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Running Kafka in Production

This section describes the key considerations before going to production with Confluent Platform.


If you’ve been following the normal development path, you’ve probably been playing with Apache Kafka® on your laptop or on a small cluster of machines laying around. But when it comes time to deploying Kafka to production, there are a few recommendations that you should consider. Nothing is a hard-and-fast rule; Kafka is used for a wide range of use cases and on a bewildering array of machines. But these recommendations provide a good starting point based on the experiences of Confluent with production clusters.


Kafka relies heavily on the filesystem for storing and caching messages. All data is immediately written to a persistent log on the filesystem without necessarily flushing to disk. In effect this just means that it is transferred into the kernel’s pagecache. A modern OS will happily divert all free memory to disk caching with little performance penalty when the memory is reclaimed. Furthermore, Kafka uses heap space very carefully and does not require setting heap sizes more than 6 GB. This will result in a file system cache of up to 28-30 GB on a 32 GB machine.

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.

A machine with 64 GB of RAM is a decent choice, but 32 GB machines are not uncommon. Less than 32 GB tends to be counterproductive (you end up needing many, many small machines).


Most Kafka deployments tend to be rather light on CPU requirements. As such, the exact processor setup matters less than the other resources. Note that if SSL is enabled, the CPU requirements can be significantly higher (the exact details depend on the CPU type and JVM implementation).

You should choose a modern processor with multiple cores. Common clusters utilize 24 core machines.

If you need to choose between faster CPUs or more cores, choose more cores. The extra concurrency that multiple cores offers will far outweigh a slightly faster clock speed.


You should use multiple drives to maximize throughput. Do not share the same drives used for Kafka data with application logs or other OS filesystem activity to ensure good latency. You can either combine these drives together into a single volume (RAID) or format and mount each drive as its own directory. Because 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 reduces the available disk space. Another potential benefit of RAID is the ability to tolerate disk failures.

You should not use RAID 5 or RAID 6 because of the significant hit on write throughput and, to a lesser extent, the I/O cost of rebuilding the array when a disk fails (the rebuild cost applies to RAID, in general, but it is worst for RAID 6 and then RAID 5).

You should use RAID 10 if the additional cost is acceptable. Otherwise, configure your Kafka server with multiple log directories, each directory mounted on a separate drive.

Finally, you should avoid network-attached storage (NAS). NAS is often slower, displays larger latencies with a wider deviation in average latency, and is a single point of failure.


A fast and reliable network is an essential performance component in a distributed system. Low latency ensures that nodes can communicate easily, while high bandwidth helps shard movement and recovery. Modern data-center networking (1 GbE, 10 GbE) is sufficient for the vast majority of clusters.

You should avoid clusters that span multiple data centers, even if the data centers are colocated in close proximity; and avoid clusters that span large geographic distances.

Kafka clusters assume that all nodes are equal. Larger latencies can exacerbate problems in distributed systems and make debugging and resolution more difficult.

From the experience of Confluent, the hassle and cost of managing cross–data center clusters is simply not worth the benefits.


You should run Kafka on XFS or ext4. XFS typically performs well with little tuning when compared to ext4 and it has become the default filesystem for many Linux distributions.

General Considerations

In general, it is better to prefer medium-to-large boxes. Avoid small machines, because you don’t want to manage a cluster with a thousand nodes, and the overhead of simply running Kafka is more apparent on such small boxes.

Avoid the large machines because they often lead to imbalanced resource usage. For example, all the memory is being used, but none of the CPU. They can also add logistical complexity if you have to run multiple nodes per machine.


Java 1.8 is supported in this version of Confluent Platform (Java 1.9 and 1.10 are currently not supported). For more information, see Java supported versions.

The recommended GC tuning (tested on a large deployment with JDK 1.8 u5) looks like this:

-Xms6g -Xmx6g -XX:MetaspaceSize=96m -XX:+UseG1GC -XX:MaxGCPauseMillis=20
       -XX:InitiatingHeapOccupancyPercent=35 -XX:G1HeapRegionSize=16M
       -XX:MinMetaspaceFreeRatio=50 -XX:MaxMetaspaceFreeRatio=80

For reference, here are the stats on one of LinkedIn’s busiest clusters (at peak):

  • 60 brokers
  • 50k partitions (replication factor 2)
  • 800k messages/sec in
  • 300 MBps inbound, 1 GBps + outbound

The tuning looks fairly aggressive, but all of the brokers in that cluster have a 90% GC pause time of about 21ms, and they’re doing less than 1 young GC per second.

Production Configuration Options

The Kafka default settings should work in most cases, especially the performance-related settings and options, but there are some logistical configurations that should be changed for production depending on your cluster layout.

General configs


The list of ZooKeeper hosts that the broker registers at. It is recommended that you configure this to all the hosts in your ZooKeeper cluster

  • Type: string
  • Importance: high
Integer ID that identifies a broker. Brokers in the same Kafka cluster must not have the same ID.
  • Type: int
  • Importance: high

The directories in which the Kafka log data is located.

  • Type: string
  • Default: “/tmp/kafka-logs”
  • Importance: high

Comma-separated list of URIs (including protocol) that the broker will listen on. Specify hostname as to bind to all interfaces or leave it empty to bind to the default interface. An example is PLAINTEXT://myhost:9092.

  • Type: string
  • Default: PLAINTEXT:// where the default for is an empty string and the default for port is 9092
  • Importance: high

Listeners to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, the value for listeners will be used.

  • Type: string
  • Default: listeners
  • Importance: high

The default number of log partitions for auto-created topics. You should increase this since it is better to over-partition a topic. Over-partitioning a topic leads to better data balancing and aids consumer parallelism. For keyed data, you should avoid changing the number of partitions in a topic.

  • Type: int
  • Default: 1
  • Valid Values: [1,…]
  • Importance: medium
  • Dynamic Update Mode: read-only

Replication configs


The default replication factor that applies to auto-created topics. You should set this to at least 2.

  • Type: int
  • Default: 1
  • Importance: medium

The minimum number of replicas in ISR needed to commit a produce request with required.acks=-1 (or all).

  • Type: int
  • Default: 1
  • Importance: medium

Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss.

  • Type: int
  • Default: 1
  • Importance: medium

File Descriptors and mmap

Kafka uses a very large number of files and a large number of sockets to communicate with the clients. All of this requires a relatively high number of available file descriptors.

Many modern Linux distributions ship with only 1,024 file descriptors allowed per process. This is too low for Kafka. You must increase your file descriptor count to something very large, such as 100,000. This process is somewhat difficult and highly dependent on your particular OS and distribution. Consult the documentation for your OS to determine how best to change the allowed file descriptor count.

You should increase your file descriptor count to to at least 100,000. This process can be difficult and is highly dependent on your particular OS and distribution. Consult the documentation for your OS to determine how best to change the allowed file descriptor count.

Multi-node Configuration

In a production environment, multiple brokers are required. During startup brokers register themselves in ZooKeeper to become a member of the cluster.

Navigate to the Apache Kafka® properties file (/etc/kafka/ and customize the following:

  • Connect to the same ZooKeeper ensemble by setting the zookeeper.connect in all nodes to the same value. Replace all instances of localhost to the hostname or FQDN (fully qualified domain name) of your node. For example, if your hostname is zookeeper:

  • Configure the broker IDs for each node in your cluster using one of these methods.

    • Dynamically generate the broker IDs: add and comment out For example:

      ############################# Server Basics #############################
      # The ID of the broker. This must be set to a unique integer for each broker.
    • Manually set the broker IDs: set a unique value for on each node.

  • Configure how other brokers and clients communicate with the broker using listeners, and optionally advertised.listeners.

    • listeners: Comma-separated list of URIs and listener names to listen on.
    • advertised.listeners: Comma-separated list of URIs and listener names for other brokers and clients to use. The advertised.listeners parameter ensures that the broker advertises an address that is accessible from both local and external hosts.

    For more information, see Production Configuration Options.