Description:
Currently I am working on using Flink with an IOT setup. Essentially, devices are sending data such as (device_id, device_type, event_timestamp, etc) and I don't have any control over when the messages get sent. I then key the steam by device_id and device_type to preform aggregations. I would like to use event-time given that is ensures the timers which are set trigger in a deterministic nature given a failure. However, given that this isn't always a high throughput stream a window could be opened for a 10 minute aggregation period, but not have its next point come until approximately 40 minutes later. Although the calculation would aggregation would eventually be completed it would output my desired result extremely late.
So my work around for this is to create an additional external source that does nothing other than pump fake messages. By having these fake messages being pumped out in alignment with my 10 minute aggregation period, even if a device hadn't sent any data, the event time windows would have something to force the windows closed. The critical part here is to make it possible that all parallel instances / operators have access to this fake message because I need to close all the windows with this single fake message. I was thinking that Broadcast state might be the most appropriate way to accomplish this goal given: "Broadcast state is replicated across all parallel instances of a function, and might typically be used where you have two streams, a regular data stream alongside a control stream that serves rules, patterns, or other configuration messages." Quote Source
Questions:
Is broadcast state the best method for ensuring all parallel instances (e.g. windows) receive my fake messages?
Once the operators have access to this fake message via the broadcast state can this fake message then be used to advance the event time watermark?
You can make this work with broadcast state, along the lines you propose, but I'm not convinced it's the best solution.
In an ideal world I'd suggest you arrange for the devices to send occasional keepalive messages, but assuming that's not possible, I think a custom Trigger would work well here. You can extend the EventTimeTrigger so that in addition to the event time timer it creates via
ctx.registerEventTimeTimer(window.maxTimestamp());
you also create a processing time timer, as a fallback, and you FIRE the window if the window still exists when that processing time timer fires.
I'm recommending this approach because it's simpler and more directly addresses the specific need. With the broadcast state approach you'll have to introduce a source for these messages, add a broadcast state descriptor and stream, add special fake watermarks for the non-broadcast stream (set to Watermark.MAX_WATERMARK), connect the broadcast and non-broadcast streams and implement a BroadcastProcessFunction (that probably doesn't really do anything), etc. It's a lot of moving parts spread across several different operators.
Related
We currently have a flink-based streaming job (the task is composed of complex FlatMapFunctions DAG), and an http interface for fetching configuration.
Now I hope to read configuration from the http interface through a source function every 5 minutes with a parallelism of 1, and then distribute it to all task managers or FlatMapFunctions of the job. In FlatMapFunctions, the configuration will be read and will never not be changed.
I have read the documentationThe Broadcast State Pattern, but the method in the documentation seems to only apply to the first Function of the broadcast, and other subsequent downstream FlatMapFunctions cannot read the state of the broadcast. As shown in the figure below, only Co-Process-Broadcast can obtain the broadcast, but map func 1 and map func 2 cannot.
Broadcast state graph
Similar to QUESTION but different, I have many downstream FlatMapFunctions and expect them all to get the broadcast configuration.
You can send the broadcast stream to multiple functions, so if your config state isn't big then that's likely what I'd do.
If the config state is very small (relative to the size of records being processed) then you could attach it to every incoming record in your BroadcastProcessFunction, so downstream operators have it in hand when processing each of their records.
I have two streams, one is main stream let's say in example of fraud detection I have transactions stream and then I have second stream which is configs, in our example it is rules. So I connect main stream to config stream in order to do processing. But when first time flink starts and we are adding job it starts consuming from transactions and configs stream parallel and when wants process transaction it sometimes see that there is no config and we have to send transaction to dead letter queue. However, what I want to achieve is, if there is patential config which I could get a bit later I want to get that config first then get transaction in order to process it rather then sending it to dead letter queue. I have the same key for transactions and configs.
long story short, is there a way telling flink when first time job starts try to consume one stream until there isn't new value then start processing main stream? How I can make them kind of sequential?
The recommended way to approach this is to connect the 2 streams and apply a RichCoFlatMap that will allow you to buffer events from main while you're waiting to receive the config events.
Check out this useful section of the Flink tutorials. The very last paragraph actually describes your problem.
It is important to recognize that you have no control over the order in which the flatMap1 and flatMap2 callbacks are called. These two input streams are racing against each other, and the Flink runtime will do what it wants to regarding consuming events from one stream or the other. In cases where timing and/or ordering matter, you may find it necessary to buffer events in managed Flink state until your application is ready to process them. (Note: if you are truly desperate, it is possible to exert some limited control over the order in which a two-input operator consumes its inputs by using a custom Operator that implements the InputSelectable interface.
So in a nutshell you should connect your 2 streams and have some kind of ListState where you can "buffer" your main elements while waiting to receive the rules. When you receive an element from the config stream, you check whether you had some pending elements "waiting" for that config in your ListState (your buffer). If you do, you can then process these elements and emit them through the collector of your flatmap.
Starting with version 1.16, you can use the hybrid source support in Flink to read all of once source (configs, in your case) before reading the second source. Though I imagine you'd have to map the events to an Either<config, transaction> so that the data stream has consistent record types.
i wonder if there is a way in flink to broadcast an event (or something like that) if specific event read from the source into all the task managers.
To be more specific I am aggregating state data with a map state and if some events are read from the source I want that all task managers perform a specific action
Is it possible?
Yes, this is possible. The broadcast state pattern is meant for exactly this sort of use case.
As David noted, using a broadcast stream is the right way to send data to all (parallel) sub-tasks. As for only broadcasting some data, take a look at side outputs as a way to do special processing for a sub-set of your data. So you could have a ProcessFunction that passes through all data un-modified, and if an incoming event is one that wants to be broadcast, then you also emit it as a side output.
I am just trying to understand the use case when to use CoProcessFunction in Flink. Explanation with an example would help me to understand the concept better.
A CoProcessFunction is similar to a RichCoFlatMap, but with the addition of also being able to use timers. The timers are useful for expiring state for stale keys, or for raising alarms when keep alive messages fail to arrive, for example.
A CoProcessFunction allows you to use one stream to influence how another is processed, or to enrich another stream. For example, an e-commerce site might have a stream of order events and a stream of shipment events, and they want to create a stream of events for orders that haven't shipped with 24 hours of the order being placed. The two streams can be keyed by the orderId, and connected together. As an order arrives it's recorded in keyed state, and a timer is created to fire 24 hours later. When a shipment event arrives, the state and timer are cleared. If a timer does fire, the state is used to send the order out to the unfilled order service.
For more on this, and examples with code, see connected streams and process function and the labs that accompany those tutorials.
I have an always one application, listening to a Kafka stream, and processing events. Events are part of a session. And I need to do calculations based off of a sessions data. I am running into a problem trying to correctly run my calculations due to the length of my sessions. 90% of my sessions are done after 5 minutes. 99% are done after 1 hour. Sessions may last more than a day, due to this being a real-time system, there is no determined end. Session are unique, and show never collide.
I am looking for a way where I can process a window multiple times, either with an initial wait period and processing any later events after that, or a pure process per event type structure. I will need to keep all previous events around(ListState), as well as previously processed values(ValueState).
I previously thought allowedLateness would allow me to do this, but it seems the lateness is only considered for when the event should have been processed, it does not extend an actual window. GlobalWindows may also work, but I am unsure if there is a way to process a window multiple times. I believe I can used an evictor with GlobalWindows to purge the Windows after a period of inactivity(although admittedly, I did not research this yet, because I was unsure of how to trigger a GlobalWindow multiple times.
Any suggestions on how to achieve what I am looking to do would be greatly appreciated, I would also be happy to clarify any points needed.
If SessionWindows won't do the job, then you can use GlobalWindows with a custom Trigger and Evictor. The Trigger interface has onElement and timer-based callbacks that can fire whenever and as often as you like. If you go down this route, then yes, you'll also need to implement an Evictor to dispose of elements when they are no longer needed.
The documentation and the source code are helpful when trying to understand how this all fits together.