Consolidated instructions on how to setup and run Hadoop on Windows 10 machines. This is exactly written from Hadoop 3.2.1 Installation on Windows 10 step by step guide. Big thanks to Raymond, the original writer. If you already have Hadoop installed and configured on your machine, you can go to the Running MapReduce Jobs section.
- Java JDK - used to run the Hadoop since it's built using Java
- 7Zip or WinRAR - unzip Hadoop binary package; anything that unzips
tar.gz - CMD or Powershell - used to test environment variables and run Hadoop
Download Hadoop from their official website and unzip the file. We'll be using Hadoop 3.2.1. Hadoop is portable so you can store it on an external hard drive. For the purpose of documentation, I will extract it to C:/Users/Anthony/Documents/cp-master.
If there are permission errors, run your unzipping program as administrator and unzip again.
Clone or download the winutils repository and copy the contents of hadoop-3.2.1/bin into the extracted location of the Hadoop binary package. In our example, it will be C:\Users\Anthony\Documents\cp-master\hadoop-3.2.1\bin
Java JDK is required to run Hadoop, so if you haven't installed it, install it.
Oracle requires you sign up and login to download it. I suggest you find an alternative resource to download it from for example here (JDK 8u261). This resource might not exist forever, so Google 'jdk version download'.
Run the installation file and the default installation directory will be C:\Program Files\Java\jdk1.8.0_261.
After installation, open up CMD or Powershell and confirm Java is intalled:
$ java -version
java version "1.8.0_261"
Java(TM) SE Runtime Environment (build 1.8.0_261-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.261-b12, mixed mode)
Open the Start Menu and type in 'environment' and press enter. A new window with System Properties should open up. Click the Environment Variables button near the bottom right.
- From step 3, find the location of where you installed Java. In this example, the default directory is
C:\Program Files\Java\jdk1.8.0_261 - Create a new User variable with the variable name as
JAVA_HOMEand the value asC:\Program Files\Java\jdk1.8.0_261
- From step 1, copy the directory you extracted the Hadoop binaries to. In this example, the directory is
C:\Users\Anthony\Documents\cp-master\hadoop-3.2.1 - Create a new User variable with the variable name as
HADOOP_HOMEand the value asC:\Users\Anthony\Documents\cp-master\hadoop-3.2.1
We'll now need to add the bin folders to the PATH environment variable.
- Click
PaththenEdit - Click
Newon the top right - Add
C:\Users\Anthony\Documents\cp-master\hadoop-3.2.1\bin - Add
C:\Program Files\Java\jdk1.8.0_261\bin
Hadoop complains about the directory if the JAVA_HOME directory has spaces. In the default installation directory, Program Files has a space which is problematic. To fix this, open the %HADOOP_HOME%\etc\hadoop\hadoop-env.cmd and change the JAVA_HOME line to the following:
set JAVA_HOME=C:\PROGRA~1\Java\jdk1.8.0_261
After setting those environment variables, you reopen CMD or Powershell and verify that the hadoop command is available:
$ hadoop -version
java version "1.8.0_261"
Java(TM) SE Runtime Environment (build 1.8.0_261-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.261-b12, mixed mode)
Now we are ready to configure the most important part - Hadoop configurations which involves Core, YARN, MapReduce, and HDFS configurations.
Each of the files are in %HADOOP_HOME%\etc\hadoop. The full path for this example is C:\Users\Anthony\Documents\cp-master\hadoop-3.2.1\etc\hadoop
Edit core-site.xml and replace the configuration element with the following:
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://0.0.0.0:19000</value>
</property>
</configuration>
Create two folders, one for the namenode directory and another for the data directory. The following are the two created folders in this example:
C:\Users\Anthony\Documents\cp-master\hadoop-3.2.1\data\dfs\namespace_logsC:\Users\Anthony\Documents\cp-master\hadoop-3.2.1\data\dfs\data
Edit hdfs-site.xml and replace the configuration element with the following:
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<!-- <value>file:///DIRECTORY 1 HERE</value> -->
<value>file:///C:/Users/Anthony/Documents/cp-master/hadoop-3.2.1/data/dfs/namespace_logs</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<!-- <value>file:///DIRECTORY 2 HERE</value> -->
<value>file:///C:/Users/Anthony/Documents/cp-master/hadoop-3.2.1/data/dfs/data</value>
</property>
</configuration>
Edit mapred-site.xml and replace the configuration element with the following:
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.application.classpath</name>
<value>%HADOOP_HOME%/share/hadoop/mapreduce/*,%HADOOP_HOME%/share/hadoop/mapreduce/lib/*,%HADOOP_HOME%/share/hadoop/common/*,%HADOOP_HOME%/share/hadoop/common/lib/*,%HADOOP_HOME%/share/hadoop/yarn/*,%HADOOP_HOME%/share/hadoop/yarn/lib/*,%HADOOP_HOME%/share/hadoop/hdfs/*,%HADOOP_HOME%/share/hadoop/hdfs/lib/*</value>
</property>
</configuration>
Edit yarn-site.xml and replace the configuration element with the following:
<configuration>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>localhost</value>
</property>
<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_MAPRED_HOME</value>
</property>
</configuration>
Run the following command and you should find the following error:
$ hdfs namenode -format
...
ERROR namenode.NameNode: Failed to start namenode.
...
To fix this, you'll need to download the a JAR file with the fix. Overwrite the existing hadoop-hdfs-3.2.1.jar in %HADOOP_HOME%\share\hadoop\hdfs with this new JAR file (you can make a backup of the current one before overwriting if you wish).
Run the following command to start HDFS daemons. When you do so, there should be two new windows that open: one for datanode and the other for namenode:
$ %HADOOP_HOME%\sbin\start-dfs.cmd
You might encounter permission issues as a normal user, so open a command line with elevated permissions. If you have the yarn package manager, you will NOT be able to run YARN daemons since both use the yarn command. To fix this, you must uninstall yarn package manager.
Run the following command (with elevated permissions) to start YARN daemons. When you do so, there should be two new windows that open: one for resource manager and the other for node manager:
$ %HADOOP_HOME%\sbin\start-yarn.cmd
The daemons also host websites that provide useful information about the cluster
http://localhost:9870/dfshealth.html#tab-overview
http://localhost:9864/datanode.html
You can stop the daemons by running the following commands:
$ %HADOOP_HOME%\sbin\stop-dfs.cmd
$ %HADOOP_HOME%\sbin\stop-yarn.cmd
After setting up your environment and running the HDFS and YARN daemons, we can start working on running MapReduce jobs on our local machine. We need to compile our code, produce a JAR file, move our inputs, and run a MapReduce job on Hadoop.
As a preface, it is best to setup some extra environment variables to make running jobs from the CLI quicker and easier. You can name these environment variables anything you want, but we will name them HADOOP_CP and HDFS_LOC to not potentially conlict with other environment variables.
Open the Start Menu and type in 'environment' and press enter. A new window with System Properties should open up. Click the Environment Variables button near the bottom right.
This is used to compile your Java files. The backticks (eg. `some command here`) do not work on Windows so we need to create a new environment variable with the results. If you need to add more packages, be sure to update the HADOOP_CP environment variable.
- Open a CLI and type in
hadoop classpath. This will produce all the locations to the Hadoop libraries required to compile code, so copy all of this - Create a new User variable with the variable name as
HADOOP_CPand the value as the results ofhadoop classpathcommand
This is used to reference the HDFS without having to constantly type the reference
- Create a new User variable with the variable name as
HDFS_LOCand the value ashdfs://localhost:19000
After creating those two extra environment variables, you can check by calling the following in your CLI:
$ echo %HADOOP_CP%
$ echo %HDFS_LOC%
Run the following commands in your CLI with your respective .java files.
$ mkdir dist/
$ javac -cp %HADOOP_CP% <some_directory>/*.java -d dist/
Run the following commands to create a JAR file with the compiled classes from Step 2.
$ cd dist
$ jar -cvf <application_name>.jar <some_directory>/*.class
added manifest
...
Make sure that HDFS and YARN daemons are running. We can now copy our inputs to the HDFS using the copyFromLocal command and verify the contents with the ls command :
$ hadoop fs -copyFromLocal <some_directory>/input %HDFS_LOC%/input
$ hadoop fs -ls %HDFS_LOC%/input
Found X items
...
Run the following commands in the dist folder when we originally compiled our code:
$ hadoop jar <application_name>.jar <pkg_name>.<class_name> %HDFS_LOC%/input %HDFS_LOC%/output
2020-10-16 17:44:40,331 INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:8032
...
2020-10-16 17:44:43,115 INFO mapreduce.Job: Running job: job_1602881955102_0001
2020-10-16 17:44:55,439 INFO mapreduce.Job: Job job_1602881955102_0001 running in uber mode : false
2020-10-16 17:44:55,441 INFO mapreduce.Job: map 0% reduce 0%
2020-10-16 17:45:04,685 INFO mapreduce.Job: map 100% reduce 0%
2020-10-16 17:45:11,736 INFO mapreduce.Job: map 100% reduce 100%
2020-10-16 17:45:11,748 INFO mapreduce.Job: Job job_1602881955102_0001 completed successfully
...
We can verify the contents of our output by using the cat command just like in shell:
$ hadoop fs -cat %HDFS_LOC%/output/part*
2020-10-16 18:19:50,225 INFO sasl.SaslDataTransferClient: SASL encryption trust check: localHostTrusted = false, remoteHostTrusted = false
...
Once we are satisfied with the results, we can copy the contents to our local machine using the copyToLocal command:
$ hadoop fs -copyToLocal %HDFS_LOC%/output <some_output_directory>/
Thanks.