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/*
* Copyright 2014 Databricks
*
* 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.databricks.spark.avro
import java.io._
import java.net.URI
import java.util.zip.Deflater
import scala.util.control.NonFatal
import com.databricks.spark.avro.DefaultSource.{AvroSchema, IgnoreFilesWithoutExtensionProperty, SerializableConfiguration}
import com.esotericsoftware.kryo.{Kryo, KryoSerializable}
import com.esotericsoftware.kryo.io.{Input, Output}
import org.apache.avro.{Schema, SchemaBuilder}
import org.apache.avro.file.{DataFileConstants, DataFileReader}
import org.apache.avro.generic.{GenericDatumReader, GenericRecord}
import org.apache.avro.mapred.{AvroOutputFormat, FsInput}
import org.apache.avro.mapreduce.AvroJob
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileStatus, Path}
import org.apache.hadoop.mapreduce.Job
import org.slf4j.LoggerFactory
import org.apache.spark.TaskContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.catalyst.expressions.GenericRow
import org.apache.spark.sql.execution.datasources.{FileFormat, OutputWriterFactory, PartitionedFile}
import org.apache.spark.sql.sources.{DataSourceRegister, Filter}
import org.apache.spark.sql.types.StructType
private[avro] class DefaultSource extends FileFormat with DataSourceRegister {
private val log = LoggerFactory.getLogger(getClass)
override def equals(other: Any): Boolean = other match {
case _: DefaultSource => true
case _ => false
}
override def inferSchema(
spark: SparkSession,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType] = {
val conf = spark.sparkContext.hadoopConfiguration
// Schema evolution is not supported yet. Here we only pick a single random sample file to
// figure out the schema of the whole dataset.
val sampleFile = if (conf.getBoolean(IgnoreFilesWithoutExtensionProperty, true)) {
files.find(_.getPath.getName.endsWith(".avro")).getOrElse {
throw new FileNotFoundException(
"No Avro files found. Hadoop option \"avro.mapred.ignore.inputs.without.extension\" is " +
"set to true. Do all input files have \".avro\" extension?"
)
}
} else {
files.headOption.getOrElse {
throw new FileNotFoundException("No Avro files found.")
}
}
// User can specify an optional avro json schema.
val avroSchema = options.get(AvroSchema).map(new Schema.Parser().parse).getOrElse {
val in = new FsInput(sampleFile.getPath, conf)
try {
val reader = DataFileReader.openReader(in, new GenericDatumReader[GenericRecord]())
try {
reader.getSchema
} finally {
reader.close()
}
} finally {
in.close()
}
}
SchemaConverters.toSqlType(avroSchema).dataType match {
case t: StructType => Some(t)
case _ => throw new RuntimeException(
s"""Avro schema cannot be converted to a Spark SQL StructType:
|
|${avroSchema.toString(true)}
|""".stripMargin)
}
}
override def shortName(): String = "avro"
override def isSplitable(
sparkSession: SparkSession,
options: Map[String, String],
path: Path): Boolean = true
override def prepareWrite(
spark: SparkSession,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory = {
val recordName = options.getOrElse("recordName", "topLevelRecord")
val recordNamespace = options.getOrElse("recordNamespace", "")
val build = SchemaBuilder.record(recordName).namespace(recordNamespace)
val outputAvroSchema = SchemaConverters.convertStructToAvro(dataSchema, build, recordNamespace)
AvroJob.setOutputKeySchema(job, outputAvroSchema)
val AVRO_COMPRESSION_CODEC = "spark.sql.avro.compression.codec"
val AVRO_DEFLATE_LEVEL = "spark.sql.avro.deflate.level"
val COMPRESS_KEY = "mapred.output.compress"
spark.conf.get(AVRO_COMPRESSION_CODEC, "snappy") match {
case "uncompressed" =>
log.info("writing uncompressed Avro records")
job.getConfiguration.setBoolean(COMPRESS_KEY, false)
case "snappy" =>
log.info("compressing Avro output using Snappy")
job.getConfiguration.setBoolean(COMPRESS_KEY, true)
job.getConfiguration.set(AvroJob.CONF_OUTPUT_CODEC, DataFileConstants.SNAPPY_CODEC)
case "deflate" =>
val deflateLevel = spark.conf.get(
AVRO_DEFLATE_LEVEL, Deflater.DEFAULT_COMPRESSION.toString).toInt
log.info(s"compressing Avro output using deflate (level=$deflateLevel)")
job.getConfiguration.setBoolean(COMPRESS_KEY, true)
job.getConfiguration.set(AvroJob.CONF_OUTPUT_CODEC, DataFileConstants.DEFLATE_CODEC)
job.getConfiguration.setInt(AvroOutputFormat.DEFLATE_LEVEL_KEY, deflateLevel)
case unknown: String =>
log.error(s"unsupported compression codec $unknown")
}
new AvroOutputWriterFactory(dataSchema, recordName, recordNamespace)
}
override def buildReader(
spark: SparkSession,
dataSchema: StructType,
partitionSchema: StructType,
requiredSchema: StructType,
filters: Seq[Filter],
options: Map[String, String],
hadoopConf: Configuration): (PartitionedFile) => Iterator[InternalRow] = {
val broadcastedConf =
spark.sparkContext.broadcast(new SerializableConfiguration(hadoopConf))
(file: PartitionedFile) => {
val log = LoggerFactory.getLogger(classOf[DefaultSource])
val conf = broadcastedConf.value.value
val userProvidedSchema = options.get(AvroSchema).map(new Schema.Parser().parse)
// TODO Removes this check once `FileFormat` gets a general file filtering interface method.
// Doing input file filtering is improper because we may generate empty tasks that process no
// input files but stress the scheduler. We should probably add a more general input file
// filtering mechanism for `FileFormat` data sources. See SPARK-16317.
if (
conf.getBoolean(IgnoreFilesWithoutExtensionProperty, true) &&
!file.filePath.endsWith(".avro")
) {
Iterator.empty
} else {
val reader = {
val in = new FsInput(new Path(new URI(file.filePath)), conf)
try {
val datumReader = userProvidedSchema match {
case Some(userSchema) => new GenericDatumReader[GenericRecord](userSchema)
case _ => new GenericDatumReader[GenericRecord]()
}
DataFileReader.openReader(in, datumReader)
} catch {
case NonFatal(e) =>
log.error("Exception while opening DataFileReader", e)
in.close()
throw e
}
}
// Ensure that the reader is closed even if the task fails or doesn't consume the entire
// iterator of records.
Option(TaskContext.get()).foreach { taskContext =>
taskContext.addTaskCompletionListener { _ =>
reader.close()
}
}
reader.sync(file.start)
val stop = file.start + file.length
val rowConverter = SchemaConverters.createConverterToSQL(
userProvidedSchema.getOrElse(reader.getSchema), requiredSchema)
new Iterator[InternalRow] {
// Used to convert `Row`s containing data columns into `InternalRow`s.
private val encoderForDataColumns = RowEncoder(requiredSchema)
private[this] var completed = false
override def hasNext: Boolean = {
if (completed) {
false
} else {
val r = reader.hasNext && !reader.pastSync(stop)
if (!r) {
reader.close()
completed = true
}
r
}
}
override def next(): InternalRow = {
if (reader.pastSync(stop)) {
throw new NoSuchElementException("next on empty iterator")
}
val record = reader.next()
val safeDataRow = rowConverter(record).asInstanceOf[GenericRow]
// The safeDataRow is reused, we must do a copy
encoderForDataColumns.toRow(safeDataRow)
}
}
}
}
}
}
private[avro] object DefaultSource {
val IgnoreFilesWithoutExtensionProperty = "avro.mapred.ignore.inputs.without.extension"
val AvroSchema = "avroSchema"
class SerializableConfiguration(@transient var value: Configuration)
extends Serializable with KryoSerializable {
@transient private[avro] lazy val log = LoggerFactory.getLogger(getClass)
private def writeObject(out: ObjectOutputStream): Unit = tryOrIOException {
out.defaultWriteObject()
value.write(out)
}
private def readObject(in: ObjectInputStream): Unit = tryOrIOException {
value = new Configuration(false)
value.readFields(in)
}
private def tryOrIOException[T](block: => T): T = {
try {
block
} catch {
case e: IOException =>
log.error("Exception encountered", e)
throw e
case NonFatal(e) =>
log.error("Exception encountered", e)
throw new IOException(e)
}
}
def write(kryo: Kryo, out: Output): Unit = {
val dos = new DataOutputStream(out)
value.write(dos)
dos.flush()
}
def read(kryo: Kryo, in: Input): Unit = {
value = new Configuration(false)
value.readFields(new DataInputStream(in))
}
}
}
