Ref from official site document.
Purpose
This document describes how to install and configure Hadoop clusters ranging
from a few nodes to extremely 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.
Prerequisites
- Install Java. See the Hadoop Wiki for known good versions.
- Download a stable version of Hadoop from Apache mirrors.
Installation
Installing a Hadoop cluster typically involves unpacking the software on all
the machines in the cluster or installing it via a packaging system as
appropriate for your operating system. It is important to divide up the
hardware into functions.
Typically one machine in the cluster is designated as the NameNode and another
machine the as 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 hardware or on shared infrastrucutre,
depending upon the load.
The rest of the machines in the cluster act as both DataNode and NodeManager.
These are the slaves.
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.xmlandmapred-default.xml.Site-specific configuration -
etc/hadoop/core-site.xml,etc/hadoop/hdfs-site.xml,etc/hadoop/yarn-site.xmlandetc/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 damones 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.
Configuring Environment of Hadoop Daemons
Administrators should use the etc/hadoop/hadoop-env.sh and optionally theetc/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:
| 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 :
export HADOOP_NAMENODE_OPTS="-XX:+UseParallelGC"
See etc/hadoop/hadoop-env.sh for other examples.
Other useful configuration parameters that you can customize include:
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 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.
In most cases, you should specify 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 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:
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:
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. |
etc/hadoop/hdfs-site.xmlConfigurations 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 his is a comma-delimited list of directories, then data will be stored in all named directories, typically on different devices. |
etc/hadoop/yarn-site.xmlConfigurations 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 |
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.xmlConfigurations 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 UIhost: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.
| 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.script.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.
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.
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.
Logging
Hadoop uses the Apache log4j via the
Apache Commons Logging framework for logging. Edit theetc/hadoop/log4j.properties file to customize the Hadoop daemons’ logging
configuration (log-formats and so on).
Operating the Hadoop Cluster
Once all the necessary configuration is complete, distribute the files to theHADOOP_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.
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:
[hdfs]$ $HADOOP_PREFIX/bin/hdfs namenode -format <cluster_name>
Start the HDFS NameNode with the following command on the designated node as
hdfs:
[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script hdfs start namenode
Start a HDFS DataNode with the following command on each designated node as
hdfs:
[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemons.sh --config $HADOOP_CONF_DIR --script hdfs start datanode
If etc/hadoop/slaves and ssh trusted access is configured (see Single Node
Setup), all of the HDFS processes can be started with a
utility script. As hdfs:
[hdfs]$ $HADOOP_PREFIX/sbin/start-dfs.sh
Start the YARN with the following command, run on the designated
ResourceManager as yarn:
[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start resourcemanager
Run a script to start a NodeManager on each designated host as yarn:
[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemons.sh --config $HADOOP_CONF_DIR start nodemanager
Start a standalone WebAppProxy server. Run on the WebAppProxy server as
yarn. If multiple servers are used with load balancing it should be run on
each of them:
[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start proxyserver
If etc/hadoop/slaves and ssh trusted access is configured (see Single Node
Setup), all of the YARN processes can be started with a
utility script. As yarn:
[yarn]$ $HADOOP_PREFIX/sbin/start-yarn.sh
Start the MapReduce JobHistory Server with the following command, run on the
designated server as mapred:
[mapred]$ $HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh --config $HADOOP_CONF_DIR start historyserver
Hadoop Shutdown
Stop the NameNode with the following command, run on the designated NameNode
as hdfs:
[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script hdfs stop namenode
Run a script to stop a DataNode as hdfs:
[hdfs]$ $HADOOP_PREFIX/sbin/hadoop-daemons.sh --config $HADOOP_CONF_DIR --script hdfs stop datanode
If etc/hadoop/slaves and ssh trusted access is configured (see Single Node
Setup), all of the HDFS processes may be stopped with a
utility script. As hdfs:
[hdfs]$ $HADOOP_PREFIX/sbin/stop-dfs.sh
Stop the ResourceManager with the following command, run on the designated
ResourceManager as yarn:
[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR stop resourcemanager
Run a script to stop a NodeManager on a slave as yarn:
[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemons.sh --config $HADOOP_CONF_DIR stop nodemanager
If etc/hadoop/slaves and ssh trusted access is configured (see Single Node
Setup), all of the YARN processes can be stopped with a
utility script. As yarn:
[yarn]$ $HADOOP_PREFIX/sbin/stop-yarn.sh
Stop the WebAppProxy server. Run on the WebAppProxy server as yarn. If
multiple servers are used with load balancing it should be run on each of
them:
[yarn]$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR stop proxyserver
Stop the MapReduce JobHistory Server with the following command, run on the
designated server as mapred:
[mapred]$ $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.