Jobtracker API error - Call to localhost/127.0.0.1:50030 failed on local exception: java.io.EOFException

Try the port number listed in your $HADOOP_HOME/conf/mapred-site.xml under the mapred.job.tracker property. Here's my pseudo mapred-site.xml conf

<property>
    <name>mapred.job.tracker</name>
    <value>localhost:9001</value>
</property>

If you look at the JobTracker.getAddress(Configuration) method, you can see it uses this property if you don't explicitly specify the jobtracker host / port:

public static InetSocketAddress getAddress(Configuration conf) {
  String jobTrackerStr =
    conf.get("mapred.job.tracker", "localhost:8012");
  return NetUtils.createSocketAddr(jobTrackerStr);
}

Apache Hadoop NextGen MapReduce (YARN)

MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2) or YARN.

The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.

The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.

The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.

 

Check this LINK for more detail

HBase Security for the Enterprise

Trend Micro developed the new security features in HBase 0.92 and has the first known deployment of secure HBase in production. We will share our motivations, use cases, experiences, and provide a 10 minute tutorial on how to set up a test secure HBase cluster and a walk through of a simple usage example. The tutorial will be carried out live on an on-demand EC2 cluster, with a video backup in case of network or EC2 unavailability.

Source : here

What is the difference between HDFS and NAS ?

The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. Following are differences between HDFS and NAS
• In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
• HDFS is designed to work with Map Reduce System, since computation are moved to data. NAS is not suitable for Map Reduce since data is stored separately from the computations.
• HDFS runs on a cluster of machines and provides redundancy using replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.

What is a Job Tracker in Hadoop? How many instances of Job Tracker run on a Hadoop Cluster?

Job Tracker is the daemon service for submitting and tracking Map Reduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The Job Tracker is single point of failure for the Hadoop Map Reduce service. If it goes down, all running jobs are halted. Job Tracker in Hadoop performs following actions(from Hadoop Wiki:)
• Client applications submit jobs to the Job tracker.
• The JobTracker talks to the NameNode to determine the location of the data
• The JobTracker locates TaskTracker nodes with available slots at or near the data
• The JobTracker submits the work to the chosen TaskTracker nodes.
• The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.
• A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable.
• When the work is completed, the JobTracker updates its status.
• Client applications can poll the JobTracker for information.

Hadoop Shell Commands

namenode -format	format the DFS filesystem
secondarynamenode	run the DFS secondary namenode
namenode	run the DFS namenode
datanode	run a DFS datanode
dfsadminmradmin	run a DFS admin client
mradmin	run a Map-Reduce admin client

HBase MapReduce Read/Write Example

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleReadWrite");
job.setJarByClass(MyReadWriteJob.class);    // class that contains mapper
                       
Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs
           

HBase MapReduce Read Example

Configuration config = HBaseConfiguration.create();
Job job = new Job(config, "ExampleRead");
job.setJarByClass(MyReadJob.class);     // class that contains mapper
   
Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs