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
>
>> On Thu, Feb 25, 2021 at 9:05 PM Rion Williams <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
>> }
>> .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)
>> )
>> .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
