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MapReduce Tutorial : Exercise - sorting

You are given data consisting of (31-bit integer, string data) pairs. These are available in plain text format:

Path Size
/home/straka/hadoop/example-inputs/numbers-small 3MB
/home/straka/hadoop/example-inputs/numbers-medium 184MB
/home/straka/hadoop/example-inputs/numbers-large 916MB

You can assume that the integers are uniformly distributed.

Your task is to sort these data. Your solution should work for TBs of data. For that reason, you must use multiple reducers. If your job is executed using r reducers, the output consists of r files, which when concatenated would produce sorted (key, value) pairs. In other words, each of the output files contains sorted (integer, data) pairs and all keys in one file are either smaller or larger than in other file.

Nonuniform data

Assuming uniformity of the integer keys is quite a big deal. Try improving your solution to sort keys with any distribution. You can use the exponentially distributed data available here:

Path Size
/home/straka/hadoop/example-inputs/nonuniform-small 3MB
/home/straka/hadoop/example-inputs/nonuniform-medium 160MB
/home/straka/hadoop/example-inputs/nonuniform-large 797MB

Assume we want to produce r output files. One of the solutions is to perform two Hadoop jobs:

  1. Go through the data and sample only a small fraction of the keys. As there are not so many of them, they can fit in one reducer.
  2. Find best r-1 integer separators using the sampled data.
  3. Run the second pass, using the separators to guide the partitioning.

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