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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.execution.streaming
import java.util.concurrent.atomic.AtomicInteger
import scala.collection.mutable.ArrayBuffer
import scala.util.control.NonFatal
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{DataFrame, Dataset, Encoder, Row, SQLContext}
import org.apache.spark.sql.catalyst.encoders.encoderFor
import org.apache.spark.sql.types.StructType
object MemoryStream {
protected val currentBlockId = new AtomicInteger(0)
protected val memoryStreamId = new AtomicInteger(0)
def apply[A : Encoder](implicit sqlContext: SQLContext): MemoryStream[A] =
new MemoryStream[A](memoryStreamId.getAndIncrement(), sqlContext)
}
/**
* A [[Source]] that produces value stored in memory as they are added by the user. This [[Source]]
* is primarily intended for use in unit tests as it can only replay data when the object is still
* available.
*/
case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
extends Source with Logging {
protected val encoder = encoderFor[A]
protected val logicalPlan = StreamingExecutionRelation(this)
protected val output = logicalPlan.output
protected val batches = new ArrayBuffer[Dataset[A]]
protected var currentOffset: LongOffset = new LongOffset(-1)
def schema: StructType = encoder.schema
def toDS()(implicit sqlContext: SQLContext): Dataset[A] = {
Dataset(sqlContext, logicalPlan)
}
def toDF()(implicit sqlContext: SQLContext): DataFrame = {
Dataset.ofRows(sqlContext, logicalPlan)
}
def addData(data: A*): Offset = {
addData(data.toTraversable)
}
def addData(data: TraversableOnce[A]): Offset = {
import sqlContext.implicits._
this.synchronized {
currentOffset = currentOffset + 1
val ds = data.toVector.toDS()
logDebug(s"Adding ds: $ds")
batches.append(ds)
currentOffset
}
}
override def toString: String = s"MemoryStream[${output.mkString(",")}]"
override def getOffset: Option[Offset] = if (batches.isEmpty) {
None
} else {
Some(currentOffset)
}
/**
* Returns the next batch of data that is available after `start`, if any is available.
*/
override def getBatch(start: Option[Offset], end: Offset): DataFrame = {
val startOrdinal =
start.map(_.asInstanceOf[LongOffset]).getOrElse(LongOffset(-1)).offset.toInt + 1
val endOrdinal = end.asInstanceOf[LongOffset].offset.toInt + 1
val newBlocks = batches.slice(startOrdinal, endOrdinal)
logDebug(
s"MemoryBatch [$startOrdinal, $endOrdinal]: ${newBlocks.flatMap(_.collect()).mkString(", ")}")
newBlocks
.map(_.toDF())
.reduceOption(_ union _)
.getOrElse {
sys.error("No data selected!")
}
}
}
/**
* A sink that stores the results in memory. This [[Sink]] is primarily intended for use in unit
* tests and does not provide durability.
*/
class MemorySink(val schema: StructType) extends Sink with Logging {
/** An order list of batches that have been written to this [[Sink]]. */
private val batches = new ArrayBuffer[Array[Row]]()
/** Returns all rows that are stored in this [[Sink]]. */
def allData: Seq[Row] = synchronized {
batches.flatten
}
def lastBatch: Seq[Row] = batches.last
def toDebugString: String = synchronized {
batches.zipWithIndex.map { case (b, i) =>
val dataStr = try b.mkString(" ") catch {
case NonFatal(e) => "[Error converting to string]"
}
s"$i: $dataStr"
}.mkString("\n")
}
override def addBatch(batchId: Long, data: DataFrame): Unit = {
if (batchId == batches.size) {
logDebug(s"Committing batch $batchId")
batches.append(data.collect())
} else {
logDebug(s"Skipping already committed batch: $batchId")
}
}
}
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