Other systems don’t put so much emphasis on clustering and replication - e.g. ?īefore diving into technical details, if Apache Kafka is a publish-subscribe system, why would we want to implement a message queue on top of it, instead of using a "proper mq", such as RabbitMQ or ActiveMq?įirst of all, Kafka has reliable and proven clustering and data replication mechanisms if you want to make sure your data is safe, and that each message is processed at-least-once - even in case of losing a number of servers - Kafka should be a good candidate as a base for your message processing system. A proof-of-concept-quality code is available on GitHub, with both embedded and stand-alone examples which should make experimentation quite straightforward. In this post, I’d like to outline a possible implementation of a Kafka-based Message Queue. That works great for many scenarios, but every now and then there are situations when you’d like to have selective message acknowledgment, and more important, redelivery of individual unacknowledged messages. That’s because of the streaming nature of a topic: you can only signal Kafka that all messages up to a given point have been processed, so that in case of failure stream processing restarts from the last committed point. It is not possible to acknowledge individual messages. However, there is one important limitation: you can only commit - or, in other words, acknowledge the processing of - all messages up to a given offset. By committing processed message offsets back to Kafka, it is relatively straightforward to implement guaranteed “at-least-once” processing. The central concept in Kafka is a topic, which can be replicated across a cluster providing safe data storage. Apache Kafka is a very popular publish/subscribe system, which can be used to reliably process a stream of data. There can be multiple producers who produce messages of different kind and the consumer can subscribe to one or more kind of messages in order to receive them. Messages from the producing end, and these can be consumed by the For fault tolerance, an offset can be maintained along with the message in the queue.Ī message queue is nothing but an asynchronous mechanism to enqueue communication To scale up, several nodes can enqueue the queue and several nodes can dequeue it. The mailEticket service can keep picking up events from the queue and process them. For each confirmed eticket, the booking service may enqueue an event for 'mailEticket' service. Here comes the role of enqueuing events for such service. The client need not wait for mail to be sent immediately to the user, to show the confirmation page. For this service to be highly available, calls need to be asynchronous. Imagine for a very popular website like Expedia, thousands of request would be going to 'mail eticket' service per second. It essentially involves search, select, pax details, payment, confirmation and 'mail eticket', in that order. When you have completed a booking, you get a message on the final screen mentioning that the booking details shall be mailed to you separately. Have you ever done a travel booking through a OTA website ( e.g: Expedia). Also, this helps in making server calls asynchronous. This way, data loss and latency are minimised. This bottleneck is resolved by use of message queues, where the producer puts through messages into a queue and consumer consumes those messages from the queue. We could use plain https, but then within a large systems, it would be very difficult to maintain data flow and offsets at the receiving end. Within a distributed system, the major challenge is to communicate between nodes. These microservices may reside on different nodes. To improve upon that, we try to scale up further by breaking the services deployed on server with microservices. We try to add more server nodes, where each node can now handle number of requests in parallel. The server calls are handled by endpoints and things are going smooth. We have a monolithic application let's suppose. In a typical Distributed system, Message Queues play a critical role.
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