摘要：
Spark资源管理1、介绍Spark资源管控分为spark集群自身可支配资源配置和job所用资源配置。exportSPARK_WORKER_CORES=6每个worker所用内存数#每个worker使用的内存数，默认是1g内存exportSPARK_WORKER_MEMORY=6g每个节点可以启动worker实例的个数#是否可以在一个节点启动几个worker进程，默认1exportSPARK_WORKER_INSTANCES=2spark守护进程本身占用的内存数spark守护进程指的是master和worker进程，该进程自身使用内存数也可以进行控制。
Spark资源管理
1、介绍
Spark资源管控分为spark集群自身可支配资源配置和job所用资源配置。
2、spark集群支配资源控制
在spark的conf/spark-env.sh文件中可以指定master和worker的支配资源数。
2.1 Spark集群可支配资源配置
每个worker使用内核数
# 每个worker使用的内核数，默认是所有内核。 export SPARK_WORKER_CORES=6每个worker所用内存数
# 每个worker使用的内存数，默认是1g内存 export SPARK_WORKER_MEMORY=6g每个节点可以启动worker实例的个数
#是否可以在一个节点启动几个worker进程，默认1 export SPARK_WORKER_INSTANCES=2spark守护进程本身占用的内存数
spark守护进程指的是master和worker进程，该进程自身使用内存数也可以进行控制。
#master和worker进程本身的内存数 ,默认1g export SPARK_DAEMON_MEMORY=200m
spark/conf/spark-env.sh配置全部内容如下：
#!/usr/bin/env bash
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This file is sourced when running various Spark programs.
# Copy it as spark-env.sh and edit that to configure Spark for your site.
export JAVA_HOME=/soft/jdk
# Options read when launching programs locally with
# ./bin/run-example or ./bin/spark-submit
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# export HADOOP_CONF_DIR=/soft/hadoop/etc/hadoop
# - 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_JAVA_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 executors to start (Default: 2)
# - SPARK_EXECUTOR_CORES, Number of cores for the executors (Default: 1).
# - SPARK_EXECUTOR_MEMORY, Memory per Executor (e.g. 1000M, 2G) (Default: 1G)
# - SPARK_DRIVER_MEMORY, Memory for Driver (e.g. 1000M, 2G) (Default: 1G)
# Options for the daemons used in the standalone deploy mode
# - SPARK_MASTER_HOST, 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
#export SPARK_MASTER_PORT=7077
#export SPARK_MASTER_WEBUI_PORT=8080
# - 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
export SPARK_WORKER_CORES=6
# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
export SPARK_WORKER_MEMORY=6g
# - 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
export SPARK_WORKER_INSTANCES=1
# - 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_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g).
export SPARK_DAEMON_MEMORY=200m
# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y")
# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
# export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=s102:2181,s103:2181,s104:2181 -Dspark.deploy.zookeeper.dir=/spark"
# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers
# Generic options for the daemons used in the standalone deploy mode
# - SPARK_CONF_DIR Alternate conf dir. (Default: ${SPARK_HOME}/conf)
# - SPARK_LOG_DIR Where log files are stored. (Default: ${SPARK_HOME}/logs)
# - SPARK_PID_DIR Where the pid file is stored. (Default: /tmp)
# - SPARK_IDENT_STRING A string representing this instance of spark. (Default: $USER)
# - SPARK_NICENESS The scheduling priority for daemons. (Default: 0)
# - SPARK_NO_DAEMONIZE Run the proposed command in the foreground. It will not output a PID file.
2.2 job资源分配设置
spark-submit命令提交job时，可以为job指定使用的资源，包括内存和内核数。但在不同的spark集群模式下，使用的配置命令是不同的。命令使用如下：
$>spark-submit --master spark://s101:7077 --executor-memory 200m --executor-cores 4
设置driver内存数
$>spark-submit --driver-cores 2 #默认是1standalone和mesos模式下
执行器内核总数设置
$>spark-submit --total-executor-cores 32
standalone和yarn模式
每个执行器内核数设置
# yarn下模式为1，standalone模式下为worker上可有的所有内核数 $>spark-submit --executor-cores 4
yarn-only
只在yarn模式下，使用的资源控制选线：
driver内核数设置
driver使用的cpu内核数，只在cluster模式下有效，默认1。
$>spark-submit --driver-cores 3执行器个数设置
启动的执行器个数，默认为2。
$>spark-submit --num-executors 3
3、资源控制细则
spark资源控制在集群配置时不进行物理资源检查，即可以配置任意的资源值。比如物理内核是16，但是配置成每个worker占用32核。如图所示：
图中箭头指向的部分是worker进程能够支配使用的资源，包括内存和内核数。
Spark job执行时，指定的资源同时受到内存和内核两方面的限制，即任何一个条件不满足，都无法启动executor进程。例如指定每个executor使用3个core，worker可以支配8个core，但是最终该worker只能启动两个executor。