Kafka vs. CloudAMQP vs. Starling

Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.

Durable, high throughput data streaming

as a log aggregator

We are using Kafka as a message queue to process our widget logs.

Used as a integration middleware by messaging interchanging.

realtime distributed broker

Distributed system messaging.

消息队列

消息队列，主要用于处理各个微服务之间的通信以及异步处理相关逻辑

Event bus

Provides durable queues that allow the front-end website to accept events and turn them into jobs for the background workers to process.

We use it as an add-on to heroku and it is an amazing message queue.

Used along with Celery, for offloading CPU intensive tasks to separate workers instead of running them on our application server.