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Apache pig with Apache tez

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Tez DAG - Directed Acyclic Graph

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  • Combination of operators
    • 2 DISTINCT + JOIN + 2 GROUP BY
  • Multiple inputs
  • Multiple HDFS outputs
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    High Depth DAG - Directed Acyclic Graph

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    Wide DAG - Directed Acyclic Graph

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    Disjoint Trees DAG - Directed Acyclic Graph

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    Bloom Filter in TEZ

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    Pig Script - Bloom UDF

     define bb BuildBloom('128', '3', 'jenkins');
    small = load 'S' as (x, y, z);
    grpd = group small all;
    fltrd = foreach grpd generate bb(small.x);
    store fltrd in ’ mybloom';
    exec;
    define bloom Bloom('mybloom');
    large = load 'L' as (a, b, c);
    flarge = filter large by bloom(L.a);
    joined = join small by x, flarge by a;
    store joined into ’ results';

    Pig Script - Bloom Join

    large = load 'L' as (a, b, c);
    small = load 'S' as (x, y, z);
    joined = join large by a, small by x using 'bloom';
    store joined into 'results';

    Bloom Filter Tuning

  • pig.bloomjoin.vectorsize.bytes –
    • The size in bytes of the bit vector to be used for the bloom filter.
    • A bigger vector size will be needed when the number of distinct keys is higher. Default value is 1048576 (1MB).
  • pig.bloomjoin.hash.type
    • The type of hash function to use.
    • Valid values are 'jenkins' and 'murmur'. Default is murmur.
  • pig.bloomjoin.hash.functions
    • The number of hash functions to be used in bloom computation.
    • It determines the probability of false positives. Higher the number lower the false positives. Too high a value can increase the CPU time.
    • Default value is 3.

    Apache PIG Hash Join

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    Apache tez - Bloom Join - Map Strategy

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    Apache pig - apache tez - Bloom Join - Reduce Strategy

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    Apache Tez - Partitioned Bloom Filters

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    Apache pig - apache tez - Bloom Join - Execution Tuning

  • pig.bloomjoin.strategy
    • Valid values are 'map' and 'reduce'. Default value is map
    • Map strategy creates bloom filters in each map and combines them in the reducer. Fast and ideal for small to medium datasets or distinct join keys.
    • Reduce strategy sends the join keys to a reducer and creates the bloom filter there. Ideal for large datasets or repeating join keys.
  • pig.bloomjoin.num.filters
    • The number of bloom filters that will be created
    • Will use that many reducers to create the bloom filters in parallel
    • Default is 1 for map strategy and 11 for reduce strategy
  • pig.bloomjoin.nocombiner
    • Used to turn off the combiner with the reduce strategy when the keys are mostly distinct
    • Default is false

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