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You use the hadoop fs --put command to write a 300 MB file using and HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this life?
Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and monitoring application resource usage?
You need to run the same job many times with minor variations. Rather than hardcoding all job configuration options in your drive code, you've decided to have your Driver subclass org.apache.hadoop.conf.Configured and implement the org.apache.hadoop.util.Tool interface.
Indentify which invocation correctly passes.mapred.job.name with a value of Example to Hadoop?
Configure the property using the -D key=value notation:
-D mapred.job.name='My Job'
You can list a whole bunch of options by calling the streaming jar with just the -info argument
Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.
JobTracker is the daemon service for submitting and tracking MapReduce 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 JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker 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.
You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs with the key consisting of the matching text, and the value containing the filename and byte offset. Determine the difference between setting the number of reduces to one and settings the number of reducers to zero.
* It is legal to set the number of reduce-tasks to zero if no reduction is desired.
In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the FileSystem.
* Often, you may want to process input data using a map function only. To do this, simply set mapreduce.job.reduces to zero. The MapReduce framework will not create any reducer tasks. Rather, the outputs of the mapper tasks will be the final output of the job.
Note:
Reduce
In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method is called for each <key, (list of values)> pair in the grouped inputs.
The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(WritableComparable, Writable).
Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just indicate that they are alive.
The output of the Reducer is not sorted.
