#microservices

#microservices

They had previously invested in a homegrown command line interface program that would run their entire environment on a continuous integration runner, but it turned out it took 15-30 minutes to get the framework up before even running a test, Chia said. There were also build failures from time-outs, and the developer of the system left the company, and nobody knew how to maintain it.

the stack of every single company I've seen is invariably AWS/GCP with at least thirty microservices (how else will you keep the code tidy?), a distributed datastore that charges per query but whose reads depend on how long it's been since the last write, a convoluted orchestrator to make sure that you never know which actual computer your code runs on, autoscaling so random midnight breakages ensure you don't get too complacent with your sleep schedule.

Airbnb has developed Impulse, an internal load testing framework designed to improve the reliability and performance of its microservices. The tool enables distributed, large-scale testing and allows engineering teams to run self-service, context-aware load tests integrated with CI pipelines. By simulating production-like traffic and interactions, Impulse helps engineers identify bottlenecks and errors before changes reach production. According to the Airbnb engineering team, Impulse is already in use in several customer support backend services and is under review for broader adoption.

I'm going to be talking about something very basic, which is almost like the fundamental ideas that I think that every developer should know if they're unfortunate enough to have to work on a distributed system, which is probably most of you. The reason I've written this talk is because I'm working on a book, which is designed as like, you've been dropped onto a project where somebody ill-advisedly made the choice to use microservices, which is always a terrible idea.

When something breaks, the impact isn't always obvious right away. What starts as a few 500 errors in one service can quickly snowball into failed transactions, frustrated users, and revenue loss.

Integrating FinOps early in microservices architectures leads to significant reductions in both cloud costs and operational inefficiencies, creating a more cost-effective environment for organizations.

A standardized CI/CD pipeline for microservices should address key challenges such as coordinating cross-service releases, managing backward compatibility, and preventing configuration duplication.

Arηs is composed of 13 entities across 6 countries, with more than 1750 consultants, enabling quick responses to market changes and requests.

Ten months after the release of MicroProfile 7.0, the MicroProfile Working Group has released version 7.1 of MicroProfile to the Java community.

The enhancement addresses a long-standing inefficiency in Java's memory model where object headers can consume over 20% of heap space in applications with many small objects.

Complex systems emerge from simple parts, revealing that modular, loosely coupled approaches in technology lead to scalable and more reliable systems.

Splitting your codebase too early can lead to reduced team velocity, missed goals, and unnecessary complexity, ultimately derailing startups looking to innovate quickly.

Shared libraries, while enhancing efficiency and consistency, can hinder scalability if not managed properly, particularly in microservices environments.

Akehurst emphasized that effective API simulation is not just about returning canned responses but about accurately reflecting the behavior of the real API.

LittleHorse is an open-source platform for orchestrating distributed systems, effectively tackling state management and microservice coordination challenges for developers.