Hi Bart,
From what I understand, you want to do a partial (per node) aggregation before
shipping the result
for the final one at the end. In addition, the keys do not seem to change
between aggregations, right?
If this is the case, this is the functionality of the Combiner in batch.
In Batch (DataSet API) this is supported, but in Streaming it is not.
If your main concern is optimizing your already up-and-running job, it would be
worth sharing your code
(or an example with the same characteristics / communication patterns if the
real code is not possible)
so that we can have a look and potentially find other parts of the pipeline
that can be optimized.
For example, given that you are concerned with the serialization overhead, it
may be worth
seeing if there are better alternatives to use.
Kostas
> On May 24, 2016, at 4:22 PM, Bart Wyatt <bart.wy...@dsvolition.com> wrote:
>
> (migrated from IRC)
>
> Hello All,
>
> My situation is this:
> I have a large amount of data partitioned in kafka by "session" (natural
> partitioning). After I read the data, I would like to do as much as possible
> before incurring re-serialization or network traffic due to the size of the
> data. I am on 1.0.3 in the java API.
>
> What I'd like to do is:
>
> while maintaining the natural partitioning (so that a single thread can
> perform this) read data from kafka, perform a window'd fold over the incoming
> data keyed by a _different_ field("key") then take the product of that
> window'd fold and allow re-partitioning to colocate data with equivalent keys
> in a new partitioning scheme where they can be reduced into a final product.
> The hope is that the products of such a windowed fold are orders of magnitude
> smaller than the data that would be serialized/sent if we re-partitioned
> before the window'd fold.
>
> Is there a way to .keyBy(...) such that it will act within the physical
> partitioning of the data and not force a re-partitioning of the data by that
> key?
>
> thanks
> -Bart
>
>
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