In early 2024, I hit ten years at Allegro, which also happens to be how long I’ve been working with microservices. This timespan also roughly corresponds to how long the company as a whole has been using them, so I think it’s a good time to outline the story of project Rubicon: a very ambitious gamble which completely changed how we work and what our software is like. The idea probably seemed rather extreme at the time, yet I am certain that without this change, Allegro would not be where it is today, or perhaps would not be there at all.
At Allegro, we use Kafka as a backbone for asynchronous communication between microservices. With up to 300k messages published and 1M messages consumed every second, it is a key part of our infrastructure. A few months ago, in our main Kafka cluster, we noticed the following discrepancy: while median response times for produce requests were in single-digit milliseconds, the tail latency was much worse. Namely, the p99 latency was up to 1 second, and the p999 latency was up to 3 seconds. This was unacceptable for a new project that we were about to start, so we decided to look into this issue. In this blog post, we would like to describe our journey — how we used Kafka protocol sniffing and eBPF to identify and remove the performance bottleneck.
Have you ever thought about ways of reducing repetitive, monotonous tasks? Maybe you would like to try to automate your own tasks? I will show you what technology we use at Allegro, what processes we have automated, and how to do it on your own.
This story shows how we strive to fix issues reported by our customers regarding inconsistent listing views on our e-commerce platform. We will use a top-down manner to guide you through our story. At the beginning, we highlight the challenges faced by our customers, followed by presenting basic information on how views are personalized on our web application. We then delve deeper into our internal architecture, aiming to clarify how it supports High Availability (HA) by using two data centers. Finally, we advertise a little Couchbase, distributed NoSQL database, and explain why it is an excellent storage solution for such an architecture.
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