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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.SparkEnv
import org.apache.spark.internal.Logging
import org.apache.spark.sql.{DataFrame, Dataset, Encoder, Row, SQLContext}
import org.apache.spark.sql.catalyst.encoders.{encoderFor, RowEncoder}
import org.apache.spark.sql.catalyst.InternalRow
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 = StreamingRelation(this)
protected val output = logicalPlan.output
protected val batches = new ArrayBuffer[Dataset[A]]
protected var currentOffset: LongOffset = new LongOffset(-1)
protected def blockManager = SparkEnv.get.blockManager
def schema: StructType = encoder.schema
def toDS()(implicit sqlContext: SQLContext): Dataset[A] = {
Dataset(sqlContext, logicalPlan)
}
def toDF()(implicit sqlContext: SQLContext): DataFrame = {
Dataset.newDataFrame(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: $ds")
batches.append(ds)
currentOffset
}
}
override def getNextBatch(start: Option[Offset]): Option[Batch] = synchronized {
val newBlocks =
batches.drop(
start.map(_.asInstanceOf[LongOffset]).getOrElse(LongOffset(-1)).offset.toInt + 1)
if (newBlocks.nonEmpty) {
logDebug(s"Running [$start, $currentOffset] on blocks ${newBlocks.mkString(", ")}")
val df = newBlocks
.map(_.toDF())
.reduceOption(_ unionAll _)
.getOrElse(sqlContext.emptyDataFrame)
Some(new Batch(currentOffset, df))
} else {
None
}
}
override def toString: String = s"MemoryStream[${output.mkString(",")}]"
}
/**
* 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(schema: StructType) extends Sink with Logging {
/** An order list of batches that have been written to this [[Sink]]. */
private var batches = new ArrayBuffer[Batch]()
/** Used to convert an [[InternalRow]] to an external [[Row]] for comparison in testing. */
private val externalRowConverter = RowEncoder(schema)
override def currentOffset: Option[Offset] = synchronized {
batches.lastOption.map(_.end)
}
override def addBatch(nextBatch: Batch): Unit = synchronized {
nextBatch.data.collect() // 'compute' the batch's data and record the batch
batches.append(nextBatch)
}
/** Returns all rows that are stored in this [[Sink]]. */
def allData: Seq[Row] = synchronized {
batches
.map(_.data)
.reduceOption(_ unionAll _)
.map(_.collect().toSeq)
.getOrElse(Seq.empty)
}
/**
* Atomically drops the most recent `num` batches and resets the [[StreamProgress]] to the
* corresponding point in the input. This function can be used when testing to simulate data
* that has been lost due to buffering.
*/
def dropBatches(num: Int): Unit = synchronized {
batches.dropRight(num)
}
def toDebugString: String = synchronized {
batches.map { b =>
val dataStr = try b.data.collect().mkString(" ") catch {
case NonFatal(e) => "[Error converting to string]"
}
s"${b.end}: $dataStr"
}.mkString("\n")
}
}
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