I will give this a shot this morning.
Considering this and the other email "Does Kafka connector leverage Kafka
message keys?" which also ends up talking about hacking around KeyedStream's
use of a HashPartitioner<>(...) is it worth looking in to providing a
KeyedStream constructor that uses a ForwardPartitioner? This was what I was
going to try this morning until you gave me a path that doesn't involve
editing flink code.
-Bart
________________________________
From: Aljoscha Krettek <aljos...@apache.org>
Sent: Wednesday, May 25, 2016 4:07 AM
To: user@flink.apache.org
Subject: Re: stream keyBy without repartition
Hi,
what Kostas said is correct.
You can however, hack it. You would have to manually instantiate a
WindowOperator and apply it on the non-keyed DataStream while still providing a
key-selector (and serializer) for state. This might sound complicated but I'll
try and walk you through the steps. Please let me know if anything is unclear,
still.
## Creating the WindowOperator
This can be copied from WindowedStream.apply(R initialValue, FoldFunction<T, R>
foldFunction, WindowFunction<R, R, K, W> function, TypeInformation<R>
resultType):
DataStream<> input = ... // create stream from sources
TypeInformation<R> resultType = TypeExtractor.getFoldReturnTypes(foldFunction,
input.getType(),
Utils.getCallLocationName(), true);
if (foldFunction instanceof RichFunction) {
throw new UnsupportedOperationException("FoldFunction of apply can not be a
RichFunction.");
}
if (windowAssigner instanceof MergingWindowAssigner) {
throw new UnsupportedOperationException("Fold cannot be used with a merging
WindowAssigner.");
}
//clean the closures
function = input.getExecutionEnvironment().clean(function);
foldFunction = input.getExecutionEnvironment().clean(foldFunction);
String callLocation = Utils.getCallLocationName();
String udfName = "WindowedStream." + callLocation;
String opName;
KeySelector<T, K> keySel = input.getKeySelector();
OneInputStreamOperator<T, R> operator;
FoldingStateDescriptor<T, R> stateDesc = new
FoldingStateDescriptor<>("window-contents",
initialValue,
foldFunction,
resultType);
opName = "TriggerWindow(" + windowAssigner + ", " + stateDesc + ", " + trigger
+ ", " + udfName + ")";
operator = new WindowOperator<>(windowAssigner,
windowAssigner.getWindowSerializer(getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType().createSerializer(getExecutionEnvironment().getConfig()),
stateDesc,
new InternalSingleValueWindowFunction<>(function),
trigger);
SingleOutputStreamOperator<> result = return input.transform(opName,
resultType, operator);
## Setting the KeySelector/Serializer for the state
This can be copied from KeyedStream.transform:
OneInputTransformation<T, R> transform = (OneInputTransformation<T, R>)
returnStream.getTransformation();
transform.setStateKeySelector(keySelector); // this would be your KeySelector
transform.setStateKeyType(keyType); // this would be a TypeInformation for your
key type
now, "result" should be your pre-combined data that was not shuffled. On this
you can key by your other type and instantiate a WindowOperator in the normal
way.
Cheers,
Aljoscha
On Tue, 24 May 2016 at 17:45 Kostas Kloudas
<k.klou...@data-artisans.com<mailto:k.klou...@data-artisans.com>> wrote:
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<mailto: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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