Hi Guozhang, The Github link I pasted was from the 0.9.0 branch. The same line seems to be throwing exceptions in my code built of the maven 0.9.0.0 package. Are you saying that something else has changed higher up the call stack that will probably not trigger so many exceptions ?
Thanks, Rajiv On Tue, Jan 26, 2016 at 10:44 PM, Guozhang Wang <wangg...@gmail.com> wrote: > Rajiv, > > Could you try to build the new consumer from 0.9.0 branch and see if the > issue can be re-produced? > > Guozhang > > On Mon, Jan 25, 2016 at 9:46 PM, Rajiv Kurian <ra...@signalfx.com> wrote: > > > The exception seems to be thrown here > > > > > https://github.com/apache/kafka/blob/0.9.0/clients/src/main/java/org/apache/kafka/common/record/MemoryRecords.java#L236 > > > > Is this not expected to hit often? > > > > On Mon, Jan 25, 2016 at 9:22 PM, Rajiv Kurian <ra...@signalfx.com> > wrote: > > > > > 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 > > >> > > >> > > >> > > > > > > > > > -- > -- Guozhang >
