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Hadoop Framework comprises of two main components, namely,
In this post we will discuss the Anatomy of a MapReduce Job in Apache Hadoop. A typical Hadoop MapReduce job is divided into a set of Map and Reduce tasks that execute on a Hadoop cluster. The execution flow occurs as follows:
Let’s concentrate on Map and Reduce phase in this blog. (We will review the input data splitting and shuffle process in detail in our future blogs).
Let us look at a simple MapReduce job execution using one of the sample examples, “teragen” in CDH3. This program is used for generating large amount of data for bench marking the clusters available in Cloudera CDH3 Quick Demo VM.
The data size to be generated and the output file location are specified as an argument to the ‘teragen’ program. The ‘teragen’ class/program runs a MapReduce job to generate the data. We will analyze this MapReduce job execution.
This output file stores the output data on HDFS. The following figure shows the execution process and all the intermediate phases of a MapReduce Job execution:
Let’s review the execution log to understand the Job execution flow:
The ‘teragen’ program launches two map tasks and 3 reduce tasks to generate the required data.
Note that the Reduce task starts after the map task completion and the number of records continue to reduce at each level.
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