Wanted to add that we are not using auto commit since we use custom
partition assignments. In fact we never call consumer.commitAsync() or
consumer.commitSync() calls. My assumption is that since we store our own
offsets these calls are not necessary. Hopefully this is not responsible
for the poor performance.
On Mon, Jan 25, 2016 at 9:20 PM, Rajiv Kurian <ra...@signalfx.com> wrote:
> We are using the new kafka consumer with the following config (as logged
> by kafka)
>
> metric.reporters = []
>
> metadata.max.age.ms = 300000
>
> value.deserializer = class
> org.apache.kafka.common.serialization.ByteArrayDeserializer
>
> group.id = myGroup.id
>
> partition.assignment.strategy = [org.apache.kafka.clients.consumer
> .RangeAssignor]
>
> reconnect.backoff.ms = 50
>
> sasl.kerberos.ticket.renew.window.factor = 0.8
>
> max.partition.fetch.bytes = 2097152
>
> bootstrap.servers = [myBrokerList]
>
> retry.backoff.ms = 100
>
> sasl.kerberos.kinit.cmd = /usr/bin/kinit
>
> sasl.kerberos.service.name = null
>
> sasl.kerberos.ticket.renew.jitter = 0.05
>
> ssl.keystore.type = JKS
>
> ssl.trustmanager.algorithm = PKIX
>
> enable.auto.commit = false
>
> ssl.key.password = null
>
> fetch.max.wait.ms = 1000
>
> sasl.kerberos.min.time.before.relogin = 60000
>
> connections.max.idle.ms = 540000
>
> ssl.truststore.password = null
>
> session.timeout.ms = 30000
>
> metrics.num.samples = 2
>
> client.id =
>
> ssl.endpoint.identification.algorithm = null
>
> key.deserializer = class sf.kafka.VoidDeserializer
>
> ssl.protocol = TLS
>
> check.crcs = true
>
> request.timeout.ms = 40000
>
> ssl.provider = null
>
> ssl.enabled.protocols = [TLSv1.2, TLSv1.1, TLSv1]
>
> ssl.keystore.location = null
>
> heartbeat.interval.ms = 3000
>
> auto.commit.interval.ms = 5000
>
> receive.buffer.bytes = 32768
>
> ssl.cipher.suites = null
>
> ssl.truststore.type = JKS
>
> security.protocol = PLAINTEXT
>
> ssl.truststore.location = null
>
> ssl.keystore.password = null
>
> ssl.keymanager.algorithm = SunX509
>
> metrics.sample.window.ms = 30000
>
> fetch.min.bytes = 512
>
> send.buffer.bytes = 131072
>
> auto.offset.reset = earliest
>
>
> We use the consumer.assign() feature to assign a list of partitions and
> call poll in a loop. We have the following setup:
>
> 1. The messages have no key and we use the byte array deserializer to get
> byte arrays from the config.
>
> 2. The messages themselves are on an average about 75 bytes. We get this
> number by diving the Kafka broker bytes-in metric by the messages-in metric.
>
> 3. Each consumer is assigned about 64 partitions of the same topic spread
> across three brokers.
>
> 4. We get very few messages per second maybe around 1-2 messages across
> all partitions on a client right now.
>
> 5. We have no compression on the topic.
>
> Our run loop looks something like this
>
> while (isRunning()) {
>
> ConsumerRecords<Void, byte[]> records = null;
>
> try {
>
> // Here timeout is about 10 seconds, so it is pretty big.
>
> records = consumer.poll(timeout);
>
> } catch (Exception e) {
>
> logger.error("Exception polling Kafka ", e);
>
> records = null;
>
> }
>
> if (records != null) {
>
> for (ConsumerRecord<Void, byte[]> record : records) {
>
> // The handler puts the byte array on a very fast ring
> buffer so it barely takes any time.
>
> handler.handleMessage(ByteBuffer.wrap(record.value()));
>
> }
>
> }
>
> }
>
>
>
> With this setup our performance has taken a horrendous hit as soon as we
> started this one thread that just polls kafka in a loop.
>
> I profiled the application using Java Mission Control and have a few
> insights.
>
> 1. There doesn't seem to be a single hotspot. The consumer just ends up
> using a lot of CPU for handing such a low number of messages. Our process
> was using 16% CPU before we added a single consumer and it went to 25% and
> above after. That's an increase of over 50% from a single consumer getting
> a single digit number of small messages per second. Here is an attachment
> of the cpu usage breakdown in the consumer (the namespace is different
> because we shade the kafka jar before using it) - http://imgur.com/tHjdVnM
> We've used bigger timeouts (100 seconds odd) and that doesn't seem to make
> much of a difference either.
>
> 2. It also seems like Kafka throws a ton of EOFExceptions. I am not sure
> whether this is expected but this seems like it would completely kill
> performance. Here is the exception tab of Java mission control.
> http://imgur.com/X3KSn37 That is 1.8 mn exceptions over a period of 3
> minutes which is about 10 thousand exceptions per second! The exception
> stack trace shows that it originates from the poll call. I don't understand
> how it can throw so many exceptions given I call poll it with a timeout of
> 10 seconds and get messages at about 1 per second.
>
> 3. The single thread seems to allocate a lot too. The single thread is
> responsible for 17.87% of our entire JVM allocation rate. Most of what it
> allocates seems to be those same EOFExceptions. Here is a chart showing the
> single thread's allocation proportion: http://imgur.com/GNUJQsz Here is a
> chart that shows a breakdown of the allocations: http://imgur.com/YjCXljE
> About 20% of the allocations are for the EOFExceptions. This seems kind of
> crazy especially given that this happens about 10 thousand times a second.
> The rest of the allocations seem to be spread all over but again seem
> excessive given how we are getting very few messages.
>
> As a comparison, we also run a wrapper over the old SimpleConsumer that
> gets a lot more data (10 -15 thousand 70 byte messages/sec on a different
> topic) and it is able to handle that load without much trouble. At this
> moment we are completely puzzled by this performance. Most of it does seem
> to be due to the crazy volumes of exceptions. Note: Our messages seem to
> all be making through. The exceptions are caught by Kafka's stack and never
> bubble though to us.
>
> Are we doing anything wrong with how we are using the new consumer (longer
> timeouts of a 100 second odd don't seem to help)?
>
> Thanks in advance,
>
> Rajiv
>
>
>
