配置hadoop+pyspark环境
配置hadoop+pyspark环境1、部署hadoop环境
配置hadoop伪分布式环境，所有服务都运行在同一个节点上。
1.1、安装JDK
安装jdk使用的是二进制免编译包，下载页面
[*]下载jdk
$ cd /opt/local/src/
$ curl -o jdk-8u171-linux-x64.tar.gzhttp://download.oracle.com/otn-pub/java/jdk/8u171-b11/512cd62ec5174c3487ac17c61aaa89e8/jdk-8u171-linux-x64.tar.gz?AuthParam=1529719173_f230ce3269ab2fccf20e190d77622fe1
[*]解压文件，配置环境变量
### 解压到指定位置
$ tar -zxf jdk-8u171-linux-x64.tar.gz -C /opt/local
### 创建软连接
$ cd /opt/local/
$ ln -s jdk1.8.0_171 jdk
### 配置环境变量，在当前用的配置文件 ~/.bashrc 增加如下配置
$ tail ~/.bashrc
# Java
export JAVA_HOME=/opt/local/jdk
export JRE_HOME=$JAVA_HOME/jre
export>
export PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin
[*]刷新环境变量
$ source ~/.bashrc
### 演那种是否生效，返回java信息说明正确
$ java -version
java version "1.8.0_171"
Java(TM) SE Runtime Environment (build 1.8.0_171-b11)
Java HotSpot(TM) 64-Bit Server VM (build 25.171-b11, mixed mode)
1.2、配置/etc/hosts
### 配置/etc/hosts 把主机名和IP地址一一对应
$ head -n 3 /etc/hosts
# ip --> hostname or domain
192.168.20.10 node
### 验证
$ ping node -c 2
PING node (192.168.20.10) 56(84) bytes of data.
64 bytes from node (192.168.20.10): icmp_seq=1 ttl=64 time=0.063 ms
64 bytes from node (192.168.20.10): icmp_seq=2 ttl=64 time=0.040 ms
1.3、设置ssh无密码登录
[*]生成SSH key
### 生成ssh key
$ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
[*]配置公钥到许可文件authorizd_keys
### 需要输入密码
ssh-copy-id node
### 验证登录，不需要密码即为成功
$ ssh node
1.4、安装配置hadoop
[*]下载hadoop
### 下载Hadoop2.7.6
$ cd /opt/local/src/
$ wget -c http://mirrors.hust.edu.cn/apache/hadoop/common/hadoop-2.7.6/hadoop-2.7.6.tar.gz
[*]创建hadoop相关目录
$ mkdir -p /opt/local/hdfs/{namenode,datanode,tmp}
$ tree /opt/local/hdfs/
/opt/local/hdfs/
├── datanode
├── namenode
└── tmp
[*]解压hadoop安装文件
### 解压到指定位置
$ cd /opt/local/src/
$ tar -zxf hadoop-2.7.6.tar.gz -C /opt/local/
### 创建软连接
$ cd /opt/local/
$ ln -s hadoop-2.7.6 hadoop
1.5、配置hadoop
1.5.1、 配置core-site.xml
$ vim /opt/local/hadoop/etc/hadoop/core-site.xml
hadoop.tmp.dir
file:/opt/local/hdfs/tmp/
fs.defaultFS
hdfs://node:9000
io.file.buffer.size
131072
1.5.2、 配置hdfs-site.xml
$ vim /opt/local/hadoop/etc/hadoop/hdfs-site.xml
dfs.replication
1
dfs.namenode.name.dir
/opt/local/hdfs/namenode
dfs.datanode.data.dir
/opt/local/hdfs/datanode
dfs.webhdfs.enabled
true
1.5.3、 配置mapred-site.xml
### mapred-site.xml需要从一个模板拷贝在修改
$ cp /opt/local/hadoop/etc/hadoop/mapred-site.xml.template/opt/local/hadoop/etc/hadoop/mapred-site.xml
$ vim /opt/local/hadoop/etc/hadoop/mapred-site.xml
mapreduce.framework.name
yarn
mapreduce.jobhistory.address
node:10020
mapreduce.jobhistory.webapp.address
node:19888
mapreduce.jobhistory.done-dir
/history/done
mapreduce.jobhistory.intermediate-done-dir
/history/done_intermediate
1.5.4、 配置yarn-site.xml
$ vim /opt/local/hadoop/etc/hadoop/yarn-site.xml
yarn.nodemanager.aux-services
mapreduce_shuffle
yarn.resourcemanager.hostname
node
yarn.resourcemanager.address
node:8032
yarn.resourcemanager.scheduler.address
node:8030
yarn.resourcemanager.resource-tracker.address
node:8031
yarn.resourcemanager.admin.address
node:8033
yarn.resourcemanager.webapp.address
node:8088
yarn.log-aggregation-enable
true
yarn.log-aggregation.retain-seconds
604800
yarn.nodemanager.pmem-check-enabled
false
yarn.nodemanager.vmem-check-enabled
false
1.5.5、 配置slaves
$ cat/opt/local/hadoop/etc/hadoop/slaves
node
1.5.6、 配置master
$ cat/opt/local/hadoop/etc/hadoop/master
node
1.5.7、 配置hadoop-env
$ vim/opt/local/hadoop/etc/hadoop/hadoop-env.sh
### 修改JAVA_HOME
export JAVA_HOME=/opt/local/jdk
1.5.8、 配置yarn-env
$ vim/opt/local/hadoop/etc/hadoop/yarn-env.sh
### 修改JAVA_HOME
export JAVA_HOME=/opt/local/jdk
1.5.9、 配置mapred-env
$ vim/opt/local/hadoop/etc/hadoop/mapred-env.sh
### 修改JAVA_HOME
export JAVA_HOME=/opt/local/jdk
1.5.10、配置hadoop环境变量
[*]增加hadoop相关配置
在 ~/.bashrc 增加hadoop环境变量，配置如下
# hadoop
export HADOOP_HOME=/opt/local/hadoop
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-DJava.library.path=$HADOOP_HOME/lib"
export JAVA_LIBRARY_PATH=$HADOOP_HOME/lib/native:$JAVA_LIBRARY_PATH
[*]启用配置
$ source ~/.bashrc
### 验证
$ hadoop version
Hadoop 2.7.6
Subversion https://shv@git-wip-us.apache.org/repos/asf/hadoop.git -r 085099c66cf28be31604560c376fa282e69282b8
Compiled by kshvachk on 2018-04-18T01:33Z
Compiled with protoc 2.5.0
From source with checksum 71e2695531cb3360ab74598755d036
This command was run using /opt/local/hadoop-2.7.6/share/hadoop/common/hadoop-common-2.7.6.jar
1.6、格式化hdfs文件系统
### 格式化hdfs，如果已有数据慎重使用，这会删除原有的数据
$ hadoop namenode -format
### namenode存储目录会产生数据
$ ls /opt/local/hdfs/namenode/
current
1.7、启动hadoop
启动hadoop主要有HDFS（Namenode、Datanode）和YARN（ResourceManager、NodeManager），可以使用start-all.sh命令启动；关闭命令stop-all.sh,也可以指定应用启动；
1.7.1、启动dfs
启动dfs包括namenode和datanode两个服务，可以使用start-dfs.sh启动，以下采用分布启动；
1.7.1.1、启动Namenode
### 启动namenode
$ hadoop-daemon.sh start namenode
starting namenode, logging to /opt/local/hadoop-2.7.6/logs/hadoop-hadoop-namenode-node.out
### 查看进程
$ jps
7547 Jps
7500 NameNode
### 启动SecondaryNameNode
$ hadoop-daemon.sh start secondarynamenode
starting secondarynamenode, logging to /opt/local/hadoop-2.7.6/logs/hadoop-hadoop-secondarynamenode-node.out
### 查看进程
$ jps
10001 SecondaryNameNode
10041 Jps
9194 NameNode
1.7.1.2、启动Datanode
### 启动datanode
$ hadoop-daemon.sh start datanode
starting datanode, logging to /opt/local/hadoop-2.7.6/logs/hadoop-hadoop-datanode-node.out
### 查看进程
$ jps
7607 DataNode
7660 Jps
7500 NameNode
10001 SecondaryNameNode
1.7.2、启动yarn
启动yarn包括ResourceManager和NodeManager，可以使用start-yarn.sh启动，以下采用分布启动；
1.7.2.1、启动ResourceManager
### 启动resourcemanager
$ yarn-daemon.sh start resourcemanager
starting resourcemanager, logging to /opt/local/hadoop-2.7.6/logs/yarn-hadoop-resourcemanager-node.out
### 查看进程
$ jps
7607 DataNode
7993 Jps
7500 NameNode
7774 ResourceManager
10001 SecondaryNameNode
1.7.2.2、启动NodeManager
### 启动nodemanager
$ yarn-daemon.sh start nodemanager
starting nodemanager, logging to /opt/local/hadoop-2.7.6/logs/yarn-hadoop-nodemanager-node.out
### 查看进程
$ jps
7607 DataNode
8041 NodeManager
8106 Jps
7500 NameNode
7774 ResourceManager
10001 SecondaryNameNode
1.7.3、启动Historyserver
### 启动 historyserver
$ mr-jobhistory-daemon.sh start historyserver
starting historyserver, logging to /opt/local/hadoop/logs/mapred-hadoop-historyserver-node.out
### 查看进程
$ jps
8278 JobHistoryServer
7607 DataNode
8041 NodeManager
7500 NameNode
8317 Jps
7774 ResourceManager
10001 SecondaryNameNode
1.7.4、hadoop相关功能说明
hadoop启动后，主要有以下功能
[*]HDFS功能：NameNode、SecondaryNameNode、DataNode
[*]YARN功能：ResourceManager、NodeManager
[*]HistoryServer:JobHistoryServer
1.8、hadoop基本操作
1.8.1、 hadoop 常用命令
命令
说明
hadoop fs -mkdir
创建HDFS 目录
hadoop fs -ls
列出HDFS 目录
hadoop fs -copyFromLocal
复制本地文件到HDFS
hadoop fs -put
复制本地文件到HDFS，put可以接收stdin（标准输入）
hadoop fs -cat
列出HDFS文件的内容
hadoop fs -copyToLocal
将HDFS上的文件复制到本地
hadoop fs -get
将HDFS上的文件复制到本地
hadoop fs -cp
负载HDFS文件
hadoop fs -rm
删除HDFS文件或目录（-R参数）
1.8.2、haodoom 命令操作
1.8.2.1 基本命令操作
[*]创建目录
$ hadoop fs -mkdir /user/hadoop
[*]创建多个目录
$ hadoop fs -mkdir -p /user/hadoop/{input,output}
[*]查看HDFS目录
$ hadoop fs -ls /
Found 2 items
drwxrwx--- - hadoop supergroup 0 2018-06-23 12:20 /history
drwxr-xr-x - hadoop supergroup 0 2018-06-23 13:20 /user
$ hadoop fs -ls /user
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2018-06-23 13:20 /user/hadoop
[*]查看所有的目录
$ hadoop fs -ls -R /
drwxrwx--- - hadoop supergroup 0 2018-06-23 12:20 /history
drwxrwx--- - hadoop supergroup 0 2018-06-23 12:20 /history/done
drwxrwxrwt - hadoop supergroup 0 2018-06-23 12:20 /history/done_intermediate
drwxr-xr-x - hadoop supergroup 0 2018-06-23 13:20 /user
drwxr-xr-x - hadoop supergroup 0 2018-06-23 13:24 /user/hadoop
drwxr-xr-x - hadoop supergroup 0 2018-06-23 13:24 /user/hadoop/input
drwxr-xr-x - hadoop supergroup 0 2018-06-23 13:24 /user/hadoop/output
[*]上传本地文件到HDFS
$ hadoop fs -copyFromLocal /opt/local/hadoop/README.txt /user/hadoop/input
[*]查看HDFS上的文件内容
$ hadoop fs -cat /user/hadoop/input/README.txt
[*]将HDFS上的文件下载到本地
$ hadoop fs -get /user/hadoop/input/README.txt ./
[*]删除文件或目录
### 删除文件会提示
$ hadoop fs -rm /user/hadoop/input/examples.desktop
18/06/23 13:47:06 INFO fs.TrashPolicyDefault: Namenode trash configuration: Deletion interval = 0 minutes, Emptier interval = 0 minutes.
Deleted /user/hadoop/input/examples.desktop
### 删除目录
$ hadoop fs -rm -R /user/hadoop
18/06/23 13:48:17 INFO fs.TrashPolicyDefault: Namenode trash configuration: Deletion interval = 0 minutes, Emptier interval = 0 minutes.
Deleted /user/hadoop
1.8.2.2、执行mapreduce任务
使用hadoop内置的wordcount程序统计字数
[*]执行计算任务
$ hadoop fs -put /opt/local/hadoop/README.txt /user/input
$ cd /opt/local/hadoop/share/hadoop/mapreduce
#### hadoop jar jar包名称 类 输入文件 输出目录
$ hadoop jar hadoop-mapreduce-examples-2.7.6.jar wordcount /user/input/ /user/output/wordcount
[*]查看当前的任务
#### 查看当然的任务情况，也可以在http://node:8088 上查看
$ yarn application -list
18/06/23 13:55:34 INFO client.RMProxy: Connecting to ResourceManager at node/192.168.20.10:8032
Total number of applications (application-types: [] and states: ):1
Application-Id Application-Name Application-Type User Queue State Final-State Progress Tracking-URL
application_1529732240998_0001 word count MAPREDUCE hadoop default RUNNING UNDEFINED 5% http://node:41713
[*]查看计算结果
#### _SUCCESS 表示成功，part开头的文件表是结果
$ hadoop fs -ls /user/output/wordcount
Found 2 items
-rw-r--r-- 1 hadoop supergroup 0 2018-06-23 13:55 /user/output/wordcount/_SUCCESS
-rw-r--r-- 1 hadoop supergroup 1306 2018-06-23 13:55 /user/output/wordcount/part-r-00000
#### 查看内容
$ hadoop fs -cat /user/output/wordcount/part-r-00000|tail
uses 1
using 2
visit 1
website 1
which 2
wiki, 1
with 1
written 1
you 1
your 1
1.9、hadoop Web界面
[*]hadoop NameNode HDFS Web界面可以查看当前HDFS和DataNode的运行情况
http://node:50070
[*]hadoop ResourceManager Web界面可以查看当前hadoop的Node节点状态，应用进程、任务执行状态
http://node:8088
2、部署spark
2.1、Scala简介和安装
2.1.1、Scala简介
spark是用是scala编写，Scala官网为：https://www.scala-lang.org/，因此需要首先安装Scala，Scala有以下特点：
[*]Scala可编译为Java bytecode 字节码，也就是说可以在JVM（Java Virtual Machine）上运行，具备跨平台能力；
[*]现有的Java的链接库都可以使用，可以继续使用丰富的Java开放源代码生态系统；
[*]Scala是一种函数式语言，在函数式语言中，函数也是值，与整数字符串处于同一地位；函数可以作为参数传递给其他函数；
[*]Scala是一种纯面向对象的语言，所有东西都是对象，而所有操作都是方法；
2.1.2、Scala安装
Scala下载地址为https://www.scala-lang.org/files/archive/；
从Spark2.0版开始，Spark默认使用Scala 2.11构建，因此下载scala-2.11版本；
[*]下载Scala
$ cd /opt/local/src/
$ wget -c https://www.scala-lang.org/files/archive/scala-2.11.11.tgz
[*]解压Scala文件
#### 解压到指定位置，并做软连接
$ tar -zxf scala-2.11.11.tgz -C /opt/local/
$ cd /opt/local/
[*]配置Scala环境变量
#### 配置 ~/.bashrc 增加如下
$ tail -n 5 ~/.bashrc
# scala
export SCALA_HOME=/opt/local/scala
export PATH=$PATH:$SCALA_HOME/bin
#### 启用配置
$ source ~/.bashrc
#### 验证
$ scala -version
Scala code runner version 2.11.11 -- Copyright 2002-2017, LAMP/EPFL
2.2、Spark安装
2.2.1、Spark下载
spark下载页面地址是http://spark.apache.org/downloads.html,需要选择用于hadoop2.7及以上版本；
$ cd /opt/local/src/
$ wget -c http://mirror.bit.edu.cn/apache/spark/spark-2.2.1/spark-2.3.1-bin-hadoop2.7.tgz
2.2.2、Spark解压配置
[*]解压spark到指定目录，并做软连接
$ tar zxf spark-2.3.1-bin-hadoop2.7.tgz -C /opt/local/
$ cd /opt/local/
$ ln -s spark-2.3.1-bin-hadoop2.7 spark
[*]配置spark环境变量
$ tail -n 5 ~/.bashrc
# spark
export SPARK_HOME=/opt/local/spark
export PATH=$PATH:$SPARK_HOME/bin
[*]启用环境变量
$ source ~/.bashrc
2.3、运行pyspark
2.3.1、本地运行pyspyark
在终端输入pyspark启动spark的python接口，启动会显示使用的python版本和spark版本信息；
pyspark --master local，local代表本地运行，使用N个线程（thread）;local
[*] 会尽量使用所有的CPU核心；
[*]本地启动pyspark
$ pyspark
Python 2.7.12 (default, Dec4 2017, 14:50:18)
on linux2
Type "help", "copyright", "credits" or "license" for more information.
2018-06-23 19:25:00 WARNNativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java>
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/_____ _____/ /__
_\ \/ _ \/ _ `/ __/'_/
/__ / .__/\_,_/_/ /_/\_\ version 2.3.1
/_/
Using Python version 2.7.12 (default, Dec4 2017 14:50:18)
SparkSession available as 'spark'.
[*]查看当前运行模式
>>> sc.master
u'local
[*]'
[*]读取本地文件
>>> textFile=sc.textFile("file:/opt/local/spark/README.md")
>>> textFile.count()
103
[*]读取HDFS文件
>>> textFile=sc.textFile("hdfs://node:9000/user/input/README.md")
>>> textFile.count()
103
2.3.2、Hadoop YARN 运行spark
spark可以在Hadoop YARN上运行，让YARN帮助它进行资源管理；
HADOOP_CONF_DIR=/opt/local/hadoop/etc/hadoop pyspark --master yarn --deploy-mode client；
HADOOP_CONF_DIR=/opt/local/hadoop/etc/hadoop 表示设置hadoop配置文件目录；
pyspark 运行的程序;
--master yarn --deploy-mode client 设置运行模式为YARN-Client
$ HADOOP_CONF_DIR=/opt/local/hadoop/etc/hadoop pyspark --master yarn --deploy-mode client
Python 2.7.12 (default, Dec4 2017, 14:50:18)
on linux2
Type "help", "copyright", "credits" or "license" for more information.
2018-06-23 20:27:48 WARNNativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java>
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
2018-06-23 20:27:52 WARNClient:66 - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
Welcome to
____ __
/ __/_____ _____/ /__
_\ \/ _ \/ _ `/ __/'_/
/__ / .__/\_,_/_/ /_/\_\ version 2.3.1
/_/
Using Python version 2.7.12 (default, Dec4 2017 14:50:18)
SparkSession available as 'spark'.
[*]查看当前运行模式
>>> sc.master
u'yarn'
[*]读取HDFS文件
>>> textFile=sc.textFile("hdfs://node:9000/user/input/README.md")
>>> textFile.count()
103
[*]yarn 查看任务运行情况
#### 也可以通过web查看：http://node:8088
$ yarn application -list
18/06/23 20:34:40 INFO client.RMProxy: Connecting to ResourceManager at node/192.168.20.10:8032
Total number of applications (application-types: [] and states: ):1
Application-Id Application-Name Application-Type User Queue State Final-State Progress Tracking-URL
application_1529756801315_0001 PySparkShell SPARK hadoop default RUNNING UNDEFINED 10% http://node:4040
2.3.3、Spark Standalone Cluster运行spark
配置Spark Standalone Cluste伪分布式环境，所有服务都运行在同一个节点上。
2.3.3.1、配置spark-env.sh
[*]复制模板文件创建spark-env.sh
$ cp /opt/local/spark/conf/spark-env.sh.template /opt/local/spark/conf/spark-env.sh
[*]配置spark-env.sh
$ tail -n 6 /opt/local/spark/conf/spark-env.sh
#### Spark Standalone Cluster
export JAVA_HOME=/opt/local/jdk
export SPARK_MASTER_HOST=node
export SPARK_WORKER_CORES=1
export SPARK_WORKER_MEMORY=512m
export SPARK_WORKER_INSTANCES=1
2.3.3.2、配置slave
#### 增加编辑，也可以拷贝模板文件
$ tail /opt/local/spark/conf/slaves
node
2.3.3.3、在Spark Standalone Cluster运行pyspark
2.3.3.3.1、启动Spark Standalone Cluster
启动Spark Standalone Cluster可以使用${SPARN_HOME}/sbin/start-all.sh一个脚本启动所有服务;也可以分布启动master和slaves；
[*]启动master
$ /opt/local/spark/sbin/start-master.sh
starting org.apache.spark.deploy.master.Master, logging to /opt/local/spark/logs/spark-hadoop-org.apache.spark.deploy.master.Master-1-node.out
~$ jps
4185 Master
[*]启动slaves
$ /opt/local/spark/sbin/start-slaves.sh
node: starting org.apache.spark.deploy.worker.Worker, logging to /opt/local/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-node.out
$ jps
4185 Master
4313 Worker
[*]通过http://node:8080查看集群状态
$ w3m http://node:8080/
2.3.1 Spark Master at spark://node:7077
· URL: spark://node:7077
· REST URL: spark://node:6066 (cluster mode)
· Alive Workers: 1
· Cores in use: 1 Total, 0 Used
· Memory in use: 256.0 MB Total, 0.0 B Used
· Applications: 0 Running, 0 Completed
· Drivers: 0 Running, 0 Completed
· Status: ALIVE
Workers (1)
Worker> 512.0
worker-20180624102100-192.168.20.10-42469 192.168.20.10:42469 ALIVE 1 (0MB
Used) (0.0 B
Used)
Running Applications (0)
Application> Executor
Completed Applications (0)
Application> Executor
2.3.3.3.2、在Spark Standalone Cluster运行pyspark
[*]运行pyspark
$ pyspark --master spark://node:7077 --num-executors 1 --total-executor-cores 1 --executor-memory 512m
Python 2.7.12 (default, Dec4 2017, 14:50:18)
on linux2
Type "help", "copyright", "credits" or "license" for more information.
2018-06-24 10:39:09 WARNNativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java>
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/_____ _____/ /__
_\ \/ _ \/ _ `/ __/'_/
/__ / .__/\_,_/_/ /_/\_\ version 2.3.1
/_/
Using Python version 2.7.12 (default, Dec4 2017 14:50:18)
SparkSession available as 'spark'.
[*]查看当前运行模式
>>> sc.master
u'spark://node:7077'
[*]读取本地文件
>>> textFile=sc.textFile("file:/opt/local/spark/README.md")
>>> textFile.count()
103
[*]读取hdfs文件
>>> textFile=sc.textFile("hdfs://node:9000/user/input/README.md")
>>> textFile.count()
103
2.3、总结
spark的运行方式有多种，主要有独立集群、YARN集群、Mesos集群，和本地模式
master可选值
描述说明
spark://host:port
spark standalone集群，默认端口为7077
yarn
YARN集群，当在YARN上运行时，需设置环境变量HADOOP_CONF_DIR指向hadoop配置目录，以获取集群信息
mesos://host:port
Mesos集群，默认端口为5050
local
本地模式，使用1个核心
local
本地模式，使用n个核心
local
[*]
本地模式，使用尽可能多的核心
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