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Kafka 3.5 Documentation

Prior releases: 0.7.x, 0.8.0, 0.8.1.X, 0.8.2.X, 0.9.0.X, 0.10.0.X, 0.10.1.X, 0.10.2.X, 0.11.0.X, 1.0.X, 1.1.X, 2.0.X, 2.1.X, 2.2.X, 2.3.X, 2.4.X, 2.5.X, 2.6.X, 2.7.X, 2.8.X, 3.0.X, 3.1.X, 3.2.X, 3.3.X, 3.4.X.

1. Getting Started

1.1 Introduction

1.2 Use Cases

Here is a description of a few of the popular use cases for Apache Kafka®. For an overview of a number of these areas in action, see this blog post.


Kafka works well as a replacement for a more traditional message broker. Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc). In comparison to most messaging systems Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications.

In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strong durability guarantees Kafka provides.

In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ or RabbitMQ.

Website Activity Tracking

The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds. This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type. These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop or offline data warehousing systems for offline processing and reporting.

Activity tracking is often very high volume as many activity messages are generated for each user page view.


Kafka is often used for operational monitoring data. This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.

Log Aggregation

Many people use Kafka as a replacement for a log aggregation solution. Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing. Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages. This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption. In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication, and much lower end-to-end latency.

Stream Processing

Many users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and then aggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing. For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an "articles" topic; further processing might normalize or deduplicate this content and publish the cleansed article content to a new topic; a final processing stage might attempt to recommend this content to users. Such processing pipelines create graphs of real-time data flows based on the individual topics. Starting in, a light-weight but powerful stream processing library called Kafka Streams is available in Apache Kafka to perform such data processing as described above. Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm and Apache Samza.

Event Sourcing

Event sourcing is a style of application design where state changes are logged as a time-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style.

Commit Log

Kafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncing mechanism for failed nodes to restore their data. The log compaction feature in Kafka helps support this usage. In this usage Kafka is similar to Apache BookKeeper project.

1.3 Quick Start

1.4 Ecosystem

There are a plethora of tools that integrate with Kafka outside the main distribution. The ecosystem page lists many of these, including stream processing systems, Hadoop integration, monitoring, and deployment tools.

1.5 Upgrading From Previous Versions

2. APIs

3. Configuration

4. Design

5. Implementation

6. Operations

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 Basic Kafka Operations

This section will review the most common operations you will perform on your Kafka cluster. All of the tools reviewed in this section are available under the bin/ directory of the Kafka distribution and each tool will print details on all possible commandline options if it is run with no arguments.

Adding and removing topics

You have the option of either adding topics manually or having them be created automatically when data is first published to a non-existent topic. If topics are auto-created then you may want to tune the default topic configurations used for auto-created topics.

Topics are added and modified using the topic tool:

  > bin/kafka-topics.sh --bootstrap-server broker_host:port --create --topic my_topic_name \
        --partitions 20 --replication-factor 3 --config x=y
The replication factor controls how many servers will replicate each message that is written. If you have a replication factor of 3 then up to 2 servers can fail before you will lose access to your data. We recommend you use a replication factor of 2 or 3 so that you can transparently bounce machines without interrupting data consumption.

The partition count controls how many logs the topic will be sharded into. There are several impacts of the partition count. First each partition must fit entirely on a single server. So if you have 20 partitions the full data set (and read and write load) will be handled by no more than 20 servers (not counting replicas). Finally the partition count impacts the maximum parallelism of your consumers. This is discussed in greater detail in the concepts section.

Each sharded partition log is placed into its own folder under the Kafka log directory. The name of such folders consists of the topic name, appended by a dash (-) and the partition id. Since a typical folder name can not be over 255 characters long, there will be a limitation on the length of topic names. We assume the number of partitions will not ever be above 100,000. Therefore, topic names cannot be longer than 249 characters. This leaves just enough room in the folder name for a dash and a potentially 5 digit long partition id.

The configurations added on the command line override the default settings the server has for things like the length of time data should be retained. The complete set of per-topic configurations is documented here.

Modifying topics

You can change the configuration or partitioning of a topic using the same topic tool.

To add partitions you can do

  > bin/kafka-topics.sh --bootstrap-server broker_host:port --alter --topic my_topic_name \
        --partitions 40
Be aware that one use case for partitions is to semantically partition data, and adding partitions doesn't change the partitioning of existing data so this may disturb consumers if they rely on that partition. That is if data is partitioned by hash(key) % number_of_partitions then this partitioning will potentially be shuffled by adding partitions but Kafka will not attempt to automatically redistribute data in any way.

To add configs:

  > bin/kafka-configs.sh --bootstrap-server broker_host:port --entity-type topics --entity-name my_topic_name --alter --add-config x=y
To remove a config:
  > bin/kafka-configs.sh --bootstrap-server broker_host:port --entity-type topics --entity-name my_topic_name --alter --delete-config x
And finally deleting a topic:
  > bin/kafka-topics.sh --bootstrap-server broker_host:port --delete --topic my_topic_name

Kafka does not currently support reducing the number of partitions for a topic.

Instructions for changing the replication factor of a topic can be found here.

Graceful shutdown

The Kafka cluster will automatically detect any broker shutdown or failure and elect new leaders for the partitions on that machine. This will occur whether a server fails or it is brought down intentionally for maintenance or configuration changes. For the latter cases Kafka supports a more graceful mechanism for stopping a server than just killing it. When a server is stopped gracefully it has two optimizations it will take advantage of:
  1. It will sync all its logs to disk to avoid needing to do any log recovery when it restarts (i.e. validating the checksum for all messages in the tail of the log). Log recovery takes time so this speeds up intentional restarts.
  2. It will migrate any partitions the server is the leader for to other replicas prior to shutting down. This will make the leadership transfer faster and minimize the time each partition is unavailable to a few milliseconds.
Syncing the logs will happen automatically whenever the server is stopped other than by a hard kill, but the controlled leadership migration requires using a special setting:
Note that controlled shutdown will only succeed if all the partitions hosted on the broker have replicas (i.e. the replication factor is greater than 1 and at least one of these replicas is alive). This is generally what you want since shutting down the last replica would make that topic partition unavailable.

Balancing leadership

Whenever a broker stops or crashes, leadership for that broker's partitions transfers to other replicas. When the broker is restarted it will only be a follower for all its partitions, meaning it will not be used for client reads and writes.

To avoid this imbalance, Kafka has a notion of preferred replicas. If the list of replicas for a partition is 1,5,9 then node 1 is preferred as the leader to either node 5 or 9 because it is earlier in the replica list. By default the Kafka cluster will try to restore leadership to the preferred replicas. This behaviour is configured with:

You can also set this to false, but you will then need to manually restore leadership to the restored replicas by running the command:
  > bin/kafka-leader-election.sh --bootstrap-server broker_host:port --election-type preferred --all-topic-partitions

Balancing Replicas Across Racks

The rack awareness feature spreads replicas of the same partition across different racks. This extends the guarantees Kafka provides for broker-failure to cover rack-failure, limiting the risk of data loss should all the brokers on a rack fail at once. The feature can also be applied to other broker groupings such as availability zones in EC2.

You can specify that a broker belongs to a particular rack by adding a property to the broker config:
When a topic is created, modified or replicas are redistributed, the rack constraint will be honoured, ensuring replicas span as many racks as they can (a partition will span min(#racks, replication-factor) different racks).

The algorithm used to assign replicas to brokers ensures that the number of leaders per broker will be constant, regardless of how brokers are distributed across racks. This ensures balanced throughput.

However if racks are assigned different numbers of brokers, the assignment of replicas will not be even. Racks with fewer brokers will get more replicas, meaning they will use more storage and put more resources into replication. Hence it is sensible to configure an equal number of brokers per rack.

Mirroring data between clusters & Geo-replication

Kafka administrators can define data flows that cross the boundaries of individual Kafka clusters, data centers, or geographical regions. Please refer to the section on Geo-Replication for further information.

Checking consumer position

Sometimes it's useful to see the position of your consumers. We have a tool that will show the position of all consumers in a consumer group as well as how far behind the end of the log they are. To run this tool on a consumer group named my-group consuming a topic named my-topic would look like this:
  > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group my-group

  TOPIC                          PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG        CONSUMER-ID                                       HOST                           CLIENT-ID
  my-topic                       0          2               4               2          consumer-1-029af89c-873c-4751-a720-cefd41a669d6   /                     consumer-1
  my-topic                       1          2               3               1          consumer-1-029af89c-873c-4751-a720-cefd41a669d6   /                     consumer-1
  my-topic                       2          2               3               1          consumer-2-42c1abd4-e3b2-425d-a8bb-e1ea49b29bb2   /                     consumer-2

Managing Consumer Groups

With the ConsumerGroupCommand tool, we can list, describe, or delete the consumer groups. The consumer group can be deleted manually, or automatically when the last committed offset for that group expires. Manual deletion works only if the group does not have any active members. For example, to list all consumer groups across all topics:
  > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --list

To view offsets, as mentioned earlier, we "describe" the consumer group like this:
  > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group my-group

  TOPIC           PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG             CONSUMER-ID                                    HOST            CLIENT-ID
  topic3          0          241019          395308          154289          consumer2-e76ea8c3-5d30-4299-9005-47eb41f3d3c4 /      consumer2
  topic2          1          520678          803288          282610          consumer2-e76ea8c3-5d30-4299-9005-47eb41f3d3c4 /      consumer2
  topic3          1          241018          398817          157799          consumer2-e76ea8c3-5d30-4299-9005-47eb41f3d3c4 /      consumer2
  topic1          0          854144          855809          1665            consumer1-3fc8d6f1-581a-4472-bdf3-3515b4aee8c1 /      consumer1
  topic2          0          460537          803290          342753          consumer1-3fc8d6f1-581a-4472-bdf3-3515b4aee8c1 /      consumer1
  topic3          2          243655          398812          155157          consumer4-117fe4d3-c6c1-4178-8ee9-eb4a3954bee0 /      consumer4
There are a number of additional "describe" options that can be used to provide more detailed information about a consumer group:
  • --members: This option provides the list of all active members in the consumer group.
          > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group my-group --members
          CONSUMER-ID                                    HOST            CLIENT-ID       #PARTITIONS
          consumer1-3fc8d6f1-581a-4472-bdf3-3515b4aee8c1 /      consumer1       2
          consumer4-117fe4d3-c6c1-4178-8ee9-eb4a3954bee0 /      consumer4       1
          consumer2-e76ea8c3-5d30-4299-9005-47eb41f3d3c4 /      consumer2       3
          consumer3-ecea43e4-1f01-479f-8349-f9130b75d8ee /      consumer3       0
  • --members --verbose: On top of the information reported by the "--members" options above, this option also provides the partitions assigned to each member.
          > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group my-group --members --verbose
          CONSUMER-ID                                    HOST            CLIENT-ID       #PARTITIONS     ASSIGNMENT
          consumer1-3fc8d6f1-581a-4472-bdf3-3515b4aee8c1 /      consumer1       2               topic1(0), topic2(0)
          consumer4-117fe4d3-c6c1-4178-8ee9-eb4a3954bee0 /      consumer4       1               topic3(2)
          consumer2-e76ea8c3-5d30-4299-9005-47eb41f3d3c4 /      consumer2       3               topic2(1), topic3(0,1)
          consumer3-ecea43e4-1f01-479f-8349-f9130b75d8ee /      consumer3       0               -
  • --offsets: This is the default describe option and provides the same output as the "--describe" option.
  • --state: This option provides useful group-level information.
          > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --describe --group my-group --state
          COORDINATOR (ID)          ASSIGNMENT-STRATEGY       STATE                #MEMBERS
          localhost:9092 (0)        range                     Stable               4
To manually delete one or multiple consumer groups, the "--delete" option can be used:
  > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --delete --group my-group --group my-other-group

  Deletion of requested consumer groups ('my-group', 'my-other-group') was successful.

To reset offsets of a consumer group, "--reset-offsets" option can be used. This option supports one consumer group at the time. It requires defining following scopes: --all-topics or --topic. One scope must be selected, unless you use '--from-file' scenario. Also, first make sure that the consumer instances are inactive. See KIP-122 for more details.

It has 3 execution options:

  • (default) to display which offsets to reset.
  • --execute : to execute --reset-offsets process.
  • --export : to export the results to a CSV format.

--reset-offsets also has following scenarios to choose from (at least one scenario must be selected):

  • --to-datetime <String: datetime> : Reset offsets to offsets from datetime. Format: 'YYYY-MM-DDTHH:mm:SS.sss'
  • --to-earliest : Reset offsets to earliest offset.
  • --to-latest : Reset offsets to latest offset.
  • --shift-by <Long: number-of-offsets> : Reset offsets shifting current offset by 'n', where 'n' can be positive or negative.
  • --from-file : Reset offsets to values defined in CSV file.
  • --to-current : Resets offsets to current offset.
  • --by-duration <String: duration> : Reset offsets to offset by duration from current timestamp. Format: 'PnDTnHnMnS'
  • --to-offset : Reset offsets to a specific offset.
Please note, that out of range offsets will be adjusted to available offset end. For example, if offset end is at 10 and offset shift request is of 15, then, offset at 10 will actually be selected.

For example, to reset offsets of a consumer group to the latest offset:

  > bin/kafka-consumer-groups.sh --bootstrap-server localhost:9092 --reset-offsets --group consumergroup1 --topic topic1 --to-latest

  TOPIC                          PARTITION  NEW-OFFSET
  topic1                         0          0

If you are using the old high-level consumer and storing the group metadata in ZooKeeper (i.e. offsets.storage=zookeeper), pass --zookeeper instead of --bootstrap-server:

  > bin/kafka-consumer-groups.sh --zookeeper localhost:2181 --list

Expanding your cluster

Adding servers to a Kafka cluster is easy, just assign them a unique broker id and start up Kafka on your new servers. However these new servers will not automatically be assigned any data partitions, so unless partitions are moved to them they won't be doing any work until new topics are created. So usually when you add machines to your cluster you will want to migrate some existing data to these machines.

The process of migrating data is manually initiated but fully automated. Under the covers what happens is that Kafka will add the new server as a follower of the partition it is migrating and allow it to fully replicate the existing data in that partition. When the new server has fully replicated the contents of this partition and joined the in-sync replica one of the existing replicas will delete their partition's data.

The partition reassignment tool can be used to move partitions across brokers. An ideal partition distribution would ensure even data load and partition sizes across all brokers. The partition reassignment tool does not have the capability to automatically study the data distribution in a Kafka cluster and move partitions around to attain an even load distribution. As such, the admin has to figure out which topics or partitions should be moved around.

The partition reassignment tool can run in 3 mutually exclusive modes:

  • --generate: In this mode, given a list of topics and a list of brokers, the tool generates a candidate reassignment to move all partitions of the specified topics to the new brokers. This option merely provides a convenient way to generate a partition reassignment plan given a list of topics and target brokers.
  • --execute: In this mode, the tool kicks off the reassignment of partitions based on the user provided reassignment plan. (using the --reassignment-json-file option). This can either be a custom reassignment plan hand crafted by the admin or provided by using the --generate option
  • --verify: In this mode, the tool verifies the status of the reassignment for all partitions listed during the last --execute. The status can be either of successfully completed, failed or in progress
Automatically migrating data to new machines
The partition reassignment tool can be used to move some topics off of the current set of brokers to the newly added brokers. This is typically useful while expanding an existing cluster since it is easier to move entire topics to the new set of brokers, than moving one partition at a time. When used to do this, the user should provide a list of topics that should be moved to the new set of brokers and a target list of new brokers. The tool then evenly distributes all partitions for the given list of topics across the new set of brokers. During this move, the replication factor of the topic is kept constant. Effectively the replicas for all partitions for the input list of topics are moved from the old set of brokers to the newly added brokers.

For instance, the following example will move all partitions for topics foo1,foo2 to the new set of brokers 5,6. At the end of this move, all partitions for topics foo1 and foo2 will only exist on brokers 5,6.

Since the tool accepts the input list of topics as a json file, you first need to identify the topics you want to move and create the json file as follows:

  > cat topics-to-move.json
  {"topics": [{"topic": "foo1"},
              {"topic": "foo2"}],
Once the json file is ready, use the partition reassignment tool to generate a candidate assignment:
  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --topics-to-move-json-file topics-to-move.json --broker-list "5,6" --generate
  Current partition replica assignment


  Proposed partition reassignment configuration


The tool generates a candidate assignment that will move all partitions from topics foo1,foo2 to brokers 5,6. Note, however, that at this point, the partition movement has not started, it merely tells you the current assignment and the proposed new assignment. The current assignment should be saved in case you want to rollback to it. The new assignment should be saved in a json file (e.g. expand-cluster-reassignment.json) to be input to the tool with the --execute option as follows:

  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --reassignment-json-file expand-cluster-reassignment.json --execute
  Current partition replica assignment


  Save this to use as the --reassignment-json-file option during rollback
  Successfully started partition reassignments for foo1-0,foo1-1,foo1-2,foo2-0,foo2-1,foo2-2

Finally, the --verify option can be used with the tool to check the status of the partition reassignment. Note that the same expand-cluster-reassignment.json (used with the --execute option) should be used with the --verify option:

  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --reassignment-json-file expand-cluster-reassignment.json --verify
  Status of partition reassignment:
  Reassignment of partition [foo1,0] is completed
  Reassignment of partition [foo1,1] is still in progress
  Reassignment of partition [foo1,2] is still in progress
  Reassignment of partition [foo2,0] is completed
  Reassignment of partition [foo2,1] is completed
  Reassignment of partition [foo2,2] is completed
Custom partition assignment and migration
The partition reassignment tool can also be used to selectively move replicas of a partition to a specific set of brokers. When used in this manner, it is assumed that the user knows the reassignment plan and does not require the tool to generate a candidate reassignment, effectively skipping the --generate step and moving straight to the --execute step

For instance, the following example moves partition 0 of topic foo1 to brokers 5,6 and partition 1 of topic foo2 to brokers 2,3:

The first step is to hand craft the custom reassignment plan in a json file:

  > cat custom-reassignment.json
Then, use the json file with the --execute option to start the reassignment process:
  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --reassignment-json-file custom-reassignment.json --execute
  Current partition replica assignment


  Save this to use as the --reassignment-json-file option during rollback
  Successfully started partition reassignments for foo1-0,foo2-1

The --verify option can be used with the tool to check the status of the partition reassignment. Note that the same custom-reassignment.json (used with the --execute option) should be used with the --verify option:

  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --reassignment-json-file custom-reassignment.json --verify
  Status of partition reassignment:
  Reassignment of partition [foo1,0] is completed
  Reassignment of partition [foo2,1] is completed

Decommissioning brokers

The partition reassignment tool does not have the ability to automatically generate a reassignment plan for decommissioning brokers yet. As such, the admin has to come up with a reassignment plan to move the replica for all partitions hosted on the broker to be decommissioned, to the rest of the brokers. This can be relatively tedious as the reassignment needs to ensure that all the replicas are not moved from the decommissioned broker to only one other broker. To make this process effortless, we plan to add tooling support for decommissioning brokers in the future.

Increasing replication factor

Increasing the replication factor of an existing partition is easy. Just specify the extra replicas in the custom reassignment json file and use it with the --execute option to increase the replication factor of the specified partitions.

For instance, the following example increases the replication factor of partition 0 of topic foo from 1 to 3. Before increasing the replication factor, the partition's only replica existed on broker 5. As part of increasing the replication factor, we will add more replicas on brokers 6 and 7.

The first step is to hand craft the custom reassignment plan in a json file:

  > cat increase-replication-factor.json
Then, use the json file with the --execute option to start the reassignment process:
  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --reassignment-json-file increase-replication-factor.json --execute
  Current partition replica assignment


  Save this to use as the --reassignment-json-file option during rollback
  Successfully started partition reassignment for foo-0

The --verify option can be used with the tool to check the status of the partition reassignment. Note that the same increase-replication-factor.json (used with the --execute option) should be used with the --verify option:

  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --reassignment-json-file increase-replication-factor.json --verify
  Status of partition reassignment:
  Reassignment of partition [foo,0] is completed
You can also verify the increase in replication factor with the kafka-topics tool:
  > bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic foo --describe
  Topic:foo	PartitionCount:1	ReplicationFactor:3	Configs:
    Topic: foo	Partition: 0	Leader: 5	Replicas: 5,6,7	Isr: 5,6,7

Limiting Bandwidth Usage during Data Migration

Kafka lets you apply a throttle to replication traffic, setting an upper bound on the bandwidth used to move replicas from machine to machine. This is useful when rebalancing a cluster, bootstrapping a new broker or adding or removing brokers, as it limits the impact these data-intensive operations will have on users.

There are two interfaces that can be used to engage a throttle. The simplest, and safest, is to apply a throttle when invoking the kafka-reassign-partitions.sh, but kafka-configs.sh can also be used to view and alter the throttle values directly.

So for example, if you were to execute a rebalance, with the below command, it would move partitions at no more than 50MB/s.
$ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 --execute --reassignment-json-file bigger-cluster.json --throttle 50000000
When you execute this script you will see the throttle engage:
  The inter-broker throttle limit was set to 50000000 B/s
  Successfully started partition reassignment for foo1-0

Should you wish to alter the throttle, during a rebalance, say to increase the throughput so it completes quicker, you can do this by re-running the execute command with the --additional option passing the same reassignment-json-file:

$ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092  --additional --execute --reassignment-json-file bigger-cluster.json --throttle 700000000
  The inter-broker throttle limit was set to 700000000 B/s

Once the rebalance completes the administrator can check the status of the rebalance using the --verify option. If the rebalance has completed, the throttle will be removed via the --verify command. It is important that administrators remove the throttle in a timely manner once rebalancing completes by running the command with the --verify option. Failure to do so could cause regular replication traffic to be throttled.

When the --verify option is executed, and the reassignment has completed, the script will confirm that the throttle was removed:

  > bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092  --verify --reassignment-json-file bigger-cluster.json
  Status of partition reassignment:
  Reassignment of partition [my-topic,1] is completed
  Reassignment of partition [my-topic,0] is completed

  Clearing broker-level throttles on brokers 1,2,3
  Clearing topic-level throttles on topic my-topic

The administrator can also validate the assigned configs using the kafka-configs.sh. There are two pairs of throttle configuration used to manage the throttling process. First pair refers to the throttle value itself. This is configured, at a broker level, using the dynamic properties:


Then there is the configuration pair of enumerated sets of throttled replicas:


Which are configured per topic.

All four config values are automatically assigned by kafka-reassign-partitions.sh (discussed below).

To view the throttle limit configuration:

  > bin/kafka-configs.sh --describe --bootstrap-server localhost:9092 --entity-type brokers
  Configs for brokers '2' are leader.replication.throttled.rate=700000000,follower.replication.throttled.rate=700000000
  Configs for brokers '1' are leader.replication.throttled.rate=700000000,follower.replication.throttled.rate=700000000

This shows the throttle applied to both leader and follower side of the replication protocol. By default both sides are assigned the same throttled throughput value.

To view the list of throttled replicas:

  > bin/kafka-configs.sh --describe --bootstrap-server localhost:9092 --entity-type topics
  Configs for topic 'my-topic' are leader.replication.throttled.replicas=1:102,0:101,

Here we see the leader throttle is applied to partition 1 on broker 102 and partition 0 on broker 101. Likewise the follower throttle is applied to partition 1 on broker 101 and partition 0 on broker 102.

By default kafka-reassign-partitions.sh will apply the leader throttle to all replicas that exist before the rebalance, any one of which might be leader. It will apply the follower throttle to all move destinations. So if there is a partition with replicas on brokers 101,102, being reassigned to 102,103, a leader throttle, for that partition, would be applied to 101,102 and a follower throttle would be applied to 103 only.

If required, you can also use the --alter switch on kafka-configs.sh to alter the throttle configurations manually.

Safe usage of throttled replication

Some care should be taken when using throttled replication. In particular:

(1) Throttle Removal:

The throttle should be removed in a timely manner once reassignment completes (by running kafka-reassign-partitions.sh --verify).

(2) Ensuring Progress:

If the throttle is set too low, in comparison to the incoming write rate, it is possible for replication to not make progress. This occurs when:

max(BytesInPerSec) > throttle

Where BytesInPerSec is the metric that monitors the write throughput of producers into each broker.

The administrator can monitor whether replication is making progress, during the rebalance, using the metric:


The lag should constantly decrease during replication. If the metric does not decrease the administrator should increase the throttle throughput as described above.

Setting quotas

Quotas overrides and defaults may be configured at (user, client-id), user or client-id levels as described here. By default, clients receive an unlimited quota. It is possible to set custom quotas for each (user, client-id), user or client-id group.

Configure custom quota for (user=user1, client-id=clientA):

  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-name user1 --entity-type clients --entity-name clientA
  Updated config for entity: user-principal 'user1', client-id 'clientA'.
Configure custom quota for user=user1:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-name user1
  Updated config for entity: user-principal 'user1'.
Configure custom quota for client-id=clientA:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type clients --entity-name clientA
  Updated config for entity: client-id 'clientA'.
It is possible to set default quotas for each (user, client-id), user or client-id group by specifying --entity-default option instead of --entity-name.

Configure default client-id quota for user=userA:

  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-name user1 --entity-type clients --entity-default
  Updated config for entity: user-principal 'user1', default client-id.
Configure default quota for user:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type users --entity-default
  Updated config for entity: default user-principal.
Configure default quota for client-id:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --alter --add-config 'producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200' --entity-type clients --entity-default
  Updated config for entity: default client-id.
Here's how to describe the quota for a given (user, client-id):
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --describe --entity-type users --entity-name user1 --entity-type clients --entity-name clientA
  Configs for user-principal 'user1', client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
Describe quota for a given user:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --describe --entity-type users --entity-name user1
  Configs for user-principal 'user1' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
Describe quota for a given client-id:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --describe --entity-type clients --entity-name clientA
  Configs for client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
If entity name is not specified, all entities of the specified type are described. For example, describe all users:
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --describe --entity-type users
  Configs for user-principal 'user1' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
  Configs for default user-principal are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
Similarly for (user, client):
  > bin/kafka-configs.sh  --bootstrap-server localhost:9092 --describe --entity-type users --entity-type clients
  Configs for user-principal 'user1', default client-id are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200
  Configs for user-principal 'user1', client-id 'clientA' are producer_byte_rate=1024,consumer_byte_rate=2048,request_percentage=200

6.2 Datacenters

Some deployments will need to manage a data pipeline that spans multiple datacenters. Our recommended approach to this is to deploy a local Kafka cluster in each datacenter, with application instances in each datacenter interacting only with their local cluster and mirroring data between clusters (see the documentation on Geo-Replication for how to do this).

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

For applications that need a global view of all data you can use mirroring to provide clusters which have aggregate data mirrored from the local clusters in all datacenters. These aggregate clusters are used for reads by applications that require the full data set.

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 obviously this will add whatever latency is required to get the cluster.

Kafka naturally batches data in both the producer and consumer so it can achieve high-throughput even over a high-latency connection. To allow this though it may be necessary to increase the TCP socket buffer sizes for the producer, consumer, and broker using the socket.send.buffer.bytes and socket.receive.buffer.bytes configurations. The appropriate way to set this is documented here.

It is generally not advisable to run a single Kafka cluster that spans multiple datacenters over a high-latency link. This will incur very high replication latency both for Kafka writes and ZooKeeper writes, and neither Kafka nor ZooKeeper will remain available in all locations if the network between locations is unavailable.

6.3 Geo-Replication (Cross-Cluster Data Mirroring)

Geo-Replication Overview

Kafka administrators can define data flows that cross the boundaries of individual Kafka clusters, data centers, or geo-regions. Such event streaming setups are often needed for organizational, technical, or legal requirements. Common scenarios include:

  • Geo-replication
  • Disaster recovery
  • Feeding edge clusters into a central, aggregate cluster
  • Physical isolation of clusters (such as production vs. testing)
  • Cloud migration or hybrid cloud deployments
  • Legal and compliance requirements

Administrators can set up such inter-cluster data flows with Kafka's MirrorMaker (version 2), a tool to replicate data between different Kafka environments in a streaming manner. MirrorMaker is built on top of the Kafka Connect framework and supports features such as:

  • Replicates topics (data plus configurations)
  • Replicates consumer groups including offsets to migrate applications between clusters
  • Replicates ACLs
  • Preserves partitioning
  • Automatically detects new topics and partitions
  • Provides a wide range of metrics, such as end-to-end replication latency across multiple data centers/clusters
  • Fault-tolerant and horizontally scalable operations

Note: Geo-replication with MirrorMaker replicates data across Kafka clusters. This inter-cluster replication is different from Kafka's intra-cluster replication, which replicates data within the same Kafka cluster.

What Are Replication Flows

With MirrorMaker, Kafka administrators can replicate topics, topic configurations, consumer groups and their offsets, and ACLs from one or more source Kafka clusters to one or more target Kafka clusters, i.e., across cluster environments. In a nutshell, MirrorMaker uses Connectors to consume from source clusters and produce to target clusters.

These directional flows from source to target clusters are called replication flows. They are defined with the format {source_cluster}->{target_cluster} in the MirrorMaker configuration file as described later. Administrators can create complex replication topologies based on these flows.

Here are some example patterns:

  • Active/Active high availability deployments: A->B, B->A
  • Active/Passive or Active/Standby high availability deployments: A->B
  • Aggregation (e.g., from many clusters to one): A->K, B->K, C->K
  • Fan-out (e.g., from one to many clusters): K->A, K->B, K->C
  • Forwarding: A->B, B->C, C->D

By default, a flow replicates all topics and consumer groups. However, each replication flow can be configured independently. For instance, you can define that only specific topics or consumer groups are replicated from the source cluster to the target cluster.

Here is a first example on how to configure data replication from a primary cluster to a secondary cluster (an active/passive setup):

# Basic settings
clusters = primary, secondary
primary.bootstrap.servers = broker3-primary:9092
secondary.bootstrap.servers = broker5-secondary:9092

# Define replication flows
primary->secondary.enabled = true
primary->secondary.topics = foobar-topic, quux-.*

Configuring Geo-Replication

The following sections describe how to configure and run a dedicated MirrorMaker cluster. If you want to run MirrorMaker within an existing Kafka Connect cluster or other supported deployment setups, please refer to KIP-382: MirrorMaker 2.0 and be aware that the names of configuration settings may vary between deployment modes.

Beyond what's covered in the following sections, further examples and information on configuration settings are available at:

Configuration File Syntax

The MirrorMaker configuration file is typically named connect-mirror-maker.properties. You can configure a variety of components in this file:

  • MirrorMaker settings: global settings including cluster definitions (aliases), plus custom settings per replication flow
  • Kafka Connect and connector settings
  • Kafka producer, consumer, and admin client settings

Example: Define MirrorMaker settings (explained in more detail later).

# Global settings
clusters = us-west, us-east   # defines cluster aliases
us-west.bootstrap.servers = broker3-west:9092
us-east.bootstrap.servers = broker5-east:9092

topics = .*   # all topics to be replicated by default

# Specific replication flow settings (here: flow from us-west to us-east)
us-west->us-east.enabled = true
us-west->us.east.topics = foo.*, bar.*  # override the default above

MirrorMaker is based on the Kafka Connect framework. Any Kafka Connect, source connector, and sink connector settings as described in the documentation chapter on Kafka Connect can be used directly in the MirrorMaker configuration, without having to change or prefix the name of the configuration setting.

Example: Define custom Kafka Connect settings to be used by MirrorMaker.

# Setting Kafka Connect defaults for MirrorMaker
tasks.max = 5

Most of the default Kafka Connect settings work well for MirrorMaker out-of-the-box, with the exception of tasks.max. In order to evenly distribute the workload across more than one MirrorMaker process, it is recommended to set tasks.max to at least 2 (preferably higher) depending on the available hardware resources and the total number of topic-partitions to be replicated.

You can further customize MirrorMaker's Kafka Connect settings per source or target cluster (more precisely, you can specify Kafka Connect worker-level configuration settings "per connector"). Use the format of {cluster}.{config_name} in the MirrorMaker configuration file.

Example: Define custom connector settings for the us-west cluster.

# us-west custom settings
us-west.offset.storage.topic = my-mirrormaker-offsets

MirrorMaker internally uses the Kafka producer, consumer, and admin clients. Custom settings for these clients are often needed. To override the defaults, use the following format in the MirrorMaker configuration file:

  • {source}.consumer.{consumer_config_name}
  • {target}.producer.{producer_config_name}
  • {source_or_target}.admin.{admin_config_name}

Example: Define custom producer, consumer, admin client settings.

# us-west cluster (from which to consume)
us-west.consumer.isolation.level = read_committed
us-west.admin.bootstrap.servers = broker57-primary:9092

# us-east cluster (to which to produce)
us-east.producer.compression.type = gzip
us-east.producer.buffer.memory = 32768
us-east.admin.bootstrap.servers = broker8-secondary:9092
Creating and Enabling Replication Flows

To define a replication flow, you must first define the respective source and target Kafka clusters in the MirrorMaker configuration file.

  • clusters (required): comma-separated list of Kafka cluster "aliases"
  • {clusterAlias}.bootstrap.servers (required): connection information for the specific cluster; comma-separated list of "bootstrap" Kafka brokers

Example: Define two cluster aliases primary and secondary, including their connection information.

clusters = primary, secondary
primary.bootstrap.servers = broker10-primary:9092,broker-11-primary:9092
secondary.bootstrap.servers = broker5-secondary:9092,broker6-secondary:9092

Secondly, you must explicitly enable individual replication flows with {source}->{target}.enabled = true as needed. Remember that flows are directional: if you need two-way (bidirectional) replication, you must enable flows in both directions.

# Enable replication from primary to secondary
primary->secondary.enabled = true

By default, a replication flow will replicate all but a few special topics and consumer groups from the source cluster to the target cluster, and automatically detect any newly created topics and groups. The names of replicated topics in the target cluster will be prefixed with the name of the source cluster (see section further below). For example, the topic foo in the source cluster us-west would be replicated to a topic named us-west.foo in the target cluster us-east.

The subsequent sections explain how to customize this basic setup according to your needs.

Configuring Replication Flows

The configuration of a replication flow is a combination of top-level default settings (e.g., topics), on top of which flow-specific settings, if any, are applied (e.g., us-west->us-east.topics). To change the top-level defaults, add the respective top-level setting to the MirrorMaker configuration file. To override the defaults for a specific replication flow only, use the syntax format {source}->{target}.{config.name}.

The most important settings are:

  • topics: list of topics or a regular expression that defines which topics in the source cluster to replicate (default: topics = .*)
  • topics.exclude: list of topics or a regular expression to subsequently exclude topics that were matched by the topics setting (default: topics.exclude = .*[\-\.]internal, .*\.replica, __.*)
  • groups: list of topics or regular expression that defines which consumer groups in the source cluster to replicate (default: groups = .*)
  • groups.exclude: list of topics or a regular expression to subsequently exclude consumer groups that were matched by the groups setting (default: groups.exclude = console-consumer-.*, connect-.*, __.*)
  • {source}->{target}.enable: set to true to enable the replication flow (default: false)


# Custom top-level defaults that apply to all replication flows
topics = .*
groups = consumer-group1, consumer-group2

# Don't forget to enable a flow!
us-west->us-east.enabled = true

# Custom settings for specific replication flows
us-west->us-east.topics = foo.*
us-west->us-east.groups = bar.*
us-west->us-east.emit.heartbeats = false

Additional configuration settings are supported, some of which are listed below. In most cases, you can leave these settings at their default values. See MirrorMakerConfig and MirrorConnectorConfig for further details.

  • refresh.topics.enabled: whether to check for new topics in the source cluster periodically (default: true)
  • refresh.topics.interval.seconds: frequency of checking for new topics in the source cluster; lower values than the default may lead to performance degradation (default: 600, every ten minutes)
  • refresh.groups.enabled: whether to check for new consumer groups in the source cluster periodically (default: true)
  • refresh.groups.interval.seconds: frequency of checking for new consumer groups in the source cluster; lower values than the default may lead to performance degradation (default: 600, every ten minutes)
  • sync.topic.configs.enabled: whether to replicate topic configurations from the source cluster (default: true)
  • sync.topic.acls.enabled: whether to sync ACLs from the source cluster (default: true)
  • emit.heartbeats.enabled: whether to emit heartbeats periodically (default: true)
  • emit.heartbeats.interval.seconds: frequency at which heartbeats are emitted (default: 1, every one seconds)
  • heartbeats.topic.replication.factor: replication factor of MirrorMaker's internal heartbeat topics (default: 3)
  • emit.checkpoints.enabled: whether to emit MirrorMaker's consumer offsets periodically (default: true)
  • emit.checkpoints.interval.seconds: frequency at which checkpoints are emitted (default: 60, every minute)
  • checkpoints.topic.replication.factor: replication factor of MirrorMaker's internal checkpoints topics (default: 3)
  • sync.group.offsets.enabled: whether to periodically write the translated offsets of replicated consumer groups (in the source cluster) to __consumer_offsets topic in target cluster, as long as no active consumers in that group are connected to the target cluster (default: false)
  • sync.group.offsets.interval.seconds: frequency at which consumer group offsets are synced (default: 60, every minute)
  • offset-syncs.topic.replication.factor: replication factor of MirrorMaker's internal offset-sync topics (default: 3)
Securing Replication Flows

MirrorMaker supports the same security settings as Kafka Connect, so please refer to the linked section for further information.

Example: Encrypt communication between MirrorMaker and the us-east cluster.

Custom Naming of Replicated Topics in Target Clusters

Replicated topics in a target cluster—sometimes called remote topics—are renamed according to a replication policy. MirrorMaker uses this policy to ensure that events (aka records, messages) from different clusters are not written to the same topic-partition. By default as per DefaultReplicationPolicy, the names of replicated topics in the target clusters have the format {source}.{source_topic_name}:

us-west         us-east
=========       =================
foo-topic  -->  us-west.foo-topic

You can customize the separator (default: .) with the replication.policy.separator setting:

# Defining a custom separator
us-west->us-east.replication.policy.separator = _

If you need further control over how replicated topics are named, you can implement a custom ReplicationPolicy and override replication.policy.class (default is DefaultReplicationPolicy) in the MirrorMaker configuration.

Preventing Configuration Conflicts

MirrorMaker processes share configuration via their target Kafka clusters. This behavior may cause conflicts when configurations differ among MirrorMaker processes that operate against the same target cluster.

For example, the following two MirrorMaker processes would be racy:

# Configuration of process 1
A->B.enabled = true
A->B.topics = foo

# Configuration of process 2
A->B.enabled = true
A->B.topics = bar

In this case, the two processes will share configuration via cluster B, which causes a conflict. Depending on which of the two processes is the elected "leader", the result will be that either the topic foo or the topic bar is replicated, but not both.

It is therefore important to keep the MirrorMaker configration consistent across replication flows to the same target cluster. This can be achieved, for example, through automation tooling or by using a single, shared MirrorMaker configuration file for your entire organization.

Best Practice: Consume from Remote, Produce to Local

To minimize latency ("producer lag"), it is recommended to locate MirrorMaker processes as close as possible to their target clusters, i.e., the clusters that it produces data to. That's because Kafka producers typically struggle more with unreliable or high-latency network connections than Kafka consumers.

First DC          Second DC
==========        =========================
primary --------- MirrorMaker --> secondary
(remote)                           (local)

To run such a "consume from remote, produce to local" setup, run the MirrorMaker processes close to and preferably in the same location as the target clusters, and explicitly set these "local" clusters in the --clusters command line parameter (blank-separated list of cluster aliases):

# Run in secondary's data center, reading from the remote `primary` cluster
$ ./bin/connect-mirror-maker.sh connect-mirror-maker.properties --clusters secondary
The --clusters secondary tells the MirrorMaker process that the given cluster(s) are nearby, and prevents it from replicating data or sending configuration to clusters at other, remote locations.
Example: Active/Passive High Availability Deployment

The following example shows the basic settings to replicate topics from a primary to a secondary Kafka environment, but not from the secondary back to the primary. Please be aware that most production setups will need further configuration, such as security settings.

# Unidirectional flow (one-way) from primary to secondary cluster
primary.bootstrap.servers = broker1-primary:9092
secondary.bootstrap.servers = broker2-secondary:9092

primary->secondary.enabled = true
secondary->primary.enabled = false

primary->secondary.topics = foo.*  # only replicate some topics
Example: Active/Active High Availability Deployment

The following example shows the basic settings to replicate topics between two clusters in both ways. Please be aware that most production setups will need further configuration, such as security settings.

# Bidirectional flow (two-way) between us-west and us-east clusters
clusters = us-west, us-east
us-west.bootstrap.servers = broker1-west:9092,broker2-west:9092
Us-east.bootstrap.servers = broker3-east:9092,broker4-east:9092

us-west->us-east.enabled = true
us-east->us-west.enabled = true

Note on preventing replication "loops" (where topics will be originally replicated from A to B, then the replicated topics will be replicated yet again from B to A, and so forth): As long as you define the above flows in the same MirrorMaker configuration file, you do not need to explicitly add topics.exclude settings to prevent replication loops between the two clusters.

Example: Multi-Cluster Geo-Replication

Let's put all the information from the previous sections together in a larger example. Imagine there are three data centers (west, east, north), with two Kafka clusters in each data center (e.g., west-1, west-2). The example in this section shows how to configure MirrorMaker (1) for Active/Active replication within each data center, as well as (2) for Cross Data Center Replication (XDCR).

First, define the source and target clusters along with their replication flows in the configuration:

# Basic settings
clusters: west-1, west-2, east-1, east-2, north-1, north-2
west-1.bootstrap.servers = ...
west-2.bootstrap.servers = ...
east-1.bootstrap.servers = ...
east-2.bootstrap.servers = ...
north-1.bootstrap.servers = ...
north-2.bootstrap.servers = ...

# Replication flows for Active/Active in West DC
west-1->west-2.enabled = true
west-2->west-1.enabled = true

# Replication flows for Active/Active in East DC
east-1->east-2.enabled = true
east-2->east-1.enabled = true

# Replication flows for Active/Active in North DC
north-1->north-2.enabled = true
north-2->north-1.enabled = true

# Replication flows for XDCR via west-1, east-1, north-1
west-1->east-1.enabled  = true
west-1->north-1.enabled = true
east-1->west-1.enabled  = true
east-1->north-1.enabled = true
north-1->west-1.enabled = true
north-1->east-1.enabled = true

Then, in each data center, launch one or more MirrorMaker as follows:

# In West DC:
$ ./bin/connect-mirror-maker.sh connect-mirror-maker.properties --clusters west-1 west-2

# In East DC:
$ ./bin/connect-mirror-maker.sh connect-mirror-maker.properties --clusters east-1 east-2

# In North DC:
$ ./bin/connect-mirror-maker.sh connect-mirror-maker.properties --clusters north-1 north-2

With this configuration, records produced to any cluster will be replicated within the data center, as well as across to other data centers. By providing the --clusters parameter, we ensure that each MirrorMaker process produces data to nearby clusters only.

Note: The --clusters parameter is, technically, not required here. MirrorMaker will work fine without it. However, throughput may suffer from "producer lag" between data centers, and you may incur unnecessary data transfer costs.

Starting Geo-Replication

You can run as few or as many MirrorMaker processes (think: nodes, servers) as needed. Because MirrorMaker is based on Kafka Connect, MirrorMaker processes that are configured to replicate the same Kafka clusters run in a distributed setup: They will find each other, share configuration (see section below), load balance their work, and so on. If, for example, you want to increase the throughput of replication flows, one option is to run additional MirrorMaker processes in parallel.

To start a MirrorMaker process, run the command:

$ ./bin/connect-mirror-maker.sh connect-mirror-maker.properties

After startup, it may take a few minutes until a MirrorMaker process first begins to replicate data.

Optionally, as described previously, you can set the parameter --clusters to ensure that the MirrorMaker process produces data to nearby clusters only.

# Note: The cluster alias us-west must be defined in the configuration file
$ ./bin/connect-mirror-maker.sh connect-mirror-maker.properties \
            --clusters us-west

Note when testing replication of consumer groups: By default, MirrorMaker does not replicate consumer groups created by the kafka-console-consumer.sh tool, which you might use to test your MirrorMaker setup on the command line. If you do want to replicate these consumer groups as well, set the groups.exclude configuration accordingly (default: groups.exclude = console-consumer-.*, connect-.*, __.*). Remember to update the configuration again once you completed your testing.

Stopping Geo-Replication

You can stop a running MirrorMaker process by sending a SIGTERM signal with the command:

$ kill <MirrorMaker pid>

Applying Configuration Changes

To make configuration changes take effect, the MirrorMaker process(es) must be restarted.

Monitoring Geo-Replication

It is recommended to monitor MirrorMaker processes to ensure all defined replication flows are up and running correctly. MirrorMaker is built on the Connect framework and inherits all of Connect's metrics, such source-record-poll-rate. In addition, MirrorMaker produces its own metrics under the kafka.connect.mirror metric group. Metrics are tagged with the following properties:

  • source: alias of source cluster (e.g., primary)
  • target: alias of target cluster (e.g., secondary)
  • topic: replicated topic on target cluster
  • partition: partition being replicated

Metrics are tracked for each replicated topic. The source cluster can be inferred from the topic name. For example, replicating topic1 from primary->secondary will yield metrics like:

  • target=secondary
  • topic=primary.topic1
  • partition=1

The following metrics are emitted:

# MBean: kafka.connect.mirror:type=MirrorSourceConnector,target=([-.w]+),topic=([-.w]+),partition=([0-9]+)

record-count            # number of records replicated source -> target
record-age-ms           # age of records when they are replicated
replication-latency-ms  # time it takes records to propagate source->target
byte-rate               # average number of bytes/sec in replicated records

# MBean: kafka.connect.mirror:type=MirrorCheckpointConnector,source=([-.w]+),target=([-.w]+)

checkpoint-latency-ms   # time it takes to replicate consumer offsets

These metrics do not differentiate between created-at and log-append timestamps.

6.4 Multi-Tenancy

Multi-Tenancy Overview

As a highly scalable event streaming platform, Kafka is used by many users as their central nervous system, connecting in real-time a wide range of different systems and applications from various teams and lines of businesses. Such multi-tenant cluster environments command proper control and management to ensure the peaceful coexistence of these different needs. This section highlights features and best practices to set up such shared environments, which should help you operate clusters that meet SLAs/OLAs and that minimize potential collateral damage caused by "noisy neighbors".

Multi-tenancy is a many-sided subject, including but not limited to:

  • Creating user spaces for tenants (sometimes called namespaces)
  • Configuring topics with data retention policies and more
  • Securing topics and clusters with encryption, authentication, and authorization
  • Isolating tenants with quotas and rate limits
  • Monitoring and metering
  • Inter-cluster data sharing (cf. geo-replication)

Creating User Spaces (Namespaces) For Tenants With Topic Naming

Kafka administrators operating a multi-tenant cluster typically need to define user spaces for each tenant. For the purpose of this section, "user spaces" are a collection of topics, which are grouped together under the management of a single entity or user.

In Kafka, the main unit of data is the topic. Users can create and name each topic. They can also delete them, but it is not possible to rename a topic directly. Instead, to rename a topic, the user must create a new topic, move the messages from the original topic to the new, and then delete the original. With this in mind, it is recommended to define logical spaces, based on an hierarchical topic naming structure. This setup can then be combined with security features, such as prefixed ACLs, to isolate different spaces and tenants, while also minimizing the administrative overhead for securing the data in the cluster.

These logical user spaces can be grouped in different ways, and the concrete choice depends on how your organization prefers to use your Kafka clusters. The most common groupings are as follows.

By team or organizational unit: Here, the team is the main aggregator. In an organization where teams are the main user of the Kafka infrastructure, this might be the best grouping.

Example topic naming structure:

  • <organization>.<team>.<dataset>.<event-name>
    (e.g., "acme.infosec.telemetry.logins")

By project or product: Here, a team manages more than one project. Their credentials will be different for each project, so all the controls and settings will always be project related.

Example topic naming structure:

  • <project>.<product>.<event-name>
    (e.g., "mobility.payments.suspicious")

Certain information should normally not be put in a topic name, such as information that is likely to change over time (e.g., the name of the intended consumer) or that is a technical detail or metadata that is available elsewhere (e.g., the topic's partition count and other configuration settings).

To enforce a topic naming structure, several options are available:

  • Use prefix ACLs (cf. KIP-290) to enforce a common prefix for topic names. For example, team A may only be permitted to create topics whose names start with payments.teamA..
  • Define a custom CreateTopicPolicy (cf. KIP-108 and the setting create.topic.policy.class.name) to enforce strict naming patterns. These policies provide the most flexibility and can cover complex patterns and rules to match an organization's needs.
  • Disable topic creation for normal users by denying it with an ACL, and then rely on an external process to create topics on behalf of users (e.g., scripting or your favorite automation toolkit).
  • It may also be useful to disable the Kafka feature to auto-create topics on demand by setting auto.create.topics.enable=false in the broker configuration. Note that you should not rely solely on this option.

Configuring Topics: Data Retention And More

Kafka's configuration is very flexible due to its fine granularity, and it supports a plethora of per-topic configuration settings to help administrators set up multi-tenant clusters. For example, administrators often need to define data retention policies to control how much and/or for how long data will be stored in a topic, with settings such as retention.bytes (size) and retention.ms (time). This limits storage consumption within the cluster, and helps complying with legal requirements such as GDPR.

Securing Clusters and Topics: Authentication, Authorization, Encryption

Because the documentation has a dedicated chapter on security that applies to any Kafka deployment, this section focuses on additional considerations for multi-tenant environments.

Security settings for Kafka fall into three main categories, which are similar to how administrators would secure other client-server data systems, like relational databases and traditional messaging systems.

  1. Encryption of data transferred between Kafka brokers and Kafka clients, between brokers, between brokers and ZooKeeper nodes, and between brokers and other, optional tools.
  2. Authentication of connections from Kafka clients and applications to Kafka brokers, as well as connections from Kafka brokers to ZooKeeper nodes.
  3. Authorization of client operations such as creating, deleting, and altering the configuration of topics; writing events to or reading events from a topic; creating and deleting ACLs. Administrators can also define custom policies to put in place additional restrictions, such as a CreateTopicPolicy and AlterConfigPolicy (see KIP-108 and the settings create.topic.policy.class.name, alter.config.policy.class.name).

When securing a multi-tenant Kafka environment, the most common administrative task is the third category (authorization), i.e., managing the user/client permissions that grant or deny access to certain topics and thus to the data stored by users within a cluster. This task is performed predominantly through the setting of access control lists (ACLs). Here, administrators of multi-tenant environments in particular benefit from putting a hierarchical topic naming structure in place as described in a previous section, because they can conveniently control access to topics through prefixed ACLs (--resource-pattern-type Prefixed). This significantly minimizes the administrative overhead of securing topics in multi-tenant environments: administrators can make their own trade-offs between higher developer convenience (more lenient permissions, using fewer and broader ACLs) vs. tighter security (more stringent permissions, using more and narrower ACLs).

In the following example, user Alice—a new member of ACME corporation's InfoSec team—is granted write permissions to all topics whose names start with "acme.infosec.", such as "acme.infosec.telemetry.logins" and "acme.infosec.syslogs.events".

# Grant permissions to user Alice
$ bin/kafka-acls.sh \
    --bootstrap-server broker1:9092 \
    --add --allow-principal User:Alice \
    --producer \
    --resource-pattern-type prefixed --topic acme.infosec.

You can similarly use this approach to isolate different customers on the same shared cluster.

Isolating Tenants: Quotas, Rate Limiting, Throttling

Multi-tenant clusters should generally be configured with quotas, which protect against users (tenants) eating up too many cluster resources, such as when they attempt to write or read very high volumes of data, or create requests to brokers at an excessively high rate. This may cause network saturation, monopolize broker resources, and impact other clients—all of which you want to avoid in a shared environment.

Client quotas: Kafka supports different types of (per-user principal) client quotas. Because a client's quotas apply irrespective of which topics the client is writing to or reading from, they are a convenient and effective tool to allocate resources in a multi-tenant cluster. Request rate quotas, for example, help to limit a user's impact on broker CPU usage by limiting the time a broker spends on the request handling path for that user, after which throttling kicks in. In many situations, isolating users with request rate quotas has a bigger impact in multi-tenant clusters than setting incoming/outgoing network bandwidth quotas, because excessive broker CPU usage for processing requests reduces the effective bandwidth the broker can serve. Furthermore, administrators can also define quotas on topic operations—such as create, delete, and alter—to prevent Kafka clusters from being overwhelmed by highly concurrent topic operations (see KIP-599 and the quota type controller_mutation_rate).

Server quotas: Kafka also supports different types of broker-side quotas. For example, administrators can set a limit on the rate with which the broker accepts new connections, set the maximum number of connections per broker, or set the maximum number of connections allowed from a specific IP address.

For more information, please refer to the quota overview and how to set quotas.

Monitoring and Metering

Monitoring is a broader subject that is covered elsewhere in the documentation. Administrators of any Kafka environment, but especially multi-tenant ones, should set up monitoring according to these instructions. Kafka supports a wide range of metrics, such as the rate of failed authentication attempts, request latency, consumer lag, total number of consumer groups, metrics on the quotas described in the previous section, and many more.

For example, monitoring can be configured to track the size of topic-partitions (with the JMX metric kafka.log.Log.Size.<TOPIC-NAME>), and thus the total size of data stored in a topic. You can then define alerts when tenants on shared clusters are getting close to using too much storage space.

Multi-Tenancy and Geo-Replication

Kafka lets you share data across different clusters, which may be located in different geographical regions, data centers, and so on. Apart from use cases such as disaster recovery, this functionality is useful when a multi-tenant setup requires inter-cluster data sharing. See the section Geo-Replication (Cross-Cluster Data Mirroring) for more information.

Further considerations

Data contracts: You may need to define data contracts between the producers and the consumers of data in a cluster, using event schemas. This ensures that events written to Kafka can always be read properly again, and prevents malformed or corrupt events being written. The best way to achieve this is to deploy a so-called schema registry alongside the cluster. (Kafka does not include a schema registry, but there are third-party implementations available.) A schema registry manages the event schemas and maps the schemas to topics, so that producers know which topics are accepting which types (schemas) of events, and consumers know how to read and parse events in a topic. Some registry implementations provide further functionality, such as schema evolution, storing a history of all schemas, and schema compatibility settings.

6.5 Kafka Configuration

Important Client Configurations

The most important producer configurations are:
  • acks
  • compression
  • batch size
The most important consumer configuration is the fetch size.

All configurations are documented in the configuration section.

A Production Server Config

Here is an example production server configuration:
  # ZooKeeper
  zookeeper.connect=[list of ZooKeeper servers]

  # Log configuration
  log.dir=[List of directories. Kafka should have its own dedicated disk(s) or SSD(s).]

  # Other configurations
  broker.id=[An integer. Start with 0 and increment by 1 for each new broker.]
  listeners=[list of listeners]
  queued.max.requests=[number of concurrent requests]
Our client configuration varies a fair amount between different use cases.

6.6 Java Version

Java 8, Java 11, and Java 17 are supported. Note that Java 8 support has been deprecated since Apache Kafka 3.0 and will be removed in Apache Kafka 4.0. Java 11 and later versions perform significantly better if TLS is enabled, so they are highly recommended (they also include a number of other performance improvements: G1GC, CRC32C, Compact Strings, Thread-Local Handshakes and more). From a security perspective, we recommend the latest released patch version as older freely available versions have disclosed security vulnerabilities. Typical arguments for running Kafka with OpenJDK-based Java implementations (including Oracle JDK) are:
  -Xmx6g -Xms6g -XX:MetaspaceSize=96m -XX:+UseG1GC
  -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:G1HeapRegionSize=16M
  -XX:MinMetaspaceFreeRatio=50 -XX:MaxMetaspaceFreeRatio=80 -XX:+ExplicitGCInvokesConcurrent
For reference, here are the stats for one of LinkedIn's busiest clusters (at peak) that uses said Java arguments:
  • 60 brokers
  • 50k partitions (replication factor 2)
  • 800k messages/sec in
  • 300 MB/sec inbound, 1 GB/sec+ outbound
All of the brokers in that cluster have a 90% GC pause time of about 21ms with less than 1 young GC per second.

6.7 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 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).


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.

It is unlikely to require much OS-level tuning, but there are three potentially important OS-level configurations:

  • File descriptor limits: Kafka uses file descriptors for log segments and open connections. If a broker hosts many partitions, consider that the broker needs at least (number_of_partitions)*(partition_size/segment_size) to track all log segments in addition to the number of connections the broker makes. We recommend at least 100000 allowed file descriptors for the broker processes as a starting point. Note: The mmap() function adds an extra reference to the file associated with the file descriptor fildes which is not removed by a subsequent close() on that file descriptor. This reference is removed when there are no more mappings to the file.
  • Max socket buffer size: can be increased to enable high-performance data transfer between data centers as described here.
  • Maximum number of memory map areas a process may have (aka vm.max_map_count). See the Linux kernel documentation. You should keep an eye at this OS-level property when considering the maximum number of partitions a broker may have. By default, on a number of Linux systems, the value of vm.max_map_count is somewhere around 65535. Each log segment, allocated per partition, requires a pair of index/timeindex files, and each of these files consumes 1 map area. In other words, each log segment uses 2 map areas. Thus, each partition requires minimum 2 map areas, as long as it hosts a single log segment. That is to say, creating 50000 partitions on a broker will result allocation of 100000 map areas and likely cause broker crash with OutOfMemoryError (Map failed) on a system with default vm.max_map_count. Keep in mind that the number of log segments per partition varies depending on the segment size, load intensity, retention policy and, generally, tends to be more than one.

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. 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 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.

Note that durability in Kafka does not require syncing data to disk, as a failed node will always recover from its replicas.

We recommend using the default flush settings which disable application fsync entirely. This means relying on the background flush done by 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.

The drawback of using application level flush settings is that it is less efficient in its disk usage pattern (it gives the OS less leeway to re-order writes) and it can introduce latency as fsync in most Linux filesystems blocks writes to the file whereas the background flushing does much more granular page-level locking.

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:

  • The I/O scheduler will batch together consecutive small writes into bigger physical writes which improves throughput.
  • The I/O scheduler will attempt to re-sequence writes to minimize movement of the disk head which improves throughput.
  • It automatically uses all the free memory on the machine

Filesystem Selection

Kafka uses regular files on disk, and as such it has no hard dependency on a specific filesystem. The two filesystems which have the most usage, however, are EXT4 and XFS. Historically, EXT4 has had more usage, but recent improvements to the XFS filesystem have shown it to have better performance characteristics for Kafka's workload with no compromise in stability.

Comparison testing was performed on a cluster with significant message loads, using a variety of filesystem creation and mount options. The primary metric in Kafka that was monitored was the "Request Local Time", indicating the amount of time append operations were taking. XFS resulted in much better local times (160ms vs. 250ms+ for the best EXT4 configuration), as well as lower average wait times. The XFS performance also showed less variability in disk performance.

General Filesystem Notes
For any filesystem used for data directories, on Linux systems, the following options are recommended to be used at mount time:
  • noatime: This option disables updating of a file's atime (last access time) attribute when the file is read. This can eliminate a significant number of filesystem writes, especially in the case of bootstrapping consumers. Kafka does not rely on the atime attributes at all, so it is safe to disable this.
XFS Notes
The XFS filesystem has a significant amount of auto-tuning in place, so it does not require any change in the default settings, either at filesystem creation time or at mount. The only tuning parameters worth considering are:
  • largeio: This affects the preferred I/O size reported by the stat call. While this can allow for higher performance on larger disk writes, in practice it had minimal or no effect on performance.
  • nobarrier: For underlying devices that have battery-backed cache, this option can provide a little more performance by disabling periodic write flushes. However, if the underlying device is well-behaved, it will report to the filesystem that it does not require flushes, and this option will have no effect.
EXT4 Notes
EXT4 is a serviceable choice of filesystem for the Kafka data directories, however getting the most performance out of it will require adjusting several mount options. In addition, these options are generally unsafe in a failure scenario, and will result in much more data loss and corruption. For a single broker failure, this is not much of a concern as the disk can be wiped and the replicas rebuilt from the cluster. In a multiple-failure scenario, such as a power outage, this can mean underlying filesystem (and therefore data) corruption that is not easily recoverable. The following options can be adjusted:
  • data=writeback: Ext4 defaults to data=ordered which puts a strong order on some writes. Kafka does not require this ordering as it does very paranoid data recovery on all unflushed log. This setting removes the ordering constraint and seems to significantly reduce latency.
  • Disabling journaling: Journaling is a tradeoff: it makes reboots faster after server crashes but it introduces a great deal of additional locking which adds variance to write performance. Those who don't care about reboot time and want to reduce a major source of write latency spikes can turn off journaling entirely.
  • commit=num_secs: This tunes the frequency with which ext4 commits to its metadata journal. Setting this to a lower value reduces the loss of unflushed data during a crash. Setting this to a higher value will improve throughput.
  • nobh: This setting controls additional ordering guarantees when using data=writeback mode. This should be safe with Kafka as we do not depend on write ordering and improves throughput and latency.
  • delalloc: Delayed allocation means that the filesystem avoid allocating any blocks until the physical write occurs. This allows ext4 to allocate a large extent instead of smaller pages and helps ensure the data is written sequentially. This feature is great for throughput. It does seem to involve some locking in the filesystem which adds a bit of latency variance.

Replace KRaft Controller Disk

When Kafka is configured to use KRaft, the controllers store the cluster metadata in the directory specified in metadata.log.dir -- or the first log directory, if metadata.log.dir is not configured. See the documentation for metadata.log.dir for details.

If the data in the cluster metdata directory is lost either because of hardware failure or the hardware needs to be replaced, care should be taken when provisioning the new controller node. The new controller node should not be formatted and started until the majority of the controllers have all of the committed data. To determine if the majority of the controllers have the committed data, run the kafka-metadata-quorum.sh tool to describe the replication status:

 > bin/kafka-metadata-quorum.sh --bootstrap-server broker_host:port describe --replication
 NodeId  LogEndOffset    Lag     LastFetchTimestamp      LastCaughtUpTimestamp   Status
 1       25806           0       1662500992757           1662500992757           Leader
 ...     ...             ...     ...                     ...                     ...

Check and wait until the Lag is small for a majority of the controllers. If the leader's end offset is not increasing, you can wait until the lag is 0 for a majority; otherwise, you can pick the latest leader end offset and wait until all replicas have reached it. Check and wait until the LastFetchTimestamp and LastCaughtUpTimestamp are close to each other for the majority of the controllers. At this point it is safer to format the controller's metadata log directory. This can be done by running the kafka-storage.sh command.

 > bin/kafka-storage.sh format --cluster-id uuid --config server_properties

It is possible for the bin/kafka-storage.sh format command above to fail with a message like Log directory ... is already formatted. This can happend when combined mode is used and only the metadata log directory was lost but not the others. In that case and only in that case, can you run the kafka-storage.sh format command with the --ignore-formatted option.

Start the KRaft controller after formatting the log directories.

 > /bin/kafka-server-start.sh server_properties

6.8 Monitoring

Kafka uses Yammer Metrics for metrics reporting in the server. The Java clients use Kafka Metrics, a built-in metrics registry that minimizes transitive dependencies pulled into client applications. Both expose metrics via JMX and can be configured to report stats using pluggable stats reporters to hook up to your monitoring system.

All Kafka rate metrics have a corresponding cumulative count metric with suffix -total. For example, records-consumed-rate has a corresponding metric named records-consumed-total.

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

Security Considerations for Remote Monitoring using JMX

Apache Kafka disables remote JMX by default. You can enable remote monitoring using JMX by setting the environment variable JMX_PORT for processes started using the CLI or standard Java system properties to enable remote JMX programmatically. You must enable security when enabling remote JMX in production scenarios to ensure that unauthorized users cannot monitor or control your broker or application as well as the platform on which these are running. Note that authentication is disabled for JMX by default in Kafka and security configs must be overridden for production deployments by setting the environment variable KAFKA_JMX_OPTS for processes started using the CLI or by setting appropriate Java system properties. See Monitoring and Management Using JMX Technology for details on securing JMX.

We do graphing and alerting on the following metrics:
Description Mbean name Normal value
Message in rate kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec,topic=([-.\w]+) Incoming message rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Byte in rate from clients kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec,topic=([-.\w]+) Byte in (from the clients) rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Byte in rate from other brokers kafka.server:type=BrokerTopicMetrics,name=ReplicationBytesInPerSec,topic=([-.\w]+) Byte in (from the other brokers) rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Controller Request rate from Broker kafka.controller:type=ControllerChannelManager,name=RequestRateAndQueueTimeMs,brokerId=([0-9]+) The rate (requests per second) at which the ControllerChannelManager takes requests from the queue of the given broker. And the time it takes for a request to stay in this queue before it is taken from the queue.
Controller Event queue size kafka.controller:type=ControllerEventManager,name=EventQueueSize Size of the ControllerEventManager's queue.
Controller Event queue time kafka.controller:type=ControllerEventManager,name=EventQueueTimeMs Time that takes for any event (except the Idle event) to wait in the ControllerEventManager's queue before being processed
Request rate kafka.network:type=RequestMetrics,name=RequestsPerSec,request={Produce|FetchConsumer|FetchFollower},version=([0-9]+)
Error rate kafka.network:type=RequestMetrics,name=ErrorsPerSec,request=([-.\w]+),error=([-.\w]+) Number of errors in responses counted per-request-type, per-error-code. If a response contains multiple errors, all are counted. error=NONE indicates successful responses.
Produce request rate kafka.server:type=BrokerTopicMetrics,name=TotalProduceRequestsPerSec,topic=([-.\w]+) Produce request rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Fetch request rate kafka.server:type=BrokerTopicMetrics,name=TotalFetchRequestsPerSec,topic=([-.\w]+) Fetch request (from clients or followers) rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Failed produce request rate kafka.server:type=BrokerTopicMetrics,name=FailedProduceRequestsPerSec,topic=([-.\w]+) Failed Produce request rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Failed fetch request rate kafka.server:type=BrokerTopicMetrics,name=FailedFetchRequestsPerSec,topic=([-.\w]+) Failed Fetch request (from clients or followers) rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Request size in bytes kafka.network:type=RequestMetrics,name=RequestBytes,request=([-.\w]+) Size of requests for each request type.
Temporary memory size in bytes kafka.network:type=RequestMetrics,name=TemporaryMemoryBytes,request={Produce|Fetch} Temporary memory used for message format conversions and decompression.
Message conversion time kafka.network:type=RequestMetrics,name=MessageConversionsTimeMs,request={Produce|Fetch} Time in milliseconds spent on message format conversions.
Message conversion rate kafka.server:type=BrokerTopicMetrics,name={Produce|Fetch}MessageConversionsPerSec,topic=([-.\w]+) Message format conversion rate, for Produce or Fetch requests, per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Request Queue Size kafka.network:type=RequestChannel,name=RequestQueueSize Size of the request queue.
Byte out rate to clients kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec,topic=([-.\w]+) Byte out (to the clients) rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Byte out rate to other brokers kafka.server:type=BrokerTopicMetrics,name=ReplicationBytesOutPerSec,topic=([-.\w]+) Byte out (to the other brokers) rate per topic. Omitting 'topic=(...)' will yield the all-topic rate.
Rejected byte rate kafka.server:type=BrokerTopicMetrics,name=BytesRejectedPerSec,topic=([-.\w]+) Rejected byte rate per topic, due to the record batch size being greater than max.message.bytes configuration. Omitting 'topic=(...)' will yield the all-topic rate.
Message validation failure rate due to no key specified for compacted topic kafka.server:type=BrokerTopicMetrics,name=NoKeyCompactedTopicRecordsPerSec 0
Message validation failure rate due to invalid magic number kafka.server:type=BrokerTopicMetrics,name=InvalidMagicNumberRecordsPerSec 0
Message validation failure rate due to incorrect crc checksum kafka.server:type=BrokerTopicMetrics,name=InvalidMessageCrcRecordsPerSec 0
Message validation failure rate due to non-continuous offset or sequence number in batch kafka.server:type=BrokerTopicMetrics,name=InvalidOffsetOrSequenceRecordsPerSec 0
Log flush rate and time kafka.log:type=LogFlushStats,name=LogFlushRateAndTimeMs
# of offline log directories kafka.log:type=LogManager,name=OfflineLogDirectoryCount 0
Leader election rate kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs non-zero when there are broker failures
Unclean leader election rate kafka.controller:type=ControllerStats,name=UncleanLeaderElectionsPerSec 0
Is controller active on broker kafka.controller:type=KafkaController,name=ActiveControllerCount only one broker in the cluster should have 1
Pending topic deletes kafka.controller:type=KafkaController,name=TopicsToDeleteCount
Pending replica deletes kafka.controller:type=KafkaController,name=ReplicasToDeleteCount
Ineligible pending topic deletes kafka.controller:type=KafkaController,name=TopicsIneligibleToDeleteCount
Ineligible pending replica deletes kafka.controller:type=KafkaController,name=ReplicasIneligibleToDeleteCount
# of under replicated partitions (|ISR| < |all replicas|) kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions 0
# of under minIsr partitions (|ISR| < min.insync.replicas) kafka.server:type=ReplicaManager,name=UnderMinIsrPartitionCount 0
# of at minIsr partitions (|ISR| = min.insync.replicas) kafka.server:type=ReplicaManager,name=AtMinIsrPartitionCount 0
Producer Id counts kafka.server:type=ReplicaManager,name=ProducerIdCount Count of all producer ids created by transactional and idempotent producers in each replica on the broker
Partition counts kafka.server:type=ReplicaManager,name=PartitionCount mostly even across brokers
Offline Replica counts kafka.server:type=ReplicaManager,name=OfflineReplicaCount 0
Leader replica counts kafka.server:type=ReplicaManager,name=LeaderCount mostly even across brokers
ISR shrink rate kafka.server:type=ReplicaManager,name=IsrShrinksPerSec 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:type=ReplicaManager,name=IsrExpandsPerSec See above
Failed ISR update rate kafka.server:type=ReplicaManager,name=FailedIsrUpdatesPerSec 0
Max lag in messages btw follower and leader replicas kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica lag should be proportional to the maximum batch size of a produce request.
Lag in messages per follower replica kafka.server:type=FetcherLagMetrics,name=ConsumerLag,clientId=([-.\w]+),topic=([-.\w]+),partition=([0-9]+) lag should be proportional to the maximum batch size of a produce request.
Requests waiting in the producer purgatory kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Produce non-zero if ack=-1 is used
Requests waiting in the fetch purgatory kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Fetch size depends on fetch.wait.max.ms in the consumer
Request total time kafka.network:type=RequestMetrics,name=TotalTimeMs,request={Produce|FetchConsumer|FetchFollower} broken into queue, local, remote and response send time
Time the request waits in the request queue kafka.network:type=RequestMetrics,name=RequestQueueTimeMs,request={Produce|FetchConsumer|FetchFollower}
Time the request is processed at the leader kafka.network:type=RequestMetrics,name=LocalTimeMs,request={Produce|FetchConsumer|FetchFollower}
Time the request waits for the follower kafka.network:type=RequestMetrics,name=RemoteTimeMs,request={Produce|FetchConsumer|FetchFollower} non-zero for produce requests when ack=-1
Time the request waits in the response queue kafka.network:type=RequestMetrics,name=ResponseQueueTimeMs,request={Produce|FetchConsumer|FetchFollower}
Time to send the response kafka.network:type=RequestMetrics,name=ResponseSendTimeMs,request={Produce|FetchConsumer|FetchFollower}
Number of messages the consumer lags behind the producer by. Published by the consumer, not broker. kafka.consumer:type=consumer-fetch-manager-metrics,client-id={client-id} Attribute: records-lag-max
The average fraction of time the network processors are idle kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent between 0 and 1, ideally > 0.3
The number of connections disconnected on a processor due to a client not re-authenticating and then using the connection beyond its expiration time for anything other than re-authentication kafka.server:type=socket-server-metrics,listener=[SASL_PLAINTEXT|SASL_SSL],networkProcessor=<#>,name=expired-connections-killed-count ideally 0 when re-authentication is enabled, implying there are no longer any older, pre-2.2.0 clients connecting to this (listener, processor) combination
The total number of connections disconnected, across all processors, due to a client not re-authenticating and then using the connection beyond its expiration time for anything other than re-authentication kafka.network:type=SocketServer,name=ExpiredConnectionsKilledCount ideally 0 when re-authentication is enabled, implying there are no longer any older, pre-2.2.0 clients connecting to this broker
The average fraction of time the request handler threads are idle kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent between 0 and 1, ideally > 0.3
Bandwidth quota metrics per (user, client-id), user or client-id kafka.server:type={Produce|Fetch},user=([-.\w]+),client-id=([-.\w]+) Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0. byte-rate indicates the data produce/consume rate of the client in bytes/sec. For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.
Request quota metrics per (user, client-id), user or client-id kafka.server:type=Request,user=([-.\w]+),client-id=([-.\w]+) Two attributes. throttle-time indicates the amount of time in ms the client was throttled. Ideally = 0. request-time indicates the percentage of time spent in broker network and I/O threads to process requests from client group. For (user, client-id) quotas, both user and client-id are specified. If per-client-id quota is applied to the client, user is not specified. If per-user quota is applied, client-id is not specified.
Requests exempt from throttling kafka.server:type=Request exempt-throttle-time indicates the percentage of time spent in broker network and I/O threads to process requests that are exempt from throttling.
ZooKeeper client request latency kafka.server:type=ZooKeeperClientMetrics,name=ZooKeeperRequestLatencyMs Latency in millseconds for ZooKeeper requests from broker.
ZooKeeper connection status kafka.server:type=SessionExpireListener,name=SessionState Connection status of broker's ZooKeeper session which may be one of Disconnected|SyncConnected|AuthFailed|ConnectedReadOnly|SaslAuthenticated|Expired.
Max time to load group metadata kafka.server:type=group-coordinator-metrics,name=partition-load-time-max maximum time, in milliseconds, it took to load offsets and group metadata from the consumer offset partitions loaded in the last 30 seconds (including time spent waiting for the loading task to be scheduled)
Avg time to load group metadata kafka.server:type=group-coordinator-metrics,name=partition-load-time-avg average time, in milliseconds, it took to load offsets and group metadata from the consumer offset partitions loaded in the last 30 seconds (including time spent waiting for the loading task to be scheduled)
Max time to load transaction metadata kafka.server:type=transaction-coordinator-metrics,name=partition-load-time-max maximum time, in milliseconds, it took to load transaction metadata from the consumer offset partitions loaded in the last 30 seconds (including time spent waiting for the loading task to be scheduled)
Avg time to load transaction metadata kafka.server:type=transaction-coordinator-metrics,name=partition-load-time-avg average time, in milliseconds, it took to load transaction metadata from the consumer offset partitions loaded in the last 30 seconds (including time spent waiting for the loading task to be scheduled)
Consumer Group Offset Count kafka.server:type=GroupMetadataManager,name=NumOffsets Total number of committed offsets for Consumer Groups
Consumer Group Count kafka.server:type=GroupMetadataManager,name=NumGroups Total number of Consumer Groups
Consumer Group Count, per State kafka.server:type=GroupMetadataManager,name=NumGroups[PreparingRebalance,CompletingRebalance,Empty,Stable,Dead] The number of Consumer Groups in each state: PreparingRebalance, CompletingRebalance, Empty, Stable, Dead
Number of reassigning partitions kafka.server:type=ReplicaManager,name=ReassigningPartitions The number of reassigning leader partitions on a broker.
Outgoing byte rate of reassignment traffic kafka.server:type=BrokerTopicMetrics,name=ReassignmentBytesOutPerSec 0; non-zero when a partition reassignment is in progress.
Incoming byte rate of reassignment traffic kafka.server:type=BrokerTopicMetrics,name=ReassignmentBytesInPerSec 0; non-zero when a partition reassignment is in progress.
Size of a partition on disk (in bytes) kafka.log:type=Log,name=Size,topic=([-.\w]+),partition=([0-9]+) The size of a partition on disk, measured in bytes.
Number of log segments in a partition kafka.log:type=Log,name=NumLogSegments,topic=([-.\w]+),partition=([0-9]+) The number of log segments in a partition.
First offset in a partition kafka.log:type=Log,name=LogStartOffset,topic=([-.\w]+),partition=([0-9]+) The first offset in a partition.
Last offset in a partition kafka.log:type=Log,name=LogEndOffset,topic=([-.\w]+),partition=([0-9]+) The last offset in a partition.

KRaft Monitoring Metrics

The set of metrics that allow monitoring of the KRaft quorum and the metadata log.
Note that some exposed metrics depend on the role of the node as defined by process.roles
KRaft Quorum Monitoring Metrics
These metrics are reported on both Controllers and Brokers in a KRaft Cluster
Metric/Attribute name Description Mbean name
Current State The current state of this member; possible values are leader, candidate, voted, follower, unattached, observer. kafka.server:type=raft-metrics,name=current-state
Current Leader The current quorum leader's id; -1 indicates unknown. kafka.server:type=raft-metrics,name=current-leader
Current Voted The current voted leader's id; -1 indicates not voted for anyone. kafka.server:type=raft-metrics,name=current-vote
Current Epoch The current quorum epoch. kafka.server:type=raft-metrics,name=current-epoch
High Watermark The high watermark maintained on this member; -1 if it is unknown. kafka.server:type=raft-metrics,name=high-watermark
Log End Offset The current raft log end offset. kafka.server:type=raft-metrics,name=log-end-offset
Number of Unknown Voter Connections Number of unknown voters whose connection information is not cached. This value of this metric is always 0. kafka.server:type=raft-metrics,name=number-unknown-voter-connections
Average Commit Latency The average time in milliseconds to commit an entry in the raft log. kafka.server:type=raft-metrics,name=commit-latency-avg
Maximum Commit Latency The maximum time in milliseconds to commit an entry in the raft log. kafka.server:type=raft-metrics,name=commit-latency-max
Average Election Latency The average time in milliseconds spent on electing a new leader. kafka.server:type=raft-metrics,name=election-latency-avg
Maximum Election Latency The maximum time in milliseconds spent on electing a new leader. kafka.server:type=raft-metrics,name=election-latency-max
Fetch Records Rate The average number of records fetched from the leader of the raft quorum. kafka.server:type=raft-metrics,name=fetch-records-rate
Append Records Rate The average number of records appended per sec by the leader of the raft quorum. kafka.server:type=raft-metrics,name=append-records-rate
Average Poll Idle Ratio The average fraction of time the client's poll() is idle as opposed to waiting for the user code to process records. kafka.server:type=raft-metrics,name=poll-idle-ratio-avg
KRaft Controller Monitoring Metrics
Metric/Attribute name Description Mbean name
Active Controller Count The number of Active Controllers on this node. Valid values are '0' or '1'. kafka.controller:type=KafkaController,name=ActiveControllerCount
Event Queue Time Ms A Histogram of the time in milliseconds that requests spent waiting in the Controller Event Queue. kafka.controller:type=ControllerEventManager,name=EventQueueTimeMs
Event Queue Processing Time Ms A Histogram of the time in milliseconds that requests spent being processed in the Controller Event Queue. kafka.controller:type=ControllerEventManager,name=EventQueueProcessingTimeMs
Fenced Broker Count The number of fenced brokers as observed by this Controller. kafka.controller:type=KafkaController,name=FencedBrokerCount
Active Broker Count The number of active brokers as observed by this Controller. kafka.controller:type=KafkaController,name=ActiveBrokerCount
Global Topic Count The number of global topics as observed by this Controller. kafka.controller:type=KafkaController,name=GlobalTopicCount
Global Partition Count The number of global partitions as observed by this Controller. kafka.controller:type=KafkaController,name=GlobalPartitionCount
Offline Partition Count The number of offline topic partitions (non-internal) as observed by this Controller. kafka.controller:type=KafkaController,name=OfflinePartitionCount
Preferred Replica Imbalance Count The count of topic partitions for which the leader is not the preferred leader. kafka.controller:type=KafkaController,name=PreferredReplicaImbalanceCount
Metadata Error Count The number of times this controller node has encountered an error during metadata log processing. kafka.controller:type=KafkaController,name=MetadataErrorCount
Last Applied Record Offset The offset of the last record from the cluster metadata partition that was applied by the Controller. kafka.controller:type=KafkaController,name=LastAppliedRecordOffset
Last Committed Record Offset The offset of the last record committed to this Controller. kafka.controller:type=KafkaController,name=LastCommittedRecordOffset
Last Applied Record Timestamp The timestamp of the last record from the cluster metadata partition that was applied by the Controller. kafka.controller:type=KafkaController,name=LastAppliedRecordTimestamp
Last Applied Record Lag Ms The difference between now and the timestamp of the last record from the cluster metadata partition that was applied by the controller. For active Controllers the value of this lag is always zero. kafka.controller:type=KafkaController,name=LastAppliedRecordLagMs
KRaft Broker Monitoring Metrics
Metric/Attribute name Description Mbean name
Last Applied Record Offset The offset of the last record from the cluster metadata partition that was applied by the broker kafka.server:type=broker-metadata-metrics,name=last-applied-record-offset
Last Applied Record Timestamp The timestamp of the last record from the cluster metadata partition that was applied by the broker. kafka.server:type=broker-metadata-metrics,name=last-applied-record-timestamp
Last Applied Record Lag Ms The difference between now and the timestamp of the last record from the cluster metadata partition that was applied by the broker kafka.server:type=broker-metadata-metrics,name=last-applied-record-lag-ms
Metadata Load Error Count The number of errors encountered by the BrokerMetadataListener while loading the metadata log and generating a new MetadataDelta based on it. kafka.server:type=broker-metadata-metrics,name=metadata-load-error-count
Metadata Apply Error Count The number of errors encountered by the BrokerMetadataPublisher while applying a new MetadataImage based on the latest MetadataDelta. kafka.server:type=broker-metadata-metrics,name=metadata-apply-error-count

Common monitoring metrics for producer/consumer/connect/streams

The following metrics are available on producer/consumer/connector/streams instances. For specific metrics, please see following sections.
Metric/Attribute name Description Mbean name
connection-close-rate Connections closed per second in the window. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
connection-close-total Total connections closed in the window. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
connection-creation-rate New connections established per second in the window. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
connection-creation-total Total new connections established in the window. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
network-io-rate The average number of network operations (reads or writes) on all connections per second. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
network-io-total The total number of network operations (reads or writes) on all connections. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
outgoing-byte-rate The average number of outgoing bytes sent per second to all servers. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
outgoing-byte-total The total number of outgoing bytes sent to all servers. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
request-rate The average number of requests sent per second. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
request-total The total number of requests sent. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
request-size-avg The average size of all requests in the window. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
request-size-max The maximum size of any request sent in the window. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
incoming-byte-rate Bytes/second read off all sockets. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
incoming-byte-total Total bytes read off all sockets. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
response-rate Responses received per second. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
response-total Total responses received. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
select-rate Number of times the I/O layer checked for new I/O to perform per second. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
select-total Total number of times the I/O layer checked for new I/O to perform. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-wait-time-ns-avg The average length of time the I/O thread spent waiting for a socket ready for reads or writes in nanoseconds. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-wait-time-ns-total The total time the I/O thread spent waiting in nanoseconds. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-waittime-total *Deprecated* The total time the I/O thread spent waiting in nanoseconds. Replacement is io-wait-time-ns-total. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-wait-ratio The fraction of time the I/O thread spent waiting. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-time-ns-avg The average length of time for I/O per select call in nanoseconds. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-time-ns-total The total time the I/O thread spent doing I/O in nanoseconds. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
iotime-total *Deprecated* The total time the I/O thread spent doing I/O in nanoseconds. Replacement is io-time-ns-total. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
io-ratio The fraction of time the I/O thread spent doing I/O. kafka.[producer|consumer|connect]:type=[producer|consumer|connect]-metrics,client-id=([-.\w]+)
connection-count The current number of active connections.