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

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  [[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 (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 tables. It accomplishes this by splitting one of the 
input table on the join predicate and streaming the other table.
  [[Anchor(Use_cases)]]
  == Use cases ==
  
@@ -26, +26 @@

  [[Anchor(Implementation)]]
  == Implementation ==
  
- Skewed join translates into two map/reduce jobs - Sample and Join. The first 
job samples the input records and computes a histogram of the underlying key 
space. The second map/reduce job partitions the input table and performs a join 
on the predicate. In order to join the two tables, one of the tables is 
partitioned and other is streamed to the map tasks. The map task of this job 
uses the =pig.quantiles= file to determine the number of reducers per key. It 
then sends the key to each of the reducers in a round robin fashion. Skewed 
joins happen in the reduce phase. 
+ Skewed join translates into two map/reduce jobs - Sample and Join. The first 
job samples the input records and computes a histogram of the underlying key 
space. The second map/reduce job partitions the input table and performs a join 
on the predicate. In order to join the two tables, one of the tables is 
partitioned and other is streamed to the map tasks. The map task of this job 
uses the ~-pig.quantiles-~ file to determine the number of reducers per key. It 
then sends the key to each of the reducers in a round robin fashion. Skewed 
joins happen in the reduce phase. 
  
- %ATTACHURL%/Slide1.jpg
+ attachment:partition.jpg
+ 
  [[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 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. 
  
  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.
  [[Anchor(Sort_phase)]]
@@ -41, +42 @@

  === 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.
  
- 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 to the logical plan. The 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.
+ 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.
  
  [[Anchor(PRop)]]
  ==== Partition Rearrange operator ====

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