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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.ui
import scala.util.Random
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.scheduler.SchedulingMode
/**
* Continuously generates jobs that expose various features of the WebUI (internal testing tool).
*
* Usage: ./run spark.ui.UIWorkloadGenerator [master]
*/
private[spark] object UIWorkloadGenerator {
val NUM_PARTITIONS = 100
val INTER_JOB_WAIT_MS = 5000
def main(args: Array[String]) {
if (args.length < 2) {
println("usage: ./spark-class org.apache.spark.ui.UIWorkloadGenerator [master] [FIFO|FAIR]")
System.exit(1)
}
val master = args(0)
val schedulingMode = SchedulingMode.withName(args(1))
val appName = "Spark UI Tester"
if (schedulingMode == SchedulingMode.FAIR) {
System.setProperty("spark.scheduler.mode", "FAIR")
}
val sc = new SparkContext(master, appName)
def setProperties(s: String) = {
if(schedulingMode == SchedulingMode.FAIR) {
sc.setLocalProperty("spark.scheduler.pool", s)
}
sc.setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, s)
}
val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS)
def nextFloat() = (new Random()).nextFloat()
val jobs = Seq[(String, () => Long)](
("Count", baseData.count),
("Cache and Count", baseData.map(x => x).cache.count),
("Single Shuffle", baseData.map(x => (x % 10, x)).reduceByKey(_ + _).count),
("Entirely failed phase", baseData.map(x => throw new Exception).count),
("Partially failed phase", {
baseData.map{x =>
val probFailure = (4.0 / NUM_PARTITIONS)
if (nextFloat() < probFailure) {
throw new Exception("This is a task failure")
}
1
}.count
}),
("Partially failed phase (longer tasks)", {
baseData.map{x =>
val probFailure = (4.0 / NUM_PARTITIONS)
if (nextFloat() < probFailure) {
Thread.sleep(100)
throw new Exception("This is a task failure")
}
1
}.count
}),
("Job with delays", baseData.map(x => Thread.sleep(100)).count)
)
while (true) {
for ((desc, job) <- jobs) {
new Thread {
override def run() {
try {
setProperties(desc)
job()
println("Job funished: " + desc)
} catch {
case e: Exception =>
println("Job Failed: " + desc)
}
}
}.start
Thread.sleep(INTER_JOB_WAIT_MS)
}
}
}
}
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