Hi Sophie,
Thank you for your response.
I tested the proposed setting for CACHE_MAX_BYTES_BUFFERING_CONFIG and it
seem to not significantly change the behavior of the application. The
latency remains very similar. The documentation states the following
regarding CACHE_MAX_BYTES_BUFFERING_CONFIG and COMMIT_INTERVAL_MS_CONFIG:
---
To enable caching but still have an upper bound on how long records will be
cached, you can set the commit interval. In this example, it is set to 1000
milliseconds:
Properties props = new Properties();
// Enable record cache of size 10 MB.
props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 10 * 1024 *
1024L);
// Set commit interval to 1 second.
props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000);
---
Which made me believe that the COMMIT_INTERVAL_MS_CONFIG would already
"override" the CACHE_MAX_BYTES_BUFFERING_CONFIG and provide an upper bound
of the latency of 1s per processing step by flushing buffers every second.
Is this the case or does these two configuration values interact in some
other way?
We are using 256 partitions for all our topics. Is this to be considered a
very high partition count? Do you think we might be affected by the bug in
2.3.0?
Thank you for the help!
Best regards,
Petter
On Wed, Oct 9, 2019 at 7:33 PM Sophie Blee-Goldman <[email protected]>
wrote:
> Hi Petter,
>
> I'd recommend turning off caching by setting
> p.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING, 0);
>
> 2.3.0 also has some known performance issues that will be fixed in 2.3.1,
> but they
> shouldn't be noticeable if you turn caching off and aren't reading/writing
> to topics
> with a very high partition count. These are fixed in 2.3.1 which should be
> released
> soon for you to upgrade, but the caching is likely the main reason for the
> latency you see.
>
> I'd also note that Streams, and Kafka in general, is typically tuned for
> high
> throughput rather than low latency, so I wouldn't be too concerned about
> a large latency unless that is a specific requirement.
>
> Cheers,
> Sophie
>
> On Wed, Oct 9, 2019 at 6:05 AM Petter Arvidsson <
> [email protected]>
> wrote:
>
> > Hi,
> >
> > I have a fairly simple kafka streams application that read messages from
> > two topics. The problem I am facing is that the delay between sending
> > events to the streams application and it producing results is very high
> (as
> > in several minutes). My question is: how can I make this latency smaller?
> >
> > The streams is doing the following:
> > ktable1 = topic1
> > -> (filter out messages using flatMap)
> > -> groupBy (with new key, adds internal rekeying topic)
> > -> aggregate (in memory store backed by internal compacted topic)
> >
> > ktabe2 = topic2
> > -> (rekey to same key as ktable1 over internal topic)
> > -> join (with ktable1)
> > -> aggregate (in memory store backed by internal compacted topic)
> >
> > ktable2.toStream.to(topic2)
> >
> > Ktable1 keep configuration that allows messages to pass through and be
> > aggregated into ktable2. Ktable2 keeps aggregates based on messages on
> > topic2. Ktable2.toStream is then used to put the aggregated messages back
> > out on topic2. The "problem" (or misunderstanding as to how kafka stream
> is
> > processing messages) is that the delay from sending a message on topic1
> to
> > the point where messages received on topic2 are passing the join is
> several
> > minutes. With the settings I have (see below) on a not that heavily
> loaded
> > system, I would assume the latency would be a couple of seconds (based on
> > the COMMIT_INTERVAL_MS_CONFIG).
> >
> > I use the following settings (as well as settings for bootstrap servers,
> > application id and so forth):
> > p.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 1000)
> > p.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest")
> > p.put(StreamsConfig.REPLICATION_FACTOR_CONFIG, 2)
> >
> > The store used for the KTables is the one returned by
> > "Stores.inMemoryKeyValueStore()".
> >
> > Kafka libraries use version "2.3.0" and the "kafka-streams-scala"
> scaladsl
> > is used to build the streams. The broker is using version "1.1.0".
> >
> > Best regards,
> > Petter
> >
>
