As a part of a broader initiative of refreshing Allegro platform, we are upgrading our internal libraries to Spring Boot 3.0 and Java 17. The task is daunting and filled with challenges, however overall progress is steady and thanks to the modular nature of our code it should end in finite time. Everyone who has performed such an upgrade knows that you need to expect the unexpected and at the end of the day prepare for lots of debugging. No amount of migration guide would prepare you for what’s coming in the field. In the words of Donald Rumsfeld there are unknown unknowns and we need to be equipped with the tools to uncover these unknowns and patch them up. In this blog post I’d like to walk you through a process that should show where the application hangs, although there seems to be nothing wrong with it. I will also show that you don’t always know what code you have – problem known as dependecy hell, place we got quite cosy in during this upgrade.
Label noise is ever-present in machine learning practice. Allegro datasets are no exception. We compared 7 methods for training classifiers robust to label noise. All of them improved the model’s performance on noisy datasets. Some of the methods decreased the model’s performance in the absence of label noise.
Hermes is a distributed publish-subscribe message broker that we use at Allegro to facilitate asynchronous communication between our microservices. As our usage of Hermes has grown over time, we faced a challenge in effectively distributing the load it handles to optimize resource utilization. In this blog post, we will present the implementation of a dynamic workload balancing algorithm that we developed to address this challenge. We will describe the approach we took, the lessons we learned along the way, and the results we achieved.
Many of us, software engineers, have experienced those days when nothing really sparks joy in coding, debugging, preparing spikes or refining tasks for the next sprints. Obviously, we would like to have as few of such days as possible and go on with our work effectively. A solution to this definitely is not tormenting our brains with guilt and forced labour. There are other ways, and I would like to invite you to explore them with me and learn a little about our nervous systems in the process. We’ll find out where the motivation comes from on a biological and psychological level. We’ll also take a look at the changes you can introduce into your day to take advantage of certain mechanisms working on a neural level and boost your motivation and productivity.
Hardware is always hard. The amount of operations, maintenance and planning that goes into supporting a data center is a daunting challenge for any enterprise. Though often unseen, without hardware there is no software.
Software Architecture is an elusive thing which, if neglected, can lead to a hard-to-develop and maintain codebase, and in more drastic circumstances to the failure of a product. This article discusses one of the backend application architecture styles which proved to be successful in providing a good foundation for building and maintaining an application in the long run: Onion Architecture.
