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From "Rohini Palaniswamy (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (PIG-5041) RoundRobinPartitioner is not deterministic when order of input records change
Date Mon, 17 Oct 2016 19:12:59 GMT

     [ https://issues.apache.org/jira/browse/PIG-5041?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Rohini Palaniswamy updated PIG-5041:
------------------------------------
    Fix Version/s: 0.17.0

> RoundRobinPartitioner is not deterministic when order of input records change
> -----------------------------------------------------------------------------
>
>                 Key: PIG-5041
>                 URL: https://issues.apache.org/jira/browse/PIG-5041
>             Project: Pig
>          Issue Type: Bug
>            Reporter: Rohini Palaniswamy
>            Assignee: Rohini Palaniswamy
>            Priority: Critical
>             Fix For: 0.17.0, 0.16.1
>
>         Attachments: PIG-5041-1.patch, PIG-5041-2.patch, PIG-5041-branch0.16.patch
>
>
> Maps can be rerun due to shuffle fetch failures. Half of the reducers can end up successfully
pulling partitions from first run of the map while other half could pull from the rerun after
shuffle fetch failures. If the data is not partitioned by the Partitioner exactly the same
way every time then it could lead to incorrect results (loss of records and duplicated records).

> There is a good probability of order of input records changing
>     - With OrderedGroupedMergedKVInput (shuffle input), they keys are sorted but values
can be in any order as the shuffle and merge depends on the order in which inputs are fetched.
Anything involving FLATTEN can produce different order of output records.
>     - With UnorderedKVInput, the records could be in any order depending on order of
shuffle fetch. 
> RoundRobinPartitioner can partition records differently everytime as order of input records
change which is very bad. We need to get rid of RoundRobinPartitioner. Since the key is empty
whenever we use  RoundRobinPartitioner we need to partitioning based on hashcode of values
to produce consistent partitioning.
> Partitioning based on hashcode is required for correctness, but disadvantage is that
it
>     - adds a lot of performance overhead with hashcode computation
>     - with the random distribution due to hashcode (as opposed to batched round robin)
input records sorted on some column could get distributed to different reducers and if union
is followed by a store, the output can have bad compression.



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