1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
|
/*
* 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 org.apache.spark.util.NonSerializable
import java.io.NotSerializableException
// 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.head * v.head)
}.collect()
FailureSuiteState.synchronized {
assert(FailureSuiteState.tasksRun === 4)
}
assert(results.toSet === Set((1, 1), (2, 4), (3, 9)))
FailureSuiteState.clear()
}
// Run a map-reduce job in which the map stage always fails.
test("failure in a map stage") {
sc = new SparkContext("local", "test")
val data = sc.makeRDD(1 to 3).map(x => { throw new Exception; (x, x) }).groupByKey(3)
intercept[SparkException] {
data.collect()
}
// Make sure that running new jobs with the same map stage also fails
intercept[SparkException] {
data.collect()
}
}
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("serializable") ||
thrown.getCause.getClass === classOf[NotSerializableException],
"Exception does not contain \"serializable\": " + thrown.getMessage)
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") ||
thrown.getCause.getClass === classOf[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") ||
thrown1.getCause.getClass === classOf[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") ||
thrown2.getCause.getClass === classOf[NotSerializableException])
FailureSuiteState.clear()
}
// TODO: Need to add tests with shuffle fetch failures.
}
|