Here is some information on actually running Kafka as a production system based on usage and experience at LinkedIn. Please send us any additional tips you know of.

6.1 Datacenters

Some deployments will need to manage a data pipeline that spans multiple datacenters. Our approach to this is to deploy a local Kafka cluster in each datacenter and machines in each location interact only with their local cluster.

For applications that need a global view of all data we use the mirror maker tool to provide clusters which have aggregate data mirrored from all datacenters. These aggregator clusters are used for reads by applications that require this.

Likewise in order to support data load into Hadoop which resides in separate facilities we provide local read-only clusters that mirror the production data centers in the facilities where this data load occurs.

This allows each facility to stand alone and operate even if the inter-datacenter links are unavailable: when this occurs the mirroring falls behind until the link is restored at which time it catches up.

This deployment pattern allows datacenters to act as independent entities and allows us to manage and tune inter-datacenter replication centrally.

This is not the only possible deployment pattern. It is possible to read from or write to a remote Kafka cluster over the WAN though TCP tuning will be necessary for high-latency links.

It is generally not advisable to run a single Kafka cluster that spans multiple datacenters as this will incur very high replication latency both for Kafka writes and Zookeeper writes and neither Kafka nor Zookeeper will remain available if the network partitions.

6.2 Kafka Configuration

Kafka 0.8 is the version we currently run. We are currently running with replication but with producers acks = 1.

Important Server Configurations

The most important server configurations for performance are those that control the disk flush rate. The more often data is flushed to disk, the more "seek-bound" Kafka will be and the lower the throughput. However very low application flush rates can lead to high latency when the flush finally does occur (because of the volume of data that must be flushed). See the section below on application versus OS flush.

Important Client Configurations

The most important producer configurations control The most important consumer configuration is the fetch size.

All configurations are documented in the configuration section.

A Production Server Config

Here is our server production server configuration:
# Replication configurations
num.replica.fetchers=4
replica.fetch.max.bytes=1048576
replica.fetch.wait.max.ms=500
replica.high.watermark.checkpoint.interval.ms=5000
replica.socket.timeout.ms=30000
replica.socket.receive.buffer.bytes=65536
replica.lag.time.max.ms=10000
replica.lag.max.messages=4000

controller.socket.timeout.ms=30000
controller.message.queue.size=10

# Log configuration
num.partitions=8
message.max.bytes=1000000
auto.create.topics.enable=true
log.index.interval.bytes=4096
log.index.size.max.bytes=10485760
log.retention.hours=168
log.flush.interval.ms=10000
log.flush.interval.messages=20000
log.flush.scheduler.interval.ms=2000
log.roll.hours=168
log.cleanup.interval.mins=30
log.segment.bytes=1073741824

# ZK configuration
zk.connection.timeout.ms=6000
zk.sync.time.ms=2000

# Socket server configuration
num.io.threads=8
num.network.threads=8
socket.request.max.bytes=104857600
socket.receive.buffer.bytes=1048576
socket.send.buffer.bytes=1048576
queued.max.requests=16
fetch.purgatory.purge.interval.requests=100
producer.purgatory.purge.interval.requests=100
Our client configuration varies a fair amount between different use cases.

Java Version

Any version of Java 1.6 or later should work fine, we are using 1.6.0_21. Here are our command line options:
java -server -Xms3072m -Xmx3072m -XX:NewSize=256m -XX:MaxNewSize=256m -XX:+UseParNewGC -XX:+UseConcMarkSweepGC 
     -XX:+UseCMSInitiatingOccupancyOnly -XX:+CMSConcurrentMTEnabled -XX:+CMSScavengeBeforeRemark 
     -XX:CMSInitiatingOccupancyFraction=30 
     -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+PrintTenuringDistribution 
     -Xloggc:logs/gc.log -Djava.awt.headless=true
     -Dcom.sun.management.jmxremote -classpath <long list of jars> the.actual.Class
	

6.4 Hardware and OS

We are using dual quad-core Intel Xeon machines with 24GB of memory.

You need sufficient memory to buffer active readers and writers. You can do a back-of-the-envelope estimate of memory needs by assuming you want to be able to buffer for 30 seconds and compute your memory need as write_throughput*30.

The disk throughput is important. We have 8x7200 rpm SATA drives. In general disk throughput is the performance bottleneck, and more disks is more better. Depending on how you configure flush behavior you may or may not benefit from more expensive disks (if you force flush often then higher RPM SAS drives may be better).

OS

Kafka should run well on any unix system and has been tested on Linux and Solaris.

We have seen a few issues running on Windows and Windows is not currently a well supported platform though we would be happy to change that.

You likely don't need to do much OS-level tuning though there are a few things that will help performance.

Two configurations that may be important:

Disks and Filesystem

We recommend using multiple drives to get good throughput and not sharing the same drives used for Kafka data with application logs or other OS filesystem activity to ensure good latency. As of 0.8 you can either RAID these drives together into a single volume or format and mount each drive as its own directory. Since Kafka has replication the redundancy provided by RAID can also be provided at the application level. This choice has several tradeoffs.

If you configure multiple data directories partitions will be assigned round-robin to data directories. Each partition will be entirely in one of the data directories. If data is not well balanced among partitions this can lead to load imbalance between disks.

RAID can potentially do better at balancing load between disks (although it doesn't always seem to) because it balances load at a lower level. The primary downside of RAID is that it is usually a big performance hit for write throughput and reduces the available disk space.

Another potential benefit of RAID is the ability to tolerate disk failures. However our experience has been that rebuilding the RAID array is so I/O intensive that it effectively disables the server, so this does not provide much real availability improvement.

Application vs. OS Flush Management

Kafka always immediately writes all data to the filesystem and supports the ability to configure the flush policy that controls when data is forced out of the OS cache and onto disk using the and flush. This flush policy can be controlled to force data to disk after a period of time or after a certain number of messages has been written. There are several choices in this configuration.

Kafka must eventually call fsync to know that data was flushed. When recovering from a crash for any log segment not known to be fsync'd Kafka will check the integrity of each message by checking its CRC and also rebuild the accompanying offset index file as part of the recovery process executed on startup.

This frequency of application-level fsyncs has a large impact on both latency and throughput. Setting a large flush interval will improve throughput as the operating system can buffer the many small writes into a single large write. This works effectively even across many partitions all taking simultaneous writes provided enough memory is available for buffering. However doing this may have a significant impact on latency as in many filesystems (including ext2, ext3, and ext4) fsync is an operation which blocks all writes to the file. Because of this allowing lots of data to accumulate and then calling flush can lead to large write latencies as new writes on that partition will be blocked as lots of accumulated data is flushed to disk.

In 0.8 we support replication as a way to ensure that data that is written is durable in the face of server crashes. As a result we allow giving out data to consumers immediately and the flush interval does not impact consumer latency. However we still MUST flush each log segment when the log rolls over to a new segment. So although you can set a relatively lenient flush interval setting no flush interval at all will lead to a full segment's worth of data being flushed all at once which can be quite slow. After 0.8 we improved our recovery procedure which allows us to avoid the blocking fsync when the log rolls. As a result in all releases after 0.8 we recommend using replication and not setting any application level flush settings---relying only on the OS and Kafka's own background flush. This provides the best of all worlds for most uses: no knobs to tune, great throughput and latency, and full recovery guarantees. We generally feel that the guarantees provided by replication are stronger than sync to local disk, however the paranoid still may prefer having both and application level fsync policies are still supported.

In general you don't need to do any low-level tuning of the filesystem, but in the next few sections we will go over some of this in case it is useful.

Understanding Linux OS Flush Behavior

In Linux, data written to the filesystem is maintained in pagecache until it must be written out to disk (due to an application-level fsync or the OS's own flush policy). The flushing of data is done by a set of background threads called pdflush (or in post 2.6.32 kernels "flusher threads").

Pdflush has a configurable policy that controls how much dirty data can be maintained in cache and for how long before it must be written back to disk. This policy is described here. When Pdflush cannot keep up with the rate of data being written it will eventually cause the writing process to block incurring latency in the writes to slow down the accumulation of data.

You can see the current state of OS memory usage by doing

  cat /proc/meminfo
The meaning of these values are described in the link above.

Using pagecache has several advantages over an in-process cache for storing data that will be written out to disk:

Ext4 Notes

Ext4 may or may not be the best filesystem for Kafka. Filesystems like XFS supposedly handle locking during fsync better. We have only tried Ext4, though.

It is not necessary to tune these settings, however those wanting to optimize performance have a few knobs that will help:

6.5 Monitoring

Kafka uses Yammer Metrics for metrics reporting in both the server and the client. This can be configured to report stats using pluggable stats reporters to hook up to your monitoring system.

The easiest way to see the available metrics to fire up jconsole and point it at a running kafka client or server; this will all browsing all metrics with JMX.

We pay particular we do graphing and alerting on the following metrics:
Description Mbean name Normal value
Message in rate "kafka.server":name="AllTopicsMessagesInPerSec", type="BrokerTopicMetrics"
Byte in rate "kafka.server":name="AllTopicsBytesInPerSec",type="BrokerTopicMetrics"
Request rate "kafka.network":name="{Produce|Fetch-consumer|Fetch-follower}-RequestsPerSec",type="RequestMetrics"
Byte out rate "kafka.server":name="AllTopicsBytesOutPerSec", type="BrokerTopicMetrics"
Log flush rate and time "kafka.log":name="LogFlushRateAndTimeMs",type="LogFlushStats"
# of under replicated partitions (|ISR| < |all replicas|) "kafka.server":name="UnderReplicatedPartitions",type="ReplicaManager" 0
Is controller active on broker "kafka.controller":name="ActiveControllerCount",type="KafkaController" only one broker in the cluster should have 1
Leader election rate "kafka.controller":name="LeaderElectionRateAndTimeMs", type="ControllerStats" non-zero when there are broker failures
Unclean leader election rate "kafka.controller":name="UncleanLeaderElectionsPerSec", type="ControllerStats" 0
Partition counts "kafka.server":name="PartitionCount",type="ReplicaManager" mostly even across brokers
Leader replica counts "kafka.server":name="LeaderCount",type="ReplicaManager" mostly even across brokers
ISR shrink rate "kafka.server":name="ISRShrinksPerSec",type="ReplicaManager" If a broker goes down, ISR for some of the partitions will shrink. When that broker is up again, ISR will be expanded once the replicas are fully caught up. Other than that, the expected value for both ISR shrink rate and expansion rate is 0.
ISR expansion rate "kafka.server":name="ISRExpandsPerSec",type="ReplicaManager" See above
Max lag in messages btw follower and leader replicas "kafka.server":name="([-.\w]+)-MaxLag",type="ReplicaFetcherManager" < replica.lag.max.messages
Lag in messages per follower replica "kafka.server":name="([-.\w]+)-ConsumerLag",type="FetcherLagMetrics" < replica.lag.max.messages
Requests waiting in the producer purgatory "kafka.server":name="PurgatorySize",type="ProducerRequestPurgatory" non-zero if ack=-1 is used
Requests waiting in the fetch purgatory "kafka.server":name="PurgatorySize",type="FetchRequestPurgatory" size depends on fetch.wait.max.ms in the consumer
Request total time "kafka.network":name="{Produce|Fetch-Consumer|Fetch-Follower}-TotalTimeMs",type="RequestMetrics" broken into queue, local, remote and response send time
Time the request waiting in the request queue "kafka.network":name="{Produce|Fetch-Consumer|Fetch-Follower}-QueueTimeMs",type="RequestMetrics"
Time the request being processed at the leader "kafka.network":name="{Produce|Fetch-Consumer|Fetch-Follower}-LocalTimeMs",type="RequestMetrics"
Time the request waits for the follower "kafka.network":name="{Produce|Fetch-Consumer|Fetch-Follower}-RemoteTimeMs",type="RequestMetrics" non-zero for produce requests when ack=-1
Time to send the response "kafka.network":name="{Produce|Fetch-Consumer|Fetch-Follower}-ResponseSendTimeMs",type="RequestMetrics"
Number of messages the consumer lags behind the broker among all partitions consumed "kafka.consumer":name="([-.\w]+)-MaxLag",type="ConsumerFetcherManager" small and not growing
The min fetch rate among all fetchers to brokers in a consumer "kafka.consumer":name="([-.\w]+)-MinFetch",type="ConsumerFetcherManager" >= 1000/fetch.wait.max.ms
We recommend monitor GC time and other stats and various server stats such as CPU utilization, I/O service time, etc. On the client side, we recommend monitor the message/byte rate (global and per topic), request rate/size/time, and on the consumer side, max lag in messages among all partitions and min fetch request rate. For a consumer to keep up, max lag needs to be less than a threshold and min fetch rate needs to be larger than 0.

Audit

The final alerting we do is on the correctness of the data delivery. We audit that every message that is sent is consumed by all consumers and measure the lag for this to occur. For important topics we alert if a certain completeness is not achieved in a certain time period. The details of this are discussed in KAFKA-260.

6.6 Zookeeper

Stable version

At LinkedIn, we are running Zookeeper 3.3.*. Version 3.3.3 has known serious issues regarding ephemeral node deletion and session expirations. After running into those issues in production, we upgraded to 3.3.4 and have been running that smoothly for over a year now.

Operationalizing Zookeeper

Operationally, we do the following for a healthy Zookeeper installation:

Redundancy in the physical/hardware/network layout: try not to put them all in the same rack, decent (but don't go nuts) hardware, try to keep redundant power and network paths, etc

I/O segregation: if you do a lot of write type traffic you'll almost definitely want the transaction logs on a different disk group than application logs and snapshots (the write to the Zookeeper service has a synchronous write to disk, which can be slow).

Application segregation: Unless you really understand the application patterns of other apps that you want to install on the same box, it can be a good idea to run Zookeeper in isolation (though this can be a balancing act with the capabilities of the hardware). Use care with virtualization: It can work, depending on your cluster layout and read/write patterns and SLAs, but the tiny overheads introduced by the virtualization layer can add up and throw off Zookeeper, as it can be very time sensitive

Zookeeper configuration and monitoring: It's java, make sure you give it 'enough' heap space (We usually run them with 3-5G, but that's mostly due to the data set size we have here). Unfortunately we don't have a good formula for it. As far as monitoring, both JMZ and the 4 letter commands are very useful, they do overlap in some cases (and in those cases we prefer the 4 letter commands, they seem more predictable, or at the very least, they work better with the LI monitoring infrastructure) Don't overbuild the cluster: large clusters, especially in a write heavy usage pattern, means a lot of intracluster communication (quorums on the writes and subsequent cluster member updates), but don't underbuild it (and risk swamping the cluster).

Try to run on a 3-5 node cluster: Zookeeper writes use quorums and inherently that means having an odd number of machines in a cluster. Remember that a 5 node cluster will cause writes to slow down compared to a 3 node cluster, but will allow more fault tolerance.

Overall, we try to keep the Zookeeper system as small as will handle the load (plus standard growth capacity planning) and as simple as possible. We try not to do anything fancy with the configuration or application layout as compared to the official release as well as keep it as self contained as possible. For these reasons, we tend to skip the OS packaged versions, since it has a tendency to try to put things in the OS standard hierarchy, which can be 'messy', for want of a better way to word it.