Hi FabianDefining a special flag for record seems like a checkpoint barrier. I think I will end up re-implementing checkpointing myself. I found the discussion in flink-dev: mail-archives.apache.org/mod_mbox/flink-dev/201511.mbox/… which seems to solve my task. Essentially they want to have a mechanism which will mark record produced by job as “last” and then wait until it’s fully propagated through DAG. Similarly to what I need. Essentially my job which produces trades can also thought as being finished once it produced all trades, then I just need to wait till latest trade produced by this job is processed.So although windows can probably also be applied, I think propagating barrier through DAG and checkpointing at final job is what I need.Can I possibly utilize internal Flink’s checkpoint barriers (i.e. like triggering a custom checkoint or finishing streaming job)?On 24 Nov 2015, at 21:53, Fabian Hueske <[hidden email]> wrote:Hi Anton,If I got your requirements right, you are looking for a solution that continuously produces updated partial aggregates in a streaming fashion. When a special event (no more trades) is received, you would like to store the last update as a final result. Is that correct?You can compute continuous updates using a reduce() or fold() function. These will produce a new update for each incoming event.For example:val s: DataStream[(Int, Long)] = ...s.keyBy(_._1).reduce( (x,y) => (x._1, y._2 + y._2) )would continuously compute a sum for every key (_._1) and produce an update for each incoming record.You could add a flag to the record and implement a ReduceFunction that marks a record as final when the no-more-trades event is received.
With a filter and a data sink you could emit such final records to a persistent data store.Btw.: You can also define custom trigger policies for windows. A custom trigger is called for each element that is added to a window and when certain timers expire. For example with a custom trigger, you can evaluate a window for every second element that is added. You can also define whether the elements in the window should be retained or removed after the evaluation.Best, Fabian2015-11-24 21:32 GMT+01:00 Anton Polyakov <[hidden email]>:In other words - buffering and waiting for window to close, then processing will be equal to simply doing one-off processing when all events are produced. I am looking for a solution when I am processing events as they are produced and when source signals "done" my processing is also nearly done.Hi Maxthanks for reply. From what I understand window works in a way that it buffers records while window is open, then apply transformation once window close is triggered and pass transformed result.
In my case then window will be open for few hours, then the whole amount of trades will be processed once window close is triggered. Actually I want to process events as they are produced without buffering them. It is more like a stream with some special mark versus windowing seems more like a batch (if I understand it correctly).On Tue, Nov 24, 2015 at 2:41 PM, Maximilian Michels <[hidden email]> wrote:Hi Anton,
You should be able to model your problem using the Flink Streaming
API. The actions you want to perform on the streamed records
correspond to transformations on Windows. You can indeed use
Watermarks to signal the window that a threshold for an action has
been reached. Otherwise an eviction policy should also do it.
Without more details about what you want to do I can only refer you to
the streaming API documentation:
Please see https://ci.apache.org/projects/flink/flink-docs-release-0.10/apis/streaming_guide.html
Thanks,
Max
On Sun, Nov 22, 2015 at 8:53 PM, Anton Polyakov
<[hidden email]> wrote:
> Hi
>
> I am very new to Flink and in fact never used it. My task (which I currently solve using home grown Redis-based solution) is quite simple - I have a system which produces some events (trades, it is a financial system) and computational chain which computes some measure accumulatively over these events. Those events form a long but finite stream, they are produced as a result of end of day flow. Computational logic forms a processing DAG which computes some measure over these events (VaR). Each trade is processed through DAG and at different stages might produce different set of subsequent events (like return vectors), eventually they all arrive into some aggregator which computes accumulated measure (reducer).
>
> Ideally I would like to process trades as they appear (i.e. stream them) and once producer reaches end of portfolio (there will be no more trades), I need to write final resulting measure and mark it as “end of day record”. Of course I also could use a classical batch - i.e. wait until all trades are produced and then batch process them, but this will be too inefficient.
>
> If I use Flink, I will need a sort of watermark saying - “done, no more trades” and once this watermark reaches end of DAG, final measure can be saved. More generally would be cool to have an indication at the end of DAG telling to which input stream position current measure corresponds.
>
> I feel my problem is very typical yet I can’t find any solution. All examples operate either on infinite streams where nobody cares about completion or classical batch examples which rely on fact all input data is ready.
>
> Can you please hint me.
>
> Thank you vm
> Anton
| Free forum by Nabble | Edit this page |