The problem or the issue addressed is on how to parallelize the computation, distribute the data, and handle failures conspire to obscure the original simple computation with large amounts of complex code to deal with these issues. Contributions are simple powerful interface that gives parallelization and distribution of large scale systems.
So to tackle the issue of parallelization, fault tolerance and distribution of data, they acquired the map and reduce primitives. The use of a functional model with user-specified map and reduce operations allows us to parallelize large computations easily and to use re-execution as the primary mechanism for fault tolerance.
The programming models would accept the key/value pair as input and produces the …show more content…
Failing that, it attempts to schedule a map task near a replica of that task’s input data. When running large MapReduce operations on a significant fraction of the workers in a cluster, most input data is read locally and consumes no network bandwidth
The task granularity is performed by subdividing the map phase by M pieces and reduce phase into R pieces. Ideally, M and R should be much larger than the number of worker machines. Having each worker perform many different tasks improves dynamic load balancing and also speeds up recovery when a worker fails: the many map tasks it has completed can be spread out across all the other worker machines.
The backup tasks, as mentioned earlier in a scenario, where the system was not reachable the map-reduce library kept on switching the workers and trying to do the job. This would in-turn would increase the amount of time required to perform the task. MapReduce operation is a straggler, that is, a machine that takes an unusually long time to complete one of the last few map or reduce tasks in the computation. Stragglers can arise for a whole host of
So to tackle the issue of parallelization, fault tolerance and distribution of data, they acquired the map and reduce primitives. The use of a functional model with user-specified map and reduce operations allows us to parallelize large computations easily and to use re-execution as the primary mechanism for fault tolerance.
The programming models would accept the key/value pair as input and produces the …show more content…
Failing that, it attempts to schedule a map task near a replica of that task’s input data. When running large MapReduce operations on a significant fraction of the workers in a cluster, most input data is read locally and consumes no network bandwidth
The task granularity is performed by subdividing the map phase by M pieces and reduce phase into R pieces. Ideally, M and R should be much larger than the number of worker machines. Having each worker perform many different tasks improves dynamic load balancing and also speeds up recovery when a worker fails: the many map tasks it has completed can be spread out across all the other worker machines.
The backup tasks, as mentioned earlier in a scenario, where the system was not reachable the map-reduce library kept on switching the workers and trying to do the job. This would in-turn would increase the amount of time required to perform the task. MapReduce operation is a straggler, that is, a machine that takes an unusually long time to complete one of the last few map or reduce tasks in the computation. Stragglers can arise for a whole host of