Standalone mode of Spark's three operating modes

1. Mode overview

Construction of a the Master+Slaverunning cluster Spark, Spark formed in the cluster.

Two, installation and use

(1) Enter the conf folder under the spark installation directory

[atguigu@hadoop102 module]$ cd spark/conf/

(2) Modify the configuration file name

[atguigu@hadoop102 conf]$ mv slaves.template slaves
[atguigu@hadoop102 conf]$ mv spark-env.sh.template spark-env.sh

(3) Modify the slave file and add the work node:

[atguigu@hadoop102 conf]$ vim slaves
hadoop102
hadoop103
hadoop104

(4) Modify the spark-env.sh file and add the following configuration:

[atguigu@hadoop102 conf]$ vim spark-env.sh
SPARK_MASTER_HOST=hadoop101
SPARK_MASTER_PORT=7077

(5) Distribute spark package

[atguigu@hadoop102 module]$ xsync spark/

(6) Start

[atguigu@hadoop102 spark]$ sbin/start-all.sh
[atguigu@hadoop102 spark]$ util.sh
================atguigu@hadoop102================
3330 Jps
3238 Worker
3163 Master
================atguigu@hadoop103================
2966 Jps
2908 Worker
================atguigu@hadoop104================
2978 Worker
3036 Jps

Web page view:hadoop102:8080

Note: If you encounter " JAVA_HOME not set" exception, you can add the following configuration to the spark-config.sh file in the sbin directory: export JAVA_HOME=XXXX(JAVA environment variable)

(7) Official request for PI case

[atguigu@hadoop102 spark]$ bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://hadoop102:7077 \
--executor-memory 1G \
--total-executor-cores 2 \
./examples/jars/spark-examples_2.11-2.1.1.jar \
100

(8) Start the spark shell

/opt/module/spark/bin/spark-shell \
--master spark://hadoop101:7077 \
--executor-memory 1g \
--total-executor-cores 2

参数解析：--master spark://hadoop102:7077 指定要连接的集群的 master

Execute the WordCount program:

scala>sc.textFile("input").flatMap(_.split("
")).map((_,1)).reduceByKey(_+_).collect

res0: Array[(String, Int)] = Array((hadoop,6), (oozie,3), (spark,3),
(hive,3), (atguigu,3), (hbase,6))

Three, JobHistoryServer configuration

(1) Modify the spark-default.conf.template name

[atguigu@hadoop102 conf]$ mv spark-defaults.conf.template sparkdefaults.conf

(2) Modify the spark-default.conf file and enable Log:

[atguigu@hadoop102 conf]$ vi spark-defaults.conf
spark.eventLog.enabled true
spark.eventLog.dir hdfs://hadoop102:9000/directory

Note:HDFS 上的目录需要提前存在 .

(3) Modify the spark-env.sh file and add the following configuration:

[atguigu@hadoop102 conf]$ vi spark-env.sh
export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=18080
-Dspark.history.retainedApplications=30
-Dspark.history.fs.logDirectory=hdfs://hadoop101:9000/directory"

Parameter Description:

spark.eventLog.dir：Application 在运行过程中所有的信息均记录在该属性指定的路径下
spark.history.ui.port=18080 WEBUI 访问的端口号为 18080
spark.history.fs.logDirectory=hdfs://hadoop102:9000/directory 配置了该属性后，在 starthistory-server.sh 时就无需再显式的指定路径，Spark History Server 页面只展示该指定路径下的信息
spark.history.retainedApplications=30 指定保存 Application 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。

(4) Distribution configuration file

[atguigu@hadoop102 conf]$ xsync spark-defaults.conf
[atguigu@hadoop102 conf]$ xsync spark-env.sh

(5) Start history service

[atguigu@hadoop102 spark]$ sbin/start-history-server.sh

(6) Perform the task again

[atguigu@hadoop102 spark]$ bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://hadoop101:7077 \
--executor-memory 1G \
--total-executor-cores 2 \
./examples/jars/spark-examples_2.11-2.1.1.jar \
100

(7) View historical services:hadoop102:18080

Four, HA configuration

(1) First, ensure that zookeeper is installed and started normally

(2) Modify the spark-env.sh file and add the following configuration:

[atguigu@hadoop102 conf]$ vi spark-env.sh
注释掉如下内容：
#SPARK_MASTER_HOST=hadoop102
#SPARK_MASTER_PORT=7077
添加上如下内容：
export SPARK_DAEMON_JAVA_OPTS="
-Dspark.deploy.recoveryMode=ZOOKEEPER
-Dspark.deploy.zookeeper.url=hadoop101,hadoop102,hadoop103
-Dspark.deploy.zookeeper.dir=/spark"

(3) Distribution of configuration files

[atguigu@hadoop102 conf]$ xsync spark-env.sh

(4) Start all nodes on hadoop102

[atguigu@hadoop102 spark]$ sbin/start-all.sh

(5) Start the master node separately on hadoop103

[atguigu@hadoop103 spark]$ sbin/start-master.sh

(6) Spark HA cluster access

/opt/module/spark/bin/spark-shell \
--master spark://hadoop101:7077,hadoop102:7077 \
--executor-memory 2g \
--total-executor-cores 2