Hadoop distributions. Bottlenecks and tuning.

Diomin AliakseyR&D

2014, Minsk

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Hadoop Matrix

OpenSource Monitoring Target Group

Apache Hadoop Yes X Developers

Cloudera Yes Good All

Hortonworks Yes Good All

MapR No Bad Enterprise

PivotalHD No Bad Enterprise

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How to find the bottleneck?

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Monitoring & Logs

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Brain

All stages

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Map stage

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Fetch stage

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Merge stage

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All stages

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All stages

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1. Increase size of cluster

2. Increase input block size

3. Increase buffer size

The most popular approaches

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1. Increase size of cluster

2. Increase input block size

3. Increase buffer size

Popular approach

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Small cluster, slow tasks

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We need more gold ……

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Large cluster, slow tasks

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1. Increase size of cluster

2. Increase input block size

3. Increase buffer size

Popular approach

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Increase input block size

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1. Increase size of cluster

2. Increase input block size

3. Increase buffer size

Popular approach

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1. Compression

Other techniques

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1. Compression

2. Combiner

Other techniques

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Wordcount

Reduce function as Combine

combine 1: <a, 1> <b, 1> <a, 1> => <a, 2> <b, 1>

combine 2: <a, 1> <b, 1> => <a, 1> <b, 1>

Reduce: <a, {1, 2}> <b, {1, 1}> => <a, 3> <b, 2>

Combiner

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Mean

combine 1: <k,40> <k,30> <k,20> => <k, 30>

combine 2: <k,2> <k,8> => <k, 5>

Reduce: <k, {30, 5}> => <k, 17.5>

Combiner

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Mean

combine 1: <k,40> <k,30> <k,20> => <k, 30>

combine 2: <k,2> <k,8> => <k, 5>

Reduce: <k, {30, 5}> => <k, 17.5>

(40 + 30 + 20 + 2 + 8)/5 = 17.5

Combiner

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Mean

combine 1:

<k,<40,1>> <k,<30,1>>, <k,<20,1>> => <k, <90,3> >

combine 2:

<k,<2,1>> <k, <8,1>> => <k, <10, 2> >

Reduce: <k, {<90,3>, <10,2>} > => <k, 20>

Combiner

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