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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.
*/
import com.jayway.jsonpath.JsonPath
import groovy.json.JsonSlurper
import org.apache.knox.gateway.shell.KnoxSession
import org.apache.knox.gateway.shell.hdfs.Hdfs
import org.apache.knox.gateway.shell.job.Job
import static java.util.concurrent.TimeUnit.SECONDS
import org.apache.knox.gateway.shell.Credentials
gateway = "https://localhost:8443/gateway/sandbox"
// You will need to copy hadoop-mapreduce-samples.jar from your cluster
// and place it under samples/ directory.
// For example you might find the jar under: /usr/iop/current/hadoop-mapreduce-client
jarFile = "samples/hadoop-mapreduce-examples.jar"
credentials = new Credentials()
credentials.add("ClearInput", "Enter username: ", "user")
.add("HiddenInput", "Enter pas" + "sword: ", "pass")
credentials.collect()
username = credentials.get("user").string()
pass = credentials.get("pass").string()
jobDir = "/user/" + username + "/test"
session = KnoxSession.login( gateway, username, pass )
println "Delete " + jobDir + ": " + Hdfs.rm( session ).file( jobDir ).recursive().now().statusCode
println "Create " + jobDir + ": " + Hdfs.mkdir( session ).dir( jobDir ).now().statusCode
putJar = Hdfs.put( session ).file( jarFile ).to( jobDir + "/lib/hadoop-mapreduce-examples.jar" ).later() {
println "Put " + jobDir + "/lib/hadoop-mapreduce-examples.jar: " + it.statusCode }
session.waitFor( putJar )
// Run teragen with 5 mappers. It will generate 500 records of 100 bytes each.
jobId = Job.submitJava(session) \
.jar( jobDir + "/lib/hadoop-mapreduce-examples.jar" ) \
.app( "teragen" ) \
.arg( "-D").arg("mapred.map.tasks=5") \
.arg( "500" ) \
.input( jobDir + "/input_terasort" ) \
.now().jobId
println "Submitted job: " + jobId
println "Polling up to 60s for job completion..."
done = false
count = 0
while( !done && count++ < 90 ) {
sleep( 1000 )
json = Job.queryStatus(session).jobId(jobId).now().string
done = JsonPath.read( json, "\$.status.jobComplete" )
print "."; System.out.flush();
}
println ""
println "Job status: " + done
text = Hdfs.ls( session ).dir( jobDir + "/input_terasort" ).now().string
json = (new JsonSlurper()).parseText( text )
println json.FileStatuses.FileStatus.pathSuffix
println "Session closed: " + session.shutdown( 10, SECONDS )