Big Data Analytics

Additional MapReduce Functionality

Combiner: The pipeline showed earlier omits a processing step which can be used for optimizing bandwidth usage by your MapReduce job. Called the Combiner, this pass runs after the Mapper and before the Reducer. Usage of the Combiner is optional. If this pass is suitable for your job, instances of the Combiner class are run on every node that has run map tasks. The Combiner will receive as input all data emitted by the Mapper instances on a given node. The output from the Combiner is then sent to the Reducers, instead of the output from the Mappers. The Combiner is a "mini-reduce" process which operates only on data generated by one machine.

Word count is a prime example for where a Combiner is useful. The Word Count program in listings 1--3 emits a (word, 1) pair for every instance of every word it sees. So if the same document contains the word "cat" 3 times, the pair ("cat", 1) is emitted three times; all of these are then sent to the Reducer. By using a Combiner, these can be condensed into a single ("cat", 3) pair to be sent to the Reducer. Now each node only sends a single value to the reducer for each word -- drastically reducing the total bandwidth required for the shuffle process, and speeding up the job. The best part of all is that we do not need to write any additional code to take advantage of this! If a reduce function is both commutative and associative, then it can be used as a Combiner as well. You can enable combining in the word count program by adding the following line to the driver:


The Combiner should be an instance of the Reducer interface. If your Reducer itself cannot be used directly as a Combiner because of commutativity or associativity, you might still be able to write a third class to use as a Combiner for your job.
Fault Tolerance

One of the primary reasons to use Hadoop to run your jobs is due to its high degree of fault tolerance. Even when running jobs on a large cluster where individual nodes or network components may experience high rates of failure, Hadoop can guide jobs toward a successful completion.

The primary way that Hadoop achieves fault tolerance is through restarting tasks. Individual task nodes (TaskTrackers) are in constant communication with the head node of the system, called the JobTracker. If a TaskTracker fails to communicate with the JobTracker for a period of time (by default, 1 minute), the JobTracker will assume that the TaskTracker in question has crashed. The JobTracker knows which map and reduce tasks were assigned to each TaskTracker.

If the job is still in the mapping phase, then other TaskTrackers will be asked to re-execute all map tasks previously run by the failed TaskTracker. If the job is in the reducing phase, then other TaskTrackers will re-execute all reduce tasks that were in progress on the failed TaskTracker.

Reduce tasks, once completed, have been written back to HDFS. Thus, if a TaskTracker has already completed two out of three reduce tasks assigned to it, only the third task must be executed elsewhere. Map tasks are slightly more complicated: even if a node has completed ten map tasks, the reducers may not have all copied their inputs from the output of those map tasks. If a node has crashed, then its mapper outputs are inaccessible. So any already-completed map tasks must be re-executed to make their results available to the rest of the reducing machines. All of this is handled automatically by the Hadoop platform.

This fault tolerance underscores the need for program execution to be side-effect free. If Mappers and Reducers had individual identities and communicated with one another or the outside world, then restarting a task would require the other nodes to communicate with the new instances of the map and reduce tasks, and the re-executed tasks would need to reestablish their intermediate state. This process is notoriously complicated and error-prone in the general case. MapReduce simplifies this problem drastically by eliminating task identities or the ability for task partitions to communicate with one another. An individual task sees only its own direct inputs and knows only its own outputs, to make this failure and restart process clean and dependable.

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.

Speculative execution is enabled by default. You can disable speculative execution for the mappers and reducers by setting the and mapred.reduce.tasks.speculative.execution JobConf options to false, respectively.