema: Schema): Any = { | ||
| schema.getTypes.toList match { | ||
| case List(s) => fromAvro(obj, s) | ||
| case List(n, s) if n.getType == NULL => fromAvro(obj, s) | ||
| case List(s, n) if n.getType == NULL => fromAvro(obj, s) | ||
| case _ => throw new SparkException( | ||
| "Unions may only consist of a concrete type and null") | ||
| } | ||
| } | ||
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| def fromAvro(obj: Any, schema: Schema): Any = { | ||
| if (obj == null) { | ||
| return null | ||
| } | ||
| schema.getType match { | ||
| case UNION => unpackUnion(obj, schema) | ||
| case ARRAY => unpackArray(obj, schema) | ||
| case FIXED => unpackFixed(obj, schema) | ||
| case MAP => unpackMap(obj, schema) | ||
| case BYTES => unpackBytes(obj) | ||
| case RECORD => unpackRecord(obj) | ||
| case STRING => obj.toString | ||
| case ENUM => obj.toString | ||
| case NULL => obj | ||
| case BOOLEAN => obj | ||
| case DOUBLE => obj | ||
| case FLOAT => obj | ||
| case INT => obj | ||
| case LONG => obj | ||
| case other => throw new SparkException( | ||
| s"Unknown Avro schema type ${other.getName}") | ||
| } | ||
| } | ||
| } |