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這里已經部署好hadoop環境，以及spark
環境如下：
192.168.1.2 master
[hadoop@master ~]$ jps 2298 SecondaryNameNode 2131 NameNode 2593 JobHistoryServer 4363 Jps 3550 HistoryServer 2481 ResourceManager 3362 Master
192.168.1.3 slave1
[hadoop@slave1 ~]$ jps 2919 Jps 2464 Worker 1993 DataNode 2109 NodeManager
192.168.1.4 slave2
[hadoop@slave2 ~]$ jps 2762 Jps 2113 NodeManager 1998 DataNode 2452 Worker
這里以spark自帶求pi值的python程序為例
[hadoop@master ~]$ cd spark
[hadoop@master spark]$ find . -name "pi.py"
[hadoop@master spark]$ cat ./examples/src/main/python/pi.py
#
# 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 sys
from random import random
from operator import add
from pyspark import SparkContext
if __name__ == "__main__":
"""
Usage: pi [slices]
"""
sc = SparkContext(appName="PythonPi")
slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
n = 100000 * slices
def f(_):
x = random() * 2 - 1
y = random() * 2 - 1
return 1 if x ** 2 + y ** 2 < 1 else 0
count = sc.parallelize(xrange(1, n+1), slices).map(f).reduce(add)
print "Pi is roughly %f" % (4.0 * count / n)
[hadoop@master spark]$ cd ./examples/src/main/python/
# 修改pi.py文件，在末尾添加
sc.stop()
[hadoop@master python]$ spark-submit --master spark://master:7077 --executor-memory 200m --driver-memory 200m pi.py
# 如報下面錯誤，綁定hosts文件127.0.0.1為localhost
Traceback (most recent call last):
File "/home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py", line 29, in <module>
sc = SparkContext(appName="PythonPi")
File "/home/hadoop/spark/python/pyspark/context.py", line 138, in __init__
self._accumulatorServer = accumulators._start_update_server()
File "/home/hadoop/spark/python/pyspark/accumulators.py", line 224, in _start_update_server
server = SocketServer.TCPServer(("localhost", 0), _UpdateRequestHandler)
File "/usr/lib64/python2.6/SocketServer.py", line 402, in __init__
self.server_bind()
File "/usr/lib64/python2.6/SocketServer.py", line 413, in server_bind
self.socket.bind(self.server_address)
File "<string>", line 1, in bind
socket.gaierror: [Errno -3] Temporary failure in name resolution
# 正常執行如下
[hadoop@master python]$ spark-submit --master spark://master:7077 --executor-memory 200m --driver-memory 200m pi.py
Spark assembly has been built with Hive, including Datanucleus jars on classpath
15/03/25 12:18:27 INFO spark.SecurityManager: Changing view acls to: hadoop
15/03/25 12:18:27 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop)
15/03/25 12:18:28 INFO slf4j.Slf4jLogger: Slf4jLogger started
15/03/25 12:18:28 INFO Remoting: Starting remoting
15/03/25 12:18:29 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://spark@master:47877]
15/03/25 12:18:29 INFO Remoting: Remoting now listens on addresses: [akka.tcp://spark@master:47877]
15/03/25 12:18:29 INFO spark.SparkEnv: Registering MapOutputTracker
15/03/25 12:18:29 INFO spark.SparkEnv: Registering BlockManagerMaster
15/03/25 12:18:29 INFO storage.DiskBlockManager: Created local directory at /tmp/spark-local-20150325121829-88cd
15/03/25 12:18:29 INFO storage.MemoryStore: MemoryStore started with capacity 116.0 MB.
15/03/25 12:18:30 INFO network.ConnectionManager: Bound socket to port 48556 with id = ConnectionManagerId(master,48556)
15/03/25 12:18:30 INFO storage.BlockManagerMaster: Trying to register BlockManager
15/03/25 12:18:30 INFO storage.BlockManagerInfo: Registering block manager master:48556 with 116.0 MB RAM
15/03/25 12:18:30 INFO storage.BlockManagerMaster: Registered BlockManager
15/03/25 12:18:30 INFO spark.HttpServer: Starting HTTP Server
15/03/25 12:18:30 INFO server.Server: jetty-8.y.z-SNAPSHOT
15/03/25 12:18:30 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:48872
15/03/25 12:18:30 INFO broadcast.HttpBroadcast: Broadcast server started at http://192.168.1.2:48872
15/03/25 12:18:30 INFO spark.HttpFileServer: HTTP File server directory is /tmp/spark-e2d76bbd-d2f6-4b2f-a018-f2d795a488aa
15/03/25 12:18:30 INFO spark.HttpServer: Starting HTTP Server
15/03/25 12:18:30 INFO server.Server: jetty-8.y.z-SNAPSHOT
15/03/25 12:18:30 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:43148
15/03/25 12:18:31 INFO server.Server: jetty-8.y.z-SNAPSHOT
15/03/25 12:18:31 INFO server.AbstractConnector: Started SelectChannelConnector@0.0.0.0:4040
15/03/25 12:18:31 INFO ui.SparkUI: Started SparkUI at http://master:4040
15/03/25 12:18:32 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/03/25 12:18:35 INFO scheduler.EventLoggingListener: Logging events to hdfs://master:9000/spark/log/pythonpi-1427311113352
15/03/25 12:18:35 INFO util.Utils: Copying /home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py to /tmp/spark-b66e65a9-91dc-479c-8938-14314fd1febb/pi.py
15/03/25 12:18:36 INFO spark.SparkContext: Added file file:/home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py at http://192.168.1.2:43148/files/pi.py with timestamp 1427311115
93515/03/25 12:18:36 INFO client.AppClient$ClientActor: Connecting to master spark://master:7077...
15/03/25 12:18:38 INFO spark.SparkContext: Starting job: reduce at /home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py:38
15/03/25 12:18:38 INFO scheduler.DAGScheduler: Got job 0 (reduce at /home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py:38) with 2 output partitions (allowLocal=false)
15/03/25 12:18:38 INFO scheduler.DAGScheduler: Final stage: Stage 0(reduce at /home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py:38)
15/03/25 12:18:38 INFO scheduler.DAGScheduler: Parents of final stage: List()
15/03/25 12:18:38 INFO scheduler.DAGScheduler: Missing parents: List()
15/03/25 12:18:38 INFO scheduler.DAGScheduler: Submitting Stage 0 (PythonRDD[1] at RDD at PythonRDD.scala:37), which has no missing parents
15/03/25 12:18:38 INFO scheduler.DAGScheduler: Submitting 2 missing tasks from Stage 0 (PythonRDD[1] at RDD at PythonRDD.scala:37)
15/03/25 12:18:38 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 2 tasks
15/03/25 12:18:38 INFO cluster.SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20150325121838-0001
15/03/25 12:18:38 INFO client.AppClient$ClientActor: Executor added: app-20150325121838-0001/0 on worker-20150325114825-slave1-50832 (slave1:50832) with 1 cores
15/03/25 12:18:38 INFO cluster.SparkDeploySchedulerBackend: Granted executor ID app-20150325121838-0001/0 on hostPort slave1:50832 with 1 cores, 200.0 MB RAM
15/03/25 12:18:38 INFO client.AppClient$ClientActor: Executor added: app-20150325121838-0001/1 on worker-20150325114823-slave2-56888 (slave2:56888) with 1 cores
15/03/25 12:18:38 INFO cluster.SparkDeploySchedulerBackend: Granted executor ID app-20150325121838-0001/1 on hostPort slave2:56888 with 1 cores, 200.0 MB RAM
15/03/25 12:18:39 INFO client.AppClient$ClientActor: Executor updated: app-20150325121838-0001/0 is now RUNNING
15/03/25 12:18:39 INFO client.AppClient$ClientActor: Executor updated: app-20150325121838-0001/1 is now RUNNING
15/03/25 12:18:43 INFO cluster.SparkDeploySchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave1:35398/user/Executor#765391125] with ID 0
15/03/25 12:18:43 INFO scheduler.TaskSetManager: Starting task 0.0:0 as TID 0 on executor 0: slave1 (PROCESS_LOCAL)
15/03/25 12:18:43 INFO scheduler.TaskSetManager: Serialized task 0.0:0 as 374986 bytes in 12 ms
15/03/25 12:18:44 INFO cluster.SparkDeploySchedulerBackend: Registered executor: Actor[akka.tcp://sparkExecutor@slave2:37669/user/Executor#2076348799] with ID 1
15/03/25 12:18:44 INFO scheduler.TaskSetManager: Starting task 0.0:1 as TID 1 on executor 1: slave2 (PROCESS_LOCAL)
15/03/25 12:18:44 INFO scheduler.TaskSetManager: Serialized task 0.0:1 as 502789 bytes in 4 ms
15/03/25 12:18:44 INFO storage.BlockManagerInfo: Registering block manager slave1:47192 with 116.0 MB RAM
15/03/25 12:18:44 INFO storage.BlockManagerInfo: Registering block manager slave2:42313 with 116.0 MB RAM
15/03/25 12:18:46 INFO scheduler.TaskSetManager: Finished TID 0 in 2534 ms on slave1 (progress: 1/2)
15/03/25 12:18:46 INFO scheduler.DAGScheduler: Completed ResultTask(0, 0)
15/03/25 12:18:46 INFO scheduler.TaskSetManager: Finished TID 1 in 2234 ms on slave2 (progress: 2/2)
15/03/25 12:18:46 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
15/03/25 12:18:46 INFO scheduler.DAGScheduler: Completed ResultTask(0, 1)
15/03/25 12:18:46 INFO scheduler.DAGScheduler: Stage 0 (reduce at /home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py:38) finished in 7.867 s
15/03/25 12:18:46 INFO spark.SparkContext: Job finished: reduce at /home/hadoop/spark-1.0.2-bin-hadoop2/examples/src/main/python/pi.py:38, took 8.181053565 s
Pi is roughly 3.147220
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/metrics/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages/stage/kill,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/static,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/executors/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/executors,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/environment/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/environment,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/storage/rdd/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/storage/rdd,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/storage/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/storage,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages/pool/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages/pool,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages/stage/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages/stage,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages/json,null}
15/03/25 12:18:46 INFO handler.ContextHandler: stopped o.e.j.s.ServletContextHandler{/stages,null}
15/03/25 12:18:46 INFO ui.SparkUI: Stopped Spark web UI at http://master:4040
15/03/25 12:18:46 INFO scheduler.DAGScheduler: Stopping DAGScheduler
15/03/25 12:18:46 INFO cluster.SparkDeploySchedulerBackend: Shutting down all executors
15/03/25 12:18:46 INFO cluster.SparkDeploySchedulerBackend: Asking each executor to shut down
15/03/25 12:18:47 INFO spark.MapOutputTrackerMasterActor: MapOutputTrackerActor stopped!
15/03/25 12:18:47 INFO network.ConnectionManager: Selector thread was interrupted!
15/03/25 12:18:47 INFO network.ConnectionManager: ConnectionManager stopped
15/03/25 12:18:47 INFO storage.MemoryStore: MemoryStore cleared
15/03/25 12:18:47 INFO storage.BlockManager: BlockManager stopped
15/03/25 12:18:47 INFO storage.BlockManagerMasterActor: Stopping BlockManagerMaster
15/03/25 12:18:47 INFO storage.BlockManagerMaster: BlockManagerMaster stopped
15/03/25 12:18:47 INFO remote.RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.
15/03/25 12:18:47 INFO remote.RemoteActorRefProvider$RemotingTerminator: Remote daemon shut down; proceeding with flushing remote transports.
15/03/25 12:18:47 INFO Remoting: Remoting shut down
15/03/25 12:18:47 INFO remote.RemoteActorRefProvider$RemotingTerminator: Remoting shut down.
15/03/25 12:18:48 INFO spark.SparkContext: Successfully stopped SparkContext查看任務監控，http://192.168.1.2:8080/
查看worker信息 http://192.168.1.3:8081/
spark on yarn實踐
[hadoop@master ~]$ cd spark/examples/src/main/scala/org/apache/spark/examples/ [hadoop@master examples]$ spark-submit --master yarn-cluster \ > --class org.apache.spark.examples.SparkPi \ > --driver-memory 400m \ > --executor-memory 400m \ > --executor-cores 1 \ > --num-executors 2 \ > /home/hadoop/spark/lib/spark-examples-1.0.2-hadoop2.2.0.jar 2 # 如報下面錯誤，修改yarn-site.xml文件 <property> <name>yarn.scheduler.maximum-allocation-mb</name> <value>800</value> </property> # value大于800即可，然后重啟yarn # 正常結果如下： [hadoop@master sbin]$ spark-submit --master yarn-cluster --class org.apache.spark.examples.SparkPi --driver-memory 400m --executor-memory 400m --executor-cores 1 --num-executors 2 /home/hadoop /spark/lib/spark-examples-1.0.2-hadoop2.2.0.jar 2Spark assembly has been built with Hive, including Datanucleus jars on classpath 15/03/25 13:06:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 15/03/25 13:06:09 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.1.2:8032 15/03/25 13:06:09 INFO yarn.Client: Got Cluster metric info from ApplicationsManager (ASM), number of NodeManagers: 2 15/03/25 13:06:09 INFO yarn.Client: Queue info ... queueName: default, queueCurrentCapacity: 0.0, queueMaxCapacity: 1.0, queueApplicationCount = 0, queueChildQueueCount = 0 15/03/25 13:06:09 INFO yarn.Client: Max mem capabililty of a single resource in this cluster 800 15/03/25 13:06:09 INFO yarn.Client: Preparing Local resources 15/03/25 13:06:10 INFO yarn.Client: Uploading file:/home/hadoop/spark/lib/spark-examples-1.0.2-hadoop2.2.0.jar to hdfs://master:9000/user/hadoop/.sparkStaging/application_1427313904247_0001/sp ark-examples-1.0.2-hadoop2.2.0.jar15/03/25 13:06:13 INFO yarn.Client: Uploading file:/home/hadoop/spark-1.0.2-bin-hadoop2/lib/spark-assembly-1.0.2-hadoop2.2.0.jar to hdfs://master:9000/user/hadoop/.sparkStaging/application_142 7313904247_0001/spark-assembly-1.0.2-hadoop2.2.0.jar15/03/25 13:06:25 INFO yarn.Client: Setting up the launch environment 15/03/25 13:06:25 INFO yarn.Client: Setting up container launch context 15/03/25 13:06:25 INFO yarn.Client: Command for starting the Spark ApplicationMaster: List($JAVA_HOME/bin/java, -server, -Xmx400m, -Djava.io.tmpdir=$PWD/tmp, -Dspark.app.name=\"org.apache.spar k.examples.SparkPi\", -Dspark.eventLog.enabled=\"true\", -Dspark.eventLog.dir=\"hdfs://master:9000/spark/log\", -Dspark.yarn.historyServer.address=\"master:18080\", -Dlog4j.configuration=log4j-spark-container.properties, org.apache.spark.deploy.yarn.ApplicationMaster, --class, org.apache.spark.examples.SparkPi, --jar , file:/home/hadoop/spark/lib/spark-examples-1.0.2-hadoop2.2.0.jar, --args '2' , --executor-memory, 400, --executor-cores, 1, --num-executors , 2, 1>, <LOG_DIR>/stdout, 2>, <LOG_DIR>/stderr)15/03/25 13:06:25 INFO yarn.Client: Submitting application to ASM 15/03/25 13:06:25 INFO impl.YarnClientImpl: Submitted application application_1427313904247_0001 to ResourceManager at master/192.168.1.2:8032 15/03/25 13:06:26 INFO yarn.Client: Application report from ASM: application identifier: application_1427313904247_0001 appId: 1 clientToAMToken: null appDiagnostics: appMasterHost: N/A appQueue: default appMasterRpcPort: 0 appStartTime: 1427313985731 yarnAppState: ACCEPTED distributedFinalState: UNDEFINED appTrackingUrl: master:8088/proxy/application_1427313904247_0001/ appUser: hadoop 15/03/25 13:06:27 INFO yarn.Client: Application report from ASM: application identifier: application_1427313904247_0001 appId: 1 clientToAMToken: null appDiagnostics: appMasterHost: N/A appQueue: default appMasterRpcPort: 0 appStartTime: 1427313985731 yarnAppState: ACCEPTED distributedFinalState: UNDEFINED appTrackingUrl: master:8088/proxy/application_1427313904247_0001/ appUser: hadoop
查看yarn監控頁面:http://192.168.1.2:8088/cluster
可以看到任務是在slave2上面執行的
訪問http://192.168.1.4:8042/node
登錄slave2查看
[hadoop@slave2 ~]$ cd /home/hadoop/hadoop/logs/userlogs/application_1427313904247_0001/container_1427313904247_0001_01_000001
[hadoop@slave2 container_1427313904247_0001_01_000001]$ ls
stderr stdout
[hadoop@slave2 container_1427313904247_0001_01_000001]$ cat stdout
Pi is roughly 3.13774
[hadoop@slave2 ~]$ cd /home/hadoop/spark/examples/src/main/scala/org/apache/spark/examples/
[hadoop@slave2 examples]$ cat SparkPi.scala
/*
* 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.examples
import scala.math.random
import org.apache.spark._
/** Computes an approximation to pi */
object SparkPi {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("Spark Pi")
val spark = new SparkContext(conf)
val slices = if (args.length > 0) args(0).toInt else 2
val n = 100000 * slices
val count = spark.parallelize(1 to n, slices).map { i =>
val x = random * 2 - 1
val y = random * 2 - 1
if (x*x + y*y < 1) 1 else 0
}.reduce(_ + _)
println("Pi is roughly " + 4.0 * count / n)
spark.stop()
}
}在yarn上面使用spark-shell
[hadoop@master ~]$ spark-shell --master yarn-client
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