Standalone模式：只使用Spark自身节点运行的集群模式，也就是所谓的独立部署Standalone模式。
下载
http://archive.apache.org/dist/spark/spark-3.0.0/
检查Java安装目录
[server@hadoop102 ~]$ cd $JAVA_HOME
[server@hadoop102 jdk1.8.0_65]$ pwd
/opt/module/jdk1.8.0_65
解压安装
[server@hadoop102 ~]$ cd /opt/software/
[server@hadoop102 software]$ tar -zxvf spark-3.0.0-bin-hadoop3.2.tgz -C /opt/module/
[server@hadoop102 software]$ cd /opt/module/
[server@hadoop102 module]$ mv spark-3.0.0-bin-hadoop3.2/ spark-standalone
[server@hadoop102 module]$ cd /opt/module/spark-standalone/conf/
[server@hadoop102 conf]$ mv slaves.template slaves
[server@hadoop102 conf]$ vim slaves
增加三台主机的host
hadoop102
hadoop103
hadoop104
server@hadoop102 conf]$ mv spark-env.sh.template spark-env.sh
[server@hadoop102 conf]$ vim spark-env.sh
增加java路径和master的host
export JAVA_HOME=/opt/module/jdk1.8.0_65
SPARK_MASTER_HOST=hadoop102
SPARK_MASTER_PORT=7077
编辑分发脚本
[server@hadoop102 ~]$ pwd
/home/server
[server@hadoop102 ~]$ mkdir bin
[server@hadoop102 ~]$ cd bin
[server@hadoop102 bin]$ pwd
/home/server/bin
[server@hadoop102 bin]$ vim xsync
#!/bin/bash
#1. 判断参数个数
if [ $# -lt 1 ]
then
echo Not Enough Argument!
exit;
fi
#2. 遍历集群所有机器
for host in hadoop102 hadoop103 hadoop104
do
echo ======= $host ======
#3. 遍历所有目录，挨个发送
for file in $@
do
#4. 判断文件是否存在
if [ -e $file ]
then
#5. 获取父目录
pdir=$(cd -P $(dirname $file);pwd)
#6. 获取当前文件的名称
fname=$(basename $file)
ssh $host "mkdir -p $pdir"
rsync -av $pdir/$fname $host:$pdir
else
echo $file does not exits!
fi
done
done
[server@hadoop102 bin]$ chmod 777 xsync
[server@hadoop102 bin]$ cd ..
[server@hadoop102 ~]$ pwd
/home/server
分发spark-standalone目录
[server@hadoop102 conf]$ cd /opt/module/
[server@hadoop102 module]$ xsync spark-standalone/
启动
[server@hadoop102 module]$ cd /opt/module/spark-standalone/
[server@hadoop102 spark-standalone]$ sbin/start-all.sh
[server@hadoop102 spark-standalone]$ jps
7570 Jps
7387 Master
7454 Worker
[server@hadoop103 module]$ jps
7276 Jps
7183 Worker
[server@hadoop104 ~]$ jps
7202 Worker
7305 Jps
提交应用
bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://hadoop102:7077 \
./examples/jars/spark-examples_2.12-3.0.0.jar \
10
--class 表示要执行程序的主类(Spark程序中包含主函数的类)
--master spark://hadoop102:7077 表示独立部署模式(Spark程序运行的环境)，连接到Spark集群
spark-examples_2.12-3.0.0.jar运行类所在的jar包
10表示程序的入口参数，用于设定当前应用的任务数量
[server@hadoop102 spark-standalone]$ bin/spark-submit \
> --class org.apache.spark.examples.SparkPi \
> --master spark://hadoop102:7077 \
> ./examples/jars/spark-examples_2.12-3.0.0.jar \
> 10
21/10/16 16:34:42 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
21/10/16 16:34:43 INFO SparkContext: Running Spark version 3.0.0
21/10/16 16:34:43 INFO ResourceUtils: ==============================================================
21/10/16 16:34:43 INFO ResourceUtils: Resources for spark.driver:
21/10/16 16:34:43 INFO ResourceUtils: ==============================================================
21/10/16 16:34:43 INFO SparkContext: Submitted application: Spark Pi
21/10/16 16:34:44 INFO SecurityManager: Changing view acls to: server
21/10/16 16:34:44 INFO SecurityManager: Changing modify acls to: server
21/10/16 16:34:44 INFO SecurityManager: Changing view acls groups to:
21/10/16 16:34:44 INFO SecurityManager: Changing modify acls groups to:
21/10/16 16:34:44 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(server); groups with view permissions: Set(); users with modify permissions: Set(server); groups with modify permissions: Set()
21/10/16 16:34:45 INFO Utils: Successfully started service 'sparkDriver' on port 38763.
21/10/16 16:34:45 INFO SparkEnv: Registering MapOutputTracker
21/10/16 16:34:46 INFO SparkEnv: Registering BlockManagerMaster
21/10/16 16:34:46 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
21/10/16 16:34:46 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
21/10/16 16:34:46 INFO SparkEnv: Registering BlockManagerMasterHeartbeat
21/10/16 16:34:46 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-15596ad6-364b-47bd-9056-e5909a388573
21/10/16 16:34:46 INFO MemoryStore: MemoryStore started with capacity 413.9 MiB
21/10/16 16:34:46 INFO SparkEnv: Registering OutputCommitCoordinator
21/10/16 16:34:47 INFO Utils: Successfully started service 'SparkUI' on port 4040.
21/10/16 16:34:48 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://hadoop102:4040
21/10/16 16:34:48 INFO SparkContext: Added JAR file:/opt/module/spark-standalone/./examples/jars/spark-examples_2.12-3.0.0.jar at spark://hadoop102:38763/jars/spark-examples_2.12-3.0.0.jar with timestamp 1634373288372
21/10/16 16:34:49 INFO StandaloneAppClient$ClientEndpoint: Connecting to master spark://hadoop102:7077...
21/10/16 16:34:50 INFO TransportClientFactory: Successfully created connection to hadoop102/192.168.100.102:7077 after 237 ms (0 ms spent in bootstraps)
21/10/16 16:34:51 INFO StandaloneSchedulerBackend: Connected to Spark cluster with app ID app-20211016163450-0000
21/10/16 16:34:51 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 34527.
21/10/16 16:34:51 INFO NettyBlockTransferService: Server created on hadoop102:34527
21/10/16 16:34:51 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
21/10/16 16:34:51 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20211016163450-0000/0 on worker-20211016162729-192.168.100.104-37825 (192.168.100.104:37825) with 1 core(s)
21/10/16 16:34:51 INFO StandaloneSchedulerBackend: Granted executor ID app-20211016163450-0000/0 on hostPort 192.168.100.104:37825 with 1 core(s), 1024.0 MiB RAM
21/10/16 16:34:51 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20211016163450-0000/1 on worker-20211016162727-192.168.100.102-45892 (192.168.100.102:45892) with 1 core(s)
21/10/16 16:34:51 INFO StandaloneSchedulerBackend: Granted executor ID app-20211016163450-0000/1 on hostPort 192.168.100.102:45892 with 1 core(s), 1024.0 MiB RAM
21/10/16 16:34:51 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20211016163450-0000/2 on worker-20211016162728-192.168.100.103-41221 (192.168.100.103:41221) with 1 core(s)
21/10/16 16:34:51 INFO StandaloneSchedulerBackend: Granted executor ID app-20211016163450-0000/2 on hostPort 192.168.100.103:41221 with 1 core(s), 1024.0 MiB RAM
21/10/16 16:34:51 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, hadoop102, 34527, None)
21/10/16 16:34:51 INFO BlockManagerMasterEndpoint: Registering block manager hadoop102:34527 with 413.9 MiB RAM, BlockManagerId(driver, hadoop102, 34527, None)
21/10/16 16:34:51 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, hadoop102, 34527, None)
21/10/16 16:34:51 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, hadoop102, 34527, None)
21/10/16 16:34:52 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20211016163450-0000/0 is now RUNNING
21/10/16 16:34:52 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20211016163450-0000/2 is now RUNNING
21/10/16 16:34:53 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20211016163450-0000/1 is now RUNNING
21/10/16 16:34:54 INFO StandaloneSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
21/10/16 16:34:59 INFO SparkContext: Starting job: reduce at SparkPi.scala:38
21/10/16 16:34:59 INFO DAGScheduler: Got job 0 (reduce at SparkPi.scala:38) with 10 output partitions
21/10/16 16:34:59 INFO DAGScheduler: Final stage: ResultStage 0 (reduce at SparkPi.scala:38)
21/10/16 16:34:59 INFO DAGScheduler: Parents of final stage: List()
21/10/16 16:34:59 INFO DAGScheduler: Missing parents: List()
21/10/16 16:34:59 INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at map at SparkPi.scala:34), which has no missing parents
21/10/16 16:35:00 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 3.1 KiB, free 413.9 MiB)
21/10/16 16:35:01 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 1816.0 B, free 413.9 MiB)
21/10/16 16:35:01 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on hadoop102:34527 (size: 1816.0 B, free: 413.9 MiB)
21/10/16 16:35:01 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1200
21/10/16 16:35:01 INFO DAGScheduler: Submitting 10 missing tasks from ResultStage 0 (MapPartitionsRDD[1] at map at SparkPi.scala:34) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9))
21/10/16 16:35:01 INFO TaskSchedulerImpl: Adding task set 0.0 with 10 tasks
21/10/16 16:35:02 INFO ResourceProfile: Default ResourceProfile created, executor resources: Map(cores -> name: cores, amount: 1, script: , vendor: , memory -> name: memory, amount: 1024, script: , vendor: ), task resources: Map(cpus -> name: cpus, amount: 1.0)
21/10/16 16:35:05 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.100.103:54042) with ID 2
21/10/16 16:35:06 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.100.104:43388) with ID 0
21/10/16 16:35:06 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.100.103:33484 with 413.9 MiB RAM, BlockManagerId(2, 192.168.100.103, 33484, None)
21/10/16 16:35:07 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.100.104:40508 with 413.9 MiB RAM, BlockManagerId(0, 192.168.100.104, 40508, None)
21/10/16 16:35:07 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, 192.168.100.103, executor 2, partition 0, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:07 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, 192.168.100.104, executor 0, partition 1, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:09 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.100.103:33484 (size: 1816.0 B, free: 413.9 MiB)
21/10/16 16:35:09 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.100.104:40508 (size: 1816.0 B, free: 413.9 MiB)
21/10/16 16:35:13 INFO TaskSetManager: Starting task 2.0 in stage 0.0 (TID 2, 192.168.100.103, executor 2, partition 2, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:13 INFO TaskSetManager: Starting task 3.0 in stage 0.0 (TID 3, 192.168.100.104, executor 0, partition 3, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:13 INFO TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 6244 ms on 192.168.100.104 (executor 0) (1/10)
21/10/16 16:35:13 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 6845 ms on 192.168.100.103 (executor 2) (2/10)
21/10/16 16:35:13 INFO TaskSetManager: Starting task 4.0 in stage 0.0 (TID 4, 192.168.100.103, executor 2, partition 4, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:13 INFO TaskSetManager: Finished task 2.0 in stage 0.0 (TID 2) in 368 ms on 192.168.100.103 (executor 2) (3/10)
21/10/16 16:35:14 INFO TaskSetManager: Starting task 5.0 in stage 0.0 (TID 5, 192.168.100.104, executor 0, partition 5, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 3.0 in stage 0.0 (TID 3) in 360 ms on 192.168.100.104 (executor 0) (4/10)
21/10/16 16:35:14 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.100.102:48108) with ID 1
21/10/16 16:35:14 INFO TaskSetManager: Starting task 6.0 in stage 0.0 (TID 6, 192.168.100.103, executor 2, partition 6, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 4.0 in stage 0.0 (TID 4) in 301 ms on 192.168.100.103 (executor 2) (5/10)
21/10/16 16:35:14 INFO TaskSetManager: Starting task 7.0 in stage 0.0 (TID 7, 192.168.100.104, executor 0, partition 7, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 5.0 in stage 0.0 (TID 5) in 272 ms on 192.168.100.104 (executor 0) (6/10)
21/10/16 16:35:14 INFO TaskSetManager: Starting task 8.0 in stage 0.0 (TID 8, 192.168.100.103, executor 2, partition 8, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 6.0 in stage 0.0 (TID 6) in 273 ms on 192.168.100.103 (executor 2) (7/10)
21/10/16 16:35:14 INFO TaskSetManager: Starting task 9.0 in stage 0.0 (TID 9, 192.168.100.104, executor 0, partition 9, PROCESS_LOCAL, 7397 bytes)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 7.0 in stage 0.0 (TID 7) in 306 ms on 192.168.100.104 (executor 0) (8/10)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 8.0 in stage 0.0 (TID 8) in 285 ms on 192.168.100.103 (executor 2) (9/10)
21/10/16 16:35:14 INFO TaskSetManager: Finished task 9.0 in stage 0.0 (TID 9) in 261 ms on 192.168.100.104 (executor 0) (10/10)
21/10/16 16:35:14 INFO DAGScheduler: ResultStage 0 (reduce at SparkPi.scala:38) finished in 14.920 s
21/10/16 16:35:14 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
21/10/16 16:35:14 INFO DAGScheduler: Job 0 is finished. Cancelling potential speculative or zombie tasks for this job
21/10/16 16:35:14 INFO TaskSchedulerImpl: Killing all running tasks in stage 0: Stage finished
21/10/16 16:35:14 INFO DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 15.503296 s
Pi is roughly 3.1398471398471397
21/10/16 16:35:15 INFO SparkUI: Stopped Spark web UI at http://hadoop102:4040
21/10/16 16:35:15 INFO StandaloneSchedulerBackend: Shutting down all executors
21/10/16 16:35:15 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Asking each executor to shut down
21/10/16 16:35:15 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
21/10/16 16:35:15 INFO MemoryStore: MemoryStore cleared
21/10/16 16:35:15 INFO BlockManager: BlockManager stopped
21/10/16 16:35:15 INFO BlockManagerMaster: BlockManagerMaster stopped
21/10/16 16:35:15 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
21/10/16 16:35:15 INFO SparkContext: Successfully stopped SparkContext
21/10/16 16:35:16 INFO ShutdownHookManager: Shutdown hook called
21/10/16 16:35:16 INFO ShutdownHookManager: Deleting directory /tmp/spark-648f23eb-d06d-42a7-8680-57275eacd379
21/10/16 16:35:16 INFO ShutdownHookManager: Deleting directory /tmp/spark-8b0a7260-c41a-4ad5-9789-5cbb31744f8c
停止服务
[server@hadoop102 spark-standalone]$ sbin/stop-all.sh
hadoop104: stopping org.apache.spark.deploy.worker.Worker
hadoop103: stopping org.apache.spark.deploy.worker.Worker
hadoop102: stopping org.apache.spark.deploy.worker.Worker
stopping org.apache.spark.deploy.master.Master
提交参数说明
bin/spark-submit \
--class <main-class> \
--master <master-url>\
... #other options
<application-jar> \
[application-arguments]
| 参数 | 解释 | 可选值举例 |
|---|---|---|
| --class | Spark程序中包含主函数的类 | |
| --master | Spark程序的运行模式(环境) | 模式:local[*]、spark://hadoop102:7077、Yarn |
| --executor-memory 1G | 指定每个executor可用内存为1G | |
| --total-executor-cores 2 | 指定所有executor使用的cpu核数为2个 | |
| --executor-cores | 指定每个executor使用的cpu核数 | |
| application-jar | 打包好的应用jar,包含依赖。这个URL在集群中全局课件。比如hdfs://共享存储系统,如果是fille://path,那么所有的节点都包含同样的jar | |
| applications-arguments | 传给main()方法的所有参数 |
配置历史服务
启动hadoop集群创建目录
[server@hadoop102 spark-standalone]$ cd ~
[server@hadoop102 ~]$ cd bin
[server@hadoop102 bin]$ myhadoop.sh start
=================启动 Hadoop集群========================
------------------启动 hdfs-----------------------------
Starting namenodes on [hadoop102]
Starting datanodes
Starting secondary namenodes [hadoop104]
------------------启动 yarn-----------------------------
Starting resourcemanager
Starting nodemanagers
------------------启动 historyserver--------------------
[server@hadoop102 bin]$ hadoop fs -mkdir /spark
[server@hadoop102 bin]$ cd /opt/module/spark-standalone/conf/
[server@hadoop102 conf]$ mv spark-defaults.conf.template spark-defaults.conf
[server@hadoop102 conf]$ vim spark-defaults.conf
增加
spark.eventLog.enabled true
spark.eventLog.dir hdfs://hadoop102/spark
[server@hadoop102 conf]$ vim spark-env.sh
增加
export SPARK_HISTORY_OPTS="
-Dspark.history.ui.port=18080
-Dspark.history.fs.logDirectory=hdfs://hadoop102/spark
-Dspark.history.retainedApplications=30"
Dspark.history.ui.port:WEBUI访问的端口号为18080
-Dspark.history.fs.logDirectory:历史服务器日志存储路径
-Dspark.history.retainedApplications:历史记录的个数，如果超过这个值，旧的应用程序信息将被删除，这个是内存中的应用数，而不是页面上显示的应用数。
分发配置文件
[server@hadoop102 conf]$ cd /opt/module/spark-standalone/
[server@hadoop102 spark-standalone]$ xsync conf/
启动集群和历史服务
[server@hadoop102 spark-standalone]$ pwd
/opt/module/spark-standalone
[server@hadoop102 spark-standalone]$ sbin/start-all.sh
[server@hadoop102 spark-standalone]$ sbin/start-history-server.sh
starting org.apache.spark.deploy.history.HistoryServer, logging to /opt/module/spark-standalone/logs/spark-server-org.apache.spark.deploy.history.HistoryServer-1-hadoop102.out
执行任务
bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://hadoop102:7077 \
./examples/jars/spark-examples_2.12-3.0.0.jar \
10
[server@hadoop102 spark-standalone]$ bin/spark-submit \
> --class org.apache.spark.examples.SparkPi \
> --master spark://hadoop102:7077 \
> ./examples/jars/spark-examples_2.12-3.0.0.jar \
> 10
[server@hadoop102 spark-standalone]$ hadoop fs -ls /spark
Found 1 items
-rw-rw---- 3 server supergroup 108036 2021-10-16 17:34 /spark/app-20211016173335-0000
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