Hadoop Cluster Setup 

• Purpose
• Prerequisites
• Installation
• Configuring Hadoop in Non-Secure Mode
  • Configuring Environment of Hadoop Daemons
  • Configuring the Hadoop Daemons
• Monitoring Health of NodeManagers
• Slaves File
• Hadoop Rack Awareness
• Logging
• Operating the Hadoop Cluster
  • Hadoop Startup
  • Hadoop Shutdown
• Web Interfaces

hadoop叢集設定

• 目的
• 先決條件
• 安裝
• 非安全模式下配置hadoop
•         配置hadoop程式的環境
•         配置hadoop程式
• 監控NodeManager的健康
• DataNode檔案
• hadoop支架感覺
• 日誌
• 操作hadoop叢集
•     叢集啟動
•     叢集關閉
• Web介面

Purpose

This document describes how to install and configure Hadoop clusters ranging from a few nodes to extremely(非常) large clusters with thousands of nodes. To play with Hadoop, you may first want to install it on a single machine (see Single Node Setup).

This document does not cover advanced topics such as Security or High Availability.

ranging from ......to...... 從...到...

該文件描述瞭如何安裝和配置從幾個到非常多的節點的hadoop叢集。為了使用hadoop,你應該首先想在一臺機器上安裝它，請參考(上一篇)

這個文件不涉及高階話題，比如安全Security 和高可用

Prerequisites

• Install Java. See the Hadoop Wiki for known good versions.
• Download a stable version of Hadoop from Apache mirrors.

先決條件

• 安裝java,檢視Hadoop Wiki檢視合適的版本
• 從apache映象中下載一個穩定的版本

Installation

Installing a Hadoop cluster typically(通常) involves unpacking the software on all the machines in the cluster or installing it via(通過) a packaging system as appropriate(適當的) for your operating system. It is important to divide up the hardware into functions.

Typically one machine in the cluster is designated(被指定) as the NameNode and another machine as the ResourceManager, exclusively. These are the masters. Other services (such as Web App Proxy Server and MapReduce Job History server) are usually run either on dedicated(專注的) hardware or on shared infrastructure（基礎建設）, depending upon the load.

either ... or ....(要麼..要麼...)

The rest of the machines in the cluster act as both DataNode and NodeManager. These are the slaves.

安裝hadoop機器通常是在叢集中的所有機器上解壓軟體，或者是適合你作業系統的打包系統。按功能區區分硬體是很重要的。

通常情況下叢集中的一臺機器被指定為NameNode,另一個機器被指定為ResourceManager,這些屬於“主人”。別的服務（比如WEB App Proxy Server或者MapReduce job History server）通常專注與一臺硬體或者是共享裝置，這些取決於負載。

叢集中其餘的叢集作為DataNode和NodeManager.他們是“工人”。

Configuring Hadoop in Non-Secure Mode

Hadoop’s Java configuration is driven by two types of important configuration files:

• Read-only default configuration - core-default.xml, hdfs-default.xml, yarn-default.xml and mapred-default.xml.

• Site-specific configuration - etc/hadoop/core-site.xml, etc/hadoop/hdfs-site.xml, etc/hadoop/yarn-site.xml and etc/hadoop/mapred-site.xml.

Additionally, you can control the Hadoop scripts found in the bin/ directory of the distribution, by setting site-specific values via the etc/hadoop/hadoop-env.sh and etc/hadoop/yarn-env.sh.

To configure the Hadoop cluster you will need to configure the environment in which the Hadoop daemons execute as well as the configuration parameters for the Hadoop daemons.

HDFS daemons are NameNode, SecondaryNameNode, and DataNode. YARN daemons are ResourceManager, NodeManager, and WebAppProxy. If MapReduce is to be used, then the MapReduce Job History Server will also be running. For large installations, these are generally running on separate hosts.

hadoop的Java配置注意是由兩個重要的配置檔案：

• 只讀配置 core-default.xml, hdfs-default.xml, yarn-default.xml and mapred-default.xml.
• 特定的配置檔案- etc/hadoop/core-site.xml, etc/hadoop/hdfs-site.xml, etc/hadoop/yarn-site.xml 和etc/hadoop/mapred-site.xml.

除此之外，你可以通過設定etc/hadoop-env.sh和etc/hadoop/yarn-env.sh中的特定值來控制bin目錄下hadoop指令碼。

為了設定hadoop叢集你需要設定hadoop程式執行的機器的環境和hadoop程式的引數。

HDFS的程式有NameNode SecondaryNameNode DataNode.YARN程式有ResourceManager NodeManager WebAppProxy.如果使用了MapReduce那麼MapReduce job History服務也會執行。節點多的話，這些通常在單獨的機器上執行。

Configuring Environment of Hadoop Daemons

Administrators should use the etc/hadoop/hadoop-env.sh and optionally the etc/hadoop/mapred-env.sh and etc/hadoop/yarn-env.sh scripts to do site-specific customization of the Hadoop daemons’ process environment.

At the very least, you must specify the JAVA_HOME so that it is correctly defined on each remote node.

Administrators can configure individual daemons using the configuration options shown below in the table:

 配置hadoop程式的環境

管理員應該使用etc/hadoop/hadoop-env.sh 或者etc/hadoop/mapred-env.sh 和etc/hadoop/yarn-env.sh去定製hadoop程式的執行環境。

至少，你必須指定JAVA_HOME在每一個遠端節點上正確的定義了。

管理員可以使用下表中的引數選項配置每個程式：

Daemon	Environment Variable
NameNode	HADOOP_NAMENODE_OPTS
DataNode	HADOOP_DATANODE_OPTS
Secondary NameNode	HADOOP_SECONDARYNAMENODE_OPTS
ResourceManager	YARN_RESOURCEMANAGER_OPTS
NodeManager	YARN_NODEMANAGER_OPTS
WebAppProxy	YARN_PROXYSERVER_OPTS
Map Reduce Job History Server	HADOOP_JOB_HISTORYSERVER_OPTS

For example, To configure Namenode to use parallelGC, the following statement should be added in hadoop-env.sh :

 例如，為了指定NameNode使用parallelGC，如下命令需要加入到hadoop-env.sh檔案。

  export HADOOP_NAMENODE_OPTS="-XX:+UseParallelGC"

See etc/hadoop/hadoop-env.sh for other examples.

Other useful configuration parameters that you can customize include:

檢視etc/hadoop/hadoop-env.sh 獲得別的示例.

你可以定製的有用的配置引數包括：

• HADOOP_PID_DIR - The directory where the daemons’ process id files are stored.
• HADOOP_LOG_DIR - The directory where the daemons’ log files are stored. Log files are automatically created if they don’t exist.
• HADOOP_HEAPSIZE / YARN_HEAPSIZE - The maximum amount of heapsize to use, in MB e.g. if the varibale（變數） is set to 1000 the heap will be set to 1000MB. This is used to configure the heap size for the daemon. By default, the value is 1000. If you want to configure the values separately for each deamon you can use.

• HADOOP_PID_DIR - 程式ID檔案儲存的目錄 
• HADOOP_LOG_DIR - 程式日誌檔案儲存的路徑，如果日誌檔案不存在的話他們將會自動建立。
• HADOOP_HEAPSIZE / YARN_HEAPSIZE - 最大堆使用量，以MB為單位，例如，如果變數設定為1000，則堆將設定為1000MB。這用於為守護程式配置堆大小。預設情況下，值為1000。如果希望為每個deamon單獨配置值，可以使用。

In most cases, you should specify（指定） the HADOOP_PID_DIR and HADOOP_LOG_DIR directories such that they can only be written to by the users that are going to run the hadoop daemons. Otherwise there is the potential（有可能的） for a symlink attack.

It is also traditional to configure HADOOP_PREFIX in the system-wide shell environment configuration. For example, a simple script inside /etc/profile.d:

在大部分情況下，你應該指定HADOOP_PID_DIR和HADOOP_LOG_DIR目錄，這些目錄只能由執行hadoop程式的使用者去建立，否則的話將會有符號連結的攻擊。

通常也會在系統全域性變數中配置HADOOP_HOME，例如在/etc/profile.d指令碼中

  HADOOP_PREFIX=/path/to/hadoop
  export HADOOP_PREFIX

Daemon	Environment Variable
ResourceManager	YARN_RESOURCEMANAGER_HEAPSIZE
NodeManager	YARN_NODEMANAGER_HEAPSIZE
WebAppProxy	YARN_PROXYSERVER_HEAPSIZE
Map Reduce Job History Server	HADOOP_JOB_HISTORYSERVER_HEAPSIZE

Configuring the Hadoop Daemons

This section deals with important parameters to be specified in the given configuration files:

這一部分處理了已給定配置檔案的重要的引數

• etc/hadoop/core-site.xml

Parameter	Value	Notes
fs.defaultFS	NameNode URI	hdfs://host:port/
io.file.buffer.size	131072	Size of read/write buffer used in SequenceFiles.128M？？

• etc/hadoop/hdfs-site.xml

• Configurations for NameNode:

Parameter	Value	Notes
dfs.namenode.name.dir	Path on the local filesystem where the NameNode stores the namespace and transactions logs persistently.	If this is a comma-delimited list of directories then the name table is replicated in all of the directories, for redundancy.
dfs.hosts / dfs.hosts.exclude	List of permitted/excluded DataNodes.	If necessary, use these files to control the list of allowable datanodes.
dfs.blocksize	268435456	HDFS blocksize of 256MB for large file-systems.
dfs.namenode.handler.count	100	More NameNode server threads to handle RPCs from large number of DataNodes.

• Configurations for DataNode:

Parameter	Value	Notes
dfs.datanode.data.dir	Comma separated list of paths on the local filesystem of a DataNode where it should store its blocks.	If this is a comma-delimited list of directories, then data will be stored in all named directories, typically on different devices.

• etc/hadoop/yarn-site.xml

• Configurations for ResourceManager and NodeManager:

Parameter	Value	Notes
yarn.acl.enable	true / false	Enable ACLs? Defaults to false.
yarn.admin.acl	Admin ACL	ACL to set admins on the cluster. ACLs are of for comma-separated-usersspacecomma-separated-groups. Defaults to special value of * which means anyone. Special value of just space means no one has access.
yarn.log-aggregation-enable	false	Configuration to enable or disable log aggregation

• Configurations for ResourceManager:

Parameter	Value	Notes
yarn.resourcemanager.address	ResourceManager host:port for clients to submit jobs.	host:port If set, overrides the hostname set in yarn.resourcemanager.hostname.
yarn.resourcemanager.scheduler.address	ResourceManager host:port for ApplicationMasters to talk to Scheduler to obtain resources.	host:port If set, overrides the hostname set in yarn.resourcemanager.hostname.
yarn.resourcemanager.resource-tracker.address	ResourceManager host:port for NodeManagers.	host:port If set, overrides the hostname set in yarn.resourcemanager.hostname.
yarn.resourcemanager.admin.address	ResourceManager host:port for administrative commands.	host:port If set, overrides the hostname set in yarn.resourcemanager.hostname.
yarn.resourcemanager.webapp.address	ResourceManager web-ui host:port.	host:port If set, overrides the hostname set in yarn.resourcemanager.hostname.
yarn.resourcemanager.hostname	ResourceManager host.	host Single hostname that can be set in place of setting all yarn.resourcemanager*address resources. Results in default ports for ResourceManager components.
yarn.resourcemanager.scheduler.class	ResourceManager Scheduler class.	CapacityScheduler (recommended), FairScheduler (also recommended), or FifoScheduler. Use a fully qualified class name, e.g., org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler.
yarn.scheduler.minimum-allocation-mb	Minimum limit of memory to allocate to each container request at the Resource Manager.	In MBs
yarn.scheduler.maximum-allocation-mb	Maximum limit of memory to allocate to each container request at the Resource Manager.	In MBs
yarn.resourcemanager.nodes.include-path / yarn.resourcemanager.nodes.exclude-path	List of permitted/excluded NodeManagers.	If necessary, use these files to control the list of allowable NodeManagers.

• Configurations for NodeManager:

Parameter	Value	Notes
yarn.nodemanager.resource.memory-mb	Resource i.e. available physical memory, in MB, for given NodeManager	Defines total available resources on the NodeManager to be made available to running containers
yarn.nodemanager.vmem-pmem-ratio	Maximum ratio by which virtual memory usage of tasks may exceed physical memory	The virtual memory usage of each task may exceed its physical memory limit by this ratio. The total amount of virtual memory used by tasks on the NodeManager may exceed its physical memory usage by this ratio.
yarn.nodemanager.local-dirs	Comma-separated list of paths on the local filesystem where intermediate data is written.	Multiple paths help spread disk i/o.
yarn.nodemanager.log-dirs	Comma-separated list of paths on the local filesystem where logs are written.	Multiple paths help spread disk i/o.
yarn.nodemanager.log.retain-seconds	10800	Default time (in seconds) to retain log files on the NodeManager Only applicable if log-aggregation is disabled.
yarn.nodemanager.remote-app-log-dir	/logs	HDFS directory where the application logs are moved on application completion. Need to set appropriate permissions. Only applicable if log-aggregation is enabled.
yarn.nodemanager.remote-app-log-dir-suffix	logs	Suffix appended to the remote log dir. Logs will be aggregated to ${yarn.nodemanager.remote-app-log-dir}/${user}/${thisParam} Only applicable if log-aggregation is enabled.
yarn.nodemanager.aux-services	mapreduce_shuffle	Shuffle service that needs to be set for Map Reduce applications.

• Configurations for History Server (Needs to be moved elsewhere):

Parameter	Value	Notes
yarn.log-aggregation.retain-seconds	-1	How long to keep aggregation logs before deleting them. -1 disables. Be careful, set this too small and you will spam the name node.
yarn.log-aggregation.retain-check-interval-seconds	-1	Time between checks for aggregated log retention. If set to 0 or a negative value then the value is computed as one-tenth of the aggregated log retention time. Be careful, set this too small and you will spam the name node.

• etc/hadoop/mapred-site.xml

• Configurations for MapReduce Applications:

Parameter	Value	Notes
mapreduce.framework.name	yarn	Execution framework set to Hadoop YARN.
mapreduce.map.memory.mb	1536	Larger resource limit for maps.
mapreduce.map.java.opts	-Xmx1024M	Larger heap-size for child jvms of maps.
mapreduce.reduce.memory.mb	3072	Larger resource limit for reduces.
mapreduce.reduce.java.opts	-Xmx2560M	Larger heap-size for child jvms of reduces.
mapreduce.task.io.sort.mb	512	Higher memory-limit while sorting data for efficiency.
mapreduce.task.io.sort.factor	100	More streams merged at once while sorting files.
mapreduce.reduce.shuffle.parallelcopies	50	Higher number of parallel copies run by reduces to fetch outputs from very large number of maps.

• Configurations for MapReduce JobHistory Server:

Parameter	Value	Notes
mapreduce.jobhistory.address	MapReduce JobHistory Server host:port	Default port is 10020.
mapreduce.jobhistory.webapp.address	MapReduce JobHistory Server Web UI host:port	Default port is 19888.
mapreduce.jobhistory.intermediate-done-dir	/mr-history/tmp	Directory where history files are written by MapReduce jobs.
mapreduce.jobhistory.done-dir	/mr-history/done	Directory where history files are managed by the MR JobHistory Server.

Monitoring Health of NodeManagers

Hadoop provides a mechanism by which administrators can configure the NodeManager to run an administrator supplied script periodically to determine if a node is healthy or not.

Administrators can determine if the node is in a healthy state by performing any checks of their choice in the script. If the script detects the node to be in an unhealthy state, it must print a line to standard output beginning with the string ERROR. The NodeManager spawns the script periodically and checks its output. If the script’s output contains the string ERROR, as described above, the node’s status is reported as unhealthy and the node is black-listed by the ResourceManager. No further tasks will be assigned to this node. However, the NodeManager continues to run the script, so that if the node becomes healthy again, it will be removed from the blacklisted nodes on the ResourceManager automatically. The node’s health along with the output of the script, if it is unhealthy, is available to the administrator in the ResourceManager web interface. The time since the node was healthy is also displayed on the web interface.

The following parameters can be used to control the node health monitoring script in etc/hadoop/yarn-site.xml.

 Hadoop提供了一種機制，通過這種機制管理員可以配置NodeManager 週期性的去執行一個管理員提供的指令碼去決定一個節點是否健康。

管理員可以通過執行指令碼中的一些檢查去決定一個節點是否處於健康狀態。如果指令碼檢測到節點屬於非健康狀態，它必須在標準輸出臺上以ERROR開頭的一行日誌。NodeManager週期性的產生指令碼並且檢查其輸出。如果指令碼輸出中包含如上所描述的ERROR字元，該節點狀態將會報告為不健康並且被ResourceManager列為黑名單。將不會有新的任務分配到該節點上。然而，NodeManager會繼續執行指令碼，以便如果該節點重新變為健康狀態，它將會從ResourceManager的黑名單節點中自動移除。節點的健康狀態伴隨著指令碼的輸出，如果它是非健康的，它在ResourceManager的web介面是可用的，從節點變為健康開始它同樣會顯示在web介面。

在etc/hadoop/yarn-site.xml中的如下引數可以用來控制節點健康監控：

Parameter	Value	Notes
yarn.nodemanager.health-checker.script.path	Node health script	Script to check for node’s health status.
yarn.nodemanager.health-checker.script.opts	Node health script options	Options for script to check for node’s health status.
yarn.nodemanager.health-checker.interval-ms	Node health script interval	Time interval for running health script.
yarn.nodemanager.health-checker.script.timeout-ms	Node health script timeout interval	Timeout for health script execution.

The health checker script is not supposed to give ERROR if only some of the local disks become bad. NodeManager has the ability to periodically check the health of the local disks (specifically checks nodemanager-local-dirs and nodemanager-log-dirs) and after reaching the threshold of number of bad directories based on the value set for the config property yarn.nodemanager.disk-health-checker.min-healthy-disks, the whole node is marked unhealthy and this info is sent to resource manager also. The boot disk is either raided or a failure in the boot disk is identified by the health checker script.

 如果本地磁碟變壞的話健康檢測指令碼將不會給出ERROR。NodeManager可以週期性的檢測本地磁碟（尤其是nodemanager-local-dirs和nodemanager-log-dirs）的健康狀況,如果到了yarn.nodemanager.disk-health-check.min-healthy-disks的臨界值，全部的node被標記為非健康，這一資訊也會傳送到ResourceManager.啟動盤被攻擊或者啟動盤的錯誤都會標記為健康檢查指令碼。

Slaves File

List all slave hostnames or IP addresses in your etc/hadoop/slaves file, one per line. Helper scripts (described below) will use the etc/hadoop/slaves file to run commands on many hosts at once. It is not used for any of the Java-based Hadoop configuration. In order to use this functionality, ssh trusts (via either passphraseless ssh or some other means, such as Kerberos) must be established for the accounts used to run Hadoop.

在etc/hadoop/workers檔案中列舉的所有的"worker"域名或IP地址資訊，一行為一個。幫助指令碼將使用etc/hadoop/workers檔案在每一個主機上執行。它不使用任何基於Java的hadoop配置。為了使用這個功能,必須為執行Hadoop的賬戶設定SSH信任（通過免密或者別的方式比如Kerberos）。

Hadoop Rack Awareness

Many Hadoop components are rack-aware and take advantage of the network topology for performance and safety. Hadoop daemons obtain the rack information of the slaves in the cluster by invoking an administrator configured module. See the Rack Awareness documentation for more specific information.

It is highly recommended configuring rack awareness prior to starting HDFS.

許多Hadoop元件利用網路拓撲學的效能和安全性是支援機架感應的。Hadoop程式通過使用管理員的配置模組是包含叢集中“工人”的機架資訊的。檢視機架感應文件（ Rack Awareness）獲取更多特定的內容。

強烈建議在開始使用HDFS之前優先配置機架感應。

Logging

Hadoop uses the Apache log4j via the Apache Commons Logging framework for logging. Edit the etc/hadoop/log4j.properties file to customize the Hadoop daemons’ logging configuration (log-formats and so on).

Hadoop通過Apache Commons Logging 框架的Apache log4j（Apache log4j ）記錄日誌。

編輯etc/hadoop/log4j.properties檔案去定製Hadoop程式的日誌配置（日誌格式化等等）。

Operating the Hadoop Cluster

Once all the necessary configuration is complete, distribute the files to the HADOOP_CONF_DIR directory on all the machines. This should be the same directory on all machines.

In general, it is recommended that HDFS and YARN run as separate users. In the majority of installations, HDFS processes execute as ‘hdfs’. YARN is typically using the ‘yarn’ account.

一旦所有的必要的配置完成了,分發所有的檔案到叢集中所有機器下的HADOOP_CONF_DIR資料夾下。這個資料夾應該在所有的機器上是一樣的。

通常情況下。HDFS和YARN應該使用不同的使用者去執行。在大部分的配置下，HDFS以hdfs賬戶執行，YARN用yarn賬戶。

Hadoop Startup

To start a Hadoop cluster you will need to start both the HDFS and YARN cluster.

The first time you bring up HDFS, it must be formatted. Format a new distributed filesystem as hdfs:

如果要啟動hadoop叢集的話你首先要同時啟動HDFS和YARN叢集。

第一次啟動HDFS的時候，必須進行格式化。將hdfs格式化為一個新的分散式檔案系統：

[hdfs]$ $HADOOP_PREFIX/bin/hdfs namenode -format <cluster_name>

Start the HDFS NameNode with the following command on the designated node as hdfs:

在指定的節點上使用hdfs使用者執行如下的命令啟動NameNode

[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script hdfs start namenode

Start a HDFS DataNode with the following command on each designated（指定） node as hdfs:

在指定的節點上使用hdfs使用者執行如下的命令啟動DataNode：

[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemons.sh --config $HADOOP_CONF_DIR --script hdfs start datanode

If etc/hadoop/slaves and ssh trusted access is configured (see Single Node Setup), all of the HDFS processes can be started with a utility script. As hdfs:

如果etc/hadoop/slaves 和ssh免密已經設定了的話（Single Node Setup），可以使用一個有用的指令碼來啟動HDFS所有的程式，

[hdfs]$ $HADOOP_PREFIX/sbin/start-dfs.sh

Start the YARN with the following command, run on the designated ResourceManager as yarn:

在指定的ResourceManager節點上使用yarn使用者執行如下命令啟動YARN:

[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start resourcemanager

Run a script to start a NodeManager on each designated host as yarn:

在每一個指定的主機上用yarn使用者執行指令碼去啟動NodeManager:

[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemons.sh --config $HADOOP_CONF_DIR start nodemanager

Start a standalone WebAppProxy server. Run on the WebAppProxy server as yarn. If multiple servers are used with load balancing it should be run on each of them:

啟動一個單獨的WebAppProxy服務。使用yarn使用者啟動WebAppProxy服務。如果有多個伺服器用來做負載均衡的話應該在每一臺機器上面執行它：

[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start proxyserver

If etc/hadoop/slaves and ssh trusted access is configured (see Single Node Setup), all of the YARN processes can be started with a utility script. As yarn:

如果etc/hadoop/workers和ssh免密都設定的話（Single Node Setup），所有的YARN程式可以使用一個有效的指令碼啟動，使用yarn使用者：

[yarn]$ $HADOOP_PREFIX/sbin/start-yarn.sh

Start the MapReduce JobHistory Server with the following command, run on the designated server as mapred:

在指定的伺服器上使用mapred使用者執行如下命令啟動MapReduce JobHistory服務：

[mapred]$ $HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh --config $HADOOP_CONF_DIR start historyserver

Hadoop Shutdown

Stop the NameNode with the following command, run on the designated NameNode as hdfs:

使用hdfs使用者在指定的NameNode伺服器上執行如下命令停止NameNode:

[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script hdfs stop namenode

Run a script to stop a DataNode as hdfs:

使用hdfs使用者執行指令碼停止DataNode

[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemons.sh --config $HADOOP_CONF_DIR --script hdfs stop datanode

If etc/hadoop/slaves and ssh trusted access is configured (see Single Node Setup), all of the HDFS processes may be stopped with a utility script. As hdfs:

如果etc/hadoop/slaves 和ssh免密已經設定的話，所有的HDFS進行可以使用一個有用的指令碼來停止。使用hdfs使用者

[hdfs]$ $HADOOP_PREFIX/sbin/stop-dfs.sh

Stop the ResourceManager with the following command, run on the designated ResourceManager as yarn:

在指定的ResourceManager機器上執行如下命令停止ResourceManager:

[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR stop resourcemanager

Run a script to stop a NodeManager on a slave as yarn:

在slave 機器上使用yarn使用者執行指令碼停止NodeManager:

[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemons.sh --config $HADOOP_CONF_DIR stop nodemanager

If etc/hadoop/slaves and ssh trusted access is configured (see Single Node Setup), all of the YARN processes can be stopped with a utility script. As yarn:

如果etc/hadoop/slaves 和ssh免密已經設定的話，所有的YARN程式可以使用一個有效的指令碼進行停止：

[yarn]$ $HADOOP_PREFIX/sbin/stop-yarn.sh

Stop the WebAppProxy server. Run on the WebAppProxy server as yarn. If multiple servers are used with load balancing it should be run on each of them:

使用yarn使用者在WebAppProxy伺服器上執行如下命令停止WebAppProxy服務，如果多個伺服器用於做負載均衡，應該在每一臺機器上執行他們：

[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR stop proxyserver

Stop the MapReduce JobHistory Server with the following command, run on the designated server as mapred:

 使用mapred使用者在指定的伺服器上執行如下命令挺屍MapReduce JobHistory服務。

[mapred]$ $HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh --config $HADOOP_CONF_DIR stop historyserver

Web Interfaces

Once the Hadoop cluster is up and running check the web-ui of the components as described below:

Daemon	Web Interface	Notes
NameNode	http://nn_host:port/	Default HTTP port is 50070.
ResourceManager	http://rm_host:port/	Default HTTP port is 8088.
MapReduce JobHistory Server	http://jhs_host:port/	Default HTTP port is 19888.

 

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