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The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task finish first are used. This is called:
Speculative execution: One problem with the Hadoop system is that by dividing the tasks across many nodes, it is possible for a few slow nodes to rate-limit the rest of the program. For example if one node has a slow disk controller, then it may be reading its input at only 10% the speed of all the other nodes. So when 99 map tasks are already complete, the system is still waiting for the final map task to check in, which takes much longer than all the other nodes.
By forcing tasks to run in isolation from one another, individual tasks do not know where their inputs come from. Tasks trust the Hadoop platform to just deliver the appropriate input. Therefore, the same input can be processed multiple times in parallel, to exploit differences in machine capabilities. As most of the tasks in a job are coming to a close, the Hadoop platform will schedule redundant copies of the remaining tasks across several nodes which do not have other work to perform. This process is known as speculative execution. When tasks complete, they announce this fact to the JobTracker. Whichever copy of a task finishes first becomes the definitive copy. If other copies were executing speculatively, Hadoop tells the TaskTrackers to abandon the tasks and discard their outputs. The Reducers then receive their inputs from whichever Mapper completed successfully, first.
Note:
* Hadoop uses 'speculative execution.' The same task may be started on multiple boxes. The first one to finish wins, and the other copies are killed.
Failed tasks are tasks that error out.
* There are a few reasons Hadoop can kill tasks by his own decisions:
a) Task does not report progress during timeout (default is 10 minutes)
b) FairScheduler or CapacityScheduler needs the slot for some other pool (FairScheduler) or queue (CapacityScheduler).
c) Speculative execution causes results of task not to be needed since it has completed on other place.
Which two of the following are true about this trivial Pig program' (choose Two)
Review the following data and Pig code:
What command to define B would produce the output (M,62,95l02) when invoking the DUMP operator on B?
Which of the following tool was designed to import data from a relational database into HDFS?
Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.
Note:
* Join Algorithms in MapReduce
A) Reduce-side join
B) Map-side join
C) In-memory join
/ Striped Striped variant variant
/ Memcached variant
* Which join to use?
/ In-memory join > map-side join > reduce-side join
/ Limitations of each?
In-memory join: memory
Map-side join: sort order and partitioning
Reduce-side join: general purpose
