spark高可用，yarn

1.配置spark-env.sh

# 配置大哥;在二哥上面,MASTER_PORT=指的是自己
SPARK_MASTER_HOST=hadoop102
# 设置zookeepr,不能换行
SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop101:2181,hadoop102:2181,hadoop103:2181 -Dspark.deploy.zookeeper.dir=/spark"
# 告诉Spark,hadoop放到哪里面了,前题是每一个Spark,的服务器上都装有hadoop
HADOOP_CONF_DIR=/data/hadoop/hadoop-3.2.1/etc/hadoop/

配置二哥MASTER_PORT=指的是自己
SPARK_MASTER_HOST=hadoop101

2.配置slaves

#配置的小弟
hadoop103
hadoop104

3.启动

1. 启动zookeeper

bin/zkServer.sh

2. 启动hadoop

sbin/start-all.sh

3. 启动spark

sbin/start-all.sh

停止spark

sbin/stop-all.sh

注意查看spark的web端的时候，由于启动了zookeeper，spark的端口与zookeeper的端口冲突，spark会把端口改成8081，在logs日志中会显示

# 查看网络的状态;是zookeeper占用了8080端口
netstat -anp|grep 8080

启动二哥在hadoop101上启动

sbin/start-master.sh

4.运行示例

# --master:把任务提交给大哥;
# --executor-memory:指定executor的内存;
# --executor-cores:指定executor的cpu,核心
bin/spark-submit \
--master spark://hadoop102:7077 \
--name myPi \
--executor-memory 500m \
--total-executor-cores 2 \
--class org.apache.spark.examples.SparkPi \
examples/jars/spark-examples_2.11-2.4.4.jar \
100000

# --master:把任务提交给大哥;
# --executor-memory:指定executor的内存;(都是workder的,executor)
# --executor-cores:指定executor的cpu,核心(都是workder的,executor)
# --deploy-mode:部署模式
bin/spark-submit \
--master spark://hadoop102:7077 \
--name myPi \
--deploy-mode cluster \
--executor-memory 500m \
--total-executor-cores 2 \
--class org.apache.spark.examples.SparkPi \
examples/jars/spark-examples_2.11-2.4.4.jar \
10000

# --driver-cores:适应于集群模式;2核
# --driver-memory:适应于集群模式;500m;
bin/spark-submit \
--master spark://hadoop102:7077 \
--name myPi \
--deploy-mode cluster \
--executor-memory 500m \
--total-executor-cores 2 \
--driver-cores 2 \
--driver-memory 500m \
--class org.apache.spark.examples.SparkPi \
examples/jars/spark-examples_2.11-2.4.4.jar \
100

# --master:把任务提交给大哥;
# --executor-memory:指定executor的内存;(都是workder的,executor)
# --executor-cores:指定executor的cpu,核心(都是workder的,executor)
# --deploy-mode:部署模式
bin/spark-submit \
--master yarn \
--name myPi \
--executor-memory 500m \
--total-executor-cores 2 \
--class org.apache.spark.examples.SparkPi \
examples/jars/spark-examples_2.11-2.4.4.jar \
10000

集群提交

# --master:把任务提交给大哥;
# --executor-memory:指定executor的内存;(都是workder的,executor)
# --executor-cores:指定executor的cpu,核心(都是workder的,executor)
# --deploy-mode:部署模式
bin/spark-submit \
--master yarn \
--name myPi \
--deploy-mode cluster \
--executor-memory 500m \
--total-executor-cores 2 \
--class org.apache.spark.examples.SparkPi \
examples/jars/spark-examples_2.11-2.4.4.jar \
10000