Repository: kafka Updated Branches: refs/heads/trunk 281fac9ed -> 16f85e84c
MINOR: Improve introduction section in docs to better cover connect and streams. Make uses and ecosystem pages stand alone. Project: http://git-wip-us.apache.org/repos/asf/kafka/repo Commit: http://git-wip-us.apache.org/repos/asf/kafka/commit/16f85e84 Tree: http://git-wip-us.apache.org/repos/asf/kafka/tree/16f85e84 Diff: http://git-wip-us.apache.org/repos/asf/kafka/diff/16f85e84 Branch: refs/heads/trunk Commit: 16f85e84ce8c01329b3298d5fca8b27cfcacddb6 Parents: 281fac9 Author: Jay Kreps <jay.kr...@gmail.com> Authored: Wed Sep 28 16:30:21 2016 -0700 Committer: Jay Kreps <jay.kr...@gmail.com> Committed: Wed Sep 28 16:30:21 2016 -0700 ---------------------------------------------------------------------- docs/api.html | 187 +++++++++++-------------------------------- docs/documentation.html | 33 ++++---- docs/ecosystem.html | 2 - docs/introduction.html | 148 +++++++++++++++++++++++++--------- docs/uses.html | 2 - 5 files changed, 170 insertions(+), 202 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/kafka/blob/16f85e84/docs/api.html ---------------------------------------------------------------------- diff --git a/docs/api.html b/docs/api.html index 8e4409d..dc65ac8 100644 --- a/docs/api.html +++ b/docs/api.html @@ -15,13 +15,25 @@ limitations under the License. --> -Apache Kafka includes new java clients (in the org.apache.kafka.clients package). These are meant to supplant the older Scala clients, but for compatibility they will co-exist for some time. These clients are available in a separate jar with minimal dependencies, while the old Scala clients remain packaged with the server. +Kafka includes four core apis: +<ol> + <li>The <a href="#producerapi">Producer</a> API allows applications to send streams of data to topics in the Kafka cluster. + <li>The <a href="#consumerapi">Consumer</a> API allows applications to read streams of data from topics in the Kafka cluster. + <li>The <a href="#streamsapi">Streams</a> API allows transforming streams of data from input topics to output topics. + <li>The <a href="#producerapi">Connect</a> API allows implementing connectors that continually pull from some source system or application into Kafka or push from Kafka into some sink system or application. +</ol> + +Kafka exposes all its functionality over a language independent protocol which has clients available in many programming languages. However only the Java clients are maintained as part of the main Kafka project, the others are available as independent open source projects. A list of non-Java clients is available <a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients";>here</a>. <h3><a id="producerapi" href="#producerapi">2.1 Producer API</a></h3> -We recommend the new Java producer for all new development. The old Scala producers have been deprecated and will be removed in a future major release. -The new Java producer is production tested and generally faster and more fully featured than the previous Scala clients. You can use this client by adding a dependency on -the client jar using the following example maven co-ordinates (you can change the version numbers with new releases): +The Producer API allows applications to send streams of data to topics in the Kafka cluster. +<p> +Examples showing how to use the producer are given in the +<a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html"; title="Kafka 0.10.0 Javadoc">javadocs</a>. +<p> +To use the producer, you can use the following maven dependency: + <pre> <dependency> <groupId>org.apache.kafka</groupId> @@ -30,26 +42,14 @@ the client jar using the following example maven co-ordinates (you can change th </dependency> </pre> -Examples showing how to use the producer are given in the -<a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html"; title="Kafka 0.10.0 Javadoc">javadocs</a>. - -<p> -For those interested in the legacy Scala producer api, information can be found <a href="http://kafka.apache.org/081/documentation.html#producerapi";> -here</a>. -</p> - <h3><a id="consumerapi" href="#consumerapi">2.2 Consumer API</a></h3> -We recommend the new Java consumer for all new development. The new Java consumer replaces the high-level ZooKeeper-based consumer and -low-level consumer APIs (also known as old Scala consumers). - -To ensure a smooth upgrade path, the old Scala consumers are still maintained (although lacking features like security) and continue to work -with the current Kafka clusters. The current plan is to deprecate them in the release after 0.10.1.0 and to remove them in a future major release. - -In the following sections we introduce new Java consumer API and the old Scala consumer APIs (both high-level ConsumerConnector and low-level SimpleConsumer). - -<h4><a id="newconsumerapi" href="#newconsumerapi">2.2.1 New Consumer API</a></h4> -This new unified consumer API removes the distinction between the 0.8 high-level and low-level consumer APIs. You can use this client by adding a dependency on the client jar using the following example maven co-ordinates (you can change the version numbers with new releases): +The Consumer API allows applications to read streams of data from topics in the Kafka cluster. +<p> +Examples showing how to use the consumer are given in the +<a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html"; title="Kafka 0.9.0 Javadoc">javadocs</a>. +<p> +To use the consumer, you can use the following maven dependency: <pre> <dependency> <groupId>org.apache.kafka</groupId> @@ -58,132 +58,37 @@ This new unified consumer API removes the distinction between the 0.8 high-level </dependency> </pre> -Examples showing how to use the consumer are given in the -<a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html"; title="Kafka 0.10.0 Javadoc">javadocs</a>. +<h3><a id="streamsapi" href="#streamsapi">Streams API</a></h3> -<h4><a id="highlevelconsumerapi" href="#highlevelconsumerapi">2.2.2 Old High Level Consumer API</a></h4> -<pre> -class Consumer { - /** - * Create a ConsumerConnector - * - * @param config at the minimum, need to specify the groupid of the consumer and the zookeeper - * connection string zookeeper.connect. - */ - public static kafka.javaapi.consumer.ConsumerConnector createJavaConsumerConnector(ConsumerConfig config); -} - -/** - * V: type of the message - * K: type of the optional key associated with the message - */ -public interface kafka.javaapi.consumer.ConsumerConnector { - /** - * Create a list of message streams of type T for each topic. - * - * @param topicCountMap a map of (topic, #streams) pair - * @param decoder a decoder that converts from Message to T - * @return a map of (topic, list of KafkaStream) pairs. - * The number of items in the list is #streams. Each stream supports - * an iterator over message/metadata pairs. - */ - public <K,V> Map<String, List<KafkaStream<K,V>>> - createMessageStreams(Map<String, Integer> topicCountMap, Decoder<K> keyDecoder, Decoder<V> valueDecoder); - - /** - * Create a list of message streams of type T for each topic, using the default decoder. - */ - public Map<String, List<KafkaStream<byte[], byte[]>>> createMessageStreams(Map<String, Integer> topicCountMap); - - /** - * Create a list of message streams for topics matching a wildcard. - * - * @param topicFilter a TopicFilter that specifies which topics to - * subscribe to (encapsulates a whitelist or a blacklist). - * @param numStreams the number of message streams to return. - * @param keyDecoder a decoder that decodes the message key - * @param valueDecoder a decoder that decodes the message itself - * @return a list of KafkaStream. Each stream supports an - * iterator over its MessageAndMetadata elements. - */ - public <K,V> List<KafkaStream<K,V>> - createMessageStreamsByFilter(TopicFilter topicFilter, int numStreams, Decoder<K> keyDecoder, Decoder<V> valueDecoder); - - /** - * Create a list of message streams for topics matching a wildcard, using the default decoder. - */ - public List<KafkaStream<byte[], byte[]>> createMessageStreamsByFilter(TopicFilter topicFilter, int numStreams); - - /** - * Create a list of message streams for topics matching a wildcard, using the default decoder, with one stream. - */ - public List<KafkaStream<byte[], byte[]>> createMessageStreamsByFilter(TopicFilter topicFilter); - - /** - * Commit the offsets of all topic/partitions connected by this connector. - */ - public void commitOffsets(); - - /** - * Shut down the connector - */ - public void shutdown(); -} - -</pre> -You can follow -<a href="https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Group+Example"; title="Kafka 0.8 consumer example">this example</a> to learn how to use the high level consumer api. -<h4><a id="simpleconsumerapi" href="#simpleconsumerapi">2.2.3 Old Simple Consumer API</a></h4> -<pre> -class kafka.javaapi.consumer.SimpleConsumer { - /** - * Fetch a set of messages from a topic. - * - * @param request specifies the topic name, topic partition, starting byte offset, maximum bytes to be fetched. - * @return a set of fetched messages - */ - public FetchResponse fetch(kafka.javaapi.FetchRequest request); - - /** - * Fetch metadata for a sequence of topics. - * - * @param request specifies the versionId, clientId, sequence of topics. - * @return metadata for each topic in the request. - */ - public kafka.javaapi.TopicMetadataResponse send(kafka.javaapi.TopicMetadataRequest request); - - /** - * Get a list of valid offsets (up to maxSize) before the given time. - * - * @param request a [[kafka.javaapi.OffsetRequest]] object. - * @return a [[kafka.javaapi.OffsetResponse]] object. - */ - public kafka.javaapi.OffsetResponse getOffsetsBefore(OffsetRequest request); - - /** - * Close the SimpleConsumer. - */ - public void close(); -} -</pre> -For most applications, the new Java Consumer API is the best option and it's the API we intend to support going forward. However, if you need to use the SimpleConsumer API, the logic will be a bit more complicated and you can follow the example <a href="https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example"; title="Kafka 0.8 SimpleConsumer example">here</a>. - -<h3><a id="streamsapi" href="#streamsapi">2.3 Streams API</a></h3> - -As of the 0.10.0 release we have added a stream processing engine to Apache Kafka called Kafka Streams, which is a client library that lets users implement their own stream processing applications for data stored in Kafka topics. -You can use Kafka Streams from within your Java applications by adding a dependency on the kafka-streams jar using the following maven co-ordinates: +The <a href="#streamsapi">Streams</a> API allows transforming streams of data from input topics to output topics. +<p> +Examples showing how to use this library are given in the +<a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/streams/KafkaStreams.html"; title="Kafka 0.10.0 Javadoc">javadocs</a> +<p> +Additional documentation on using the Streams API is available <a href="/documentation.html#streams">here</a>. +<p> +To use Kafka Streams you can use the following maven dependency: <pre> <dependency> <groupId>org.apache.kafka</groupId> <artifactId>kafka-streams</artifactId> - <version>0.10.0.1</version> + <version>0.10.0.0</version> </dependency> </pre> -Examples showing how to use this library are given in the -<a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/streams/KafkaStreams.html"; title="Kafka 0.10.0 Javadoc">javadocs</a> and <a href="streams.html" title="Kafka Streams Overview">kafka streams overview</a>. +<h3><a id="connectapi" href="#connectapi">Connect API</a></h3> + +The Connect API allows implementing connectors that continually pull from some source data system into Kafka or push from Kafka into some sink data system. +<p> +Many users of Connect won't need to use this API directly, though, they can use pre-built connectors without needing to write any code. Additional information on using Connect is available <a href="/documentation.html#connect">here</a>. +<p> +Those who want to implement custom connectors can see the <a href="http://kafka.apache.org/0100/javadoc/index.html?org/apache/kafka/connect";>javadoc</a>. <p> - Please note that Kafka Streams is a new component of Kafka, and its public APIs may change in future releases. - We use the <b>@InterfaceStability.Unstable</b> annotation to denote classes whose APIs may change without backward-compatibility in future releases. -</p> + +<h3><a id="legacyapis" href="#streamsapi">Legacy APIs</a></h3> + +<p> +A more limited legacy producer and consumer api is also included in Kafka. These old Scala APIs are deprecated and only still available for compatability purposes. Information on them can be found here <a href="http://kafka.apache.org/081/documentation.html#producerapi";> +here</a>. +</p> \ No newline at end of file http://git-wip-us.apache.org/repos/asf/kafka/blob/16f85e84/docs/documentation.html ---------------------------------------------------------------------- diff --git a/docs/documentation.html b/docs/documentation.html index 95d1251..f4f1ddc 100644 --- a/docs/documentation.html +++ b/docs/documentation.html @@ -31,16 +31,13 @@ Prior releases: <a href="/07/documentation.html">0.7.x</a>, <a href="/08/documen <li><a href="#upgrade">1.5 Upgrading</a> </ul> </li> - <li><a href="#api">2. API</a> + <li><a href="#api">2. APIs</a> <ul> <li><a href="#producerapi">2.1 Producer API</a> <li><a href="#consumerapi">2.2 Consumer API</a> - <ul> - <li><a href="#newconsumerapi">2.2.1 New Consumer API</a> - <li><a href="#highlevelconsumerapi">2.2.2 Old High Level Consumer API</a> - <li><a href="#simpleconsumerapi">2.2.3 Old Simple Consumer API</a> - </ul> <li><a href="#streamsapi">2.3 Streams API</a> + <li><a href="#connectapi">2.4 Connect API</a> + <li><a href="#legacyapis">2.5 Legacy APIs</a> </ul> </li> <li><a href="#configuration">3. Configuration</a> @@ -110,11 +107,6 @@ Prior releases: <a href="/07/documentation.html">0.7.x</a>, <a href="/08/documen <li><a href="#ext4">Ext4 Notes</a> </ul> <li><a href="#monitoring">6.6 Monitoring</a> - <ul> - <li><a href="#selector_monitoring">Common monitoring metrics for producer/consumer/connect</a></li> - <li><a href="#new_producer_monitoring">New producer monitoring</a></li> - <li><a href="#new_consumer_monitoring">New consumer monitoring</a></li> - </ul> <li><a href="#zk">6.7 ZooKeeper</a> <ul> <li><a href="#zkversion">Stable Version</a> @@ -158,13 +150,18 @@ Prior releases: <a href="/07/documentation.html">0.7.x</a>, <a href="/08/documen </ul> <h2><a id="gettingStarted" href="#gettingStarted">1. Getting Started</a></h2> -<!--#include virtual="introduction.html" --> -<!--#include virtual="uses.html" --> -<!--#include virtual="quickstart.html" --> -<!--#include virtual="ecosystem.html" --> -<!--#include virtual="upgrade.html" --> - -<h2><a id="api" href="#api">2. API</a></h2> + <h3><a id="introduction" href="#introduction">1.1 Introduction</a></h3> + <!--#include virtual="introduction.html" --> + <h3><a id="uses" href="#uses">1.2 Use Cases</a></h3> + <!--#include virtual="uses.html" --> + <h3><a id="quickstart" href="#quickstart">1.3 Quick Start</a></h3> + <!--#include virtual="quickstart.html" --> + <h3><a id="ecosystem" href="#ecosystem">1.4 Ecosystem</a></h3> + <!--#include virtual="ecosystem.html" --> + <h3><a id="upgrade" href="#upgrade">1.5 Upgrading From Previous Versions</a></h3> + <!--#include virtual="upgrade.html" --> + +<h2><a id="api" href="#api">2. APIs</a></h2> <!--#include virtual="api.html" --> http://git-wip-us.apache.org/repos/asf/kafka/blob/16f85e84/docs/ecosystem.html ---------------------------------------------------------------------- diff --git a/docs/ecosystem.html b/docs/ecosystem.html index 73d5706..5fbcec5 100644 --- a/docs/ecosystem.html +++ b/docs/ecosystem.html @@ -15,6 +15,4 @@ limitations under the License. --> -<h3><a id="ecosystem" href="#ecosystem">1.4 Ecosystem</a></h3> - There are a plethora of tools that integrate with Kafka outside the main distribution. The <a href="https://cwiki.apache.org/confluence/display/KAFKA/Ecosystem";> ecosystem page</a> lists many of these, including stream processing systems, Hadoop integration, monitoring, and deployment tools. http://git-wip-us.apache.org/repos/asf/kafka/blob/16f85e84/docs/introduction.html ---------------------------------------------------------------------- diff --git a/docs/introduction.html b/docs/introduction.html index 3fbf7a3..147210f 100644 --- a/docs/introduction.html +++ b/docs/introduction.html @@ -14,43 +14,65 @@ See the License for the specific language governing permissions and limitations under the License. --> - -<h3><a id="introduction" href="#introduction">1.1 Introduction</a></h3> -Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. +Kafka is <i>a distributed streaming platform</i>. What exactly does that mean? +<p> +We think of a streaming platform as having three key capabilities: +<ol> + <li>It let's you publish and subscribe to streams of records. In this respect it is similar to a message queue or enterprise messaging system. + <li>It let's you store streams of records in a fault-tolerant way. + <li>It let's you process streams of records as they occur. +</ol> +<p> +What is Kafka good for? +<p> +It gets used for two broad classes of application: +<ol> + <li>Building real-time streaming data pipelines that reliably get data between systems or applications + <li>Building real-time streaming applications that transform or react to the streams of data +</ol> <p> -What does all that mean? +To understand how Kafka does these things, let's dive in and explore Kafka's capabilities from the bottom up. <p> -First let's review some basic messaging terminology: +First a few concepts: <ul> - <li>Kafka maintains feeds of messages in categories called <i>topics</i>. - <li>We'll call processes that publish messages to a Kafka topic <i>producers</i>. - <li>We'll call processes that subscribe to topics and process the feed of published messages <i>consumers</i>. - <li>Kafka is run as a cluster comprised of one or more servers each of which is called a <i>broker</i>. + <li>Kafka is run as a cluster on one or more servers. + <li>The Kafka cluster stores streams of <i>records</i> in categories called <i>topics</i>. + <li>Each record consists of a key, a value, and a timestamp. </ul> - -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: -<div style="text-align: center; width: 100%"> - <img src="images/producer_consumer.png"> +Kafka has four core APIs: +<div style="float: right"> + <img src="images/kafka-apis.png" style="width:400px"> </div> - -Communication between the clients and the servers is done with a simple, high-performance, language agnostic <a href="https://kafka.apache.org/protocol.html";>TCP protocol</a>. We provide a Java client for Kafka, but clients are available in <a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients";>many languages</a>. +<ul> + <li>The <a href="/documentation.html#producerapi">Producer API</a> allows an application to publish a stream records to one or more Kafka topics. + <li>The <a href="/documentation.html#consumerapi">Consumer API</a> allows an application to subscribe to one or more topics and process the stream of records produced to them. + <li>The <a href="/documentation.html#streams">Streams API</a> allows an application to act as a <i>stream processor</i>, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams. + <li>The <a href="/documentation.html#connect">Connector API</a> allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to +</ul> +<p> +In Kafka the communication between the clients and the servers is done with a simple, high-performance, language agnostic <a href="https://kafka.apache.org/protocol.html";>TCP protocol</a>. This protocol is versioned and maintains backwards compatibility with older version. We provide a Java client for Kafka, but clients are available in <a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients";>many languages</a>. <h4><a id="intro_topics" href="#intro_topics">Topics and Logs</a></h4> -Let's first dive into the high-level abstraction Kafka provides—the topic. +Let's first dive into the core abstraction Kafka provides for a stream of records—the topic. <p> -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: +A topic is a category or feed name to which records are published. Topics in Kafka are always multi-subscriber; that is, a topic can have zero, one, or many consumers that subscribe to the data written to it. +<p> +For each topic, the Kafka cluster maintains a partitioned log that looks like this: <div style="text-align: center; width: 100%"> <img src="images/log_anatomy.png"> </div> -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 <i>offset</i> that uniquely identifies each message within the partition. +Each partition is an ordered, immutable sequence of records that is continually appended to—a structured commit log. The records in the partitions are each assigned a sequential id number called the <i>offset</i> that uniquely identifies each record within the partition. <p> -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. +The Kafka cluster retains all published records—whether or not they have been consumed—using a configurable retention period. For example if the retention policy is set to two days, then for the two days after a record 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 storing data for a long time is not a problem. <p> -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. +<div style="float:right"> + <img src="images/log_consumer.png" style="width:400px"> +</div> +In fact, the only metadata retained on a per-consumer basis is the offset or position of that consumer in the log. This offset is controlled by the consumer: normally a consumer will advance its offset linearly as it reads records, but, in fact, since the position is controlled by the consumer it can consume records in any order it likes. For example a consumer can reset to an older offset to reprocess data from the past or skip ahead to the most recent record and start consuming from "now". <p> 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. <p> -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 server that hosts 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 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. <h4><a id="intro_distribution" href="#intro_distribution">Distribution</a></h4> @@ -60,40 +82,88 @@ Each partition has one server which acts as the "leader" and zero or more server <h4><a id="intro_producers" href="#intro_producers">Producers</a></h4> -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. +Producers publish data to the topics of their choice. The producer is responsible for choosing which record 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 record). More on the use of partitioning in a second! <h4><a id="intro_consumers" href="#intro_consumers">Consumers</a></h4> -Messaging traditionally has two models: <a href="http://en.wikipedia.org/wiki/Message_queue";>queuing</a> and <a href="http://en.wikipedia.org/wiki/Publish%E2%80%93subscribe_pattern";>publish-subscribe</a>. 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 <i>consumer group</i>. -<p> -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. +Consumers label themselves with a <i>consumer group</i> name, and each record 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. <p> -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 the same consumer group, then the records will effectively be load balanced over the consumer instances. <p> -If all the consumer instances have different consumer groups, then this works like publish-subscribe and all messages are broadcast to all consumers. -<p> -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 a cluster of consumers instead of a single process. -<p> - +If all the consumer instances have different consumer groups, then each record will be broadcast to all the consumer processes. <div style="float: right; margin: 20px; width: 500px" class="caption"> <img src="images/consumer-groups.png"><br> 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. </div> <p> -Kafka has stronger ordering guarantees than a traditional messaging system, too. +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 a cluster of consumers instead of a single process. <p> -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. +The way consumption is implemented in Kafka is by dividing up the partitions in the log over the consumer instances so that each instance is the exclusive consumer of a "fair share" of partitions at any point in time. This process of maintaining membership in the group is handled by the Kafka protocol dynamically. If new instances join the group they will take over some partitions from other members of the group; if an instance dies, its partitions will be distributed to the remaining instances. <p> -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. -<p> -Kafka only provides a total order over messages <i>within</i> 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. +Kafka only provides a total order over records <i>within</i> 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 records this can be achieved with a topic that has only one partition, though this will mean only one consumer process per consumer group. <h4><a id="intro_guarantees" href="#intro_guarantees">Guarantees</a></h4> At a high-level Kafka gives the following guarantees: <ul> - <li>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. - <li>A consumer instance sees messages in the order they are stored in the log. - <li>For a topic with replication factor N, we will tolerate up to N-1 server failures without losing any messages committed to the log. + <li>Messages sent by a producer to a particular topic partition will be appended in the order they are sent. That is, if a record M1 is sent by the same producer as a record M2, and M1 is sent first, then M1 will have a lower offset than M2 and appear earlier in the log. + <li>A consumer instance sees records in the order they are stored in the log. + <li>For a topic with replication factor N, we will tolerate up to N-1 server failures without losing any records committed to the log. </ul> More details on these guarantees are given in the design section of the documentation. + +<h4><a id="kafka_mq" href="#kafka_mq">Kafka as a Messaging System</a></h4> + +How does Kafka's notion of streams compare to a traditional enterprise messaging system? +<p> +Messaging traditionally has two models: <a href="http://en.wikipedia.org/wiki/Message_queue";>queuing</a> and <a href="http://en.wikipedia.org/wiki/Publish%E2%80%93subscribe_pattern";>publish-subscribe</a>. In a queue, a pool of consumers may read from a server and each record goes to one of them; in publish-subscribe the record is broadcast to all consumers. Each of these two models has a strength and a weakness. The strength of queuing is that it allows you to divide up the processing of data over multiple consumer instances, which lets you scale your processing. Unfortunately queues aren't multi-subscriber—once one process reads the data it's gone. Publish-subscribe allows you broadcast data to multiple processes, but has no way of scaling processing since every message goes to every subscriber. +<p> +The consumer group concept in Kafka generalizes these two concepts. As with a queue the consumer group allows you to divide up processing over a collection of processes (the members of the consumer group). As with publish-subscribe, Kafka allows you to broadcast messages to multiple consumer groups. +<p> +The advantage of Kafka's model is that every topic has both these properties—it can scale processing and is also multi-subscriber—there is no need to choose one or the other. +<p> +Kafka has stronger ordering guarantees than a traditional messaging system, too. +<p> +A traditional queue retains records in-order on the server, and if multiple consumers consume from the queue then the server hands out records in the order they are stored. However, although the server hands out records in order, the records are delivered asynchronously to consumers, so they may arrive out of order on different consumers. This effectively means the ordering of the records 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. +<p> +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. + +<h4>Kafka as a Storage System</h4> + +Any message queue that allows publishing messages decoupled from consuming them is effectively acting as a storage system for the in-flight messages. What is different about Kafka is that it is a very good storage system. +<p> +Data written to Kafka is written to disk and replicated for fault-tolerance. Kafka allows producers to wait on acknowledgement so that a write isn't considered complete until it is fully replicated and guaranteed to persist even if the server written to fails. +<p> +The disk structures Kafka uses scale well—Kafka will perform the same whether you have 50 KB or 50 TB of persistent data on the server. +<p> +As a result of taking storage seriously and allowing the clients to control their read position, you can think of Kafka as a kind of special purpose distributed filesystem dedicated to high-performance, low-latency commit log storage, replication, and propagation. + +<h4>Kafka for Stream Processing</h4> +<p> +It isn't enough to just read, write, and store streams of data, the purpose is to enable real-time processing of streams. +<p> +In Kafka a stream processor is anything that takes continual streams of data from input topics, performs some processing on this input, and produces continual streams of data to output topics. +<p> +For example a retail application might take in input streams of sales and shipments, and output a stream of reorders and price adjustments computed off this data. +<p> +It is possible to do simple processing directly using the producer and consumer APIs. However for more complex transformations Kafka provides a fully integrated <a href="/streams.html">Streams API</a>. This allows building applications that do non-trivial processing that compute aggregations off of streams or join streams together. +<p> +This facility helps solve the hard problems this type of application faces: handling out-of-order data, reprocessing input as code changes, performing stateful computations, etc. +<p> +The streams API builds on the core primitives Kafka provides: it uses the producer and consumer APIs for input, uses Kafka for stateful storage, and uses the same group mechanism for fault tolerance among the stream processor instances. + +<h4>Putting the Pieces Together</h4> + +This combination of messaging, storage, and stream processing may seem unusual but it is essential to Kafka's role as a streaming platform. +<p> +A distributed file system like HDFS allows storing static files for batch processing. Effectively a system like this allows storing and processing <i>historical</i> data from the past. +<p> +A traditional enterprise messaging system allows processing future messages that will arrive after you subscribe. Applications built in this way process future data as it arrives. +<p> +Kafka combines both of these capabilities, and the combination is critical both for Kafka usage as a platform for streaming applications as well as for streaming data pipelines. +<p> +By combining storage and low-latency subscriptions, streaming applications can treat both past and future data the same way. That is a single application can process historical, stored data but rather than ending when it reaches the last record it can keep processing as future data arrives. This is a generalized notion of stream processing that subsumes batch processing as well as message-driven applications. +<p> +Likewise for streaming data pipelines the combination of subscription to real-time events make it possible to use Kafka for very low-latency pipelines; but the ability to store data reliably make it possible to use it for critical data where the delivery of data must be guaranteed or for integration with offline systems that load data only periodically or may go down for extended periods of time for maintenance. The stream processing facilities make it possible to transform data as it arrives. +<p> +For more information on the guarantees, apis, and capabilities Kafka provides see the rest of the <a href="/documentation.html">documentation</a>. http://git-wip-us.apache.org/repos/asf/kafka/blob/16f85e84/docs/uses.html ---------------------------------------------------------------------- diff --git a/docs/uses.html b/docs/uses.html index 5b97272..6214ee6 100644 --- a/docs/uses.html +++ b/docs/uses.html @@ -15,8 +15,6 @@ limitations under the License. --> -<h3><a id="uses" href="#uses">1.2 Use Cases</a></h3> - 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 <a href="http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying";>this blog post</a>. <h4><a id="uses_messaging" href="#uses_messaging">Messaging</a></h4>
