Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/11006#discussion_r51478334
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala
---
@@ -0,0 +1,138 @@
+/*
+ * 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 org.apache.spark.{Logging, SparkEnv}
+import org.apache.spark.sql.{DataFrame, Dataset, Encoder, Row, SQLContext}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.{encoderFor, RowEncoder}
+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 getCurrentOffset: Offset = currentOffset
+
+ def toDS()(implicit sqlContext: SQLContext): Dataset[A] = {
+ new Dataset(sqlContext, logicalPlan)
+ }
+
+ def toDF()(implicit sqlContext: SQLContext): DataFrame = {
+ new DataFrame(sqlContext, logicalPlan)
+ }
+
+ 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 durablility.
+ */
+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 {
+ 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.remove(batches.size - num, num)
--- End diff --
Can be simplified as `batches.dropRight(num)`.
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