A recent study published by researchers from Washington University in St. Louis and Uber Technologies reveals a critical but often overlooked pattern in microservices architecture: non-fatal errors. While these errors do not cause system failures, they introduce noticeable performance overhead that can impact user experience and operational costs.
Distributed traces are essential for understanding the behaviour, performance, and reliability of microservices architecture. They can be used to surface meaningful observations about service dependencies, call graphs, and runtime dynamics, enabling software engineers and scientists to develop new tools for optimisation and fault diagnosis.
Recent analyses of Meta and Alibaba’s production microservices architectures identified patterns of heavy-tail and power law distributions. These patterns manifest in service scale, and request patterns, providing a glimpse into the inherent characteristics of large-scale distributed systems. For details review of Meta and Alibaba’s microservices ecosystem please see here and here.
Alibaba has built a complex system of microservices to support its large user base and manage its diverse business operations. This article explores key learning from Alibaba’s microservices architecture, presenting critical observations from its design, scalability, performance optimisation, and resource allocation model.
The rise of service-oriented architecture (SOA) and microservices architecture has led to a major shift in software development, enabling the creation of complex, distributed systems composed of independent, loosely coupled services. These architectures offer numerous benefits, including scalability, flexibility, and resilience.
As microservices architectures have become increasingly common in modern software systems, they have brought benefits and challenges. One of the most pressing challenges has been maintaining performance at scale while dealing with complex service dependencies and network communication overhead.
In contemporary technology environments, organisations are increasingly challenged with the complexities of privacy engineering. The evolving data governance and regulatory ecosystems demand not only technical ingenuity but also a deep understanding of legal frameworks and organizational dynamics.
At the heart of privacy lies the principle of purpose limitation, dictating that data should only be processed for explicitly stated purposes. This principle presents a considerable challenge, especially for organisations operating at the scale of Meta, which handles vast amounts of data from billions of users.
Recently I migrated this website from Ghost to Hugo. This site is now generated by Hugo, stored by Github, deployed on Cloudflare Pages, and content managed via Decap CMS. Hugo, Decap CMS, Cloudflare Pages, and GitHub together create a powerful and efficient stack for building, managing, and deploying static websites.
Differential Privacy is a powerful framework for ensuring privacy in data analysis by adding controlled noise to computations. Its mathematical foundation guarantees that the presence or absence of any individual’s data in a dataset does not significantly affect the outcome of an analysis. Here are six key equations that capture the essence of differential privacy and its mechanisms, along with references to their origins and explanations.
