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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.storage
import org.scalatest.FunSuite
import org.apache.spark.{SharedSparkContext, SparkConf, LocalSparkContext, SparkContext}
class FlatmapIteratorSuite extends FunSuite with LocalSparkContext {
/* Tests the ability of Spark to deal with user provided iterators from flatMap
* calls, that may generate more data then available memory. In any
* memory based persistance Spark will unroll the iterator into an ArrayBuffer
* for caching, however in the case that the use defines DISK_ONLY persistance,
* the iterator will be fed directly to the serializer and written to disk.
*
* This also tests the ObjectOutputStream reset rate. When serializing using the
* Java serialization system, the serializer caches objects to prevent writing redundant
* data, however that stops GC of those objects. By calling 'reset' you flush that
* info from the serializer, and allow old objects to be GC'd
*/
test("Flatmap Iterator to Disk") {
val sconf = new SparkConf().setMaster("local-cluster[1,1,512]")
.setAppName("iterator_to_disk_test")
sc = new SparkContext(sconf)
val expand_size = 100
val data = sc.parallelize((1 to 5).toSeq).
flatMap( x => Stream.range(0, expand_size))
var persisted = data.persist(StorageLevel.DISK_ONLY)
println(persisted.count())
assert(persisted.count()===500)
assert(persisted.filter(_==1).count()===5)
}
test("Flatmap Iterator to Memory") {
val sconf = new SparkConf().setMaster("local-cluster[1,1,512]")
.setAppName("iterator_to_disk_test")
sc = new SparkContext(sconf)
val expand_size = 100
val data = sc.parallelize((1 to 5).toSeq).
flatMap(x => Stream.range(0, expand_size))
var persisted = data.persist(StorageLevel.MEMORY_ONLY)
println(persisted.count())
assert(persisted.count()===500)
assert(persisted.filter(_==1).count()===5)
}
test("Serializer Reset") {
val sconf = new SparkConf().setMaster("local-cluster[1,1,512]")
.setAppName("serializer_reset_test")
.set("spark.serializer.objectStreamReset", "10")
sc = new SparkContext(sconf)
val expand_size = 500
val data = sc.parallelize(Seq(1,2)).
flatMap(x => Stream.range(1, expand_size).
map(y => "%d: string test %d".format(y,x)))
var persisted = data.persist(StorageLevel.MEMORY_ONLY_SER)
assert(persisted.filter(_.startsWith("1:")).count()===2)
}
}
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