To start Hadoop in Linux, you need to follow these steps:
- Download and extract Hadoop: Visit the Apache Hadoop website and download the latest stable release of Hadoop. Extract the downloaded tarball to a directory of your choice.
- Configure Hadoop: Go to the extracted Hadoop directory and navigate to the /etc/hadoop folder. Edit the hadoop-env.sh file to set the Java home path by defining the JAVA_HOME variable.
- Configure core-site.xml: Open the core-site.xml file located in the /etc/hadoop folder. Add the configuration property fs.defaultFS and set it to the Hadoop filesystem URI.
- Configure hdfs-site.xml: Open the hdfs-site.xml file located in the /etc/hadoop folder. Set the dfs.replication property to define the number of replicas for data blocks. You may also customize other properties as per your requirements.
- Format the Hadoop file system: Open the terminal and run the command hdfs namenode -format to format the Hadoop file system.
- Start Hadoop daemons: Run the command start-dfs.sh to start the Hadoop distributed file system (HDFS). Run the command start-yarn.sh to start the YARN resource manager.
- Verify Hadoop deployment: Open a web browser and navigate to localhost:9870 to access the Hadoop web user interface. This interface provides information about the Hadoop cluster.
That's it! You have successfully started Hadoop in Linux. You can now perform various data processing tasks using Hadoop's distributed computing framework.
How to start Hadoop's ResourceManager in Linux?
To start Hadoop's ResourceManager in Linux, follow these steps:
- Open a terminal window.
- Navigate to the Hadoop installation directory.
- Go to the sbin folder within the Hadoop installation directory.
- Run the following command to start the ResourceManager: ./yarn-daemon.sh start resourcemanager
- Wait for the ResourceManager to start. You can check the logs in the console for any errors or information.
Once the ResourceManager is started successfully, it will be accessible at the default ResourceManager web UI URL: http://localhost:8088/.
How to view Hadoop logs in Linux?
To view Hadoop logs in Linux, you can follow the steps below:
- SSH into the machine where Hadoop is installed using the command: ssh username@server_ip_address.
- Navigate to the Hadoop logs directory using the command: cd /path/to/hadoop/logs. The exact path may vary depending on your Hadoop installation.
- Use the ls command to list the available log files. Hadoop logs are typically stored in multiple files such as hadoop-hadoop-namenode-servername.log, hadoop-hadoop-datanode-servername.log, hadoop-yarn-nodemanager-servername.log, etc.
- Once you have identified the log file you want to view, use a text editor like nano or vi to open the log file. For example, to open the Namenode log file, use the command: nano hadoop-hadoop-namenode-servername.log.
- The text editor will open the log file, allowing you to view the contents. You can scroll through the log file using the arrow keys or the scroll wheel of your mouse.
- To search for specific patterns or errors within the log file, you can use text search operations provided by the text editor. In nano, you can press Ctrl + W to search for a specific pattern or use other search options available in the editor.
Note: Make sure you have the necessary permissions to access the log files. Additionally, it is recommended to use tools like Apache Ambari or Cloudera Manager to view and analyze Hadoop logs in a more interactive and user-friendly manner.
How to start Hadoop's job history server in Linux?
To start Hadoop's Job History Server in Linux, you can follow these steps:
- Open your terminal and navigate to the Hadoop installation directory.
- Go to the sbin directory within the Hadoop installation directory.
- Run the following command to start the Job History Server: ./mr-jobhistory-daemon.sh start historyserver
- The Job History Server will start running, and you should see some logs on the terminal indicating the server has started successfully.
- You can access the Job History Server by opening a web browser and navigating to the following URL: http://localhost:19888 Here, "localhost" refers to the machine on which you are running Hadoop. However, if you are running Hadoop on a remote machine, replace "localhost" with the IP address or hostname of that machine.
- The Job History Server dashboard will be displayed in your web browser, showing the history of completed MapReduce jobs.
Note: Before starting the Job History Server, make sure that you have successfully started the Hadoop cluster and all the related daemons, such as NameNode and DataNode. The Job History Server utilizes the log files generated by the MapReduce jobs, so it requires a running Hadoop cluster.
What is the role of Hadoop's ResourceManager?
The ResourceManager in Hadoop is responsible for managing the cluster's resources and is a critical component of Hadoop's resource management and job scheduling. Its main role includes:
- Resource Allocation: The ResourceManager manages the allocation of resources in the cluster, making sure the available resources are assigned to various applications and tasks effectively. It maintains information about the available resources such as memory, CPU, and bandwidth.
- Scheduling: The ResourceManager schedules the applications and tasks based on their resource requirements and availability in the cluster. It ensures fair and efficient sharing of resources among all the applications and prevents resource contention.
- Fault-tolerance: The ResourceManager monitors the health and status of the cluster's nodes and detects failures. In case of node failures, it automatically reallocates the resources to other healthy nodes to continue the smooth execution of applications.
- Integration with the ApplicationMaster: The ResourceManager works closely with the ApplicationMaster, which is responsible for managing the lifecycle of individual applications. It provides necessary resources and container details to the ApplicationMaster for launching and executing tasks.
- Dynamically scaling resources: The ResourceManager can dynamically negotiate the allocation of resources with compute frameworks, allowing clusters to be easily resized and adapted according to workload changes.
Overall, the ResourceManager acts as a central authority for resource management in a Hadoop cluster, ensuring efficient utilization of resources and reliable execution of applications.