Re: Hourly top-k statistics of DataStream

Posted by Jamie Grier on
URL: http://deprecated-apache-flink-user-mailing-list-archive.369.s1.nabble.com/Hourly-top-k-statistics-of-DataStream-tp7415p7443.html

Suggestions in-line below...

On Mon, Jun 6, 2016 at 7:26 PM, Yukun Guo <[hidden email]> wrote:

Hi,

I'm working on a project which uses Flink to compute hourly log statistics
like top-K. The logs are fetched from Kafka by a FlinkKafkaProducer and packed
into a DataStream.

The problem is, I find the computation quite challenging to express with
Flink's DataStream API:

1. If I use something like `logs.timeWindow(Time.hours(1))`, suppose that the
data volume is really high, e.g., billions of logs might be generated in one
hour, will the window grow too large and can't be handled efficiently?

In the general case you can use:

    stream

        .timeWindow(...)

        .apply(reduceFunction, windowFunction)

which can take a ReduceFunction and a WindowFunction.  The ReduceFunction is used to reduce the state on the fly and thereby keep the total state size low.  This can commonly be used in analytics applications to reduce the state size that you're accumulating for each window.  In the specific case of TopK, however, you cannot do this if you want an exact result.  To get an exact result I believe you have to actually keep around all of the data and then calculate TopK at the end in your WindowFunction.  If you are able to use approximate algorithms for your use case than you can calculate a probabilistic incremental TopK based on some sort of sketch-based algorithm.

2. We have to create a `KeyedStream` before applying `timeWindow`. However,
the distribution of some keys are skewed hence using them may compromise
the performance due to unbalanced partition loads. (What I want is just
rebalance the stream across all partitions.)

A good and simple way to approach this may be to come up with a composite key for your data that *is* uniformly distributed.  You can imagine something simple like 'natural_key:random_number'.  Then keyBy(natural_key) and reduce() again.  For example:

    stream

        .keyBy(key, rand())      // partition by composite key that is uniformly distributed

        .timeWindow(1 hour)

        .reduce()                     // pre-aggregation

        .keyBy(key)                // repartition

        .timeWindow(1 hour)

        .reduce()                     // final aggregation

 

3. The top-K algorithm can be straightforwardly implemented with `DataSet`'s
`mapPartition` and `reduceGroup` API as in
[FLINK-2549](https://github.com/apache/flink/pull/1161/), but not so easy if
taking the DataStream approach, even with the stateful operators. I still
cannot figure out how to reunion streams once they are partitioned.

    I'm not sure I know what you're trying to do here.  What do you mean by re-union?

 

4. Is it possible to convert a DataStream into a DataSet? If yes, how can I
make Flink analyze the data incrementally rather than aggregating the logs for
one hour before starting to process?

There is no direct way to turn a DataStream into a DataSet.  I addressed the point about doing the computation incrementally above, though.  You do this with a ReduceFunction.  But again, there doesn't exist an exact incremental TopK algorithm that I'm aware of.  This can be done with sketching, though.

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