Huge backpressure when using AggregateFunction with Session Window

[Ori Popowski]

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!