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Spark安装部署

LuckyJune

LuckyJune

2015-02-03

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Spark是UC Berkeley AMPLab开发的类MapRed计算框架。MapRed框架适用于batch job，但是由于它自身的框架限制，第一，pull-based heartbeat作业调度。第二，shuffle中间结果全部落地disk，导致了高延迟，启动开销很大。而Spark是为迭代式，交互式计算所生的。第一，它采用了actor model 的akka 作为通讯框架。第二， 它 使用了RDD分布式内存，操作之间的数据不需要dump到磁盘上，而是通过RDD Partition分 布 在各个节点内存中 ， 极大的提高了数据间的流转， 同 时RDD之间维护了血统关系，一旦RDD fail掉了，能通过父RDD自动重建， 保 证了fault tolerance，而在Spark之上有丰富的应用，比如 Shark，Spark Stre aming，MLBase。我们在生产环境中已经使用Shark来作为Hive的一种补充，它共享了hive 的metastore，serde ， 使用方式也和hive几乎一样，如果 data input size不是很大的情况下，相同语句 确实比hive 会快很多，后续会单独写一篇来详解。

 

Spark software stack 

Spark安装部署
 

本篇介绍下spark的安装 ： 

Spark可以运 行在统一资源调度 器 上， 比如YARN, Mesos，也可以独立部署S tandalone模式，由于我们YARN集群还没上，所以暂时用Standalone模式，它是master/slave模式，由一个spark master和一组spark worker构成 ，S tandalone模式只支持FIFO调度策略，而且默认情况下提交一个job会占用掉 spark集群所有的core，这样就变一个集群只能运行一个job了 ， 需要设置 spark.cores.max值来做 调整 

 
部署环境:

Spark Master: test85.hadoop

Spark Worker: test88.hadoop, test89.hadoop, test90.hadoop, test91.hadoop

 

1. 保证Master和Worker节点SSH打通

2. 由于Spark会使用hadoop client来和hdfs交互，所以每个节点需要安装Hadoop Client

3. 安装Scala，Scala 2.10.2版本和Spark有冲突，所以只能安装Scala 2.9.3

wget http://spark-project.org/download/spark-0.7.3-prebuilt-hadoop1.tgz​
tar xzvf spark-0.7.3-prebuilt-hadoop1.tgz​
ln -s spark-0.7.3 spark-release

在/etc/profile加上环境变量 

export SPARK_HOME=/usr/local/spark-release
export SCALA_HOME=/usr/local/scala
export PATH=$PATH:$SPARK_HOME/bin:$SCALA_HOME/bin

设置Spark的configuration文件，在$ SPARK_HOME/conf/ spark-env.sh中 
设置Spark的configuration文件，在$SPARK_HOME/conf/spark-env.sh中
export JAVA_HOME=/usr/local/jdk
export SCALA_HOME=/usr/local/scala
export SPARK_EXAMPLES_JAR=$SPARK_HOME/examples/target/scala-2.9.3/spark-examples_2.9.3-0.7.3.jar
export SPARK_SSH_OPTS="-p58422 -o StrictHostKeyChecking=no"
export SPARK_MASTER_IP=test85.hadoop

export SPARK_MASTER_WEBUI_PORT=8088
export SPARK_WORKER_WEBUI_PORT=8099

export SPARK_WORKER_CORES=4
export SPARK_WORKER_MEMORY=8g

export LD_LIBRARY_PATH=/usr/local/hadoop/lzo/lib
export SPARK_LIBRARY_PATH=/usr/local/hadoop/hadoop-release/lib/native/Linux-amd64-64
SPARK_WORKER_CORES设置成物理worker cpu核数，SPARK_WORKER_MEMORY为worker节点上spark jobs可用的物理内存总量 

slaves文件中加入worker地址 

# A Spark Worker will be started on each of the machines listes below
test88.hadoop
test89.hadoop
test90.hadoop
test91.hadoop

同步 配置文件和Spark,Scala 到整个集群 

启动spark master

bin/start-master.sh

13/09/23 09:46:57 INFO Slf4jEventHandler: Slf4jEventHandler started
13/09/23 09:46:57 INFO ActorSystemImpl: RemoteServerStarted@akka://[email protected]:7077
13/09/23 09:46:57 INFO Master: Starting Spark master at spark://test85.hadoop:7077
13/09/23 09:46:57 INFO IoWorker: IoWorker thread 'spray-io-worker-0' started
13/09/23 09:46:57 INFO HttpServer: akka://sparkMaster/user/HttpServer started on /0.0.0.0:8088

启动spark workers

bin/start-slaves.sh

13/09/23 09:47:54 INFO Slf4jEventHandler: Slf4jEventHandler started
13/09/23 09:47:55 INFO ActorSystemImpl: RemoteServerStarted@akka://[email protected]:36665
13/09/23 09:47:55 INFO Worker: Starting Spark worker test89.hadoop:36665 with 4 cores, 8.0 GB RAM
13/09/23 09:47:55 INFO Worker: Spark home: /usr/local/spark-0.7.3
13/09/23 09:47:55 INFO Worker: Connecting to master spark://test85.hadoop:7077
13/09/23 09:47:56 INFO ActorSystemImpl: RemoteClientStarted@akka://[email protected]:7077
13/09/23 09:47:57 INFO IoWorker: IoWorker thread 'spray-io-worker-0' started
13/09/23 09:47:58 INFO HttpServer: akka://sparkWorker/user/HttpServer started on /0.0.0.0:8099
13/09/23 09:47:58 INFO Worker: Successfully registered with master

运行计算PI的job

./run spark.examples.SparkPi spark://test85.hadoop:7077

[hadoop@test85 spark-release]$ ./run spark.examples.SparkPi spark://test85.hadoop:7077
13/09/23 10:15:59 INFO Slf4jEventHandler: Slf4jEventHandler started
13/09/23 10:15:59 INFO SparkEnv: Registering BlockManagerMaster
13/09/23 10:15:59 INFO MemoryStore: MemoryStore started with capacity 323.9 MB.
13/09/23 10:15:59 INFO DiskStore: Created local directory at /tmp/spark-local-20130923101559-6a72
13/09/23 10:15:59 INFO ConnectionManager: Bound socket to port 54795 with id = ConnectionManagerId(test85.hadoop,54795)
13/09/23 10:15:59 INFO BlockManagerMaster: Trying to register BlockManager
13/09/23 10:15:59 INFO BlockManagerMaster: Registered BlockManager
13/09/23 10:15:59 INFO HttpBroadcast: Broadcast server started at http://10.1.77.85:58290
13/09/23 10:15:59 INFO SparkEnv: Registering MapOutputTracker
13/09/23 10:15:59 INFO HttpFileServer: HTTP File server directory is /tmp/spark-22ef9d2b-0e57-42e2-ae90-a9cd99233c1c
13/09/23 10:16:00 INFO IoWorker: IoWorker thread 'spray-io-worker-0' started
13/09/23 10:16:00 INFO HttpServer: akka://spark/user/BlockManagerHTTPServer started on /0.0.0.0:46611
13/09/23 10:16:00 INFO BlockManagerUI: Started BlockManager web UI at http://test85.hadoop:46611
13/09/23 10:16:00 INFO SparkContext: Added JAR /usr/local/spark-release/examples/target/scala-2.9.3/spark-examples_2.9.3-0.7.3.jar at http://10.1.77.85:51299/jars/spark-examples_2.9.3-0.7.3.jar with timestamp 1379902560222
13/09/23 10:16:00 INFO Client$ClientActor: Connecting to master spark://test85.hadoop:7077
13/09/23 10:16:00 INFO SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20130923101600-0000
13/09/23 10:16:00 INFO Client$ClientActor: Executor added: app-20130923101600-0000/0 on worker-20130923094755-test89.hadoop-36665 (test89.hadoop) with 4 cores
13/09/23 10:16:00 INFO SparkDeploySchedulerBackend: Granted executor ID app-20130923101600-0000/0 on host test89.hadoop with 4 cores, 512.0 MB RAM
13/09/23 10:16:00 INFO Client$ClientActor: Executor added: app-20130923101600-0000/1 on worker-20130923094752-test90.hadoop-39876 (test90.hadoop) with 4 cores
13/09/23 10:16:00 INFO SparkDeploySchedulerBackend: Granted executor ID app-20130923101600-0000/1 on host test90.hadoop with 4 cores, 512.0 MB RAM
13/09/23 10:16:00 INFO Client$ClientActor: Executor added: app-20130923101600-0000/2 on worker-20130923094751-test91.hadoop-53527 (test91.hadoop) with 4 cores
13/09/23 10:16:00 INFO SparkDeploySchedulerBackend: Granted executor ID app-20130923101600-0000/2 on host test91.hadoop with 4 cores, 512.0 MB RAM
13/09/23 10:16:00 INFO Client$ClientActor: Executor added: app-20130923101600-0000/3 on worker-20130923094752-test88.hadoop-43591 (test88.hadoop) with 4 cores
13/09/23 10:16:00 INFO SparkDeploySchedulerBackend: Granted executor ID app-20130923101600-0000/3 on host test88.hadoop with 4 cores, 512.0 MB RAM
13/09/23 10:16:00 INFO SparkContext: Starting job: reduce at SparkPi.scala:22
13/09/23 10:16:00 INFO DAGScheduler: Got job 0 (reduce at SparkPi.scala:22) with 2 output partitions (allowLocal=false)
13/09/23 10:16:00 INFO DAGScheduler: Final stage: Stage 0 (map at SparkPi.scala:18)
13/09/23 10:16:00 INFO DAGScheduler: Parents of final stage: List()
13/09/23 10:16:00 INFO DAGScheduler: Missing parents: List()
13/09/23 10:16:00 INFO DAGScheduler: Submitting Stage 0 (MappedRDD[1] at map at SparkPi.scala:18), which has no missing parents
13/09/23 10:16:00 INFO DAGScheduler: Submitting 2 missing tasks from Stage 0 (MappedRDD[1] at map at SparkPi.scala:18)
13/09/23 10:16:00 INFO ClusterScheduler: Adding task set 0.0 with 2 tasks
13/09/23 10:16:00 INFO Client$ClientActor: Executor updated: app-20130923101600-0000/2 is now RUNNING
13/09/23 10:16:00 INFO Client$ClientActor: Executor updated: app-20130923101600-0000/3 is now RUNNING
13/09/23 10:16:00 INFO Client$ClientActor: Executor updated: app-20130923101600-0000/1 is now RUNNING
13/09/23 10:16:00 INFO Client$ClientActor: Executor updated: app-20130923101600-0000/0 is now RUNNING
13/09/23 10:16:02 INFO SparkDeploySchedulerBackend: Registered executor: Actor[akka://[email protected]:44054/user/Executor] with ID 1
13/09/23 10:16:02 INFO TaskSetManager: Starting task 0.0:0 as TID 0 on executor 1: test90.hadoop (preferred)
13/09/23 10:16:02 INFO TaskSetManager: Serialized task 0.0:0 as 1339 bytes in 35 ms
13/09/23 10:16:02 INFO TaskSetManager: Starting task 0.0:1 as TID 1 on executor 1: test90.hadoop (preferred)
13/09/23 10:16:02 INFO TaskSetManager: Serialized task 0.0:1 as 1339 bytes in 1 ms
13/09/23 10:16:02 INFO SparkDeploySchedulerBackend: Registered executor: Actor[akka://[email protected]:34433/user/Executor] with ID 2
13/09/23 10:16:02 INFO SparkDeploySchedulerBackend: Registered executor: Actor[akka://[email protected]:53079/user/Executor] with ID 0
13/09/23 10:16:02 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager test91.hadoop:49214 with 323.9 MB RAM
13/09/23 10:16:02 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager test90.hadoop:33628 with 323.9 MB RAM
13/09/23 10:16:02 INFO SparkDeploySchedulerBackend: Registered executor: Actor[akka://[email protected]:38074/user/Executor] with ID 3
13/09/23 10:16:03 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager test88.hadoop:55313 with 323.9 MB RAM
13/09/23 10:16:03 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager test89.hadoop:37899 with 323.9 MB RAM
13/09/23 10:16:03 INFO TaskSetManager: Finished TID 1 in 1128 ms (progress: 1/2)
13/09/23 10:16:03 INFO TaskSetManager: Finished TID 0 in 1175 ms (progress: 2/2)
13/09/23 10:16:03 INFO DAGScheduler: Completed ResultTask(0, 1)
13/09/23 10:16:03 INFO DAGScheduler: Completed ResultTask(0, 0)
13/09/23 10:16:03 INFO DAGScheduler: Stage 0 (map at SparkPi.scala:18) finished in 2.939 s
13/09/23 10:16:03 INFO SparkContext: Job finished: reduce at SparkPi.scala:22, took 2.976491771 s
Pi is roughly 3.14368

参考文档：

http://spark.incubator.apache.org/docs/latest/spark-standalone.html 

http://rdc.taobao.com/team/jm/archives/1823 

http://zhuguangbin.github.io/blog/2013/07/16/spark-deploy/ 

 

本文链接 http://blog.csdn.net/lalaguozhe/article/details/11921493 ，转载请注明 

LuckyJune

LuckyJune

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LuckyJune

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