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Parallel Execution: It uses a "sliding window" (fanout) of threads to run commands in parallel rather than one-by-one (serially).
Host Grouping: You can target hosts using ranges (e.g., web[01-10]) or groups defined in files like /etc/genders.
Thread Safety: It is designed to be highly efficient, handling timeouts on specific nodes without hanging the entire process.
Companion Tools: It usually comes with pdcp (parallel copy), which lets you copy files to multiple machines at once.

Important Prerequisites

To use pdsh effectively, you usually need:

SSH Keys: You should have passwordless SSH access to the target machines.
RCMD Module: On Ubuntu, you often need to specify the ssh module by adding -R ssh to your command, or by setting the environment variable PDSH_RCMD_TYPE=ssh.

Download

frank@ZZHPC:~/download$ wget https://dlcdn.apache.org/hadoop/common/stable/hadoop-3.4.2.tar.gz

frank@ZZHPC:~/download$ tar -xzf hadoop-3.4.2.tar.gz

Prepare to Start the Hadoop Cluster

In the distribution, edit the file etc/hadoop/hadoop-env.sh to define some parameters as follows:

JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64

.bashrc:

export HADOOP_HOME=~/download/hadoop-3.4.2
PATH=$PATH:$HADOOP_HOME/bin
export PATH

frank@ZZHPC:~$ hadoop
Usage: hadoop [OPTIONS] SUBCOMMAND [SUBCOMMAND OPTIONS]
 or    hadoop [OPTIONS] CLASSNAME [CLASSNAME OPTIONS]
  where CLASSNAME is a user-provided Java class

  OPTIONS is none or any of:

--config dir                     Hadoop config directory
--debug                          turn on shell script debug mode
--help                           usage information
buildpaths                       attempt to add class files from build tree
hostnames list[,of,host,names]   hosts to use in worker mode
hosts filename                   list of hosts to use in worker mode
loglevel level                   set the log4j level for this command
workers                          turn on worker mode

  SUBCOMMAND is one of:


    Admin Commands:

daemonlog     get/set the log level for each daemon

    Client Commands:

archive       create a Hadoop archive
checknative   check native Hadoop and compression libraries availability
classpath     prints the class path needed to get the Hadoop jar and the required libraries
conftest      validate configuration XML files
credential    interact with credential providers
distch        distributed metadata changer
distcp        copy file or directories recursively
dtutil        operations related to delegation tokens
envvars       display computed Hadoop environment variables
fedbalance    balance data between sub-clusters
fs            run a generic filesystem user client
gridmix       submit a mix of synthetic job, modeling a profiled from production load
jar <jar>     run a jar file. NOTE: please use "yarn jar" to launch YARN applications, not this command.
jnipath       prints the java.library.path
kdiag         Diagnose Kerberos Problems
kerbname      show auth_to_local principal conversion
key           manage keys via the KeyProvider
rbfbalance    move directories and files across router-based federation namespaces
rumenfolder   scale a rumen input trace
rumentrace    convert logs into a rumen trace
s3guard       S3 Commands
version       print the version

    Daemon Commands:

kms           run KMS, the Key Management Server
registrydns   run the registry DNS server

SUBCOMMAND may print help when invoked w/o parameters or with -h.

This will display the usage documentation for the hadoop script.

Now you are ready to start your Hadoop cluster in one of the three supported modes:

Standalone Operation

By default, Hadoop is configured to run in a non-distributed mode, as a single Java process. This is useful for debugging.

The following example copies the unpacked conf directory to use as input and then finds and displays every match of the given regular expression. Output is written to the given output directory.

  $ mkdir input
  $ cp etc/hadoop/*.xml input
  $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.4.2.jar grep input output 'dfs[a-z.]+'
  $ cat output/*

Essentially, you are telling Hadoop to run a specific MapReduce program (a "job") using its built-in examples library.

Breaking Down the Command

$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.4.2.jar grep input output 'dfs[a-z.]+'

Component	Description
bin/hadoop	This is the Hadoop script that executes commands. Since you are in standalone mode, it runs as a single Java process on your local machine.
jar	This tells Hadoop that you want to run a program stored in a Java Archive (JAR) file.
share/hadoop/.../examples-3.4.2.jar	This is the file path to the compiled library that contains several pre-written MapReduce programs (like wordcount, pi, and grep).
grep	This is the specific "main class" or program inside the JAR file you want to execute. It mimics the Unix `grep` command but uses MapReduce.
input	The directory containing your source files (the `.xml` files you copied earlier).
output	The directory where Hadoop will save the results. Note: This directory must not exist before you run the command, or Hadoop will throw an error.
'dfs[a-z.]+'	The regular expression (regex) the program is looking for. It searches for any string starting with "dfs" followed by letters or dots.

How it Works (The MapReduce Flow)

Even in standalone mode, Hadoop follows the MapReduce logic to process your request:

Map Phase: Hadoop reads every line of every XML file in the input folder. It looks for strings that match your regex (dfs...).
Reduce Phase: It counts how many times each matching string appeared.
Output: It writes the final counts of those matches into the output directory.

What happens next?

After this command finishes, you use the fourth command (cat output/*) to view the results. You will likely see a list of configuration properties from the Hadoop XML files that start with "dfs," such as dfs.replication or dfs.permissions.

Pseudo-Distributed Operation

Hadoop can also be run on a single-node in a pseudo-distributed mode where each Hadoop daemon runs in a separate Java process.

Configuration

Use the following:

etc/hadoop/core-site.xml:

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://localhost:9000</value>
    </property>
</configuration>

etc/hadoop/hdfs-site.xml:

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

Setup passphraseless ssh

Now check that you can ssh to the localhost without a passphrase:

  $ ssh localhost

If you cannot ssh to localhost without a passphrase, execute the following commands:

  $ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
  $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
  $ chmod 0600 ~/.ssh/authorized_keys

Execution

The following instructions are to run a MapReduce job locally. If you want to execute a job on YARN, see YARN on Single Node.

Format the filesystem:
```
  $ bin/hdfs namenode -format
```
Start NameNode daemon and DataNode daemon:
```
  $ sbin/start-dfs.sh
```
The hadoop daemon log output is written to the $HADOOP_LOG_DIR directory (defaults to $HADOOP_HOME/logs).
Browse the web interface for the NameNode; by default it is available at:
- NameNode - http://localhost:9870/
Make the HDFS directories required to execute MapReduce jobs:
```
  $ bin/hdfs dfs -mkdir -p /user/<username>
```

Copy the input files into the distributed filesystem:

  $ bin/hdfs dfs -mkdir input
  $ bin/hdfs dfs -put etc/hadoop/*.xml input

Run some of the examples provided:

  $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.4.2.jar grep input output 'dfs[a-z.]+'

Examine the output files: Copy the output files from the distributed filesystem to the local filesystem and examine them:
```
  $ bin/hdfs dfs -get output output
  $ cat output/*
```
or

View the output files on the distributed filesystem:
```
  $ bin/hdfs dfs -cat output/*
```
When you’re done, stop the daemons with:
```
  $ sbin/stop-dfs.sh
```

YARN on a Single Node

You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.

The following instructions assume that 1. ~ 4. steps of the above instructions are already executed.

Configure parameters as follows:

etc/hadoop/mapred-site.xml:

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.application.classpath</name>
        <value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*:$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value>
    </property>
</configuration>

etc/hadoop/yarn-site.xml:

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.env-whitelist</name>
        <value>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</value>
    </property>
</configuration>

Start ResourceManager daemon and NodeManager daemon:
```
  $ sbin/start-yarn.sh
```
Browse the web interface for the ResourceManager; by default it is available at:
- ResourceManager - http://localhost:8088/
Run a MapReduce job.
When you’re done, stop the daemons with:
```
  $ sbin/stop-yarn.sh
```

Fully-Distributed Operation

For information on setting up fully-distributed, non-trivial clusters see Cluster Setup.

posted on 2025-12-28 10:26 ZhangZhihuiAAA 阅读(1) 评论(0) 收藏举报

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