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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.
*/
package org.apache.spark.sql.sources
import java.text.NumberFormat
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileStatus, Path}
import org.apache.hadoop.io.{NullWritable, Text}
import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, TextOutputFormat}
import org.apache.spark.sql.{sources, Row, SQLContext}
import org.apache.spark.sql.catalyst.{expressions, InternalRow}
import org.apache.spark.sql.catalyst.expressions.{Cast, Expression, GenericInternalRow, InterpretedPredicate, InterpretedProjection, JoinedRow, Literal}
import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
import org.apache.spark.sql.execution.datasources._
import org.apache.spark.sql.types.{DataType, StructType}
import org.apache.spark.util.SerializableConfiguration
class SimpleTextSource extends FileFormat with DataSourceRegister {
override def shortName(): String = "test"
override def inferSchema(
sqlContext: SQLContext,
options: Map[String, String],
files: Seq[FileStatus]): Option[StructType] = {
Some(DataType.fromJson(options("dataSchema")).asInstanceOf[StructType])
}
override def prepareWrite(
sqlContext: SQLContext,
job: Job,
options: Map[String, String],
dataSchema: StructType): OutputWriterFactory = new OutputWriterFactory {
override def newInstance(
path: String,
bucketId: Option[Int],
dataSchema: StructType,
context: TaskAttemptContext): OutputWriter = {
new SimpleTextOutputWriter(path, context)
}
}
override def buildReader(
sqlContext: SQLContext,
dataSchema: StructType,
partitionSchema: StructType,
requiredSchema: StructType,
filters: Seq[Filter],
options: Map[String, String]): (PartitionedFile) => Iterator[InternalRow] = {
SimpleTextRelation.requiredColumns = requiredSchema.fieldNames
SimpleTextRelation.pushedFilters = filters.toSet
val fieldTypes = dataSchema.map(_.dataType)
val inputAttributes = dataSchema.toAttributes
val outputAttributes = requiredSchema.flatMap { field =>
inputAttributes.find(_.name == field.name)
}
val conf = new Configuration(sqlContext.sessionState.hadoopConf)
val broadcastedConf =
sqlContext.sparkContext.broadcast(new SerializableConfiguration(conf))
(file: PartitionedFile) => {
val predicate = {
val filterCondition: Expression = filters.collect {
// According to `unhandledFilters`, `SimpleTextRelation` only handles `GreaterThan` filter
case sources.GreaterThan(column, value) =>
val dataType = dataSchema(column).dataType
val literal = Literal.create(value, dataType)
val attribute = inputAttributes.find(_.name == column).get
expressions.GreaterThan(attribute, literal)
}.reduceOption(expressions.And).getOrElse(Literal(true))
InterpretedPredicate.create(filterCondition, inputAttributes)
}
// Uses a simple projection to simulate column pruning
val projection = new InterpretedProjection(outputAttributes, inputAttributes)
val unsafeRowIterator =
new HadoopFileLinesReader(file, broadcastedConf.value.value).map { line =>
val record = line.toString
new GenericInternalRow(record.split(",", -1).zip(fieldTypes).map {
case (v, dataType) =>
val value = if (v == "") null else v
// `Cast`ed values are always of internal types (e.g. UTF8String instead of String)
Cast(Literal(value), dataType).eval()
})
}.filter(predicate).map(projection)
// Appends partition values
val fullOutput = requiredSchema.toAttributes ++ partitionSchema.toAttributes
val joinedRow = new JoinedRow()
val appendPartitionColumns = GenerateUnsafeProjection.generate(fullOutput, fullOutput)
unsafeRowIterator.map { dataRow =>
appendPartitionColumns(joinedRow(dataRow, file.partitionValues))
}
}
}
}
class SimpleTextOutputWriter(path: String, context: TaskAttemptContext) extends OutputWriter {
private val recordWriter: RecordWriter[NullWritable, Text] =
new AppendingTextOutputFormat(new Path(path)).getRecordWriter(context)
override def write(row: Row): Unit = {
val serialized = row.toSeq.map { v =>
if (v == null) "" else v.toString
}.mkString(",")
recordWriter.write(null, new Text(serialized))
}
override def close(): Unit = {
recordWriter.close(context)
}
}
class AppendingTextOutputFormat(outputFile: Path) extends TextOutputFormat[NullWritable, Text] {
val numberFormat = NumberFormat.getInstance()
numberFormat.setMinimumIntegerDigits(5)
numberFormat.setGroupingUsed(false)
override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
val configuration = context.getConfiguration
val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
val taskAttemptId = context.getTaskAttemptID
val split = taskAttemptId.getTaskID.getId
val name = FileOutputFormat.getOutputName(context)
new Path(outputFile, s"$name-${numberFormat.format(split)}-$uniqueWriteJobId")
}
}
object SimpleTextRelation {
// Used to test column pruning
var requiredColumns: Seq[String] = Nil
// Used to test filter push-down
var pushedFilters: Set[Filter] = Set.empty
// Used to test failed committer
var failCommitter = false
// Used to test failed writer
var failWriter = false
// Used to test failure callback
var callbackCalled = false
}
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