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From Ameet Kini <>
Subject Re: examples of map-side join of two hadoop sequence files
Date Mon, 21 Oct 2013 15:16:44 GMT
Right, except both my sequence files are large and so doing a "collect()"
and then broadcasting one of them would be costly. Since I have two large
sorted sequence files with a one-to-one relationship among the keys, I need
to perform the "merge" portion of a good old "sort-merge" join. And it is
actually a very simple merge, since each key is unique within the file.

I was looking at the mapPartitions API:
def  mapPartitions[U](f: (Iterator[T]) => Iterator[U], preservesPartitioning:
Boolean)(implicit arg0: ClassManifest[U]):

If somehow the function f has access to the underlying partition
information (e.g., HadoopPartition.inputSplit), then it could open a reader
on the actual hdfs file corresponding to that inputSplit, and manually do
the join. But looks like HadoopPartition is declared private. Is there some
other way to figure out which underlying HDFS file corresponds to the
partition being iterated upon in mapPartitions?


On Mon, Oct 21, 2013 at 12:54 AM, Reynold Xin <> wrote:

> How about the following:
> val smallFile = sc.sequenceFile(....).collect()
> val largeFile = sc.sequenceFile(...)
> val small = sc.broadcast(smallFile)
> largeFile.mapPartitions { iter =>
>   // build up a hash table for small. called it smallTable
>   iter.filter(row => smallTable.contains(row.joinKey)).map { row =>
>     join smallTable.get(row.joinKey) with row itself
>   }
> }
> On Fri, Oct 18, 2013 at 2:22 PM, Ameet Kini <> wrote:
>> Forgot to add an important point. My sequence files are sorted (they're
>> actually Hadoop map files). Since they're sorted, it makes sense to do a
>> fetch at the partition-level of the inner sequence file.
>> Thanks,
>> Ameet
>> On Fri, Oct 18, 2013 at 5:20 PM, Ameet Kini <> wrote:
>>> I've seen discussions where the suggestion is to do a map-side join, but
>>> haven't seen an example yet, and can certainly use one. I have two sequence
>>> files where the key is unique within each file, so the join is a one-to-one
>>> join, and can hence benefit from a map-side join. However both sequence
>>> files can be large, so reading one of them completely in the driver and
>>> broadcasting it out would be expensive.
>>> I don't think there is a map-side join implementation in Spark but
>>> earlier suggestions have been to write one using mapPartitions on one of
>>> the operands as the outer loop. If that is the case, how would I fetch the
>>> split corresponding to the keys in the outer's partition. I'd prefer to do
>>> a fetch-per-partition rather than a fetch-per-tuple.
>>> In any case, some feedback, and preferably, an example of a map-side
>>> join without broadcasting would help.
>>> Thanks,
>>> Ameet

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