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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
import org.scalatest.FunSuite
import SparkContext._
import org.apache.spark.util.NonSerializable
// Common state shared by FailureSuite-launched tasks. We use a global object
// for this because any local variables used in the task closures will rightfully
// be copied for each task, so there's no other way for them to share state.
object FailureSuiteState {
var tasksRun = 0
var tasksFailed = 0
def clear() {
synchronized {
tasksRun = 0
tasksFailed = 0
}
}
}
class FailureSuite extends FunSuite with LocalSparkContext {
// Run a 3-task map job in which task 1 deterministically fails once, and check
// whether the job completes successfully and we ran 4 tasks in total.
test("failure in a single-stage job") {
sc = new SparkContext("local[1,2]", "test")
val results = sc.makeRDD(1 to 3, 3).map { x =>
FailureSuiteState.synchronized {
FailureSuiteState.tasksRun += 1
if (x == 1 && FailureSuiteState.tasksFailed == 0) {
FailureSuiteState.tasksFailed += 1
throw new Exception("Intentional task failure")
}
}
x * x
}.collect()
FailureSuiteState.synchronized {
assert(FailureSuiteState.tasksRun === 4)
}
assert(results.toList === List(1,4,9))
FailureSuiteState.clear()
}
// Run a map-reduce job in which a reduce task deterministically fails once.
test("failure in a two-stage job") {
sc = new SparkContext("local[1,2]", "test")
val results = sc.makeRDD(1 to 3).map(x => (x, x)).groupByKey(3).map {
case (k, v) =>
FailureSuiteState.synchronized {
FailureSuiteState.tasksRun += 1
if (k == 1 && FailureSuiteState.tasksFailed == 0) {
FailureSuiteState.tasksFailed += 1
throw new Exception("Intentional task failure")
}
}
(k, v(0) * v(0))
}.collect()
FailureSuiteState.synchronized {
assert(FailureSuiteState.tasksRun === 4)
}
assert(results.toSet === Set((1, 1), (2, 4), (3, 9)))
FailureSuiteState.clear()
}
test("failure because task results are not serializable") {
sc = new SparkContext("local[1,1]", "test")
val results = sc.makeRDD(1 to 3).map(x => new NonSerializable)
val thrown = intercept[SparkException] {
results.collect()
}
assert(thrown.getClass === classOf[SparkException])
assert(thrown.getMessage.contains("NotSerializableException"))
FailureSuiteState.clear()
}
test("failure because task closure is not serializable") {
sc = new SparkContext("local[1,1]", "test")
val a = new NonSerializable
// Non-serializable closure in the final result stage
val thrown = intercept[SparkException] {
sc.parallelize(1 to 10, 2).map(x => a).count()
}
assert(thrown.getClass === classOf[SparkException])
assert(thrown.getMessage.contains("NotSerializableException"))
// Non-serializable closure in an earlier stage
val thrown1 = intercept[SparkException] {
sc.parallelize(1 to 10, 2).map(x => (x, a)).partitionBy(new HashPartitioner(3)).count()
}
assert(thrown1.getClass === classOf[SparkException])
assert(thrown1.getMessage.contains("NotSerializableException"))
// Non-serializable closure in foreach function
val thrown2 = intercept[SparkException] {
sc.parallelize(1 to 10, 2).foreach(x => println(a))
}
assert(thrown2.getClass === classOf[SparkException])
assert(thrown2.getMessage.contains("NotSerializableException"))
FailureSuiteState.clear()
}
// TODO: Need to add tests with shuffle fetch failures.
}
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