It supports multi-subscribers and automatically balances the consumers during failure.It offers high throughput for both publishing and subscribing.It is designed as a distributed system which is very easy to scale out.How Kafka Differs from Traditional Messaging System ?Īpache Kafka differs from traditional messaging system in: The sync mode is way slower but it guarantees durability for data, while the async mode is extremely fast, but it might lose a small percentage of data in case of a node outages. It can be operated in sync and async modes. Producer as the name suggests, sends data to the brokers. Consequently, Kafka can support a large number of consumers and retain large amounts of data with very little overhead. Make sure spark-core2.10 and spark-streaming2.10 are marked as provided dependencies as those are already present in a Spark installation. Kafka does not attempt to track which messages were read by each consumer and only retain unread messages rather, Kafka retains all messages for a set amount of time, and consumers are responsible to track their location in each log. Deploying: Similar to the first approach, you can package spark-streaming-kafka2.10 and its dependencies into the application JAR and the launch the application using spark-submit. Each message in a partition is assigned a unique offset.
What makes Kafka unique is that Kafka treats each topic partition as a log (an ordered set of messages). Since Kafka is a distributed system, topics are partitioned and replicated across multiple nodes.
Producers write data to topics and consumers read from topics. Like many publish-subscribe messaging systems, Kafka maintains feeds of messages in topics. Kafka is a distributed publish-subscribe messaging system that is designed to be fast, scalable, and durable. What is Kafka? kafka is a messaging system that was originally developed at LinkedIn to serve as the foundation for LinkedIn’s activity stream and operational data processing pipeline.