Hi Ori, … answering from remote …
* If not completely mistaken, Scala Vector is immutable, creating a copy
whenever you append, but
* This is not the main problem, the vectors collected so far get
deserialized with every incoming event (from state storage) and afterward
serialized into stat storage
* This won’t matter so much if you only collect 2 or 3 events into a
session window, but with maybe 1000 such events it does (you didn’t share your
numbers 😊 )
* For the ProcessFunction implementation you could use a Vector Builder and
the assign the result.
* Regarding the "without touching the previously stored event" question,
more detailed (I was in a rush)
* Windowing with ProcessFunction collects every event assigned to a
session window into a list state … iterating/aggregating over the collected
event only once when the window is triggered (i.e. the session is finished)
* While collecting the events into the list state it add()-s the new
event to the list state
* For rocksdb this involves only serializing the single added event and
appending the binary representation to the list state of the respective (key,
session window key (namespace in Flink speak)), i.e.
* The previously stored events for the session window are not touched
when a new event is added
* Next question: the overhead can easily be the cause of such backpressure,
depending on the numbers:
* Serialized size of your accumulator, proportional to the number of
aggregated events
* Size and entropy, frquency of your key space -> cache hits vs. cache
fails in RocksDb
* Of course there could be additional sources of backpressure
I hope this helps, … I’ll be back next week
Thias
From: Ori Popowski <ori....@gmail.com>
Sent: Donnerstag, 21. Oktober 2021 15:32
To: Schwalbe Matthias <matthias.schwa...@viseca.ch>
Cc: user <user@flink.apache.org>
Subject: Re: Huge backpressure when using AggregateFunction with Session Window
Thanks for taking the time to answer this.
* You're correct that the SimpleAggregator is not used in the job setup. I
didn't copy the correct piece of code.
* I understand the overhead involved. But I do not agree with the O(n^2)
complexity. Are you implying that Vector append is O(n) by itself?
* I understand your points regarding ProcessFunction except for the
"without touching the previously stored event". Also with AggregateFunction +
concatenation I don't touch the elements other than the new element. I forgot
to mention by the way, that the issue reproduces also with Lists which should
be much faster for appends and concats.
Could overhead by itself account for the backpressure?
From this job the only conclusion is that Flink just cannot do aggregating
operations which collect values, only simple operations which produce a scalar
values (like sum/avg). It seems weird to me Flink would be so limited in such
way.
On Wed, Oct 20, 2021 at 7:03 PM Schwalbe Matthias
<matthias.schwa...@viseca.ch<mailto:matthias.schwa...@viseca.ch>> wrote:
Hi Ori,
Just a couple of comments (some code is missing for a concise explanation):
* SimpleAggregator is not used in the job setup below (assuming another job
setup)
* SimpleAggregator is called for each event that goes into a specific
session window, however
* The scala vectors will ever grow with the number of events that end up
in a single window, hence
* Your BigO complexity will be O(n^2), n: number of events in window (or
worse)
* For each event the accumulator is retrieved from window state and
stored to window state (and serialized, if on RocksDB Backend)
* On the other hand when you use a process function
* Flink keeps a list state of events belonging to the session window, and
* Only when the window is triggered (on session gap timeout) all events
are retrieved from window state and processed
* On RocksDbBackend the new events added to the window are appended to
the existing window state key without touching the previously stored events,
hence
* Serialization is only done once per incoming event, and
* BigO complexity is around O(n)
… much simplified
When I started with similar questions I spent quite some time in the debugger,
breaking into the windowing functions and going up the call stack, in order to
understand how Flink works … time well spent
I hope this helps …
I won’t be able to follow up for the next 1 ½ weeks, unless you try to meet me
on FlinkForward conference …
Thias
From: Ori Popowski <ori....@gmail.com<mailto:ori....@gmail.com>>
Sent: Mittwoch, 20. Oktober 2021 16:17
To: user <user@flink.apache.org<mailto:user@flink.apache.org>>
Subject: Huge backpressure when using AggregateFunction with Session Window
I have a simple Flink application with a simple keyBy, a SessionWindow, and I
use an AggregateFunction to incrementally aggregate a result, and write to a
Sink.
Some of the requirements involve accumulating lists of fields from the events
(for example, all URLs), so not all the values in the end should be primitives
(although some are, like total number of events, and session duration).
This job is experiencing a huge backpressure 40 minutes after launching.
I've found out that the append and concatenate operations in the logic of my
AggregateFunction's add() and merge() functions are what's ruining the job
(i.e. causing the backpressure).
I've managed to create a reduced version of my job, where I just append and
concatenate some of the event values and I can confirm that a backpressure
starts just 40 minutes after launching the job:
class SimpleAggregator extends AggregateFunction[Event, Accumulator,
Session] with LazyLogging {
override def createAccumulator(): Accumulator = (
Vector.empty,
Vector.empty,
Vector.empty,
Vector.empty,
Vector.empty
)
override def add(value: Event, accumulator: Accumulator): Accumulator = {
(
accumulator._1 :+ value.getEnvUrl,
accumulator._2 :+ value.getCtxVisitId,
accumulator._3 :+ value.getVisionsSId,
accumulator._4 :+ value.getTime.longValue(),
accumulator._5 :+ value.getTime.longValue()
)
}
override def merge(a: Accumulator, b: Accumulator): Accumulator = {
(
a._1 ++ b._1,
a._2 ++ b._2,
a._3 ++ b._3,
a._4 ++ b._4,
a._5 ++ b._5
)
}
override def getResult(accumulator: Accumulator): Session = {
Session.newBuilder()
.setSessionDuration(1000)
.setSessionTotalEvents(1000)
.setSId("-" + UUID.randomUUID().toString)
.build()
}
}
This is the job overall (simplified version):
class App(
source: SourceFunction[Event],
sink: SinkFunction[Session]
) {
def run(config: Config): Unit = {
val senv = StreamExecutionEnvironment.getExecutionEnvironment
senv.setMaxParallelism(256)
val dataStream = senv.addSource(source).uid("source")
dataStream
.assignAscendingTimestamps(_.getTime)
.keyBy(event => (event.getWmUId, event.getWmEnv,
event.getSId).toString())
.window(EventTimeSessionWindows.withGap(config.sessionGap.asFlinkTime))
.allowedLateness(0.seconds.asFlinkTime)
.process(new ProcessFunction).uid("process-session")
.addSink(sink).uid("sink")
senv.execute("session-aggregation")
}
}
After 3 weeks of grueling debugging, profiling, checking the serialization and
more I couldn't solve the backpressure issue.
However, I got an idea and used Flink's ProcessWindowFunction which just
aggregates all the events behind the scenes and just gives them to me as an
iterator, where I can then do all my calculations.
Surprisingly, there's no backpressure. So even though the ProcessWindowFunction
actually aggregates more data, and also does concatenations and appends, for
some reason there's no backpressure.
To finish this long post, what I'm trying to understand here is why when I
collected the events using an AggregateFunction there was a backpressure, and
when Flink does this for me with ProcessWindowFunction there's no backpressure?
It seems to me something is fundamentally wrong here, since it means I cannot
do any non-reducing operations without creating backpressure. I think it
shouldn't cause the backpressure I experienced. I'm trying to understand what I
did wrong here.
Thanks!
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Diese Nachricht ist ausschliesslich für den Adressaten bestimmt und beinhaltet
unter Umständen vertrauliche Mitteilungen. Da die Vertraulichkeit von
e-Mail-Nachrichten nicht gewährleistet werden kann, übernehmen wir keine
Haftung für die Gewährung der Vertraulichkeit und Unversehrtheit dieser
Mitteilung. Bei irrtümlicher Zustellung bitten wir Sie um Benachrichtigung per
e-Mail und um Löschung dieser Nachricht sowie eventueller Anhänge. Jegliche
unberechtigte Verwendung oder Verbreitung dieser Informationen ist streng
verboten.
This message is intended only for the named recipient and may contain
confidential or privileged information. As the confidentiality of email
communication cannot be guaranteed, we do not accept any responsibility for the
confidentiality and the intactness of this message. If you have received it in
error, please advise the sender by return e-mail and delete this message and
any attachments. Any unauthorised use or dissemination of this information is
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