![]() Each truck may send a message to Kafka every 20 seconds, each message will contain the truck ID and the truck position (latitude and longitude). We can create a topic named - trucks_gps to which the trucks publish their positions. Each truck is fitted with a GPS locator that reports its position to Kafka. For example, an application that displays truck locations on a dashboard or another application that sends notifications if an event of interest occurs.Ī traffic company wants to track its fleet of trucks. There may be different consumers of the topic. In this case, the trucks (or rather, a GPS application inside of each truck) are the producers). How Consumers, Producers and Topics interact in KafkaĪ traffic company wants to track its fleet of trucks. With these core concepts, we can illustrate a basic example of how the Kafka architecture functions: The position of a record within a partition is known as the offset consumers always read from lower offsets to higher offsets, and cannot read data backwards. Consumers read in order from each partition, though if a consumer is reading from multiple partitions then message order cannot be guaranteed. Each partition may live on different servers, also known as Kafka brokers.ĭata in Apache Kafka is written and read in order, with new records appended to the end of a partition by producers. Partitions are numbered starting from 0 to N-1, where N is the number of partitions. The number of partitions of a topic is specified at the time of topic creation. A single topic may have more than one partition it is common to see topics with 100 partitions, and you can have hundreds of thousands if you wanted. Topics themselves are organized into a number of partitions. Kafka topics can contain any kind of message in any format, and the sequence of all these messages is called a data stream. The data in the topics are stored in the key-value form in binary format. A Kafka Cluster contains Topics, which organize related events or messages. Internally, everything in Kafka is organized into a Kafka Cluster. Producers and Consumers exist outside of Kafka. As with Producers, Consumer applications integrate a Kafka client library, with libraries available for almost all widely used programming languages. ![]() Apache Kafka supports all widely used programming languages.Ĭonsumers are the applications that receive data from Kafka, by reading data from one or more Kafka topics. ![]() Producers are external data sources outside of Kafka, typically applications that will integrate an Apache Kafka library in order to send data via Kafka’s Producer API. We won’t cover every aspect in detail here, as that would make this guide far too large, but you’ll find links to in-depth looks at each aspect of Kafka’s architecture. At its heart, Kafka is simply about moving data from a producer of data to a consumer of data, which is managed via a Kafka topic. Perhaps one of the reasons for the success of Kafka is that its architecture is actually relatively simple, while still being highly scalable and fault-tolerant. Apache Kafka is now an open-source project maintained by Confluent. The creators of Kafka left LinkedIn to form their own company called Confluent to focus full-time on Kafka and its ecosystem. It is used extensively in production workloads in companies such as Netflix, Apple, Uber, and Airbnb, in addition to LinkedIn. Kafka has been deployed in sizes ranging from just three nodes to thousands of nodes. Kafka is highly scalable and fault-tolerant to node loss. ![]() Kafka provides a simple message queue interface on top of its append-only log-structured storage medium. Since its initial development by LinkedIn in 2011, Apache Kafka has become the standard for enterprises and other businesses looking to build data streaming pipelines.Īpache Kafka is now used by more than 80% of the Fortune 100 companies. Apache Kafka is an open-source distributed system consisting of servers and clients, primarily used for data streaming. ![]()
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