Embed multicolor icons using a single DOM element
Icons are an integral part of most modern UIs. What is the best way to embed icons nowadays?
Icons are an integral part of most modern UIs. What is the best way to embed icons nowadays?
This article is a form of a public postmortem in which we would like to share our bumpy way of revealing the cause of a mysterious performance problem. Besides unveiling part of our technical stack based on open-source solutions, we also show how some false assumptions made such a bug triage process much harder. Besides all NOT TO DOs, you can find some exciting information about performance hunting and reproducing performance issues on a small scale. As a perk, we prepared a repository where you can reproduce the problem and make yourself familiar with tools that allowed us to confirm the cause. The last part (lessons learned) is the most valuable if you prefer to learn from the mistakes of others.
B-tree is a structure that helps to search through great amounts of data. It was invented over 40 years ago, yet it is still employed by the majority of modern databases. Although there are newer index structures, like LSM trees, B-tree is unbeaten when handling most of the database queries.
The idea for this article arose during a meeting where we learned that our supervisor would be leaving the company to pursue new opportunities. In response, a colleague lamented that what we would miss most is the knowledge departing with the leader. Unfortunately, that’s how it goes. Not only do we lose a colleague, but we also lose valuable knowledge and experience. However, this isn’t a story about my supervisor; it’s a story about all those individuals who are experts in their fields, who understand the paths to success and paths that lead to catastrophic failures. When they leave, they take with them knowledge that you won’t find in any book, note, or Jira ticket. And this leads to a fundamental question: What can be done to avoid this “black hole” of knowledge? How can we ensure it doesn’t vanish along with them? That’s what this article is all about.
MongoDB is the most popular database used at Allegro. We have hundreds of MongoDB databases running on our on—premise servers. In 2022 we decided that we need to migrate all our MongoDB databases from existing shared clusters to new MongoDB clusters hosted on Kubernetes pods with separated resources. To perform the migration of all databases we needed a tool for transfering all the data and keeping consistency between old and new databases. That’s how mongo-migration-stream project was born.
After six years as a Team Leader, I went back to hands-on engineering work, and I’m very happy about taking this step. While it may appear surprising at first, it was a well-thought-out decision, and actually I’ve already performed such a maneuver once before.
In the era of ubiquitous cloud services and an increasingly growing PaaS and serverless-oriented approach, performance and resources seem to be becoming less and less important. After all, we can scale horizontally and vertically at any time, without worrying about potential performance challenges that the business may introduce.
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.
Sometimes great results in code performance come with a small amount of work. We’d like to tell you a story about how we changed the Allegro mobile homepage and reduced usage of Allegro service infrastructure with only a few lines of code.
Let’s look at what transactions in MongoDB are and how they differ from SQL transactions.
Building a complex web platform can be a real challenge, especially when parts of it are delivered by independent teams. Picking out the correct architecture is crucial, but maintaining it can be even more challenging. Frontend microservices, aka microfrontends, is an architecture that gives a lot of flexibility, but can cause performance issues in the future, if not managed well. This article presents an approach to the microfrontends architecture to keep the frontend technology stack efficient based on the complexity of user interface.
What would you say if we stored 1 000 records in a database, and the database claimed that there were only 998 of them? Or, if we created a database storing sets of values and in some cases the database would claim that some element was in that set, while in fact it was not? It definitely must be a bug, right? It turns out such behavior is not necessarily an error, as long as we use a database that implements probabilistic algorithms and data structures. In this post we will learn about two probability-based techniques, perform some experiments and consider when it is worth using a database that lies to us a bit.
Currently, in the Android world, the topic of modularization is very popular. Many bloggers describe their experiences with it and analyze what Google recommends. Our team started the modularization process before it was hot. I will describe our reasons, decisions, problems and give you some advice. We will see if modularization makes sense and what it brings to the table. I will also post some statistics showing what it looked like before and after the modularization process.
Cloud providers like to brag about high availability and unlimited scaling of their services – and they are correct, as these features are indeed significant advantages of cloud solutions. Their computational power is so high that for most use cases, it’s almost unlimited. In this blog post, I would like to tell you about our experiences with Azure Cosmos DB and batch processing.
In this article, we want to share our approach to using server-driven UI in native mobile apps. In 2019 we created the first version of the in-house server-driven rendering tool called MBox and used it to render the homepage in the Allegro app on Android and iOS. We have come a long way since then, and now we use this tool to render more and more screens in the Allegro apps. After almost three years of working on MBox, we want to share how it works and the key advantages and challenges of using this approach.
With this article, I would like to introduce you to EventStorming and explain to you how to get started. I am not discovering anything new, just gathering available knowledge in one place. What I will show you is a few tips on how to conduct and facilitate EventStorming workshops.
Each of us has probably experienced a time in our career when we wanted to get rid of the Garbage Collector from our application because it was running too long, too often, and perhaps even led to temporary system freezes. What if we could still benefit from the GC, but in special cases, also be able to store data beyond its control? We could still take advantage of its convenience and, at the same time, be able to easily get rid of long GC pauses. It turns out that it is possible. In this article, we will look at whether and when it is worth storing data beyond the reach of the Garbage Collector’s greedy hands.
At Allegro we decided to introduce GraphQL as our API Gateway for building several internal client systems. By building such a solution we’ve learnt a lot about this technology and we would like to share it with you in this article.
Nowadays, technical debt can be considered the bread and butter of most IT-powered enterprises around the world. Almost every company that survived the startup phase and managed to deliver its first products to customers will face at some point technical challenges related to past architectural decisions. Although code engineering gets better every year, we cannot argue with the obvious fact of life: the market will always force many of us to deliver tech products faster than we wish. Time To Market has always been a key success factor for many product companies and it puts a lot of pressure on Engineering to keep up with challenging deadlines.
This blogpost is the result of a research collaboration between the Allegro Machine Learning Research team and the Institute of Mathematics of the Polish Academy of Sciences (IMPAN), Warsaw.