What is Job Tracker in Apache Hadoop?
JobTracker is a daemon service that is used for submitting and tracking MapReduce(MR) jobs in the Apache Hadoop framework. In a typical production cluster, JobTracker runs on a separate machine…
What is Hadoop Task Tracker?
Task Tracker is a daemon in the Hadoop cluster node that accepts various tasks from Job Tracker. These tasks range from Map, Reduce, or Shuffle operations. They also run their…
Hadoop Tutorials
Apache Hadoop is an open-source framework that is used to store and process large datasets whose size ranges from gigabytes to petabytes of data. This framework uses multiple commodity computers…
What is Heartbeat in Hadoop Framework?
What is HeartBeat? In Hadoop framework heartbeat is a signal that is sent by DataNode to NameNode and also by Task Tracker to the Job tracker. DataNode sends the signal…
What is Speculative Execution in Hadoop?
Speculative execution is a way of coping with individual machine performance. There might be many machines in large clusters with a hundred or thousands of machines, that are not performing…
What are HDFS Data Blocks?
Hadoop Block Size Configuration and Components Block is defined as the smallest site/location on the hard drive that is available to read and write data. Data in HDFS(Hadoop Distributed File…
Rack Awareness in Hadoop HDFS
What is Rack? Before looking into the Rack awareness in Hadoop HDFS, let us understand the rack itself. A rack is a storage area where all the data nodes are…
Difference between Apache Hadoop/HDFS and HBase
Apache Hadoop/HDFS and HBase are both parts of the Big data framework. They both are used to store a massive amount of data. In spite of this similarity, they have…
Apache Hadoop 3 Changes
Apache Hadoop 3 incorporated a number of enhancements over the Hadoop-2.x. We will talk about the important enhancement that was implemented as part of Hadoop 3 over Hadoop 2 in…
Finding Right hardware for Hadoop Cluster
Many things needs to be considered before finding the right hardware for Hadoop clusters. Hadoop workloads tend to vary a lot betweek different jobs. It takes experience to correctly anticipate the amounts of storage, processing power, and inter-node communication that will be required for different kinds of jobs.