microservices
TrialTechniques
A software architecture that splits an application into independently deployable services.
Why it's here
Placed in Trial: 12 article(s) of evidence from 3 source(s), led by research-stage coverage, with 2 in the last 30 days. Confidence 70%.
Evidence (12)
- 4The New Stack·10/8/2026researchDesigning Scalable APIs for AI Agents
The article explains why AI agent traffic behaves differently from normal web traffic and argues that microservices-style design is better suited to handling it. It uses an OpenAI-compatible API example and a prototype backed by Oracle AI Database Free to show how shared state, retries, and bursty tool calls affect scalability.
- 7InfoQ·10/2/2026framework_updateUber Eats Rebuilds Search Pipeline to Halve Latency
Uber rebuilt major parts of the Uber Eats search pipeline and says the changes cut end-to-end latency by 50%. The work included Above-the-Fold measurement, reduced retrieval, parallel hydration, advertising data redesign, infrastructure tuning, and an agentic coding workflow, with further exploration of microbatching, product-based retrieval, and HTTP multipart streaming.
- 5InfoQ·8/28/2026framework_updateHow Netflix’s Commerce Architecture Evolved from DVDs to Global Streaming
Kasia Trapszo outlines how Netflix transformed its commerce platform from a U.S. DVD-by-mail service into global infrastructure. The talk covers handling international payment differences, meeting strict regulatory requirements, breaking up monoliths along domain boundaries, and re-architecting systems to support large live-event traffic.
- 4The New Stack·8/6/2026researchWhy platform teams resist “just rewrite it” modernization
The article argues that many organizations now operate both cloud-native Kubernetes platforms and legacy VM environments, creating duplicated infrastructure, staffing, and governance costs. It says “rewrite it” is often framed as an easy modernization fix, but in practice rewrites are expensive, risky, and slowed by architecture, validation, and data migration work that AI cannot eliminate.
- 3InfoQ·8/4/2026framework_updateMicroservices Platforms and Team Topologies
Chris Richardson explains how internal platforms and Team Topologies can speed up microservices delivery while reducing cognitive load on stream-aligned teams. He outlines six platform patterns spanning security, observability, build, and deployment, and highlights common pitfalls in platform engineering.
- 3InfoQ·7/31/2026framework_updateHolly Cummins on idea circularity in tech
Holly Cummins argues that many current tech trends repeat earlier patterns, linking historic architecture tradeoffs to today’s cloud, microservices, and AI hype cycles. She also connects financial and technical debt to broader forms of debt, urging engineering leaders to adapt to changing assumptions and rely on proven engineering practices.
- 6InfoQ·7/28/2026breakthroughHow a Payroll Team Split Three Monoliths into 120 Microservices
A payroll and HR software team gradually decomposed three monolithic systems into more than 120 domain microservices over five years. The migration used a pull-based approach, with each new feature built as a separate service to avoid changing legacy code and keep costs low.
- 2Hacker News·7/20/2026researchWhy perfection can prevent over-engineering
The article argues that over-engineering is not about caring too much, but about solving the wrong problem under unclear requirements. It says that with well-defined constraints, a system can have a single best solution, and that many software design choices should be treated as product decisions based on user needs.
- 7InfoQ·7/20/2026product_launchDoorDash Builds 1.5M RPS Proxy Cache with Envoy and Valkey
DoorDash developed Entity Cache, a transparent proxy caching platform designed to cut redundant service-to-service traffic in its microservices environment. Built on Envoy and Valkey, it operates inside the company’s service mesh and is reported to handle more than 1.5 million requests per second with very high availability.
- 4The New Stack·7/16/2026researchThe Real Bottleneck in Independent Deployments Is Validation
The article argues that many platform teams already have strong deployment tooling, but still ship changes in batches because they cannot validate each change independently with enough confidence. It says shared environments and release trains were practical responses to scarce validation capacity, and that coding agents may worsen the problem by increasing change volume and batch size.
- 4InfoQ·7/14/2026framework_updateLessons Learned from Migrating to Micro-Frontends
Luca Mezzalira shares practical lessons from helping teams move from monolithic web applications to distributed frontend architectures. The talk covers the difference between components and micro-frontends, a six-step decision framework for client- versus server-side rendering, and the use of edge compute for safer iterative rollouts.
- 6The New Stack·7/11/2026researchAI code reviews expose a new software bottleneck
The article argues that merge quality should be treated as an explicit contract, especially as coding agents increase pull request volume. It says traditional pipelines usually verify only the first three layers of confidence, while real system behavior testing remains the costly missing layer that often catches microservice bugs.