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From Apache Wiki <wikidi...@apache.org>
Subject [Pig Wiki] Update of "PigSkewedJoinSpec" by SriranjanManjunath
Date Thu, 07 May 2009 20:01:21 GMT
Dear Wiki user,

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The following page has been changed by SriranjanManjunath:
http://wiki.apache.org/pig/PigSkewedJoinSpec

------------------------------------------------------------------------------
  [[Anchor(Intro)]]
  == Introduction ==
  
- Parallel joins are vulnerable to the presence of skew in the underlying data. If the underlying
data is sufficiently skewed, load imbalances will swamp any of the parallelism gains [#References
(1)]. In order to counteract this problem, skewed join computes a histogram of the key space
and uses this data to allocate reducers for a given key. Skewed join does not place a restriction
on the size of the input tables. It accomplishes this by splitting one of the input table
on the join predicate and streaming the other table.
+ Parallel joins are vulnerable to the presence of skew in the underlying data. If the underlying
data is sufficiently skewed, load imbalances will swamp any of the parallelism gains [#References
(1)]. In order to counteract this problem, skewed join computes a histogram of the key space
and uses this data to allocate reducers for a given key. Skewed join does not place a restriction
on the size of the input keys. It accomplishes this by splitting one of the input on the join
predicate and streaming the other input.
  [[Anchor(Use_cases)]]
  == Use cases ==
  
@@ -32, +32 @@

  
  [[Anchor(Sampler_phase)]]
  === Sampler phase ===
- If the underlying data is sufficiently skewed, load imbalances will result in a few reducers
getting a lot of keys. As a first task, the sampler creates a histogram of the key distribution
and stores it in the ~-pig.keydist-~ file. This key distribution will be used to allocate
the right number of reducers for a key. For the table which is partitioned, the partitioner
uses the key distribution to copy the output to the reducer buffer regions in a round robin
fashion. For the table which is streamed, the mapper task uses the ~-pig.keydist-~ file to
copy the data to each of the reduce partitions. 
+ If the underlying data is sufficiently skewed, load imbalances will result in a few reducers
getting a lot of keys. As a first task, the sampler creates a histogram of the key distribution
and stores it in the ~-pig.keydist-~ file. This key distribution will be used to allocate
the right number of reducers for a key. For the table which is partitioned, the partitioner
uses the key distribution to send the data to the reducer in a round robin fashion. For the
table which is streamed, the mapper task uses the ~-pig.keydist-~ file to copy the data to
each of the reduce partitions. 
  
- As a first stab at the implementation, we will be using the uniform random sampler used
by Order BY. The sampler currently does not output the key distribution. It will be modified
to support the same.
+ As a first stab at the implementation, we will be using the uniform random sampler used
by Order BY. The sampler currently does not output the key distribution nor the size of the
sample record. It will be modified to support the same.
  [[Anchor(Sort_phase)]]
  === Sort phase ===
  The keys are sorted based on the input predicate.
  [[Anchor(Join_phase)]]
  === Join Phase ===
- Skewed join happens in the reduce phase. As a convention, the first table in the join command
is partitioned and sent to the various reducers. Partitioning allows us to support massive
tables without having to worry about the memory limitations. The partitioner is overridden
to send the data in a round robin fashion to each of the reducers associated with a key. The
partitioner obtains the reducer information from the key distribution file. To counteract
the issues with reducer starvation (i.e. the keys that require more than 1 reducer are granted
the reducers whereas the other keys are starved for the reducers), the user is allowed to
set a config parameter pig.mapreduce.skewedjoin.uniqreducers. The value is a percentage of
unique reducers the partitioner should use. For ex: if the value is 90, 10% of the total reducers
will be used for highly skewed data.
+ Skewed join happens in the reduce phase. As a convention, the first table in the join command
is partitioned and sent to the various reducers. Partitioning allows us to support massive
tables without having to worry about the memory limitations. The partitioner is overridden
to send the data in a round robin fashion to each of the reducers associated with a key. The
partitioner obtains the reducer information from the key distribution file. To counteract
the issues with reducer starvation (i.e. the keys that require more than 1 reducer are granted
the reducers whereas the other keys are starved for the reducers), the user is allowed to
set a config parameter pig.mapreduce.skewedjoin.uniqreducers. The value is a percentage of
unique reducers the partitioner should use. For ex: if the value is 90, 10% of the total reducers
will be used for highly skewed data. If the input is highly skewed and the number of reducers
is very low, the task will bail out and report an error.
  
  For the streaming table, since more than one reducer can be associated with a key, the streamed
table records (that match the key) needs to be copied over to each of these reducers. The
mapper function uses the key distribution in ~-pig.keydist-~ file to copy the records over
to each of the partition. It accomplishes this be inserting a [#PRop PRop] to the logical
plan. The [#PRop PRop] sets a partition index to each of the key/value pair which is then
used by the partitioner to send the pair to the right reducer.
  

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