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https://issues.apache.org/jira/browse/KAFKA-3159?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15134562#comment-15134562
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Rajiv Kurian edited comment on KAFKA-3159 at 2/5/16 5:49 PM:
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I think I've found the underlying issue (might not be the only one in play). It
turns out that when I don't have any messages in the log, the Kafka broker
sends back a reply with no messages immediately instead of respecting the
fetch_max_wait_ms or the fetch_min_bytes. The EOFExceptions were probably just
raised from parsing empty message sets. I can reproduce this consistently.
Steps:
1. Create a topic with a small retention say 5 minutes or wait for said topic
to have all its logs cleaned.
2. Start consuming on the topic without any messages being sent to the topic.
3. Observe that Kafka sends back an empty reply to every fetch request almost
immediately. This can be observed with tcp-dump, or monitoring the
networking-in/out or ngrep etc. I also verified it by writing my own client and
observing that my requests get immediate replies when the log is empty.
4. As soon as you start sending messages to the topic, the problem goes away.
We've actually hit this problem in the past (seeing massive number of network
traffic) when we were subscribed to a single topic that gets no messages. We
didn't know the underlying issue then but I am pretty sure it is this problem.
This is a problem if any consumer is sending fetch requests to at least one
broker that is a leader for the partitions being queried but has no messages
retained in its log. In real life it can also be a problem. Here are a few use
cases:
i) Metadata like topics that are always consumed but very rarely ever written
to. We've run into this in the past like I said.
ii) During feature development one can switch on the consumers, and put the
producers behind a feature flag. This was the problem we ran into. The consumer
code went ahead before the producer code was integrated/switched on and we had
to roll back because of the massive regression.
Moreover it goes against all intuition that doing fetch requests against an
empty topic-partition should not be more expensive than actually getting data.
was (Author: ra...@signalfx.com):
I think I've found the isse. It turns out that when I don't have any messages
in the log, the kafka broker sends back a reply with no messages immediately
instead of respecting the fetch_max_wait_ms or the fetch_min_bytes. The
EOFExceptions were probably just raised from parsing empty message sets. I can
reproduce this consistently. Steps:
1. Create a topic with a small retention say 5 minutes or wait for said topic
to have all its logs cleaned.
2. Start consuming on the topic without any messages being sent to the topic.
3. Observe that Kafka sends back an empty reply to every fetch request almost
immediately. This can be observed with tcp-dump, or monitoring the
networking-in/out or ngrep etc. I also verified it by writing my own client and
observing that my requests get immediate replies when the log is empty.
4. As soon as you start sending messages to the topic, the problem goes away.
We've actually hit this problem in the past (seeing massive number of network
traffic) when we were subscribed to a single topic that gets no messages. We
didn't know the underlying issue then but I am pretty sure it is this problem.
This is a problem if any consumer is sending fetch requests to at least one
broker that is a leader for the partitions being queried but has no messages
retained in its log. In real life it can also be a problem. Here are a few use
cases:
i) Metadata like topics that are always consumed but very rarely ever written
to. We've run into this in the past like I said.
ii) During feature development one can switch on the consumers, and put the
producers behind a feature flag. This was the problem we ran into. The consumer
code went ahead before the producer code was integrated/switched on and we had
to roll back because of the massive regression.
Moreover it goes against all intuition that doing fetch requests against an
empty topic-partition should not be more expensive than actually getting data.
> Kafka consumer 0.9.0.0 client poll is very CPU intensive under certain
> conditions
> ----------------------------------------------------------------------------------
>
> Key: KAFKA-3159
> URL: https://issues.apache.org/jira/browse/KAFKA-3159
> Project: Kafka
> Issue Type: Bug
> Components: clients
> Affects Versions: 0.9.0.0
> Environment: Linux, Oracle JVM 8.
> Reporter: Rajiv Kurian
> Assignee: Jason Gustafson
> Fix For: 0.9.0.1
>
> Attachments: Memory-profile-patched-client.png, Screen Shot
> 2016-02-01 at 11.09.32 AM.png
>
>
> 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 dividing 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) {
> // This never hits for us
> 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/BxWs9Q0 So 20.54% of our entire
> process CPU is used on polling these 64 partitions (across 3 brokers) with
> single digit number of 70-80 byte odd messages. 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 a single digit number of messages per second. The exception seems to
> be thrown from here:
> https://github.com/apache/kafka/blob/0.9.0/clients/src/main/java/org/apache/kafka/common/record/MemoryRecords.java#L236
> 3. The single thread seems to allocate a lot too. The single thread is
> responsible for 17.87% of our entire JVM allocation rate. During other runs
> it has gone up to 20% 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. But given that the
> 20% of the allocations (exceptions) is around 10k/second, the thread itself
> is allocating about 50k objects/second which seems 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 (30 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. At least some part of that seems to
> be due to the crazy volumes of exceptions but the CPU profiling breakdown
> seems to suggest that there are plenty of other causes including the
> Fetcher.initFetches() call and the ConsumerNetworkClient.poll() call. Note:
> Our messages seem to all be making through. We haven't measured the end to
> end latency. The exceptions are caught by Kafka's stack and never bubble up
> to us.
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