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From Gabriel Reid <gabriel.r...@gmail.com>
Subject Re: Simulating Avro reads in MemPipeline
Date Wed, 04 Jun 2014 15:05:21 GMT
On Wed, Jun 4, 2014 at 4:53 PM, Josh Wills <josh.wills@gmail.com> wrote:
> As long as we're on the subject, the fact that we don't
> serialize/deserialize DoFns before we run MemPipelines has burned me a few
> times when I would make a change and forget about the serialization
> implications until I tried to run the job. One of the things I like about
> the local Spark implementation is that it does this serialization check for
> you. So if we were to have some mode that could be enabled to allow us to
> simulate DoFn serialization and object re-use situations locally, even at
> the cost of extra runtime overhead, I would be happy.

Yeah, that sounds like a good plan -- it could probably be done via an
overload of MemPipeline.getInstance() that would take a boolean flag
to replication MR or not.


>
>
> On Wed, Jun 4, 2014 at 4:27 AM, Gabriel Reid <gabriel.reid@gmail.com> wrote:
>
>> Hi David,
>>
>> Yeah, the whole object reuse situation in MapReduce is quite a drag.
>> By the way, this isn't specific to Avro -- it's just as big of an
>> issue with Writables.
>>
>> I'm kind of torn on the idea of building this into MemPipeline. On the
>> one hand, the closer the behavior of different pipeline
>> implementations are, the better, and I think MemPipeline is currently
>> indeed largely used for fast testing of MR workflows.
>>
>> On the other hand, the MemPipeline can also be seen as an optimized
>> implementation for running a pipeline faster if everything fits in
>> memory, so adding in extra serialization/deserialization just to be
>> similar to the MRPipeline would be unfortunately. Additionally,
>> there's also the SparkPipeline implementation to consider -- I assume
>> that spark doesn't have this same object reuse situation, although I'm
>> not actually sure.
>>
>> I think that my general feeling is that I'd rather not add object
>> reuse into MemPipeline, but I do think it would be great if we could
>> bring the behavior of the two implementations in line. Any other
>> thoughts on how we could do this?
>>
>> - Gabriel
>>
>>
>> On Wed, Jun 4, 2014 at 11:51 AM, David Whiting <davidwhiting@gmail.com>
>> wrote:
>> > We are facing some problems where errors are not found with MemPipeline
>> > tests which do happen on the cluster due to the way Avro reuses objects
>> for
>> > reading; meaning that if you do a parallelDo and a PGroupedTable, then
>> the
>> > Iterable of values you get is actually the same object returned to you
>> with
>> > different contents. If you then try and store certain instances of this
>> to
>> > be emitted later, then you will get unexpected results without taking a
>> > copy.
>> >
>> > To try and catch these problems on the local side, we've written a
>> wrapper
>> > for Iterable<? extends SpecificRecord> which uses the SpecificDatumWriter
>> > and SpecificDatumReader to write to and from ByteBuffers for each
>> iteration
>> > and offering the last record for reuse. This allows us to run the
>> offending
>> > MapFn or DoFn in isolation with a wrapped Iterable as input to identify
>> and
>> > fix the problem.
>> >
>> > This, however, is a little awkward and it would be much neater if this
>> was
>> > driven from the Crunch side. At the point in the MapShuffler where the
>> > Iterable is wrapped in a SingleUseIterable, it could also be wrapped in
>> one
>> > of these AvroReadSimulatingIterable, meaning that people could find these
>> > problems before even running them live for the first time. However, this
>> is
>> > a problem that is specific to Avro so it obviously makes no sense to hack
>> > it right into HFunction.
>> >
>> > Anyone have any thoughts on how this could be integrated nicely, or
>> indeed
>> > if it should be integrated at all?
>>

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