Big Data Hadoop & Spark

Know about of the Running of Hadoop Daemons

Hadoop daemons

In this video blog, we will be discussing how to start your Hadoop daemons. We can come to the conclusion that the Hadoop cluster is running by looking at the Hadoop daemon itself. A Daemon is nothing but a process. So, Hadoop daemons are nothing but Hadoop processes. As Hadoop is built using Java, all the Hadoop daemons are Java processes.

We can check the list of Java processes running in your system by using the command jps.

If you are able to see the Hadoop daemons running after executing the jps command, we can safely assume that the Hadoop cluster is running.

Some of the basic Hadoop daemons are as follows:

  • Name node

  • Data node

  • Resource manager

  • Node manager

We can find these daemons in the sbin directory of Hadoop. After moving into the sbin directory, we can start all the Hadoop daemons by using the command start-all.sh.

After executing the command, all the daemons start one by one. After all the daemons have started, we can check their presence by typing jps, which gives the list of all Java processes that are running.

We can also stop all the daemons using the command stop-all.s. We can also start or stop each daemon separately.

Now, let’s look at the start and stop commands for each of the Hadoop daemon.

Hadoop

Namenode:

Start:hadoop-daemon.sh start namenode

stop:hadoop-daemon.sh stop namenode

Datanode:

Start:hadoop-daemon.sh start datanode

Stop:hadoop-daemon.sh stop datanode

Resource manager:

start:yarn-daemon.sh start resourcemanager

stop:yarn-daemon.sh stop resoucemnager

Node manager:

start:yarn-daemon.sh start nodemanager

stop:yarn-daemon.sh stop nodemanager

We can see that the Name node and Data node are segregated as Hadoop daemons, and the Resource manager and the Node manager are segregated as YARN daemons.

We hope this post helped you in understanding how to Run your hadoop daemon . Keep visiting our site acadgild for more updates on Big Data and other technologies.

Hadoop

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