Hi Rion,

I think what David was refering to is that you do the entire time handling yourself in process function. That means not using the `context.timerService()` or `onTimer()` that Flink provides but calling your own logic based on the timestamps that enter your process function and the stored state.

Regards,
Timo


On 26.02.21 00:29, Rion Williams wrote:

Hi David,

Thanks for your prompt reply, it was very helpful and the PseudoWindow example is excellent. I believe it closely aligns with an approach that I was tinkering with but seemed to be missing a few key pieces. In my case, I'm essentially going to want to be aggregating the messages that are coming into the window (a simple string-concatenation aggregation would work). Would I need another form of state to hold that, as looking through this example with naive eyes, it seems that this function is currently storing multiple windows in state via the MapState provided:

// Keyed, managed state, with an entry for each window, keyed by the window's end time.
// There is a separate MapState object for each driver.
private transient MapState<Long, Float> sumOfTips;

If I wanted to perform an aggregation for each key/tenant, would a MapState be appropriate? Such as a MapState<Long, String> if I was doing a string aggregation, so that within my processElement function I could use something similar for building these aggregations and ultimately triggering them:

// Keep track of a tenant/source specific watermark
private lateinit var currentWatermark: ValueState<Long>
// Keep track of the contents of each of the windows where the key represents the close // of the window and the contents represents an accumulation of the records for that window
private lateinit var windowContents: MapState<Long, String>

If that's the case, this is what I've thrown together thus far and I feel like it's moving in the right direction:

class MagicWindow(private val duration: Long, private val lateness: Long):
     KeyedProcessFunction<String, Event, FileOutput>(){

     // Keep track of a tenant/source specific watermark
     private lateinit var currentWatermark: ValueState<Long>
    // Keep track of the contents of each of the windows where the key represents the close     // of the window and the contents represents an accumulation of the records for that window
     private lateinit var windowContents: MapState<Long, String>

     override fun open(config: Configuration) {
         currentWatermark = runtimeContext.getState(watermark)
         currentWatermark.update(Long.MIN_VALUE)
     }

    override fun processElement(element: Event, context: Context, out: Collector<FileOutput>) {
         // Resolve the event time
         val eventTime: Long = getEventTime(element)

         // Update watermark (if applicable)
         if (currentWatermark.value() < eventTime){
             currentWatermark.update(eventTime)
         }

         // Define a timer for this window
         val timerService = context.timerService()

         if (eventTime <= timerService.currentWatermark()) {
             // This event is late; its window has already been triggered.
         } else {
            // Determine the "actual" window closure and start a timer for it
             // (eventTime + window
            val endOfWindow= eventTime - (eventTime % duration) + duration - 1

             // Schedule a callback for when the window has been completed.
             timerService.registerEventTimeTimer(endOfWindow)

            // Add this element to the corresponding aggregation for this window             windowContents.put(endOfWindow, windowContents[endOfWindow] + "$element")
         }
     }

    override fun onTimer(timestamp: Long, context: OnTimerContext, out: Collector<FileOutput>) {
         val key = context.currentKey
         val currentAggregation: String = windowContents.get(timestamp)

         // Output things here and clear the current aggregation for this
         // tenant/source combination in this window
     }

     companion object {
         private val watermark = ValueStateDescriptor(
             "watermark",
             Long::class.java
         )

         private val windowContents = MapStateDescriptor(
             "window-contents",
             Long::class.java,
             String::class.java
         )

         fun getEventTime(element: Event): Long {
             return Instant(element.`source$1`.createdTimestamp).millis
         }
     }
}

Is something glaringly off with this? I’ll need to do some additionally reading on the timers, but any additional clarification would be greatly appreciated.

Thanks so much for your initial response again!

Rion

On Feb 25, 2021, at 3:27 PM, David Anderson <dander...@apache.org> wrote:


Rion,

What you want isn't really achievable with the APIs you are using. Without some sort of per-key (per-tenant) watermarking -- which Flink doesn't offer -- the watermarks and windows for one tenant can be held up by the failure of another tenant's events to arrive in a timely manner.

However, your pipeline is pretty straightforward, and it shouldn't be particularly difficult to accomplish what you want. What you can do is to ignore the built-in watermarking and windowing APIs, and build equivalent functionality in the form of a KeyedProcessFunction.

The Flink docs include an example [1] showing how to implement your own tumbling event time windows with a process function. That implementation assumes you can rely on watermarks for triggering the windows; you'll have to do that differently.

What you can do instead is to track, in ValueState, the largest timestamp you've seen so far (for each key/tenant). Whenever that advances, you can subtract the bounded-out-of-orderness duration from that timestamp, and then check to see if the resulting value is now large enough to trigger any of the windows for that key/tenant.

Handling allowed lateness should be pretty straightforward.

Hope this helps,
David

[1] https://ci.apache.org/projects/flink/flink-docs-release-1.12/learn-flink/event_driven.html#example <https://ci.apache.org/projects/flink/flink-docs-release-1.12/learn-flink/event_driven.html#example>

On Thu, Feb 25, 2021 at 9:05 PM Rion Williams <rionmons...@gmail.com <mailto:rionmons...@gmail.com>> wrote:

    Hey folks, I have a somewhat high-level/advice question regarding
    Flink and if it has the mechanisms in place to accomplish what I’m
    trying to do. I’ve spent a good bit of time using Apache Beam, but
    recently pivoted over to native Flink simply because some of the
    connectors weren’t as mature or didn’t support some of the
    functionality that I needed.

    Basically - I have a single Kafka topic with 10 partitions that
    I’m consuming from. This is a multi-tenant topic containing data
    that comes in at various times from various tenants and is not at
    all guaranteed to be in order, at least with regards to “event
    time”, which is what I care about.

    What I’m trying to accomplish is this: *Given a multi-tenant topic
    with records eventually distributed across partitions, is it
    possible to consume and window each of these records independently
    of one another without one tenant potentially influencing another
    and write out to separate files per tenant/source (i.e. some other
    defined property on the records)?”
    *
    My pipeline currently looks something like this:

    @JvmStatic
    fun main(args: Array<String>) {
        val pipeline = StreamExecutionEnvironment
            .getExecutionEnvironment()
            //.createLocalEnvironmentWithWebUI(Configuration())

        val properties = buildPropertiesFromArgs(args)
        val stream = pipeline
            .addSource(readFromKafka("events", properties))
            .assignTimestampsAndWatermarks(
                WatermarkStrategy
.forBoundedOutOfOrderness<Event>(Duration.ofSeconds(...))
                    .withTimestampAssigner { event: Event, _: Long ->
                        // Assign the created timestamp as the event
    timestamp
                        Instant(event.createdTimestamp).millis
                    }
            )

        // There are multiple data sources that each have their own
    windows and allowed lateness
        // so ensure that each source only handles records for it
        DataSources.forEach { source ->
            stream
                .filter { event ->
                    event.source == source.name <http://source.name>
                }
                .keyBy { event ->
                    //print("Keying record with id ${record.`id$1`} by
    tenant ${record.`source$1`.tenantName}")
                    event.tenant
                }
                .window(
TumblingEventTimeWindows.of(Time.minutes(source.windowDuration))
                )
                .allowedLateness(
                    Time.minutes(source.allowedLateness)
                )
                .process(
                    // This just contains some logic to take the
    existing windows and construct a file
                    // using the window range and keys (tenant/source)
    with the values being
                    // an aggregation of all of the records
                    WindowedEventProcessFunction(source.name
    <http://source.name>)
                )
                .map { summary ->
                    // This would be a sink to write to a file
                }
        }
        pipeline.execute("event-processor")
    }

    My overarching question is really - *Can I properly separate the
    data with custom watermark strategies and ensure that keying (or
    some other construct) is enough to allow each tenant/source
    combination to be treated as it’s own stream with it’s own
    watermarking? *I know I could possibly break the single topic up
    into multiple disparate topics, however that level of granularity
    would likely result in several thousand (7000+) topics so I'm
    hoping that some of the constructs available within Flink may help
    with this (WatermarkStrategies, etc.)

    Any recommendations / advice would be extremely helpful as I'm
    quite new to the Flink world, however I have quite a bit of
    experience in Apache Beam, Kafka Streams, and a smattering of
    other streaming technologies.

    Thanks much,

    Rion


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