I'm using flink with event time keyed windows.
It seems like some of the windows are not being emitted.
Is the watermark being advanced for each key individually?
For example, if my key is (id,type), and a specific pair of id and type are not being ingested to the source, will their specific window watermark will not advance?
If this is the case, how can i make sure that all my keyd windows will get evicted after some time? (we have many keys so sending a periodic dummy message for each key is not an option).
I'll appreciate any help
Flink has separate watermarks for each task (i.e., each parallel instance) -- otherwise there would have to some sort of horribly expensive global coordination -- but not for each key. In the case of a keyed window, each instance of the window operator will be handling the events for some disjoint subset of the keyspace, and all of the windows for those keys will be using the same watermark.
Keep in mind that empty windows do not produce results. So if there is some key for which there are no events during a window, that window will not produce results for that key.
Or it could be that you have an idle source holding back the watermarks. If one of your source tasks becomes idle, then its watermark won't advance. You could inspect the current watermark in the web UI, and check to see if it is advancing in every task.
Related
I have a custom flink Source, and I have a SerializableTimestampAssigner that assigns event timestamps to records emitted by the source. The source may emit records out of order because of the nature of the underlying data storage, however with BATCH mode, I expect Flink to sort these records by event timestamp before any operator processes them.
Excerpted from Flink document on execution mode:
In BATCH mode, where the input dataset is known in advance, there is no need for such a heuristic as, at the very least, elements can be sorted by timestamp so that they are processed in temporal order.
However, this doesn't seem to be the case. If I create a datastream out of the Source (StreamExecutionEnvironment.fromSource) with my timestamp assigner, and then datastream.addSink(x => println(extractTimestamp(x)), the output isn't strictly ascending. Is my understanding of the document wrong? Or does flink expect me (the users) to sort the input dataset themselves?
BATCH execution mode first sorts by key, and within each key, it sorts by timestamp. By operating this way, it only needs to keep state for one key at a time, so this keeps the runtime simple and efficient.
If your pipeline isn't using keyed streams, then you won't be using keyed state or timers, so the ordering shouldn't matter (and I'm not sure what happens).
For keyed co-streams, they are both keyed in the same way, and both streams are sorted by those keys and the keys are advanced in lockstep.
Broadcast streams are sent in their entirety before anything else.
I'm using Flink with a kinesis source and event time keyed windows. The application will be listening to a live stream of data, windowing (event time windows) and processing each keyed stream. I have another use-case where i also need to be able to support backfill of older data for certain key streams (These will be new key streams with event-time < watermark).
Given that I'm using Watermarks, this poses to be a problem since Flink doesn't support per - key watermark. Hence any keyed stream for backfill will end up being ignored since the event time for this stream will be < application watermark maintained by the live stream.
I have gone through other similar questions but wasn't able to get a possible approach.
Here are possible approaches I'm considering but still have some open questions.
Possible Approach - 1
(i) Maintain a copy of the application specifically for backfill purpose. The backfill job will happen rarely (~ a few times a month). The stream of data sent to the application copy will have an indicator for start and stop in the stream. Using that I plan on starting / resetting the watermark.
Open Question ? Is it possible to reset the watermark using an indicator from the stream ? I understand that this is not best practise but can't think of an alternative solution.
Follow up to : Clear Flink watermark state in DataStream [No definitive solution provided.]
Possible Approach - 2
Have parallel instances for each key since its possible for having different watermark per task. -> Not going with this since i'll be having > 5k keyed streams.
Let me know if any other details are needed.
You can address this by running the backfill jobs in BATCH execution mode. When the DataStream API operates in batch mode, the input is bounded (finite), and known in advance. This allows Flink to sort the input by key and by timestamp, and the processing will proceed correctly according to event time without any concern for watermarks or late events.
I see that there are lot of discussions going on about adding support for watermarks per key. But do flink support per partition watermarks?
Currently - then minimum of all the watermarks(non idle partitions) is taken into account. Because of this the last hanging records in a window are stuck as well.(when incremented the watermark using periodicemit)
Any info on this is really appreciated!
Some of the sources, such as the FlinkKafkaConsumer, support per-partition watermarking. You get this by calling assignTimestampsAndWatermarks on the source, rather than on the stream produced by the source.
What this does is that each consumer instance tracks the maximum timestamp within each partition, and take as its watermark the minimum of these maximums, less the configured bounded out-of-orderness. Idle partitions will be ignored, if you configure it to do so.
Not only does this yield more accurate watermarking, but if your events are in-order within each partition, this also makes it possible to take advantage of the WatermarkStrategy.forMonotonousTimestamps() strategy.
See Watermark Strategies and the Kafka Connector for more details.
As for why the last window isn't being triggered, this is related to watermarking, but not to per-partition watermarking. The problem is simply that windows are triggered by watermarks, and the watermarks are trailing behind the timestamps in the events. So the watermarks can never catch up to the final events, and can never trigger the last window.
This isn't a problem for unbounded streaming jobs, since they never stop and never have a last window. And it isn't a problem for batch jobs, since they are aware of all of the data. But for bounded streaming jobs, you need to do something to work around this issue. Broadly speaking, what you must do is to inform Flink that the input stream has ended -- whenever the Flink sources detect that they have reached the end of an event-time-based input stream, they emit one last watermark whose value is MAX_WATERMARK, and this will trigger any open windows.
One way to do this is to use a KafkaDeserializationSchema with an implementation of isEndOfStream that returns true when the job reaches its end.
Flink provides an example here : https://www.ververica.com/blog/stream-processing-introduction-event-time-apache-flink that describes the scenario that someone is playing a game, loses connection due to subway and then when he is back online all the data is back and can be sorted and processed.
My understanding with this is that if there's more players there are two options:
All the other ones will be delayed waiting for this user to get back connection and send the data allowing the watermark to be pushed;
This user is classified as idle allowing the watermark to move forward and when he gets connected all his data will go to late data stream;
I would like to have the following option:
Each user is processed independently with its own watermark for his session window. Ideally I would even use ingestion time (so when he gets connection back I will put all the data into one unique session that would later order by the event timestamp once the session closes) and there would be a gap between the current time and the last timestamp (ingestion) of the window I'm processing (the session window guarantees this based on the time gap that terminates the session); I also don't want the watermark to be stuck once one user loses connection and I also don't want to manage idle states: just continue processing all the other events normally and once this user gets back do not classify any data as late data due to the watermark being advanced in time compared with the moment the user lost connection;
How could I implement the requirement above? I've been having a hard time working no scenarios like this due to watermark being global. Is there an easy explanation for not having watermarks for each key ?
Thank you in advance!
The closest Flink's watermarking comes to supporting this directly is probably the support for per-kafka-partition watermarking -- which isn't really a practical solution to the situation you describe (since having a kafka partition per user isn't realistic).
What can be done is to simply ignore watermarking, and implement the logic yourself, using a KeyedProcessFunction.
BTW, there was recently a thread about this on both the flink-user and flink-dev mailing lists under the subject Per Key Grained Watermark Support.
Consider I have a data stream that contains event time data in it. I want to gather input data stream in window time of 8 milliseconds and reduce every window data. I do that using the following code:
aggregatedTuple
.keyBy( 0).timeWindow(Time.milliseconds(8))
.reduce(new ReduceFunction<Tuple2<Long, JSONObject>>()
Point: The key of the data stream is the timestamp of processing time mapped to last 8 submultiples of a timestamp of processing millisecond, for example 1531569851297 will mapped to 1531569851296.
But it's possible the data stream arrived late and enter to the wrong window time. For example, suppose I set the window time to 8 milliseconds. If data enter the Flink engine in order or at least with a delay less than window time (8 milliseconds) it will be the best case. But suppose data stream event time (that is a field in the data stream, also) has arrived with the latency of 30 milliseconds. So it will enter the wrong window and I think if I check the event time of every data stream, as it wants to enter the window, I can filter at such a late data.
So I have two question:
How can I filter data stream as it wants to enter the window and check if the data created at the right timestamp for the window?
How can I gather such late data in a variable to do some processing on them?
Flink has two different, related abstractions that deal with different aspects of computing windowed analytics on streams with event-time timestamps: watermarks and allowed lateness.
First, watermarks, which come into play whenever working with event-time data (whether or not you are using windows). Watermarks provide information to Flink about the progress of event-time, and give you, the application writer, a means of coping with out-of-order data. Watermarks flow with the data stream, and each one marks a position in the stream and carries a timestamp. A watermark serves as an assertion that at that point in the stream, the stream is now (probably) complete up to that timestamp -- or in other words, the events that follow the watermark are unlikely to be from before the time indicated by the watermark. The most common watermarking strategy is to use a BoundedOutOfOrdernessTimestampExtractor, which assumes that events arrive within some fixed, bounded delay.
This now provides a definition of lateness -- events that follow a watermark with timestamps less than the watermarks' timestamp are considered late.
The window API provides a notion of allowed lateness, which is set to zero by default. If allowed lateness is greater than zero, then the default Trigger for event-time windows will accept late events into their appropriate windows, up to the limit of the allowed lateness. The window action will fire once at the usual time, and then again for each late event, up to the end of the allowed lateness interval. After which, late events are discarded (or collected to a side output if one is configured).
How can I filter data stream as it wants to enter the window and check
if the data created at the right timestamp for the window?
Flink's window assigners are responsible for assigning events to the appropriate windows -- the right thing will happen automatically. New window instances will be created as needed.
How can I gather such late data in a variable to do some processing on them?
You can either be sufficiently generous in your watermarking so as to avoid having any late data, and/or configure the allowed lateness to be long enough to accommodate the late events. Be aware, however, that Flink will be forced to keep all windows open that are still accepting late events, which will delay garbage collecting old windows and may consume considerable memory.
Note that this discussion assumes you want to work with time windows -- e.g. the 8msec long windows you are working with. Flink also supports count windows (e.g. group events into batches of 100), session windows, and custom window logic. Watermarks and lateness don't play any role if you are using count windows, for example.
If you want per-key results for your analytics, then use keyBy to partition the stream by key (e.g., by userId) before applying windowing. For example
stream
.keyBy(e -> e.userId)
.timeWindow(Time.seconds(10))
.reduce(...)
will produce separate results for each userId.
Update: Note that in recent versions of Flink it is now possible for windows to collect late events to a side output.
Some relevant documentation:
Event Time and Watermarks
Allowed Lateness