Introduction Background
Development
Example MapReduce job Worker registration
Job submission
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Fault tolerance Conclusion
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Background / MapReducecluster MapReduce cluster
TABLE OF CONTENTS
1 Workflow
2 Fault tolerance
In this section, we will cover some of the important high-level concepts of how the system is expected to work. It will be useful to familiarize yourself with the terminology below before diving into the remainder of the spec.
Our MapReduce cluster consists of one coordinator and several workers. The cluster can run several different applications, each of which is defined by a map function and a reduce function.
The general flow for running a job on the MapReduce cluster looks like this:
1 A client can make an SubmitJob RPC request to the coordinator that specifies the following parameters:
• Input files: Files containing data to be processed
• Output directory: Directory to write final outputs to
• Desired application: Application to run on the given input files (e.g. word count)
• Number of reduce tasks: Number of sets to divide mapped keys into
2 The coordinator will then assign map tasks to each worker. A map task involves running the application’s map function on the data contained in a single input file. The results of these map tasks should be stored in memory by each individual worker. There should be one map task per input file, so the number of map tasks is equal to the number of input files. Each map task is identified by a unique map task number.
3 Once all map tasks are complete, the coordinator will assign a reduce task (i.e. a set of keys to call the application’s reduce function on) to each worker. To retrieve the results of map tasks from other workers, workers executing a reduce task must send RPCs to request the necessary data. Each reduce task is identified by a unique reduce task number.
4 Finally, the reduce workers will write their results to disk, after which the client can do whatever postprocessing it needs to on the MapReduce output files found in the output directory.
Fault tolerance
The MapReduce cluster should also be fault tolerant. This means it can tolerate worker crashes and failures, reassigning tasks to alive workers as necessary. It does this by having workers send periodic heartbeats to the coordinator, allowing the coordinator to determine when a worker is no longer alive.
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MapReduce cluster
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