com.uber.hoodie.AvroConversionUtils.scala Maven / Gradle / Ivy
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/*
* Copyright (c) 2017 Uber Technologies, Inc. ([email protected])
*
* Licensed 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.
*
*
*/
package com.uber.hoodie
import java.nio.ByteBuffer
import java.sql.{Date, Timestamp}
import java.util
import com.databricks.spark.avro.SchemaConverters
import com.databricks.spark.avro.SchemaConverters.IncompatibleSchemaException
import org.apache.avro.Schema.Type._
import org.apache.avro.generic.GenericData.{Fixed, Record}
import org.apache.avro.generic.{GenericData, GenericRecord}
import org.apache.avro.{Schema, SchemaBuilder}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.catalyst.expressions.GenericRow
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
import scala.collection.JavaConverters._
object AvroConversionUtils {
def createRdd(df: DataFrame, structName: String, recordNamespace: String): RDD[GenericRecord] = {
val dataType = df.schema
val encoder = RowEncoder.apply(dataType).resolveAndBind()
df.queryExecution.toRdd.map(encoder.fromRow)
.mapPartitions { records =>
if (records.isEmpty) Iterator.empty
else {
val convertor = createConverterToAvro(dataType, structName, recordNamespace)
records.map { x => convertor(x).asInstanceOf[GenericRecord] }
}
}
}
def createDataFrame(rdd: RDD[GenericRecord], schemaStr: String, ss : SparkSession): Dataset[Row] = {
if (rdd.isEmpty()) {
ss.emptyDataFrame
} else {
ss.createDataFrame(rdd.mapPartitions { records =>
if (records.isEmpty) Iterator.empty
else {
val schema = Schema.parse(schemaStr)
val dataType = convertAvroSchemaToStructType(schema)
val convertor = createConverterToRow(schema, dataType)
records.map { x => convertor(x).asInstanceOf[Row] }
}
}, convertAvroSchemaToStructType(Schema.parse(schemaStr))).asInstanceOf[Dataset[Row]]
}
}
def getNewRecordNamespace(elementDataType: DataType,
currentRecordNamespace: String,
elementName: String): String = {
elementDataType match {
case StructType(_) => s"$currentRecordNamespace.$elementName"
case _ => currentRecordNamespace
}
}
/**
* NOTE : This part of code is copied from com.databricks.spark.avro.SchemaConverters.scala (133:310) (spark-avro)
*
* Returns a converter function to convert row in avro format to GenericRow of catalyst.
*
* @param sourceAvroSchema Source schema before conversion inferred from avro file by passed in
* by user.
* @param targetSqlType Target catalyst sql type after the conversion.
* @return returns a converter function to convert row in avro format to GenericRow of catalyst.
*/
def createConverterToRow(sourceAvroSchema: Schema,
targetSqlType: DataType): AnyRef => AnyRef = {
def createConverter(avroSchema: Schema,
sqlType: DataType, path: List[String]): AnyRef => AnyRef = {
val avroType = avroSchema.getType
(sqlType, avroType) match {
// Avro strings are in Utf8, so we have to call toString on them
case (StringType, STRING) | (StringType, ENUM) =>
(item: AnyRef) => if (item == null) null else item.toString
// Byte arrays are reused by avro, so we have to make a copy of them.
case (IntegerType, INT) | (BooleanType, BOOLEAN) | (DoubleType, DOUBLE) |
(FloatType, FLOAT) | (LongType, LONG) =>
identity
case (BinaryType, FIXED) =>
(item: AnyRef) =>
if (item == null) {
null
} else {
item.asInstanceOf[Fixed].bytes().clone()
}
case (BinaryType, BYTES) =>
(item: AnyRef) =>
if (item == null) {
null
} else {
val byteBuffer = item.asInstanceOf[ByteBuffer]
val bytes = new Array[Byte](byteBuffer.remaining)
byteBuffer.get(bytes)
bytes
}
case (struct: StructType, RECORD) =>
val length = struct.fields.length
val converters = new Array[AnyRef => AnyRef](length)
val avroFieldIndexes = new Array[Int](length)
var i = 0
while (i < length) {
val sqlField = struct.fields(i)
val avroField = avroSchema.getField(sqlField.name)
if (avroField != null) {
val converter = createConverter(avroField.schema(), sqlField.dataType,
path :+ sqlField.name)
converters(i) = converter
avroFieldIndexes(i) = avroField.pos()
} else if (!sqlField.nullable) {
throw new IncompatibleSchemaException(
s"Cannot find non-nullable field ${sqlField.name} at path ${path.mkString(".")} " +
"in Avro schema\n" +
s"Source Avro schema: $sourceAvroSchema.\n" +
s"Target Catalyst type: $targetSqlType")
}
i += 1
}
(item: AnyRef) => {
if (item == null) {
null
} else {
val record = item.asInstanceOf[GenericRecord]
val result = new Array[Any](length)
var i = 0
while (i < converters.length) {
if (converters(i) != null) {
val converter = converters(i)
result(i) = converter(record.get(avroFieldIndexes(i)))
}
i += 1
}
new GenericRow(result)
}
}
case (arrayType: ArrayType, ARRAY) =>
val elementConverter = createConverter(avroSchema.getElementType, arrayType.elementType,
path)
val allowsNull = arrayType.containsNull
(item: AnyRef) => {
if (item == null) {
null
} else {
item.asInstanceOf[java.lang.Iterable[AnyRef]].asScala.map { element =>
if (element == null && !allowsNull) {
throw new RuntimeException(s"Array value at path ${path.mkString(".")} is not " +
"allowed to be null")
} else {
elementConverter(element)
}
}
}
}
case (mapType: MapType, MAP) if mapType.keyType == StringType =>
val valueConverter = createConverter(avroSchema.getValueType, mapType.valueType, path)
val allowsNull = mapType.valueContainsNull
(item: AnyRef) => {
if (item == null) {
null
} else {
item.asInstanceOf[java.util.Map[AnyRef, AnyRef]].asScala.map { x =>
if (x._2 == null && !allowsNull) {
throw new RuntimeException(s"Map value at path ${path.mkString(".")} is not " +
"allowed to be null")
} else {
(x._1.toString, valueConverter(x._2))
}
}.toMap
}
}
case (sqlType, UNION) =>
if (avroSchema.getTypes.asScala.exists(_.getType == NULL)) {
val remainingUnionTypes = avroSchema.getTypes.asScala.filterNot(_.getType == NULL)
if (remainingUnionTypes.size == 1) {
createConverter(remainingUnionTypes.head, sqlType, path)
} else {
createConverter(Schema.createUnion(remainingUnionTypes.asJava), sqlType, path)
}
} else avroSchema.getTypes.asScala.map(_.getType) match {
case Seq(t1) => createConverter(avroSchema.getTypes.get(0), sqlType, path)
case Seq(a, b) if Set(a, b) == Set(INT, LONG) && sqlType == LongType =>
(item: AnyRef) => {
item match {
case null => null
case l: java.lang.Long => l
case i: java.lang.Integer => new java.lang.Long(i.longValue())
}
}
case Seq(a, b) if Set(a, b) == Set(FLOAT, DOUBLE) && sqlType == DoubleType =>
(item: AnyRef) => {
item match {
case null => null
case d: java.lang.Double => d
case f: java.lang.Float => new java.lang.Double(f.doubleValue())
}
}
case other =>
sqlType match {
case t: StructType if t.fields.length == avroSchema.getTypes.size =>
val fieldConverters = t.fields.zip(avroSchema.getTypes.asScala).map {
case (field, schema) =>
createConverter(schema, field.dataType, path :+ field.name)
}
(item: AnyRef) => if (item == null) {
null
} else {
val i = GenericData.get().resolveUnion(avroSchema, item)
val converted = new Array[Any](fieldConverters.length)
converted(i) = fieldConverters(i)(item)
new GenericRow(converted)
}
case _ => throw new IncompatibleSchemaException(
s"Cannot convert Avro schema to catalyst type because schema at path " +
s"${path.mkString(".")} is not compatible " +
s"(avroType = $other, sqlType = $sqlType). \n" +
s"Source Avro schema: $sourceAvroSchema.\n" +
s"Target Catalyst type: $targetSqlType")
}
}
case (left, right) =>
throw new IncompatibleSchemaException(
s"Cannot convert Avro schema to catalyst type because schema at path " +
s"${path.mkString(".")} is not compatible (avroType = $left, sqlType = $right). \n" +
s"Source Avro schema: $sourceAvroSchema.\n" +
s"Target Catalyst type: $targetSqlType")
}
}
createConverter(sourceAvroSchema, targetSqlType, List.empty[String])
}
def createConverterToAvro(dataType: DataType,
structName: String,
recordNamespace: String): Any => Any = {
dataType match {
case BinaryType => (item: Any) =>
item match {
case null => null
case bytes: Array[Byte] => ByteBuffer.wrap(bytes)
}
case IntegerType | LongType |
FloatType | DoubleType | StringType | BooleanType => identity
case ByteType => (item: Any) =>
if (item == null) null else item.asInstanceOf[Byte].intValue
case ShortType => (item: Any) =>
if (item == null) null else item.asInstanceOf[Short].intValue
case _: DecimalType => (item: Any) => if (item == null) null else item.toString
case TimestampType => (item: Any) =>
if (item == null) null else item.asInstanceOf[Timestamp].getTime
case DateType => (item: Any) =>
if (item == null) null else item.asInstanceOf[Date].getTime
case ArrayType(elementType, _) =>
val elementConverter = createConverterToAvro(
elementType,
structName,
getNewRecordNamespace(elementType, recordNamespace, structName))
(item: Any) => {
if (item == null) {
null
} else {
val sourceArray = item.asInstanceOf[Seq[Any]]
val sourceArraySize = sourceArray.size
val targetList = new util.ArrayList[Any](sourceArraySize)
var idx = 0
while (idx < sourceArraySize) {
targetList.add(elementConverter(sourceArray(idx)))
idx += 1
}
targetList
}
}
case MapType(StringType, valueType, _) =>
val valueConverter = createConverterToAvro(
valueType,
structName,
getNewRecordNamespace(valueType, recordNamespace, structName))
(item: Any) => {
if (item == null) {
null
} else {
val javaMap = new util.HashMap[String, Any]()
item.asInstanceOf[Map[String, Any]].foreach { case (key, value) =>
javaMap.put(key, valueConverter(value))
}
javaMap
}
}
case structType: StructType =>
val builder = SchemaBuilder.record(structName).namespace(recordNamespace)
val schema: Schema = SchemaConverters.convertStructToAvro(
structType, builder, recordNamespace)
val fieldConverters = structType.fields.map(field =>
createConverterToAvro(
field.dataType,
field.name,
getNewRecordNamespace(field.dataType, recordNamespace, field.name)))
(item: Any) => {
if (item == null) {
null
} else {
val record = new Record(schema)
val convertersIterator = fieldConverters.iterator
val fieldNamesIterator = dataType.asInstanceOf[StructType].fieldNames.iterator
val rowIterator = item.asInstanceOf[Row].toSeq.iterator
while (convertersIterator.hasNext) {
val converter = convertersIterator.next()
record.put(fieldNamesIterator.next(), converter(rowIterator.next()))
}
record
}
}
}
}
def convertStructTypeToAvroSchema(structType: StructType,
structName: String,
recordNamespace: String): Schema = {
val builder = SchemaBuilder.record(structName).namespace(recordNamespace)
SchemaConverters.convertStructToAvro(structType, builder, recordNamespace)
}
def convertAvroSchemaToStructType(avroSchema: Schema): StructType = {
SchemaConverters.toSqlType(avroSchema).dataType.asInstanceOf[StructType];
}
}
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