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 High Availability.
Important: all production Hadoop clusters use Kerberos to authenticate callers and secure access to HDFS data as well as restriction access to computation services (YARN etc.).
These instructions do not cover integration with any Kerberos services, -everyone bringing up a production cluster should include connecting to their organisation’s Kerberos infrastructure as a key part of the deployment.
See Security for details on how to secure a cluster.
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 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 hardware or on shared infrastructure, depending upon the load.
The rest of the machines in the cluster act as both DataNode and NodeManager. These are the workers.
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.
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:
| Daemon | Environment Variable |
|---|---|
| NameNode | HDFS_NAMENODE_OPTS |
| DataNode | HDFS_DATANODE_OPTS |
| Secondary NameNode | HDFS_SECONDARYNAMENODE_OPTS |
| ResourceManager | YARN_RESOURCEMANAGER_OPTS |
| NodeManager | YARN_NODEMANAGER_OPTS |
| WebAppProxy | YARN_PROXYSERVER_OPTS |
| Map Reduce Job History Server | MAPRED_HISTORYSERVER_OPTS |
For example, To configure Namenode to use parallelGC and a 4GB Java Heap, the following statement should be added in hadoop-env.sh :
export HDFS_NAMENODE_OPTS="-XX:+UseParallelGC -Xmx4g"
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_MAX - The maximum amount of memory to use for the Java heapsize. Units supported by the JVM are also supported here. If no unit is present, it will be assumed the number is in megabytes. By default, Hadoop will let the JVM determine how much to use. This value can be overriden on a per-daemon basis using the appropriate _OPTS variable listed above. For example, setting HADOOP_HEAPSIZE_MAX=1g and HADOOP_NAMENODE_OPTS="-Xmx5g" will configure the NameNode with 5GB heap.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_HOME in the system-wide shell environment configuration. For example, a simple script inside /etc/profile.d:
HADOOP_HOME=/path/to/hadoop export HADOOP_HOME
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.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. |
| 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 |
| 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. |
| 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. |
yarn.nodemanager.env-whitelist |
Environment properties to be inherited by containers from NodeManagers | For mapreduce application in addition to the default values HADOOP_MAPRED_HOME should to be added. Property value should JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_HOME,PATH,LANG,TZ,HADOOP_MAPRED_HOME |
| 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. |
| 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. |
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.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.
List all worker hostnames or IP addresses in your etc/hadoop/workers file, one per line. Helper scripts (described below) will use the etc/hadoop/workers 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.
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 workers 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.
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).
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.
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]$ $HADOOP_HOME/bin/hdfs namenode -format
Start the HDFS NameNode with the following command on the designated node as hdfs:
[hdfs]$ $HADOOP_HOME/bin/hdfs --daemon start namenode
Start a HDFS DataNode with the following command on each designated node as hdfs:
[hdfs]$ $HADOOP_HOME/bin/hdfs --daemon start datanode
If etc/hadoop/workers and ssh trusted access is configured (see Single Node Setup), all of the HDFS processes can be started with a utility script. As hdfs:
[hdfs]$ $HADOOP_HOME/sbin/start-dfs.sh
Start the YARN with the following command, run on the designated ResourceManager as yarn:
[yarn]$ $HADOOP_HOME/bin/yarn --daemon start resourcemanager
Run a script to start a NodeManager on each designated host as yarn:
[yarn]$ $HADOOP_HOME/bin/yarn --daemon start nodemanager
Start a standalone WebAppProxy server. Run on the WebAppProxy server as yarn. If multiple servers are used with load balancing it should be run on each of them:
[yarn]$ $HADOOP_HOME/bin/yarn --daemon start proxyserver
If etc/hadoop/workers and ssh trusted access is configured (see Single Node Setup), all of the YARN processes can be started with a utility script. As yarn:
[yarn]$ $HADOOP_HOME/sbin/start-yarn.sh
Start the MapReduce JobHistory Server with the following command, run on the designated server as mapred:
[mapred]$ $HADOOP_HOME/bin/mapred --daemon start historyserver
Stop the NameNode with the following command, run on the designated NameNode as hdfs:
[hdfs]$ $HADOOP_HOME/bin/hdfs --daemon stop namenode
Run a script to stop a DataNode as hdfs:
[hdfs]$ $HADOOP_HOME/bin/hdfs --daemon stop datanode
If etc/hadoop/workers and ssh trusted access is configured (see Single Node Setup), all of the HDFS processes may be stopped with a utility script. As hdfs:
[hdfs]$ $HADOOP_HOME/sbin/stop-dfs.sh
Stop the ResourceManager with the following command, run on the designated ResourceManager as yarn:
[yarn]$ $HADOOP_HOME/bin/yarn --daemon stop resourcemanager
Run a script to stop a NodeManager on a worker as yarn:
[yarn]$ $HADOOP_HOME/bin/yarn --daemon stop nodemanager
If etc/hadoop/workers and ssh trusted access is configured (see Single Node Setup), all of the YARN processes can be stopped with a utility script. As yarn:
[yarn]$ $HADOOP_HOME/sbin/stop-yarn.sh
Stop the WebAppProxy server. Run on the WebAppProxy server as yarn. If multiple servers are used with load balancing it should be run on each of them:
[yarn]$ $HADOOP_HOME/bin/yarn stop proxyserver
Stop the MapReduce JobHistory Server with the following command, run on the designated server as mapred:
[mapred]$ $HADOOP_HOME/bin/mapred --daemon stop historyserver
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 9870. |
| ResourceManager | http://rm_host:port/ | Default HTTP port is 8088. |
| MapReduce JobHistory Server | http://jhs_host:port/ | Default HTTP port is 19888. |