Spark学习2：Spark 配置以及repl运行和IDE运行
Spark 运行
本文针对Standalone模式下的Spark
Spark配置
安装Spark之后，首先保证Spark的配置项目正确。Spark的配置文件存储在$Spark_HOME/conf/目录下
spark-env.sh.template
slaves
spark-defaults.conf.template
spark-env.sh.template
#!/usr/bin/env bash # This file is sourced when running various Spark programs. # Copy it as spark-env.sh and edit that to configure Spark for your site. # Options read when launching programs locally with # ./bin/run-example or ./bin/spark-submit # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files # - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node # - SPARK_PUBLIC_DNS, to set the public dns name of the driver program # - SPARK_CLASSPATH, default classpath entries to append # Options read by executors and drivers running inside the cluster # - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node # - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program # - SPARK_CLASSPATH, default classpath entries to append # - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data # - MESOS_NATIVE_LIBRARY, to point to your libmesos.so if you use Mesos # Options read in YARN client mode # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files # - SPARK_EXECUTOR_INSTANCES, Number of workers to start (Default: 2) # - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1). # - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G) # - SPARK_DRIVER_MEMORY, Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb) # - SPARK_YARN_APP_NAME, The name of your application (Default: Spark) # - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’) # - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job. # - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job. # Options for the daemons used in the standalone deploy mode: # - SPARK_MASTER_IP, to bind the master to a different IP address or hostname # - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master # - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y") # - SPARK_WORKER_CORES, to set the number of cores to use on this machine # - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g) # - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node # - SPARK_WORKER_DIR, to set the working directory of worker processes # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y") # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y") # - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y") # - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers
按照注释的提示，把上面的template文件拷贝重命名，去掉template后缀，并修改内容如下
spark-env.sh
export JAVA_HOME=/xxxxxxx export SPARK_MASTER_IP=masterip or hostname export SPARK_WORKER_CORE=1 export SPARK_WORKER_INSTANCE=1 export SPARK_WORKER_MEMORY=512M #每个worker最多可以使用多少内存 export SPARK_MASTER_PORT=8888 export SPARK_JAVA_OPTS="-verbose:gc -XX:PrintGCDetails -XX:+PrintGCTimesStamps -XX:+UseCompressedOops"
slaves
# A Spark Worker will be started on each of the machines listed below. localhost
启动Spark
在Spark运行之前，首先需要让Spark集群启动，如果需要用到hadoop的HDFS的话，也需要把HDFS启动起来。
启动master
./sbin/start-master.sh
注册worker，同一台机器可以注册若干个worker
./bin/spark-class org.apache.spark.deploy.worker.Worker spark://masterip:7077 &
检查Spark的运行情况
查看master进程
ps -ef | grep -i spark或者
jps
或者打开页面查看
http://masterip:8080/
其他相关命令
在SPARK_HOME/sbin目录:
- sbin/start-master.sh-在机器上执行脚本，启动 master .
- sbin/start-slaves.sh- 启动conf/slaves中指定的每一个slave .
- sbin/start-all.sh- 同时启动master 以及 上面所说文件中指定的slave
- sbin/stop-master.sh- 停止通过bin/start-master.sh脚本启动的master
- sbin/stop-slaves.sh- 停止通过bin/start-slaves.sh启动的slave .
- sbin/stop-all.sh- 停止上述的两种启动脚本启动的master和slave
DFS
启动DFS就可以了，不需要启动整个hadoop
./start-dfs.sh
REPL 运行
$Spark_HOME/bin/MASTER=local[4] ADD_JARS code.jar ./spark-shell #MASTER=spark://host:port #export SPARK_MEM=xxg，指的是这个application在每个node上可以使用的内存
注意
在最新版本1.1.0中SparkContext.scala中增加了下面的内容
private def warnSparkMem(value: String): String = {
logWarning("Using SPARK_MEM to set amount of memory to use per executor process is " +
"deprecated, please use spark.executor.memory instead.")
value
}
private[spark] val executorMemory = conf.getOption("spark.executor.memory")
.orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY")))
.orElse(Option(System.getenv("SPARK_MEM")).map(warnSparkMem))
.map(Utils.memoryStringToMb)
.getOrElse(512)
可以看出Spark最新的机制，建议使用新的spark.executor.memory取代SPARK_EXECUTOR_MEMORY，并且系统默认支持512M内存，其中Utils.memoryStringToMb方法可以把G转换成M。
$./spark-shell
如果以standalone方式启动的话，最好把master的内容写全。可以把export SPARK_MEM=xxg写入到./spark-shell中，方便起见。如果只是启动spark shell的话ADD_JARS可以不写
IDE运行
这里的IDE运行指的是，在IDE中编译打包好自己的程序后，然后以普通java包的形式运行。与Spark shell相对，就是说要自己声称自己新的Spark context。
比如在命令行下执行如下命令
java -jar ./chinahadoop.jar ./chinahadoop.jar hdfs://server1:9000/labs/a.txt hdfs://server1:9000/labs/output/上面的jar包要指定main函数。
具体项目参考下面的github链接
https://github.com/suxingfate/spark101/blob/master/src/main/scala/cn/chinahadoop/spark/Analysis.scala
以及pom中的配置依赖和打包方式
https://github.com/suxingfate/spark101/blob/master/pom.xml