Step 7: Configure mapred-site.xml. From the base of the Hadoop installation, edit the etc/hadoop/mapred-site.xml file. A new configuration option for Hadoop 2 is the capability to specify a framework name for MapReduce, setting the mapreduce.framework.name property. In this install, we will use the value of 'yarn' to tell MapReduce that it. InfoA newer version of installation guide for latest Hadoop 3.2.1 is available.I recommend using that to install as it has a number of new features. Refer to the following article for more details.
- Hadoop - Enviornment Setup - Hadoop is supported by GNU/Linux platform and its flavors. Therefore, we have to install a Linux operating system for setting up Hadoop environment.
- This article describes how to run a Revolution R Enterprise script in a Hadoop cluster from a Windows client outside the cluster using a PuTTY ssh client. Install and configure Revolution R Enterprise 7.3 in the Hadoop cluster per the Revolution R Enterprise 7.3 Hadoop Configuration Guide. Verify the operation of RRE in the cluster when the.
- Building and configuring Hadoop on Windows Select Start — All Programs — Microsoft Windows SDK v7.1 and open the Windows SDK 7 command prompt as the administrator. Change the directory to C: hadoop (if it doesn t exist, create it).
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␡- Steps to Configure a Single-Node YARN Cluster
This chapter is from the book
This chapter is from the book
Steps to Configure a Single-Node YARN Cluster
The following type of installation is often referred to as 'pseudo-distributed' because it mimics some of the functionality of a distributed Hadoop cluster. A single machine is, of course, not practical for any production use, nor is it parallel. A small-scale Hadoop installation can provide a simple method for learning Hadoop basics, however.
The recommended minimal installation hardware is a dual-core processor with 2 GB of RAM and 2 GB of available hard drive space. The system will need a recent Linux distribution with Java installed (e.g., Red Hat Enterprise Linux or rebuilds, Fedora, Suse Linux Enterprise, OpenSuse, Ubuntu). Red Hat Enterprise Linux 6.3 is used for this installation example. A bash shell environment is also assumed. The first step is to download Apache Hadoop.
Note that the following commands and files are available for download from the book repository; see Appendix A for details.
Step 1: Download Apache Hadoop
Download the latest distribution from the Hadoop website (http://hadoop.apache.org/). For example, as root do the following:
Next create and extract the package in /opt/yarn:
Step 2: Set JAVA_HOME
For Hadoop 2, the recommended version of Java can be found at http://wiki.apache.org/hadoop/HadoopJavaVersions. In general, a Java Development Kit 1.6 (or greater) should work. For this install, we will use Open Java 1.6.0_24, which is part of Red Hat Enterprise Linux 6.3. Make sure you have a working Java JDK installed; in this case, it is the Java-1.6.0-openjdk RPM. To include JAVA_HOME for all bash users (other shells must be set in a similar fashion), make an entry in /etc/profile.d as follows:
To make sure JAVA_HOME is defined for this session, source the new script:
Step 3: Create Users and Groups
It is best to run the various daemons with separate accounts. Three accounts (yarn, hdfs, mapred) in the group hadoop can be created as follows:
Step 4: Make Data and Log Directories
Hadoop needs various data and log directories with various permissions. Enter the following lines to create these directories:
Next, move to the YARN installation root and create the log directory and set the owner and group as follows:
Step 5: Configure core-site.xml
From the base of the Hadoop installation path (e.g., /opt/yarn/hadoop-2.2.0), edit the etc/hadoop/core-site.xml file. The original installed file will have no entries other than the tags. Two properties need to be set. The first is the fs.default.name property, which sets the host and request port name for the NameNode (metadata server for HDFS). The second is hadoop.http.staticuser.user, which will set the default user name to hdfs. Copy the following lines to the Hadoop etc/hadoop/core-site.xml file and remove the original empty tags.
Step 6: Configure hdfs-site.xml
From the base of the Hadoop installation path, edit the etc/hadoop/hdfs-site.xml file. In the single-node pseudo-distributed mode, we don't need or want the HDFS to replicate file blocks. By default, HDFS keeps three copies of each file in the file system for redundancy. There is no need for replication on a single machine; thus the value of dfs.replication will be set to 1.
In hdfs-site.xml, we specify the NameNode, Secondary NameNode, and DataNode data directories that we created in Step 4. These are the directories used by the various components of HDFS to store data. Copy the following lines into Hadoop etc/hadoop/hdfs-site.xml and remove the original empty tags.
Step 7: Configure mapred-site.xml
From the base of the Hadoop installation, edit the etc/hadoop/mapred-site.xml file. A new configuration option for Hadoop 2 is the capability to specify a framework name for MapReduce, setting the mapreduce.framework.name property. In this install, we will use the value of 'yarn' to tell MapReduce that it will run as a YARN application. First, copy the template file to the mapred-site.xml.
Next, copy the following lines into Hadoop etc/hadoop/mapred-site.xml file and remove the original empty tags.
Step 8: Configure yarn-site.xml
From the base of the Hadoop installation, edit the etc/hadoop/yarn-site.xml file. The yarn.nodemanager.aux-services property tells NodeManagers that there will be an auxiliary service called mapreduce.shuffle that they need to implement. After we tell the NodeManagers to implement that service, we give it a class name as the means to implement that service. This particular configuration tells MapReduce how to do its shuffle. Because NodeManagers won't shuffle data for a non-MapReduce job by default, we need to configure such a service for MapReduce. Copy the following lines to the Hadoop etc/hadoop/yarn-site.xml file and remove the original empty tags.
Step 9: Modify Java Heap Sizes
The Hadoop installation uses several environment variables that determine the heap sizes for each Hadoop process. These are defined in the etc/hadoop/*-env.sh files used by Hadoop. The default for most of the processes is a 1 GB heap size; because we're running on a workstation that will probably have limited resources compared to a standard server, however, we need to adjust the heap size settings. The values that follow are adequate for a small workstation or server.
Edit the etc/hadoop/hadoop-env.sh file to reflect the following (don't forget to remove the '#' at the beginning of the line):
Next, edit mapred-env.sh to reflect the following:
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Finally, edit yarn-env.sh to reflect the following:
The following line will need to be added to yarn-env.sh:
Step 10: Format HDFS
For the HDFS NameNode to start, it needs to initialize the directory where it will hold its data. The NameNode service tracks all the metadata for the file system. The format process will use the value assigned to dfs.namenode.name.dir in etc/hadoop/hdfs-site.xml earlier (i.e., /var/data/hadoop/hdfs/nn). Formatting destroys everything in the directory and sets up a new file system. Format the NameNode directory as the HDFS superuser, which is typically the 'hdfs' user account.
From the base of the Hadoop distribution, change directories to the 'bin' directory and execute the following commands:
If the command worked, you should see the following near the end of a long list of messages:
Step 11: Start the HDFS Services
Once formatting is successful, the HDFS services must be started. There is one service for the NameNode (metadata server), a single DataNode (where the actual data is stored), and the SecondaryNameNode (checkpoint data for the NameNode). The Hadoop distribution includes scripts that set up these commands as well as name other values such as PID directories, log directories, and other standard process configurations. From the bin directory in Step 10, execute the following as user hdfs:
The command should show the following:
The secondarynamenode and datanode services can be started in the same way:
If the daemon started successfully, you should see responses that will point to the log file. (Note that the actual log file is appended with '.log,' not '.out.'). As a sanity check, issue a jps command to confirm that all the services are running. The actual PID (Java Process ID) values will be different than shown in this listing:
If the process did not start, it may be helpful to inspect the log files. For instance, examine the log file for the NameNode. (Note that the path is taken from the preceding command.)
All Hadoop services can be stopped using the hadoop-daemon.sh script. For example, to stop the datanode service, enter the following (as user hdfs in the /opt/yarn/hadoop-2.2.0/sbin directory):
The same can be done for the NameNode and SecondaryNameNode.
Step 12: Start YARN Services
As with HDFS services, the YARN services need to be started. One Resource-Manager and one NodeManager must be started as user yarn (exiting from user hdfs first):
As when the HDFS daemons were started in Step 1, the status of the running daemons is sent to their respective log files. To check whether the services are running, issue a jps command. The following shows all the services necessary to run YARN on a single server:
If there are missing services, check the log file for the specific service. Similar to the case with HDFS services, the services can be stopped by issuing a stop argument to the daemon script:
Step 13: Verify the Running Services Using the Web Interface
Both HDFS and the YARN ResourceManager have a web interface. These interfaces are a convenient way to browse many of the aspects of your Hadoop installation. To monitor HDFS, enter the following (or use your favorite web browser):
Connecting to port 50070 will bring up a web interface similar to Figure 2.1.
A web interface for the ResourceManager can be viewed by entering the following:
A webpage similar to that shown in Figure 2.2 will be displayed.
Figure 2.2 Webpage for YARN ResourceManager
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