Kafka | Simply Explained
Blog / Kafka | Simply Explained
So why is Kafka such a pivotal piece of technology that is used by more than 80% of all Fortune 100 companies?TL;DR Summary Core Concepts
ProducersClusters & BrokersTopics & PartitionsConsumers and Consumer GroupsOffsetsMechanics of Kafka Advantages of Kafka Kafka in the Real World
- Kafka is a powerful distributed event streaming platform that handles high volumes of data with ease. Its architecture of producers, brokers, topics, partitions, consumers, and consumer groups enables efficient and scalable real-time data processing.
- The fundamental strength of Kafka lies in its ability to decouple data streams and process them in parallel, providing both reliability and speed.
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- Producers are applications or services that send data (messages) to Kafka. They publish messages to specific topics within the Kafka cluster.
- A Kafka cluster is a group of one or more servers working together. Each server in the cluster is called a broker. Brokers are responsible for receiving messages from producers, storing them safely, and serving them to consumers.
- Kafka organizes messages into topics, which are like categories or channels (e.g. topic for website clicks). Each topic is split into multiple partitions to distribute the data across brokers, allowing for scalability and parallel processing (think of something like ad clicks millions a second and so scaling would be important).
- Consumers are applications or services that read and process messages from Kafka topics. They subscribe to the topics they are interested in and consume messages at their own pace.
- Consumer groups allow multiple consumers to coordinate and share the workload, ensuring that each message is processed by only one consumer within the group.
- Each message within a partition is assigned a unique offset, a sequential number that identifies its position in the partition. Offsets help consumers keep track of which messages they have already processed.
- Message Storage: Kafka stores messages durably on disk and allows consumers to read them at their own pace. This decouples the production of data from its consumption.
- Replication: For fault tolerance, Kafka replicates data across multiple brokers. Each partition has one leader and zero or more followers. If a leader fails, a follower automatically takes over.
- High Throughput and Low Latency: Kafka can process millions of messages per second with minimal delay.
- Scalability: Its partitioned log model allows for horizontal scaling by adding more brokers and partitions.
- Durability and Fault Tolerance: Data replication across brokers ensures that the system can recover from failures without data loss.
- Netflix: Uses Kafka to process and stream real-time data for features like user activity tracking, recommendations, and operational monitoring.
- Uber: Utilizes Kafka to handle real-time event processing for trip data, location tracking, and communication between their microservices.
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