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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.streaming
import scala.language.implicitConversions
import org.scalatest.BeforeAndAfter
import org.apache.spark.sql._
import org.apache.spark.sql.execution.streaming._
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
import org.apache.spark.util.Utils
class MemorySinkSuite extends StreamTest with SharedSQLContext with BeforeAndAfter {
import testImplicits._
after {
sqlContext.streams.active.foreach(_.stop())
}
test("directly add data in Append output mode") {
implicit val schema = new StructType().add(new StructField("value", IntegerType))
val sink = new MemorySink(schema, InternalOutputModes.Append)
// Before adding data, check output
assert(sink.latestBatchId === None)
checkAnswer(sink.latestBatchData, Seq.empty)
checkAnswer(sink.allData, Seq.empty)
// Add batch 0 and check outputs
sink.addBatch(0, 1 to 3)
assert(sink.latestBatchId === Some(0))
checkAnswer(sink.latestBatchData, 1 to 3)
checkAnswer(sink.allData, 1 to 3)
// Add batch 1 and check outputs
sink.addBatch(1, 4 to 6)
assert(sink.latestBatchId === Some(1))
checkAnswer(sink.latestBatchData, 4 to 6)
checkAnswer(sink.allData, 1 to 6) // new data should get appended to old data
// Re-add batch 1 with different data, should not be added and outputs should not be changed
sink.addBatch(1, 7 to 9)
assert(sink.latestBatchId === Some(1))
checkAnswer(sink.latestBatchData, 4 to 6)
checkAnswer(sink.allData, 1 to 6)
// Add batch 2 and check outputs
sink.addBatch(2, 7 to 9)
assert(sink.latestBatchId === Some(2))
checkAnswer(sink.latestBatchData, 7 to 9)
checkAnswer(sink.allData, 1 to 9)
}
test("directly add data in Update output mode") {
implicit val schema = new StructType().add(new StructField("value", IntegerType))
val sink = new MemorySink(schema, InternalOutputModes.Update)
// Before adding data, check output
assert(sink.latestBatchId === None)
checkAnswer(sink.latestBatchData, Seq.empty)
checkAnswer(sink.allData, Seq.empty)
// Add batch 0 and check outputs
sink.addBatch(0, 1 to 3)
assert(sink.latestBatchId === Some(0))
checkAnswer(sink.latestBatchData, 1 to 3)
checkAnswer(sink.allData, 1 to 3)
// Add batch 1 and check outputs
sink.addBatch(1, 4 to 6)
assert(sink.latestBatchId === Some(1))
checkAnswer(sink.latestBatchData, 4 to 6)
checkAnswer(sink.allData, 1 to 6) // new data should get appended to old data
// Re-add batch 1 with different data, should not be added and outputs should not be changed
sink.addBatch(1, 7 to 9)
assert(sink.latestBatchId === Some(1))
checkAnswer(sink.latestBatchData, 4 to 6)
checkAnswer(sink.allData, 1 to 6)
// Add batch 2 and check outputs
sink.addBatch(2, 7 to 9)
assert(sink.latestBatchId === Some(2))
checkAnswer(sink.latestBatchData, 7 to 9)
checkAnswer(sink.allData, 1 to 9)
}
test("directly add data in Complete output mode") {
implicit val schema = new StructType().add(new StructField("value", IntegerType))
val sink = new MemorySink(schema, InternalOutputModes.Complete)
// Before adding data, check output
assert(sink.latestBatchId === None)
checkAnswer(sink.latestBatchData, Seq.empty)
checkAnswer(sink.allData, Seq.empty)
// Add batch 0 and check outputs
sink.addBatch(0, 1 to 3)
assert(sink.latestBatchId === Some(0))
checkAnswer(sink.latestBatchData, 1 to 3)
checkAnswer(sink.allData, 1 to 3)
// Add batch 1 and check outputs
sink.addBatch(1, 4 to 6)
assert(sink.latestBatchId === Some(1))
checkAnswer(sink.latestBatchData, 4 to 6)
checkAnswer(sink.allData, 4 to 6) // new data should replace old data
// Re-add batch 1 with different data, should not be added and outputs should not be changed
sink.addBatch(1, 7 to 9)
assert(sink.latestBatchId === Some(1))
checkAnswer(sink.latestBatchData, 4 to 6)
checkAnswer(sink.allData, 4 to 6)
// Add batch 2 and check outputs
sink.addBatch(2, 7 to 9)
assert(sink.latestBatchId === Some(2))
checkAnswer(sink.latestBatchData, 7 to 9)
checkAnswer(sink.allData, 7 to 9)
}
test("registering as a table in Append output mode") {
val input = MemoryStream[Int]
val query = input.toDF().write
.format("memory")
.outputMode("append")
.queryName("memStream")
.startStream()
input.addData(1, 2, 3)
query.processAllAvailable()
checkDataset(
spark.table("memStream").as[Int],
1, 2, 3)
input.addData(4, 5, 6)
query.processAllAvailable()
checkDataset(
spark.table("memStream").as[Int],
1, 2, 3, 4, 5, 6)
query.stop()
}
test("registering as a table in Complete output mode") {
val input = MemoryStream[Int]
val query = input.toDF()
.groupBy("value")
.count()
.write
.format("memory")
.outputMode("complete")
.queryName("memStream")
.startStream()
input.addData(1, 2, 3)
query.processAllAvailable()
checkDataset(
spark.table("memStream").as[(Int, Long)],
(1, 1L), (2, 1L), (3, 1L))
input.addData(4, 5, 6)
query.processAllAvailable()
checkDataset(
spark.table("memStream").as[(Int, Long)],
(1, 1L), (2, 1L), (3, 1L), (4, 1L), (5, 1L), (6, 1L))
query.stop()
}
ignore("stress test") {
// Ignore the stress test as it takes several minutes to run
(0 until 1000).foreach { _ =>
val input = MemoryStream[Int]
val query = input.toDF().write
.format("memory")
.queryName("memStream")
.startStream()
input.addData(1, 2, 3)
query.processAllAvailable()
checkDataset(
spark.table("memStream").as[Int],
1, 2, 3)
input.addData(4, 5, 6)
query.processAllAvailable()
checkDataset(
spark.table("memStream").as[Int],
1, 2, 3, 4, 5, 6)
query.stop()
}
}
test("error when no name is specified") {
val error = intercept[AnalysisException] {
val input = MemoryStream[Int]
val query = input.toDF().write
.format("memory")
.startStream()
}
assert(error.message contains "queryName must be specified")
}
test("error if attempting to resume specific checkpoint") {
val location = Utils.createTempDir(namePrefix = "steaming.checkpoint").getCanonicalPath
val input = MemoryStream[Int]
val query = input.toDF().write
.format("memory")
.queryName("memStream")
.option("checkpointLocation", location)
.startStream()
input.addData(1, 2, 3)
query.processAllAvailable()
query.stop()
intercept[AnalysisException] {
input.toDF().write
.format("memory")
.queryName("memStream")
.option("checkpointLocation", location)
.startStream()
}
}
private def checkAnswer(rows: Seq[Row], expected: Seq[Int])(implicit schema: StructType): Unit = {
checkAnswer(
sqlContext.createDataFrame(sparkContext.makeRDD(rows), schema),
intsToDF(expected)(schema))
}
private implicit def intsToDF(seq: Seq[Int])(implicit schema: StructType): DataFrame = {
require(schema.fields.size === 1)
sqlContext.createDataset(seq).toDF(schema.fieldNames.head)
}
}
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