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Introduction

This document covers the protocol implemented in Kafka 0.8 and beyond. It is meant to give a readable guide to the protocol that covers the available requests, their binary format, and the proper way to make use of them to implement a client. This document assumes you understand the basic design and terminology described here.

The protocol used in 0.7 and earlier is similar to this, but we chose to make a one time (we hope) break in compatibility to be able to clean up cruft and generalize things.

Overview

The Kafka protocol is fairly simple, there are only six core client requests APIs.

1. Metadata - Describes the currently available brokers, their host and port information, and gives information about which broker hosts which partitions.
2. Send - Send messages to a broker
3. Fetch - Fetch messages from a broker, one which fetches data, one which gets cluster metadata, and one which gets offset information about a topic.
4. Offsets - Get information about the available offsets for a given topic partition.
5. Offset Commit - Commit a set of offsets for a consumer group
6. Offset Fetch - Fetch a set of offsets for a consumer group

Each of these will be described in detail below. Additionally, as of 0.9, Kafka supports general group management for consumers and Kafka Connect. The client API consists of five requests:

1. GroupCoordinator - Locate the current coordinator of a group.
2. JoinGroup - Become a member of a group, creating it if there are no active members.
3. SyncGroup - Synchronize state for all members of a group (e.g. distribute partition assignments to consumers).
4. Heartbeat - Keep a member alive in the group. 
5. LeaveGroup - Directly depart a group.

Finally, there are several administrative APIs which can be used to monitor/administer the Kafka cluster (this list will grow when KIP-4 is completed).

1. DescribeGroups - Used to inspect the current state of a set of groups (e.g. to view consumer partition assignments).  
2. ListGroups - List the current groups managed by a broker.

Preliminaries

Network

Kafka uses a binary protocol over TCP. The protocol defines all apis as request response message pairs. All messages are size delimited and are made up of the following primitive types.

The client initiates a socket connection and then writes a sequence of request messages and reads back the corresponding response message. No handshake is required on connection or disconnection. TCP is happier if you maintain persistent connections used for many requests to amortize the cost of the TCP handshake, but beyond this penalty connecting is pretty cheap.

The client will likely need to maintain a connection to multiple brokers, as data is partitioned and the clients will need to talk to the server that has their data. However it should not generally be necessary to maintain multiple connections to a single broker from a single client instance (i.e. connection pooling).

The server guarantees that on a single TCP connection, requests will be processed in the order they are sent and responses will return in that order as well. The broker's request processing allows only a single in-flight request per connection in order to guarantee this ordering. Note that clients can (and ideally should) use non-blocking IO to implement request pipelining and achieve higher throughput. i.e., clients can send requests even while awaiting responses for preceding requests since the outstanding requests will be buffered in the underlying OS socket buffer. All requests are initiated by the client, and result in a corresponding response message from the server except where noted.

The server has a configurable maximum limit on request size and any request that exceeds this limit will result in the socket being disconnected.

Partitioning and bootstrapping

Kafka is a partitioned system so not all servers have the complete data set. Instead recall that topics are split into a pre-defined number of partitions, P, and each partition is replicated with some replication factor, N. Topic partitions themselves are just ordered "commit logs" numbered 0, 1, ..., P.

All systems of this nature have the question of how a particular piece of data is assigned to a particular partition. Kafka clients directly control this assignment, the brokers themselves enforce no particular semantics of which messages should be published to a particular partition. Rather, to publish messages the client directly addresses messages to a particular partition, and when fetching messages, fetches from a particular partition. If two clients want to use the same partitioning scheme they must use the same method to compute the mapping of key to partition.

These requests to publish or fetch data must be sent to the broker that is currently acting as the leader for a given partition. This condition is enforced by the broker, so a request for a particular partition to the wrong broker will result in an the NotLeaderForPartition error code (described below). 

How can the client find out which topics exist, what partitions they have, and which brokers currently host those partitions so that it can direct its requests to the right hosts? This information is dynamic, so you can't just configure each client with some static mapping file. Instead all Kafka brokers can answer a metadata request that describes the current state of the cluster: what topics there are, which partitions those topics have, which broker is the leader for those partitions, and the host and port information for these brokers.

In other words, the client needs to somehow find one broker and that broker will tell the client about all the other brokers that exist and what partitions they host. This first broker may itself go down so the best practice for a client implementation is to take a list of two or three urls to bootstrap from. The user can then choose to use a load balancer or just statically configure two or three of their kafka hosts in the clients.

The client does not need to keep polling to see if the cluster has changed; it can fetch metadata once when it is instantiated cache that metadata until it receives an error indicating that the metadata is out of date. This error can come in two forms: (1) a socket error indicating the client cannot communicate with a particular broker, (2) an error code in the response to a request indicating that this broker no longer hosts the partition for which data was requested.

1. Cycle through a list of "bootstrap" kafka urls until we find one we can connect to. Fetch cluster metadata.
2. Process fetch or produce requests, directing them to the appropriate broker based on the topic/partitions they send to or fetch from.
3. If we get an appropriate error, refresh the metadata and try again.

Partitioning Strategies

As mentioned above the assignment of messages to partitions is something the producing client controls. That said, how should this functionality be exposed to the end-user?

Partitioning really serves two purposes in Kafka:

1. It balances data and request load over brokers
2. It serves as a way to divvy up processing among consumer processes while allowing local state and preserving order within the partition. We call this semantic partitioning.

For a given use case you may care about only one of these or both.

To accomplish simple load balancing a simple approach would be for the client to just round robin requests over all brokers. Another alternative, in an environment where there are many more producers than brokers, would be to have each client chose a single partition at random and publish to that. This later strategy will result in far fewer TCP connections.

Semantic partitioning means using some key in the message to assign messages to partitions. For example if you were processing a click message stream you might want to partition the stream by the user id so that all data for a particular user would go to a single consumer. To accomplish this the client can take a key associated with the message and use some hash of this key to choose the partition to which to deliver the message.

Batching

Our apis encourage batching small things together for efficiency. We have found this is a very significant performance win. Both our API to send messages and our API to fetch messages always work with a sequence of messages not a single message to encourage this. A clever client can make use of this and support an "asynchronous" mode in which it batches together messages sent individually and sends them in larger clumps. We go even further with this and allow the batching across multiple topics and partitions, so a produce request may contain data to append to many partitions and a fetch request may pull data from many partitions all at once.

The client implementer can choose to ignore this and send everything one at a time if they like.

Versioning and Compatibility

The protocol is designed to enable incremental evolution in a backward compatible fashion. Our versioning is on a per-api basis, each version consisting of a request and response pair. Each request contains an API key that identifies the API being invoked and a version number that indicates the format of the request and the expected format of the response.

The intention is that clients would implement a particular version of the protocol, and indicate this version in their requests. Our goal is primarily to allow API evolution in an environment where downtime is not allowed and clients and servers cannot all be changed at once.

The server will reject requests with a version it does not support, and will always respond to the client with exactly the protocol format it expects based on the version it included in its request. The intended upgrade path is that new features would first be rolled out on the server (with the older clients not making use of them) and then as newer clients are deployed these new features would gradually be taken advantage of.

Currently all versions are baselined at 0, as we evolve these APIs we will indicate the format for each version individually.

The Protocol

Protocol Primitive Types

The protocol is built out of the following primitive types.

Fixed Width Primitives

int8, int16, int32, int64 - Signed integers with the given precision (in bits) stored in big endian order.

Variable Length Primitives

bytes, string - These types consist of a signed integer giving a length N followed by N bytes of content. A length of -1 indicates null. string uses an int16 for its size, and bytes uses an int32.

Arrays

This is a notation for handling repeated structures. These will always be encoded as an int32 size containing the length N followed by N repetitions of the structure which can itself be made up of other primitive types. In the BNF grammars below we will show an array of a structure foo as [foo].

Notes on reading the request format grammars

The BNFs below give an exact context free grammar for the request and response binary format. For each API I will give the request and response together followed by all the sub-definitions. The BNF is intentionally not compact in order to give human-readable name (for example I define a production for ErrorCode even though it is just an int16 in order to give it a symbolic name). As always in a BNF a sequence of productions indicates concatenation, so the MetadataRequest given below would be a sequence of bytes containing first a VersionId, then a ClientId, and then an array of TopicNames (each of which has its own definition). Productions are always given in camel case and primitive types in lower case. When there are multiple possible productions these are separated with '|' and may be enclosed in parenthesis for grouping. The top-level definition is always given first and subsequent sub-parts are indented.

Common Request and Response Structure

All requests and responses originate from the following grammar which will be incrementally describe through the rest of this document:

RequestOrResponse => Size (RequestMessage | ResponseMessage)
  Size => int32

Requests

Requests all have the following format:

RequestMessage => ApiKey ApiVersion CorrelationId ClientId RequestMessage
  ApiKey => int16
  ApiVersion => int16
  CorrelationId => int32
  ClientId => string
  RequestMessage => MetadataRequest | ProduceRequest | FetchRequest | OffsetRequest | OffsetCommitRequest | OffsetFetchRequest

The various request and response messages will be described below.

Responses

Response => CorrelationId ResponseMessage
CorrelationId => int32
ResponseMessage => MetadataResponse | ProduceResponse | FetchResponse | OffsetResponse | OffsetCommitResponse | OffsetFetchResponse

The response will always match the paired request (e.g. we will send a MetadataResponse in return to a MetadataRequest).

Message sets

One structure common to both the produce and fetch requests is the message set format. A message in kafka is a key-value pair with a small amount of associated metadata. A message set is just a sequence of messages with offset and size information. This format happens to be used both for the on-disk storage on the broker and the on-the-wire format.

A message set is also the unit of compression in Kafka, and we allow messages to recursively contain compressed message sets to allow batch compression.

N.B., MessageSets are not preceded by an int32 like other array elements in the protocol.

MessageSet => [Offset MessageSize Message]
  Offset => int64
  MessageSize => int32

Message format

v0
Message => Crc MagicByte Attributes Key Value
  Crc => int32
  MagicByte => int8
  Attributes => int8
  Key => bytes
  Value => bytes
 
v1 (supported since 0.10.0)
Message => Crc MagicByte Attributes Key Value
  Crc => int32
  MagicByte => int8
  Attributes => int8
  Timestamp => int64
  Key => bytes
  Value => bytes

In Kafka 0.11, the structure of the 'MessageSet' and 'Message' were significantly changed. Not only were new fields added to support new features like exactly once semantics and record headers, but the recursive nature of the previous versions of the message format was eliminated in favor of a flat structure. A 'MessageSet' is now called a 'RecordBatch', which contains one or more 'Records' (and not 'Messages'). When compression is enabled, the RecordBatch header remains uncompressed, but the Records are compressed together. Further, multiple fields in the 'Record' are varint encoded, which leads to significant space savings for larger batches. 

The new message format has a Magic value of 2. Its structure is as follows:

RecordBatch =>
  FirstOffset => int64
  Length => int32
  PartitionLeaderEpoch => int32 
  Magic => int8  
  CRC => int32
  Attributes => int16
  LastOffsetDelta => int32 
  FirstTimestamp => int64 
  MaxTimestamp => int64 
  ProducerId => int64 
  ProducerEpoch => int16 
  FirstSequence => int32 
  Records => [Record]
 
Record => 
  Length => varint
  Attributes => int8
  TimestampDelta => varint
  OffsetDelta => varint
  KeyLen => varint
  Key => data
  ValueLen => varint
  Value => data
  Headers => [Header] 
 
Header => HeaderKey HeaderVal
  HeaderKeyLen => varint
  HeaderKey => string
  HeaderValueLen => varint
  HeaderValue => data

The semantics of the newly added fields are described below: 

Compression

Kafka supports compressing messages for additional efficiency, however this is more complex than just compressing a raw message. Because individual messages may not have sufficient redundancy to enable good compression ratios, compressed messages must be sent in special batches (although you may use a batch of one if you truly wish to compress a message on its own). The messages to be sent are wrapped (uncompressed) in a MessageSet structure, which is then compressed and stored in the Value field of a single "Message" with the appropriate compression codec set. The receiving system parses the actual MessageSet from the decompressed value. The outer MessageSet should contain only one compressed "Message" (see KAFKA-1718 for details).

Kafka currently supports two compression codecs with the following codec numbers:

The APIs

This section gives details on each of the individual APIs, their usage, their binary format, and the meaning of their fields.

Metadata API

This API answers the following questions:

• What topics exist?
• How many partitions does each topic have?
• Which broker is currently the leader for each partition?
• What is the host and port for each of these brokers?

This is the only request that can be addressed to any broker in the cluster.

Since there may be many topics the client can give an optional list of topic names in order to only return metadata for a subset of topics.

The metadata returned is at the partition level, but grouped together by topic for convenience and to avoid redundancy. For each partition the metadata contains the information for the leader as well as for all the replicas and the list of replicas that are currently in-sync.

Note: If "auto.create.topics.enable" is set in the broker configuration, a topic metadata request will create the topic with the default replication factor and number of partitions. 

Topic Metadata Request

TopicMetadataRequest => [TopicName]
  TopicName => string

Metadata Response

The response contains metadata for each partition, with partitions grouped together by topic. This metadata refers to brokers by their broker id. The brokers each have a host and port.

MetadataResponse => [Broker][TopicMetadata]
  Broker => NodeId Host Port  (any number of brokers may be returned)
  	NodeId => int32
  	Host => string
  	Port => int32
  TopicMetadata => TopicErrorCode TopicName [PartitionMetadata]
  	TopicErrorCode => int16
  PartitionMetadata => PartitionErrorCode PartitionId Leader Replicas Isr
  	PartitionErrorCode => int16
  	PartitionId => int32
  	Leader => int32
  	Replicas => [int32]
  	Isr => [int32]

Possible Error Codes

* UnknownTopic (3)
* LeaderNotAvailable (5)
* InvalidTopic (17)
* TopicAuthorizationFailed (29)

Produce API

The produce API is used to send message sets to the server. For efficiency it allows sending message sets intended for many topic partitions in a single request.

The produce API uses the generic message set format, but since no offset has been assigned to the messages at the time of the send the producer is free to fill in that field in any way it likes.

Produce Request

v0, v1 (supported in 0.9.0 or later) and v2 (supported in 0.10.0 or later)
ProduceRequest => RequiredAcks Timeout [TopicName [Partition MessageSetSize MessageSet]]
  RequiredAcks => int16
  Timeout => int32
  Partition => int32
  MessageSetSize => int32

Produce Request on or after v1 indicates the client can parse the quota throttle time in the Produce Response.

Produce Request on or after v2 indicates the client can parse the timestamp field in the produce Response.

Produce Response

v0
ProduceResponse => [TopicName [Partition ErrorCode Offset]]
  TopicName => string
  Partition => int32
  ErrorCode => int16
  Offset => int64
 
v1 (supported in 0.9.0 or later)
ProduceResponse => [TopicName [Partition ErrorCode Offset]] ThrottleTime
  TopicName => string
  Partition => int32
  ErrorCode => int16
  Offset => int64
  ThrottleTime => int32
 
v2 (supported in 0.10.0 or later)
ProduceResponse => [TopicName [Partition ErrorCode Offset Timestamp]] ThrottleTime
  TopicName => string
  Partition => int32
  ErrorCode => int16
  Offset => int64
  Timestamp => int64
  ThrottleTime => int32

Possible Error Codes: (TODO)

Fetch API

The fetch API is used to fetch a chunk of one or more logs for some topic-partitions. Logically one specifies the topics, partitions, and starting offset at which to begin the fetch and gets back a chunk of messages. In general, the return messages will have offsets larger than or equal to the starting offset. However, with compressed messages, it's possible for the returned messages to have offsets smaller than the starting offset. The number of such messages is typically small and the caller is responsible for filtering out those messages.

Fetch requests follow a long poll model so they can be made to block for a period of time if sufficient data is not immediately available.

As an optimization the server is allowed to return a partial message at the end of the message set. Clients should handle this case.

One thing to note is that the fetch API requires specifying the partition to consume from. The question is how should a consumer know what partitions to consume from? In particular how can you balance the partitions over a set of consumers acting as a group so that each consumer gets a subset of partitions. We have done this assignment dynamically using zookeeper for the scala and java client. The downside of this approach is that it requires a fairly fat client and a zookeeper connection. We haven't yet created a Kafka API to allow this functionality to be moved to the server side and accessed more conveniently. A simple consumer client can be implemented by simply requiring that the partitions be specified in config, though this will not allow dynamic reassignment of partitions should that consumer fail. We hope to address this gap in the next major release.

Fetch Request

FetchRequest => ReplicaId MaxWaitTime MinBytes [TopicName [Partition FetchOffset MaxBytes]]
  ReplicaId => int32
  MaxWaitTime => int32
  MinBytes => int32
  TopicName => string
  Partition => int32
  FetchOffset => int64
  MaxBytes => int32

Fetch Response

v0
FetchResponse => [TopicName [Partition ErrorCode HighwaterMarkOffset MessageSetSize MessageSet]]
  TopicName => string
  Partition => int32
  ErrorCode => int16
  HighwaterMarkOffset => int64
  MessageSetSize => int32
 
v1 (supported in 0.9.0 or later) and v2 (supported in 0.10.0 or later)
FetchResponse => ThrottleTime [TopicName [Partition ErrorCode HighwaterMarkOffset MessageSetSize MessageSet]]
  ThrottleTime => int32
  TopicName => string
  Partition => int32
  ErrorCode => int16
  HighwaterMarkOffset => int64
  MessageSetSize => int32

Fetch Response v1 only contains message format v0.

Fetch Response v2 might either contain message format v0 or message format v1.

Possible Error Codes

* OFFSET_OUT_OF_RANGE (1)
* UNKNOWN_TOPIC_OR_PARTITION (3)
* NOT_LEADER_FOR_PARTITION (6)
* REPLICA_NOT_AVAILABLE (9)
* UNKNOWN (-1)

Offset API (AKA ListOffset)

This API describes the valid offset range available for a set of topic-partitions. As with the produce and fetch APIs requests must be directed to the broker that is currently the leader for the partitions in question. This can be determined using the metadata API.

For version 0, the response contains the starting offset of each segment for the requested partition as well as the "log end offset" i.e. the offset of the next message that would be appended to the given partition. In version 1, which was initially supported in 0.10.1.0, Kafka supports a time index to search offsets by the timestamp used in messages, and a change was made to this API to support this. Note that this API is only supported for topics which have enabled the 0.10 message format, and the UNSUPPORTED_FOR_MESSAGE_FORMAT will be returned otherwise.

Offset Request

// v0 
ListOffsetRequest => ReplicaId [TopicName [Partition Time MaxNumberOfOffsets]]
  ReplicaId => int32
  TopicName => string
  Partition => int32
  Time => int64
  MaxNumberOfOffsets => int32

 
// v1 (supported in 0.10.1.0 and later)
ListOffsetRequest => ReplicaId [TopicName [Partition Time]] 
  ReplicaId => int32 
  TopicName => string 
  Partition => int32 
  Time => int64

Offset Response

// v0
OffsetResponse => [TopicName [PartitionOffsets]]
  PartitionOffsets => Partition ErrorCode [Offset]
  Partition => int32
  ErrorCode => int16
  Offset => int64

 
// v1
ListOffsetResponse => [TopicName [PartitionOffsets]] 
  PartitionOffsets => Partition ErrorCode Timestamp [Offset]
  Partition => int32 
  ErrorCode => int16 
  Timestamp => int64 
  Offset => int64

Possible Error Codes

* UNKNOWN_TOPIC_OR_PARTITION (3)
* NOT_LEADER_FOR_PARTITION (6)
* UNKNOWN (-1)

* UNSUPPORTED_FOR_MESSAGE_FORMAT (43)

Offset Commit/Fetch API

These APIs allow for centralized management of offsets. Read more Offset Management. As per comments on KAFKA-993 these API calls are not fully functional in releases until Kafka 0.8.1.1. It will be available in the 0.8.2 release.

Group Coordinator Request

The offsets for a given consumer group are maintained by a specific broker called the group coordinator. i.e., a consumer needs to issue its offset commit and fetch requests to this specific broker. It can discover the current coordinator by issuing a group coordinator request.

GroupCoordinatorRequest => GroupId
  GroupId => string

Group Coordinator Response

GroupCoordinatorResponse => ErrorCode CoordinatorId CoordinatorHost CoordinatorPort
  ErrorCode => int16
  CoordinatorId => int32
  CoordinatorHost => string
  CoordinatorPort => int32

Possible Error Codes

* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* GROUP_AUTHORIZATION_FAILED (30)

Offset Commit Request

v0 (supported in 0.8.1 or later)
OffsetCommitRequest => ConsumerGroupId [TopicName [Partition Offset Metadata]]
  ConsumerGroupId => string
  TopicName => string
  Partition => int32
  Offset => int64
  Metadata => string

 
v1 (supported in 0.8.2 or later)
OffsetCommitRequest => ConsumerGroupId ConsumerGroupGenerationId ConsumerId [TopicName [Partition Offset TimeStamp Metadata]]
  ConsumerGroupId => string
  ConsumerGroupGenerationId => int32
  ConsumerId => string
  TopicName => string
  Partition => int32
  Offset => int64
  TimeStamp => int64
  Metadata => string

v2 (supported in 0.9.0 or later)
OffsetCommitRequest => ConsumerGroup ConsumerGroupGenerationId ConsumerId RetentionTime [TopicName [Partition Offset Metadata]]
  ConsumerGroupId => string
  ConsumerGroupGenerationId => int32
  ConsumerId => string
  RetentionTime => int64
  TopicName => string
  Partition => int32
  Offset => int64
  Metadata => string

In v0 and v1, the time stamp of each partition is defined as the commit time stamp, and the offset coordinator will retain the committed offset until its commit time stamp + offset retention time specified in the broker config; if the time stamp field is not set, brokers will set the commit time as the receive time before committing the offset, users can explicitly set the commit time stamp if they want to retain the committed offset longer on the broker than the configured offset retention time.

In v2, we removed the time stamp field but add a global retention time field (see KAFKA-1634 for details); brokers will then always set the commit time stamp as the receive time, but the committed offset can be retained until its commit time stamp + user specified retention time in the commit request. If the retention time is not set (-1), the broker offset retention time will be used as default.

Note that when this API is used for a "simple consumer," which is not part of a consumer group, then the generationId must be set to -1 and the memberId must be empty (not null). Additionally, if there is an active consumer group with the same groupId, then the commit will be rejected (typically with an UNKNOWN_MEMBER_ID or ILLEGAL_GENERATION error). 

Offset Commit Response

v0, v1 and v2:
OffsetCommitResponse => [TopicName [Partition ErrorCode]]]
  TopicName => string
  Partition => int32
  ErrorCode => int16

Possible Error Codes

* OFFSET_METADATA_TOO_LARGE (12)
* GROUP_LOAD_IN_PROGRESS (14)
* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* NOT_COORDINATOR_FOR_GROUP (16)
* ILLEGAL_GENERATION (22)
* UNKNOWN_MEMBER_ID (25)
* REBALANCE_IN_PROGRESS (27)
* INVALID_COMMIT_OFFSET_SIZE (28)
* TOPIC_AUTHORIZATION_FAILED (29)
* GROUP_AUTHORIZATION_FAILED (30)

Offset Fetch Request

Per the comment on

v0 and v1 (supported in 0.8.2 or after):
OffsetFetchRequest => ConsumerGroup [TopicName [Partition]]
  ConsumerGroup => string
  TopicName => string
  Partition => int32

Offset Fetch Response

v0 and v1 (supported in 0.8.2 or after):
OffsetFetchResponse => [TopicName [Partition Offset Metadata ErrorCode]]
  TopicName => string
  Partition => int32
  Offset => int64
  Metadata => string
  ErrorCode => int16

Note that if there is no offset associated with a topic-partition under that consumer group the broker does not set an error code (since it is not really an error), but returns empty metadata and sets the offset field to -1.

There is no format difference between Offset Fetch Request v0 and v1. Functionality wise, Offset Fetch Request v0 will fetch offset from zookeeper, Offset Fetch Request v1 will fetch offset from Kafka.

Possible Error Codes

* UNKNOWN_TOPIC_OR_PARTITION (3) <- only for request v0
* GROUP_LOAD_IN_PROGRESS (14)
* NOT_COORDINATOR_FOR_GROUP (16)
* ILLEGAL_GENERATION (22)
* UNKNOWN_MEMBER_ID (25)
* TOPIC_AUTHORIZATION_FAILED (29)
* GROUP_AUTHORIZATION_FAILED (30)

Group Membership API

These requests are used by clients to participate in a client group managed by Kafka. From a high level, each group in the cluster is assigned one the brokers (its group coordinator) to facilitate group management. Once the coordinator has been located (using the group coordinator request from above), group members can join the group and synchronize state, and then use heartbeats to stay active in the group. When the client shuts down, it uses a leave group request to deregister from the group. More detail on protocol semantics is outlined in Kafka Client-side Assignment Proposal.

The primary use case for the membership API is consumer groups, but the requests are intentionally generic to support other cases (e.g. Kafka Connect groups). The cost of this generality is that specific group semantics are pushed into the client. For example, the JoinGroup/SyncGroup requests defined below have no explicit fields supporting partition assignment for consumer groups. Instead, they contain generic byte arrays in which assignments can be embedded by the consumer client implementation.

But although this allows each client implementation to define its own embedded schema, compatibility with Kafka tooling requires clients to use the standard embedded schema used by the client shipped with Kafka. The consumer-groups.sh utility, for example, assumes this format to display partition assignments. We therefore recommend that clients should follow the same schema so that these tools will work for all client implementations.

Join Group Request

The join group request is used by a client to become a member of a group. When new members join an existing group, all previous members are required to rejoin by sending a new join group request. When a member first joins the group, the memberId will be empty (i.e. ""), but a rejoining member should use the same memberId from the previous generation. 

v0 supported in 0.9.0.0 and greater
JoinGroupRequest => GroupId SessionTimeout MemberId ProtocolType GroupProtocols
  GroupId => string
  SessionTimeout => int32
  MemberId => string
  ProtocolType => string
  GroupProtocols => [ProtocolName ProtocolMetadata]
    ProtocolName => string
    ProtocolMetadata => bytes
 
v1 supported in 0.10.1.0 and greater
JoinGroupRequest => GroupId SessionTimeout MemberId ProtocolType GroupProtocols
  GroupId => string
  SessionTimeout => int32
  RebalanceTimeout => int32
  MemberId => string
  ProtocolType => string
  GroupProtocols => [ProtocolName ProtocolMetadata]
    ProtocolName => string
    ProtocolMetadata => bytes

The SessionTimeout field is used to indicate client liveness. If the coordinator does not receive at least one heartbeat (see below) before expiration of the session timeout, then the member will be removed from the group. Prior to version 0.10.1, the session timeout was also used as the timeout to complete a needed rebalance. Once the coordinator begins rebalancing, each member in the group has up to the session timeout in order to send a new JoinGroup request. If they fail to do so, they will be removed from the group. In 0.10.1, a new version of the JoinGroup request was created with a separate RebalanceTimeout field. Once a rebalance begins, each client has up to this duration to rejoin, but note that if the session timeout is lower than the rebalance timeout, the client must still continue to send heartbeats.

The ProtocolType field defines the embedded protocol that the group implements. The group coordinator ensures that all members in the group support the same protocol type. The meaning of the protocol name and metadata contained in the GroupProtocols field depends on the protocol type. Note that the join group request allows for multiple protocol/metadata pairs. This enables rolling upgrades without downtime. The coordinator chooses a single protocol which all members support. The upgraded member includes both the new version and the old version of the protocol. Once all members have upgraded, the coordinator will choose whichever protocol is listed first in the GroupProtocols array.

Consumer groups: Below we define the embedded protocol used by consumer groups. We recommend all consumer implementations follow this format so that tooling will work correctly across all clients.

ProtocolType => "consumer"
 
ProtocolName => AssignmentStrategy
  AssignmentStrategy => string
 
ProtocolMetadata => Version Subscription UserData
  Version => int16
  Subscription => [Topic]
    Topic => string
  UserData => bytes

The UserData field can be used by custom partition assignment strategies. For example, in a sticky partitioning implementation, this field can contain the assignment from the previous generation. In a resource-based assignment strategy, it could include the number of cpus on the machine hosting each consumer instance.

Kafka Connect uses the "connect" protocol type and its protocol details are internal to the Connect implementation.

Join Group Response

After receiving join group requests from all members in the group, the coordinator will select one member to be the group leader and a protocol which is supported by all members. The leader will receive the full list of members along with the associated metadata for the protocol chosen. Other members, followers, will receive an empty array of members. It is the responsibility of the leader to inspect the metadata of each member and assign state using SyncGroup request below.

Upon every completion of the join group phase, the coordinator increments a GenerationId for the group. This is returned as a field in the response to each member, and is sent in heartbeats and offset commit requests. When the coordinator rebalances a group, the coordinator will send an error code indicating that the member needs to rejoin. If the member does not rejoin before a rebalance completes, then it will have an old generationId, which will cause ILLEGAL_GENERATION errors when included in new requests.

v0 and v1 supported in 0.9.0 and greater
JoinGroupResponse => ErrorCode GenerationId GroupProtocol LeaderId MemberId Members
  ErrorCode => int16
  GenerationId => int32
  GroupProtocol => string
  LeaderId => string
  MemberId => string
  Members => [MemberId MemberMetadata]
    MemberId => string
    MemberMetadata => bytes

Consumer groups: The coordinator is responsible for choosing a protocol (i.e. partition assignment strategy) compatible across all members. The leader then is the member which actually performs the selected assignment. Multiple assignment strategies can be included in the join group request to support either an upgrade of an existing version or a change to a different assignment strategy.

Possible Error Codes:

* GROUP_LOAD_IN_PROGRESS (14)
* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* NOT_COORDINATOR_FOR_GROUP (16)
* INCONSISTENT_GROUP_PROTOCOL (23)
* UNKNOWN_MEMBER_ID (25)
* INVALID_SESSION_TIMEOUT (26)
* GROUP_AUTHORIZATION_FAILED (30)

SyncGroup Request

The sync group request is used by the group leader to assign state (e.g. partition assignments) to all members of the current generation. All members send SyncGroup immediately after joining the group, but only the leader provides the group's assignment.

SyncGroupRequest => GroupId GenerationId MemberId GroupAssignment
  GroupId => string
  GenerationId => int32
  MemberId => string
  GroupAssignment => [MemberId MemberAssignment]
    MemberId => string
    MemberAssignment => bytes

Consumer Groups: The format of the MemberAssignment field for consumer groups is included below:

MemberAssignment => Version PartitionAssignment
  Version => int16
  PartitionAssignment => [Topic [Partition]]
    Topic => string
    Partition => int32
  UserData => bytes

All client implementations using the "consumer" protocol type should support this schema.

Sync Group Response

Each member in the group will receive the assignment from the leader in the sync group response. 

SyncGroupResponse => ErrorCode MemberAssignment
  ErrorCode => int16
  MemberAssignment => bytes

Possible Error Codes:

* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* NOT_COORDINATOR_FOR_GROUP (16)
* ILLEGAL_GENERATION (22)
* UNKNOWN_MEMBER_ID (25)
* REBALANCE_IN_PROGRESS (27)
* GROUP_AUTHORIZATION_FAILED (30)

Heartbeat Request

Once a member has joined and synced, it will begin sending periodic heartbeats to keep itself in the group. If not heartbeat has been received by the coordinator with the configured session timeout, the member will be kicked out of the group.

HeartbeatRequest => GroupId GenerationId MemberId
  GroupId => string
  GenerationId => int32
  MemberId => string  

Heartbeat Response

HeartbeatResponse => ErrorCode
  ErrorCode => int16

Possible Error Codes:

* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* NOT_COORDINATOR_FOR_GROUP (16)
* ILLEGAL_GENERATION (22)
* UNKNOWN_MEMBER_ID (25)
* REBALANCE_IN_PROGRESS (27)
* GROUP_AUTHORIZATION_FAILED (30)

LeaveGroup Request

To explicitly leave a group, the client can send a leave group request. This is preferred over letting the session timeout expire since it allows the group to rebalance faster, which for the consumer means that less time will elapse before partitions can be reassigned to an active member.

LeaveGroupRequest => GroupId MemberId
  GroupId => string
  MemberId => string

LeaveGroup Response

LeaveGroupResponse => ErrorCode
  ErrorCode => int16

Possible Error Codes:

* GROUP_LOAD_IN_PROGRESS (14)
* CONSUMER_COORDINATOR_NOT_AVAILABLE (15)
* NOT_COORDINATOR_FOR_CONSUMER (16)
* UNKNOWN_CONSUMER_ID (25)
* GROUP_AUTHORIZATION_FAILED (30)

Administrative API

ListGroups Request

This API can be used to find the current groups managed by a broker. To get a list of all groups in the cluster, you must send ListGroup to all brokers.

ListGroupsRequest => 

ListGroups Response

ListGroupsResponse => ErrorCode Groups
  ErrorCode => int16
  Groups => [GroupId ProtocolType]
    GroupId => string
    ProtocolType => string

Possible Error Codes:

* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* AUTHORIZATION_FAILED (29)

DescribeGroups Request

DescribeGroupsRequest => [GroupId]
  GroupId => string

DescribeGroups Response

DescribeGroupsResponse => [ErrorCode GroupId State ProtocolType Protocol Members]
  ErrorCode => int16
  GroupId => string
  State => string
  ProtocolType => string
  Protocol => string
  Members => [MemberId ClientId ClientHost MemberMetadata MemberAssignment]
    MemberId => string
    ClientId => string
    ClientHost => string
    MemberMetadata => bytes
    MemberAssignment => bytes  

Possible Error Codes:

* GROUP_LOAD_IN_PROGRESS (14)
* GROUP_COORDINATOR_NOT_AVAILABLE (15)
* NOT_COORDINATOR_FOR_GROUP (16)
* AUTHORIZATION_FAILED (29)

Constants

Api Keys And Current Versions

The following are the numeric codes that the ApiKey in the request can take for each of the above request types.

Error Codes

We use numeric codes to indicate what problem occurred on the server. These can be translated by the client into exceptions or whatever the appropriate error handling mechanism in the client language. Here is a table of the error codes currently in use:

Introduction

This document covers the protocol implemented in Kafka 0.8. It is meant to give a readable guide to the protocol that covers the available requests, their binary format, and the proper way to make use of them to implement a client. This document assumes you understand the basic design and terminology described here.

Overview

The Kafka protocol is fairly simple, there are only four client requests APIs.

1. Metadata - Describes the currently available brokers, their host and port information, and gives information about which broker hosts which partitions.
2. Send - Send messages to a broker
3. Fetch - Fetch messages from a broker, one which fetches data, one which gets cluster metadata, and one which gets offset information about a topic.
4. Offsets - Get information about the available offsets for a given topic partition.

Each of these will be described in detail below.

Preliminaries

Kafka uses a binary protocol over TCP. The protocol defines all apis as request response message pairs. All messages are size delimited and are made up of the following primitive types.

The client initiates a socket connection and then writes a sequence of request messages and reads back the corresponding response message. No handshake is required on connection or disconnection.

The client will likely need to maintain a connection to multiple brokers, however it should not be necessary to maintain multiple connections to a single broker from a single client instance (i.e. connection pooling).

The assignment of messages to partitions is a client concern. When it receives a produce or fetch request the broker will check that it is the leader for the partitions in the request. However the assignment of messages to partitions is purely in the control of the client. Most clients will want to partition by the message key, but the exact nature of the partition mapping is not specified. Generally it will be some kind of simple hash over the partitions. If two clients want to use the same partitioning scheme they must use the same partition assignment algorithm. In the scala client we have made it pluggable via a partitioner interface that controls the mapping of key to partition.

As indicated above the client must address most requests to the broker responsible for the appropriate data. The only exception is the metadata request which can be issued against any server. The result of this request gives the partition ownership information that can be used to direct subsequent requests. If the client gets an error it should refresh its metadata.

The server has a configurable maximum limit on request size and any request that exceeds this limit will result in the socket being disconnected.

Protocol Primitives

The protocol is built out of the following primitive structures.

Fixed Width Primitives

int8, int16, int32, int64, uint8, uint16, uint32, uint64 - Integers with the given precision (in bits) stored in big endian order. unitXX variants are unsigned and have double the range.

Variable Length Primitives

bytes16, bytes32, string16 - These types consist of a signed integer giving a length N followed by N bytes of content. -1 indicates null. bytes16 and string16 use a two byte int16 size and bytes32 uses a four byte int32 size. string16 is identical in format to bytes16 but the bytes should be interpreted as UTF8 encoded characters.

Arrays

This is a notation for handling repeated structures. These will always be encoded as an uint32 size containing the length N followed by N repetitions of the structure which can itself be made up of other primitive types. In the BNF grammars below we will show an array of a structure foo as \[foo\].

Notes on reading the request format grammars

The BNFs below give an exact context free grammar for the request and response binary format. For each API I will give the request and response together followed by all the sub-definitions. The BNF is intentionally not compact in order to give human-readable name (for example I define a production for ErrorCode even though it is just an int16 in order to give it a symbolic name). As always in a BNF a sequence of productions indicates concatenation, so the MetadataRequest given below would be a sequence of bytes containing first a VersionId, then a ClientId, and then an array of TopicNames (each of which has its own definition). Productions are always given in camel case and primitive types in lower case. When there are multiple possible productions these are separated with '|' and may be inclosed in parenthesis for grouping. The top-level definition is always given first and subsequent sub-parts are indented.

Common Request and Response Structure

All requests and responses originate from the following grammar which will be incrementally describe through the rest of this document:

RequestOrResponse => MessageSize (RequestMessage | ResponseMessage)
  MessageSize => uint32

A request looks like this:

RequestMessage => ApiKey ApiVersion ClientId RequestMessage
  ApiKey => uint16
  ApiVersion => uint16
  ClientId => string
  RequestMessage => MetadataRequest | ProduceRequest | FetchRequest | OffsetRequest

The various request and response messages will be described below.

And the response:

Response => VersionId ResponseMessage
ResponseMessage => MetadataResponse | ProduceResponse | FetchResponse | OffsetResponse

...

Message sets

...

One structure common to both the produce and fetch requests is the message set format. A message in kafka is a key-value pair with a small amount of associated metadata. A message set is just a sequence of messages with offset and size information. This format happens to be used both for the on-disk storage on the broker and the on-the-wire format.

A message set is also the unit of compression in Kafka, and we allow messages to recursively contain compressed message sets to allow batch compression.

MessageSet => [Offset MessageSize Message]  Offset => int64
  MessageSize => int32

The individual messages in the set are defined as follows:

Message => Crc MagicByte Attributes Key Value
  Crc => int32
  MagicByte => int8
  Attributes => int8
  Key => bytes32
  Value => bytes32

Metadata API

This API answers the question "who has what data and where are they?". Specifically this request will tell for each topic how many partitions it has, which brokers currently host each of these partitions, and which of these is the master. Since Kafka is a partitioned system requests need to be directed to the appropriate server--the one currently acting as the master for the partition you want to interact with. Since cluster membership in Kafka is dynamic, you can't just give all the clients a config file with all the brokers (some of them may be down, or partitions may have moved); instead you need to ask the cluster about its current state at run time. Hence the first thing a client needs to do when it connects is ask, "where is everyone?" using this metadata API.

This is the only request that can be made to any server without regard to partition ownership and all servers will give the same answer (disregarding timing differences). Fetch and produce requests always interact with particular partitions, and sending these to the wrong broker will result in an invalid metadata error. The client is expected to cache the cluster metadata locally, using it to direct requests to the correct hosts, until it gets an invalid metadata error or can't reach a particular broker, at which point it should fetch the metadata again and update its cache.

This presents a bit of a catch-22, since the only way to find out which Kafka servers exists is to ask a Kafka server, so how can a client ever connect the first time? To do this a client should take a "bootstrap urls" configuration from which it can find out the list of currently available servers. Importantly this need not contain all the servers in the cluster, maybe just two or three for redundancy. The client should try each of these until it finds one it can connect to. This will ensure that even if one of the bootstrap servers is down the client can still fetch the cluster metadata.

For deployment you may not want to hardcode such a list and may prefer to rely on dns or a VIP or something like that to find a bootstap server.

So the lifecycle of most clients looks something like this:

1. Cycle through a list of bootstrap kafka urls until we find one we can connect to. Fetch cluster metadata.
2. Process fetch or produce requests, directing them to the appropriate broker based on the topic/partitions they send to or fetch from.
3. If we get an appropriate error, refresh the metadata and try again.

Since there may be many topics the client can give an optional list of topic names in order to only return metadata for a subset of topics.

The metdata returned is at the partition level, but grouped together by topic for convenience and to avoid redundancy. For each partition the metadata contains the information for the leader as well as for all the replicas and the list of replicas that are currently in-sync.

Metadata Request

MetadataRequest => [TopicName]
  TopicName => string

Metadata Response

MetadataResponse => [TopicMetadata]
  TopicMetadata => TopicErrorCode TopicName [PartitionMetadata]
  PartitionMetadata => PartitionErrorCode PartitionId LeaderExists Leader Replicas Isr
  PartitionErrorCode => int16
  PartitionId => unit32
  LeaderExists => int8
  Leader => Broker
  Replicas => [Broker]
  Isr => [Broker]
  Broker => NodeId CreatorId Host Port
  NodeId => uint32
  CreatorId => string
  Host => string
  Port => uint32

Produce API

The produce API is used to send message sets to the server. For efficiency it allows sending message sets intended for many topic partitions in a single request.

The produce API uses the generic message set format, but since no offset has been assigned to the messages at the time of the send the producer is free to fill in that field in any way it likes.

Produce Request

ProduceRequest => RequiredAcks Timeout [TopicName [Partition MessageSetSize MessageSet]]
  RequiredAcks => int16
  Timeout => uint32
  Partition => uint32
  MessageSetSize => uint32

Produce Response

ProduceResponse => [TopicName [Partition ErrorCode Offset]]
  TopicName => string
  Partition => unit32
  ErrorCode => int16
  Offset => int64

Fetch API

The fetch API is used to fetch a chunk of one or more logs for some topic-partitions. Logically one specifies the topics, partitions, and starting offset at which to begin the fetch and gets back a chunk of messages.

Fetch requests follow a long poll model so they can be made to block for a period of time if sufficient data is not immediately available.

One thing to note is that the fetch API requires specifying the partition to consume from. The question is how should a consumer know what partitions to consume from? In particular how can you balance the partitions over a set of consumers acting as a group so that each consumer gets a subset of partitions. We have done this assignment dynamically using zookeeper for the scala and java client. The downside of this approach is that it requires a fairly fat client and a zookeeper connection. We haven't yet created a Kafka API to allow this functionality to be moved to the server side and accessed more conveniently. A simple consumer client can be implemented by simply requiring that the partitions be specified in config, though this will not allow dynamic reassignment of partitions should that consumer fail. We hope to address this gap in the next major release.

Fetch Request

FetchRequest => ReplicaId MaxWaitTime MinBytes [TopicName [Partition FetchOffset MaxBytes]]
  ReplicaId => int32
  MaxWaitTime => uint32
  MinBytes => uint32
  TopicName => string
  Partition => uint32

  FetchOffset => int64
  MaxBytes => uint32

Fetch Response

FetchResponse => [TopicName [Partition ErrorCode FetchedOffset HighwaterMarkOffset MessageSetSize MessageSet]]
  TopicName => string
  Partition => unit32
  ErrorCode => int16
  FetchedOffset => uint64
  HighwaterMarkOffset => int64
  MessageSetSize => int32

Offset API

This API describes the valid offset rage available for a set of topic-partitions. As with the produce and fetch APIs requests must be directed to the broker that is currently the leader for the partitions in question. This can be determined using the metadata API.

The response contains the starting offset of each segment for the requested partition as well as the "log end offset" i.e. the offset of the next message that would be appended to the given partition.

We agree that this API is slightly funky.

Offset Request

OffsetRequest => [TopicName [Partition Time MaxNumberOfOffsets]]
  TopicName => string
  Partition => uint32
  Time => uint64
  MaxNumberOfOffsets => int32

Offset Response

OffsetResponse => [TopicName [PartitionOffsets]]
  PartitionOffsets => Partition ErrorCode [Offset]
  Partition => int32
  ErrorCode => int16
  Offset => int64

Error Codes

We use numeric codes to indicate what problem occurred on the server. These can be translated by the client into exceptions or whatever the appropriate error handling mechanism in the client language. Here is a table of the error codes currently in use:

Some Common Philosophical Questions

Some people have asked why we don't use HTTP. There are a number of reasons, the best is that client implementors can make use of some of the more advanced TCP features--the ability to multiplex requests, the ability to simultaneously poll many connections, etc. We have also found HTTP libraries in many languages to be surprisingly shabby.

...

Another question is why we don't adopt XMPP, STOMP, AMQP or an existing protocol. The answer to this varies by protocol, but in general the problem is that the protocol does determine large parts of the implementation and we couldn't do what we are doing if we didn't have control over the protocol. Our belief is that it is possible to do better than existing messaging systems have in providing a truly distributed messaging system, and to do this we need to build something that works differentdifferently.

A final question is why we don't use a system like Protocol Buffers or Thrift to define our request messages. These packages excel at helping you to managing lots and lots of serialized messages. However we have only a few messages. Support across languages is somewhat spotty (depending on the package). Finally the mapping between binary log format and wire protocol is something we manage somewhat carefully and this would not be possible with these systems. Finally we prefer the style of versioning APIs explicitly and checking this to inferring new values as nulls as it allows more nuanced control of compatibilitof compatibility.