Kafka 0.9.0 Documentation

By | March 11, 2016

1. Getting Started
1.1 Introduction
1.2 Use Cases
1.3 Quick Start
1.4 Ecosystem
1.5 Upgrading
2. API
2.1 Producer API
2.2 Consumer API
2.2.1 Old High Level Consumer API
2.2.2 Old Simple Consumer API
2.2.3 New Consumer API
3. Configuration
3.1 Broker Configs
3.2 Producer Configs
3.3 Consumer Configs
3.3.1 Old Consumer Configs
3.3.2 New Consumer Configs
3.4 Kafka Connect Configs
4. Design
4.1 Motivation
4.2 Persistence
4.3 Efficiency
4.4 The Producer
4.5 The Consumer
4.6 Message Delivery Semantics
4.7 Replication
4.8 Log Compaction
4.9 Quotas
5. Implementation
5.1 API Design
5.2 Network Layer
5.3 Messages
5.4 Message format
5.5 Log
5.6 Distribution
6. Operations
6.1 Basic Kafka Operations
Adding and removing topics
Modifying topics
Graceful shutdown
Balancing leadership
Checking consumer position
Mirroring data between clusters
Expanding your cluster
Decommissioning brokers
Increasing replication factor
6.2 Datacenters
6.3 Important Configs
Important Server Configs
Important Client Configs
A Production Server Configs
6.4 Java Version
6.5 Hardware and OS
Disks and Filesystems
Application vs OS Flush Management
Linux Flush Behavior
Ext4 Notes
6.6 Monitoring
6.7 ZooKeeper
Stable Version
7. Security
7.1 Security Overview
7.2 Encryption and Authentication using SSL
7.3 Authentication using SASL
7.4 Authorization and ACLs
7.5 ZooKeeper Authentication
New Clusters
Migrating Clusters
Migrating the ZooKeeper Ensemble
8. Kafka Connect
8.1 Overview
8.2 User Guide
8.3 Connector Development Guide
1. Getting Started

1.1 Introduction

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
What does all that mean?

First let’s review some basic messaging terminology:

Kafka maintains feeds of messages in categories called topics.
We’ll call processes that publish messages to a Kafka topic producers.
We’ll call processes that subscribe to topics and process the feed of published messages consumers..
Kafka is run as a cluster comprised of one or more servers each of which is called a broker.
So, at a high level, producers send messages over the network to the Kafka cluster which in turn serves them up to consumers like this:

Communication between the clients and the servers is done with a simple, high-performance, language agnostic TCP protocol. We provide a Java client for Kafka, but clients are available in many languages.
Topics and Logs

Let’s first dive into the high-level abstraction Kafka provides—the topic.
A topic is a category or feed name to which messages are published. For each topic, the Kafka cluster maintains a partitioned log that looks like this:

Each partition is an ordered, immutable sequence of messages that is continually appended to—a commit log. The messages in the partitions are each assigned a sequential id number called the offset that uniquely identifies each message within the partition.
The Kafka cluster retains all published messages—whether or not they have been consumed—for a configurable period of time. For example if the log retention is set to two days, then for the two days after a message is published it is available for consumption, after which it will be discarded to free up space. Kafka’s performance is effectively constant with respect to data size so retaining lots of data is not a problem.

In fact the only metadata retained on a per-consumer basis is the position of the consumer in the log, called the “offset”. This offset is controlled by the consumer: normally a consumer will advance its offset linearly as it reads messages, but in fact the position is controlled by the consumer and it can consume messages in any order it likes. For example a consumer can reset to an older offset to reprocess.

This combination of features means that Kafka consumers are very cheap—they can come and go without much impact on the cluster or on other consumers. For example, you can use our command line tools to “tail” the contents of any topic without changing what is consumed by any existing consumers.

The partitions in the log serve several purposes. First, they allow the log to scale beyond a size that will fit on a single server. Each individual partition must fit on the servers that host it, but a topic may have many partitions so it can handle an arbitrary amount of data. Second they act as the unit of parallelism—more on that in a bit.


The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. Each partition is replicated across a configurable number of servers for fault tolerance.
Each partition has one server which acts as the “leader” and zero or more servers which act as “followers”. The leader handles all read and write requests for the partition while the followers passively replicate the leader. If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.


Producers publish data to the topics of their choice. The producer is responsible for choosing which message to assign to which partition within the topic. This can be done in a round-robin fashion simply to balance load or it can be done according to some semantic partition function (say based on some key in the message). More on the use of partitioning in a second.

Messaging traditionally has two models: queuing and publish-subscribe. In a queue, a pool of consumers may read from a server and each message goes to one of them; in publish-subscribe the message is broadcast to all consumers. Kafka offers a single consumer abstraction that generalizes both of these—the consumer group.
Consumers label themselves with a consumer group name, and each message published to a topic is delivered to one consumer instance within each subscribing consumer group. Consumer instances can be in separate processes or on separate machines.

If all the consumer instances have the same consumer group, then this works just like a traditional queue balancing load over the consumers.

If all the consumer instances have different consumer groups, then this works like publish-subscribe and all messages are broadcast to all consumers.

More commonly, however, we have found that topics have a small number of consumer groups, one for each “logical subscriber”. Each group is composed of many consumer instances for scalability and fault tolerance. This is nothing more than publish-subscribe semantics where the subscriber is cluster of consumers instead of a single process.

A two server Kafka cluster hosting four partitions (P0-P3) with two consumer groups. Consumer group A has two consumer instances and group B has four.
Kafka has stronger ordering guarantees than a traditional messaging system, too.

A traditional queue retains messages in-order on the server, and if multiple consumers consume from the queue then the server hands out messages in the order they are stored. However, although the server hands out messages in order, the messages are delivered asynchronously to consumers, so they may arrive out of order on different consumers. This effectively means the ordering of the messages is lost in the presence of parallel consumption. Messaging systems often work around this by having a notion of “exclusive consumer” that allows only one process to consume from a queue, but of course this means that there is no parallelism in processing.

Kafka does it better. By having a notion of parallelism—the partition—within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group. By doing this we ensure that the consumer is the only reader of that partition and consumes the data in order. Since there are many partitions this still balances the load over many consumer instances. Note however that there cannot be more consumer instances in a consumer group than partitions.

Kafka only provides a total order over messages within a partition, not between different partitions in a topic. Per-partition ordering combined with the ability to partition data by key is sufficient for most applications. However, if you require a total order over messages this can be achieved with a topic that has only one partition, though this will mean only one consumer process per consumer group.


At a high-level Kafka gives the following guarantees:
Messages sent by a producer to a particular topic partition will be appended in the order they are sent. That is, if a message M1 is sent by the same producer as a message M2, and M1 is sent first, then M1 will have a lower offset than M2 and appear earlier in the log.
A consumer instance sees messages in the order they are stored in the log.
For a topic with replication factor N, we will tolerate up to N-1 server failures without losing any messages committed to the log.
More details on these guarantees are given in the design section of the documentation.
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 end up doing stage-wise processing of data where data is consumed from topics of raw data and then aggregated, enriched, or otherwise transformed into new Kafka topics for further consumption. For example a processing flow for article recommendation might crawl article content from RSS feeds and publish it to an “articles” topic; further processing might help normalize or deduplicate this content to a topic of cleaned article content; a final stage might attempt to match this content to users. This creates a graph of real-time data flow out of the individual topics. Storm and Samza are popular frameworks for implementing these kinds of transformations.
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

This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data.
Step 1: Download the code

Download the release and un-tar it.
> tar -xzf kafka_2.11-
> cd kafka_2.11-
Step 2: Start the server

Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don’t already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.

> bin/zookeeper-server-start.sh config/zookeeper.properties
[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)

Now start the Kafka server:
> bin/kafka-server-start.sh config/server.properties
[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)

Step 3: Create a topic

Let’s create a topic named “test” with a single partition and only one replica:
> bin/kafka-topics.sh –create –zookeeper localhost:2181 –replication-factor 1 –partitions 1 –topic test
We can now see that topic if we run the list topic command:
> bin/kafka-topics.sh –list –zookeeper localhost:2181
Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.
Step 4: Send some messages

Kafka comes with a command line client that will take input from a file or from standard input and send it out as messages to the Kafka cluster. By default each line will be sent as a separate message.
Run the producer and then type a few messages into the console to send to the server.

> bin/kafka-console-producer.sh –broker-list localhost:9092 –topic test
This is a message
This is another message
Step 5: Start a consumer

Kafka also has a command line consumer that will dump out messages to standard output.
> bin/kafka-console-consumer.sh –zookeeper localhost:2181 –topic test –from-beginning
This is a message
This is another message
If you have each of the above commands running in a different terminal then you should now be able to type messages into the producer terminal and see them appear in the consumer terminal.

All of the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail.

Step 6: Setting up a multi-broker cluster

So far we have been running against a single broker, but that’s no fun. For Kafka, a single broker is just a cluster of size one, so nothing much changes other than starting a few more broker instances. But just to get feel for it, let’s expand our cluster to three nodes (still all on our local machine).
First we make a config file for each of the brokers:

> cp config/server.properties config/server-1.properties
> cp config/server.properties config/server-2.properties
Now edit these new files and set the following properties:


The broker.id property is the unique and permanent name of each node in the cluster. We have to override the port and log directory only because we are running these all on the same machine and we want to keep the brokers from all trying to register on the same port or overwrite each others data.
We already have Zookeeper and our single node started, so we just need to start the two new nodes:

> bin/kafka-server-start.sh config/server-1.properties &

> bin/kafka-server-start.sh config/server-2.properties &

Now create a new topic with a replication factor of three:
> bin/kafka-topics.sh –create –zookeeper localhost:2181 –replication-factor 3 –partitions 1 –topic my-replicated-topic
Okay but now that we have a cluster how can we know which broker is doing what? To see that run the “describe topics” command:
> bin/kafka-topics.sh –describe –zookeeper localhost:2181 –topic my-replicated-topic
Topic:my-replicated-topic PartitionCount:1 ReplicationFactor:3 Configs:
Topic: my-replicated-topic Partition: 0 Leader: 1 Replicas: 1,2,0 Isr: 1,2,0
Here is an explanation of output. The first line gives a summary of all the partitions, each additional line gives information about one partition. Since we have only one partition for this topic there is only one line.
“leader” is the node responsible for all reads and writes for the given partition. Each node will be the leader for a randomly selected portion of the partitions.
“replicas” is the list of nodes that replicate the log for this partition regardless of whether they are the leader or even if they are currently alive.
“isr” is the set of “in-sync” replicas. This is the subset of the replicas list that is currently alive and caught-up to the leader.
Note that in my example node 1 is the leader for the only partition of the topic.
We can run the same command on the original topic we created to see where it is:

> bin/kafka-topics.sh –describe –zookeeper localhost:2181 –topic test
Topic:test PartitionCount:1 ReplicationFactor:1 Configs:
Topic: test Partition: 0 Leader: 0 Replicas: 0 Isr: 0
So there is no surprise there—the original topic has no replicas and is on server 0, the only server in our cluster when we created it.
Let’s publish a few messages to our new topic:

> bin/kafka-console-producer.sh –broker-list localhost:9092 –topic my-replicated-topic

my test message 1
my test message 2
Now let’s consume these messages:
> bin/kafka-console-consumer.sh –zookeeper localhost:2181 –from-beginning –topic my-replicated-topic

my test message 1
my test message 2
Now let’s test out fault-tolerance. Broker 1 was acting as the leader so let’s kill it:
> ps | grep server-1.properties
7564 ttys002 0:15.91 /System/Library/Frameworks/JavaVM.framework/Versions/1.6/Home/bin/java…
> kill -9 7564
Leadership has switched to one of the slaves and node 1 is no longer in the in-sync replica set:
> bin/kafka-topics.sh –describe –zookeeper localhost:2181 –topic my-replicated-topic
Topic:my-replicated-topic PartitionCount:1 ReplicationFactor:3 Configs:
Topic: my-replicated-topic Partition: 0 Leader: 2 Replicas: 1,2,0 Isr: 2,0
But the messages are still be available for consumption even though the leader that took the writes originally is down:
> bin/kafka-console-consumer.sh –zookeeper localhost:2181 –from-beginning –topic my-replicated-topic

my test message 1
my test message 2
Step 7: Use Kafka Connect to import/export data

Writing data from the console and writing it back to the console is a convenient place to start, but you’ll probably want to use data from other sources or export data from Kafka to other systems. For many systems, instead of writing custom integration code you can use Kafka Connect to import or export data. Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs connectors, which implement the custom logic for interacting with an external system. In this quickstart we’ll see how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic and export data from a Kafka topic to a file. First, we’ll start by creating some seed data to test with:
> echo -e “foo\nbar” > test.txt
Next, we’ll start two connectors running in standalone mode, which means they run in a single, local, dedicated process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data. The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector class to instantiate, and any other configuration required by the connector.
> bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties
These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file. During startup you’ll see a number of log messages, including some indicating that the connectors are being instantiated. Once the Kafka Connect process has started, the source connector should start reading lines from
and producing them to the topic
, and the sink connector should start reading messages from the topic
and write them to the file
. We can verify the data has been delivered through the entire pipeline by examining the contents of the output file:
> cat test.sink.txt
Note that the data is being stored in the Kafka topic
, so we can also run a console consumer to see the data in the topic (or use custom consumer code to process it):
> bin/kafka-console-consumer.sh –zookeeper localhost:2181 –topic connect-test –from-beginning

The connectors continue to process data, so we can add data to the file and see it move through the pipeline:
> echo “Another line” >> test.txt
You should see the line appear in the console consumer output and in the sink file.
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

Upgrading from 0.8.0, 0.8.1.X or 0.8.2.X to has potential breaking changes (please review before upgrading) and an inter-broker protocol change from previous versions. This means that upgraded brokers and clients may not be compatible with older versions. It is important that you upgrade your Kafka cluster before upgrading your clients. If you are using MirrorMaker downstream clusters should be upgraded first as well.
For a rolling upgrade:

Update server.properties file on all brokers and add the following property: inter.broker.protocol.version=0.8.2.X
Upgrade the brokers. This can be done a broker at a time by simply bringing it down, updating the code, and restarting it.
Once the entire cluster is upgraded, bump the protocol version by editing inter.broker.protocol.version and setting it to
Restart the brokers one by one for the new protocol version to take effect
Note: If you are willing to accept downtime, you can simply take all the brokers down, update the code and start all of them. They will start with the new protocol by default.

Note: Bumping the protocol version and restarting can be done any time after the brokers were upgraded. It does not have to be immediately after.

Potential breaking changes in

Java 1.6 is no longer supported.
Scala 2.9 is no longer supported.
Broker IDs above 1000 are now reserved by default to automatically assigned broker IDs. If your cluster has existing broker IDs above that threshold make sure to increase the reserved.broker.max.id broker configuration property accordingly.
Configuration parameter replica.lag.max.messages was removed. Partition leaders will no longer consider the number of lagging messages when deciding which replicas are in sync.
Configuration parameter replica.lag.time.max.ms now refers not just to the time passed since last fetch request from replica, but also to time since the replica last caught up. Replicas that are still fetching messages from leaders but did not catch up to the latest messages in replica.lag.time.max.ms will be considered out of sync.
Compacted topics no longer accept messages without key and an exception is thrown by the producer if this is attempted. In 0.8.x, a message without key would cause the log compaction thread to subsequently complain and quit (and stop compacting all compacted topics).
MirrorMaker no longer supports multiple target clusters. As a result it will only accept a single –consumer.config parameter. To mirror multiple source clusters, you will need at least one MirrorMaker instance per source cluster, each with its own consumer configuration.
Tools packaged under org.apache.kafka.clients.tools.* have been moved to org.apache.kafka.tools.*. All included scripts will still function as usual, only custom code directly importing these classes will be affected.
The default Kafka JVM performance options (KAFKA_JVM_PERFORMANCE_OPTS) have been changed in kafka-run-class.sh.
The kafka-topics.sh script (kafka.admin.TopicCommand) now exits with non-zero exit code on failure.
The kafka-topics.sh script (kafka.admin.TopicCommand) will now print a warning when topic names risk metric collisions due to the use of a ‘.’ or ‘_’ in the topic name, and error in the case of an actual collision.
The kafka-console-producer.sh script (kafka.tools.ConsoleProducer) will use the new producer instead of the old producer be default, and users have to specify ‘old-producer’ to use the old producer.
By default all command line tools will print all logging messages to stderr instead of stout.

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