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Hive >> mail # user >> map side join with group by


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Re: map side join with group by
Nitin

Yeah. My original question is that is there a way to force Hive (or rather
to say, is it possible) to execute map side join at mapper phase and group
by in reduce phase. So instead of launching a map only job (join) and map
reduce job (group by), doing it altogether in a single MR job. This is
obviously not what Hive does but I am wondering if it is a nice feature to
have.

The point you made (different keys in join and group by) only matters when
it is the time in reduce phase, right? As map side join takes care of join
at mapper phase, it sounds to me natural that group by can be done in the
reduce phase in the same job. The only hassle that I can think of is that
map output have to be resorted (based on group by keys).

Chen

On Thu, Dec 13, 2012 at 1:42 PM, Nitin Pawar <[EMAIL PROTECTED]>wrote:

> chen in mapside join .. there are no reducers .. its MAP ONLY job
>
>
> On Thu, Dec 13, 2012 at 11:54 PM, Chen Song <[EMAIL PROTECTED]>wrote:
>
>> Understood that fact that it is impossible in the same MR job if both
>> join and group by are gonna happen in the reduce phase (because the join
>> keys and group by keys are different). But for map side join, the joins
>> would be complete by the end of the map phase, and outputs should be ready
>> to be distributed to reducers based on group by keys.
>>
>> Chen
>>
>>
>> On Thu, Dec 13, 2012 at 11:04 AM, Nitin Pawar <[EMAIL PROTECTED]>wrote:
>>
>>> Thats because for the first job the join keys are different and second
>>> job group by keys are different, you just cant assume join keys and group
>>> keys will be same so they are two different jobs
>>>
>>>
>>> On Thu, Dec 13, 2012 at 8:26 PM, Chen Song <[EMAIL PROTECTED]>wrote:
>>>
>>>> Yeah, my abridged version of query might be a little broken but my
>>>> point is that when a query has a map join and group by, even in its
>>>> simplified incarnation, it will launch two jobs. I was just wondering why
>>>> map join and group by cannot be accomplished in one MR job.
>>>>
>>>> Best,
>>>> Chen
>>>>
>>>>
>>>> On Thu, Dec 13, 2012 at 12:30 AM, Nitin Pawar <[EMAIL PROTECTED]>wrote:
>>>>
>>>>> I think Chen wanted to know why this is two phased query if I
>>>>> understood it correctly
>>>>>
>>>>> When you run a mapside join .. it just performs the join query ..
>>>>> after that to execute the group by part it launches the second job.
>>>>> I may be wrong but this is how I saw it whenever I executed group by
>>>>> queries
>>>>>
>>>>>
>>>>> On Thu, Dec 13, 2012 at 7:11 AM, Mark Grover <
>>>>> [EMAIL PROTECTED]> wrote:
>>>>>
>>>>>> Hi Chen,
>>>>>> I think we would need some more information.
>>>>>>
>>>>>> The query is referring to a table called "d" in the MAPJOIN hint but
>>>>>> there is not such table in the query. Moreover, Map joins only make
>>>>>> sense when the right table is the one being "mapped" (in other words,
>>>>>> being kept in memory) in case of a Left Outer Join, similarly if the
>>>>>> left table is the one being "mapped" in case of a Right Outer Join.
>>>>>> Let me know if this is not clear, I'd be happy to offer a better
>>>>>> explanation.
>>>>>>
>>>>>> In your query, the where clause on a column called "hour", at this
>>>>>> point I am unsure if that's a column of table1 or table2. If it's
>>>>>> column on table1, that predicate would get pushed up (if you have
>>>>>> hive.optimize.ppd property set to true), so it could possibly be done
>>>>>> in 1 MR job (I am not sure if that's presently the case, you will have
>>>>>> to check the explain plan). If however, the where clause is on a
>>>>>> column in the right table (table2 in your example), it can't be pushed
>>>>>> up since a column of the right table can have different values before
>>>>>> and after the LEFT OUTER JOIN. Therefore, the where clause would need
>>>>>> to be applied in a separate MR job.
>>>>>>
>>>>>> This is just my understanding, the full proof answer would lie in
>>>>>> checking out the explain plans and the Semantic Analyzer code.

Chen Song
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB