配置spark历史服务(spark二)
1. 编辑spark-defaults.conf位置文件
添加spark.eventLog.enabled和spark.eventLog.dir的配置
修改spark.eventLog.dir为我们之前在hdfs配置的端口
hdfs配置参考hadoop(七)集群配置同步（hadoop完全分布式四）|9
[shaozhiqi@hadoop102 conf]$ pwd
/opt/module/spark-2.4.3-bin-hadoop2.7/conf
[shaozhiqi@hadoop102 conf]$ vim spark-defaults.conf
# spark.master spark://master:7077
# spark.eventLog.enabled true
# spark.eventLog.dir hdfs://namenode:8021/directory
# spark.serializer org.apache.spark.serializer.KryoSerializer
# spark.driver.memory 5g
# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"
spark.eventLog.enabled true
spark.eventLog.dir hdfs://hadoop102:9000/directory
2. 分发我们conf修改的配置文件
分发配置参考hadoop(六)rsync远程同步|xsync集群分发(完全分布式准备三)|8
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ testxsync conf/
找个机器看下是否同步成功
[shaozhiqi@hadoop103 spark-2.4.3-bin-hadoop2.7]$ cd conf
[shaozhiqi@hadoop103 conf]$ cat spark-defaults.conf
# spark.master spark://master:7077
# spark.eventLog.enabled true
# spark.eventLog.dir hdfs://namenode:8021/directory
# spark.serializer org.apache.spark.serializer.KryoSerializer
# spark.driver.memory 5g
# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"
spark.eventLog.enabled true
spark.eventLog.dir hdfs://hadoop102:9000/directory
[shaozhiqi@hadoop103 conf]$
3. 启动我们的hdfs
防止启动报错，先删除data logs 然后格式化namenode
bin/hdfs namenode –format
[shaozhiqi@hadoop102 hadoop-3.1.2]$ start-dfs.sh
启动成功，查看进程
[shaozhiqi@hadoop102 hadoop-3.1.2]$ start-dfs.sh
Starting namenodes on [hadoop102]
Starting datanodes
hadoop103: WARNING: /opt/module/hadoop-3.1.2/logs does not exist. Creating.
hadoop104: WARNING: /opt/module/hadoop-3.1.2/logs does not exist. Creating.
Starting secondary namenodes [hadoop104]
[shaozhiqi@hadoop102 hadoop-3.1.2]$ jps
3088 Master
3168 Worker
4452 Jps
3366 CoarseGrainedExecutorBackend
4200 DataNode
4076 NameNode
3773 GetConf
[shaozhiqi@hadoop102 hadoop-3.1.2]$
Yarn等我们提交任务到yarn时再启动
4. 查看我们的hdfs namenode ui
image.png
5. 创建hdfs文件夹，和我们上面配置的spark-defaults.conf中的一样
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ hadoop fs -mkdir /directory
再次查看：
image.png
6. 再次修改spark-env.sh添加历史服务参数
[shaozhiqi@hadoop102 conf]$ vi spark-env.sh
export JAVA_HOME=/opt/module/jdk1.8.0_211
export SPARK_MASTER_HOS=hadoop102
export SPARK_MASTER_PORT=7077
export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=4000 -Dspark.history.retainedApplications=3 -Dspark.history.fs.logDirectory=hdfs://hadoop102:9000/directory"
7. 同步我们的spark-env.sh
shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ testxsync conf/spark-env.sh
8. 执行一个spark进程
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ bin/spark-submit \
> --class org.apache.spark.examples.SparkPi \
> --master spark://hadoop102:7077 \
> --executor-memory 1G \
> --total-executor-cores 2 \
> ./examples/jars/spark-examples_2.11-2.4.3.jar \
> 100
9. 查看spark ui多了我们的进程
image.png
点击spark pi进程，由于我们的任务还在执行，可以直接跳转
image.png
10. 发现好久都没有执行完看下日志
19/07/01 07:15:53 WARN TaskSchedulerImpl:Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
难道是没有资源了？
点击kill掉spark shell和我们的spark Pi,然后单独提交spark Pi任务试下
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ bin/spark-submit \
> --class org.apache.spark.examples.SparkPi \
> --master spark://hadoop102:7077 \
> --executor-memory 1G \
> --total-executor-cores 2 \
> ./examples/jars/spark-examples_2.11-2.4.3.jar \
> 100
image.png
可以看到50多秒句结束了
当任务执行结束现在去访问spark 的4000，发现发问不了
11. 开启历史服务就可以访问已结束的任务了
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ sbin/start-history-server.sh
starting org.apache.spark.deploy.history.HistoryServer, logging to /opt/module/spark-2.4.3-bin-hadoop2.7/logs/spark-shaozhiqi-org.apache.spark.deploy.history.HistoryServer-1-hadoop102.out
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ jps
可以看到多了HistoryServer
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$ jps
3505 Worker
4708 HistoryServer
4775 Jps
4027 DataNode
3437 Master
3901 NameNode
[shaozhiqi@hadoop102 spark-2.4.3-bin-hadoop2.7]$
12. 访问history ui,成功
image.png
13. 查看hdfsz有无生成执行结果文件
文件已生成历史服务配置成功
image.png
