[jira] [Created] (KAFKA-13289) Bulk processing data through a join with kafka-streams results in `Skipping record for expired segment`

["Matthew Sheppard (Jira)" <ji...@apache.org>]

Matthew Sheppard created KAFKA-13289:
----------------------------------------

             Summary: Bulk processing data through a join with kafka-streams results in `Skipping record for expired segment`
                 Key: KAFKA-13289
                 URL: https://issues.apache.org/jira/browse/KAFKA-13289
             Project: Kafka
          Issue Type: Bug
          Components: streams
    Affects Versions: 2.8.0
            Reporter: Matthew Sheppard


When pushing bulk data through a kafka-steams app, I see it log the following message many times...

`WARN org.apache.kafka.streams.state.internals.AbstractRocksDBSegmentedBytesStore - Skipping record for expired segment.`

...and data which I expect to have been joined through a leftJoin step appears to be lost.

I've seen this in practice either when my application has been shut down for a while and then is brought back up, or when I've used something like the [app-reset-rool](https://docs.confluent.io/platform/current/streams/developer-guide/app-reset-tool.html) in an attempt to have the application reprocess past data.

I was able to reproduce this behaviour in isolation by generating 1000 messages to two topics spaced an hour apart (with the original timestamps in order), then having kafka streams select a key for them and try to leftJoin the two rekeyed streams.

Self contained source code for that reproduction is available at https://github.com/mattsheppard/ins14809/blob/main/src/test/java/ins14809/Ins14809Test.java

The actual kafka-streams topology in there looks like this.

```
            final StreamsBuilder builder = new StreamsBuilder();
            final KStream<String, String> leftStream = builder.stream(leftTopic);
            final KStream<String, String> rightStream = builder.stream(rightTopic);

            final KStream<String, String> rekeyedLeftStream = leftStream
                    .selectKey((k, v) -> v.substring(0, v.indexOf(":")));

            final KStream<String, String> rekeyedRightStream = rightStream
                    .selectKey((k, v) -> v.substring(0, v.indexOf(":")));

            JoinWindows joinWindow = JoinWindows.of(Duration.ofSeconds(5));

            final KStream<String, String> joined = rekeyedLeftStream.leftJoin(
                    rekeyedRightStream,
                    (left, right) -> left + "/" + right,
                    joinWindow
            );
```

...and the eventual output I produce looks like this...

```
...
523 [523,left/null]
524 [524,left/null, 524,left/524,right]
525 [525,left/525,right]
526 [526,left/null]
527 [527,left/null]
528 [528,left/528,right]
529 [529,left/null]
530 [530,left/null]
531 [531,left/null, 531,left/531,right]
532 [532,left/null]
533 [533,left/null]
534 [534,left/null, 534,left/534,right]
535 [535,left/null]
536 [536,left/null]
537 [537,left/null, 537,left/537,right]
538 [538,left/null]
539 [539,left/null]
540 [540,left/null]
541 [541,left/null]
542 [542,left/null]
543 [543,left/null]
...
```

...where as, given the input data, I expect to see every row end with the two values joined, rather than the right value being null.

Note that I understand it's expected that we initially get the left/null values for many values since that's the expected semantics of kafka-streams left join, at least until https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Streams+Join+Semantics#KafkaStreamsJoinSemantics-ImprovedLeft/OuterStream-StreamJoin(v3.1.xandnewer)spurious

I've noticed that if I set a very large grace value on the join window the problem is solved, but since the input I provide is not out of order I did not expect to need to do that, and I'm weary of the resource requirements doing so in practice on an application with a lot of volume.

My suspicion is that something is happening such that when one partition is processed it causes the stream time to be pushed forward to the newest message in that partition, meaning when the next partition is then examined it is found to contain many records which are 'too old' compared to the stream time. 

I ran across https://kafkacommunity.blogspot.com/2020/02/re-skipping-record-for-expired-segment_88.html from a year and a half ago which seems to describe the same problem, but I'm hoping the self-contained reproduction might make the issue easier to tackle!



--
This message was sent by Atlassian Jira
(v8.3.4#803005)