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Major Description
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
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)
}
}
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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