# Microservices Published articles for Microservices. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance DevFeed: [How API Design Is Evolving for AI Agents, Security, Reliability, and Compliance]() Original publisher: [Read original article]() Author: J Simpson Published: 2026-09-17T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [API](), [Architecture & Design](), [Artificial Intelligence](), [reliability](), [Security]() Tags: [agents](), [ai-agents](), [api](), [api-architecture](), [api-as-a-product](), [api-design](), [api-discovery](), [api-security](), [architecture](), [authorization](), [blog](), [mcp](), [microservices](), [rate-limiting](), [reliability](), [security]() ### AI overview This article examines six ways API design is evolving as APIs serve AI agents and other machine consumers, with implications for security, reliability, and regulatory compliance. ### Source excerpt For over two decades, API design and architecture remained remarkably consistent. Perhaps it's the outsized influence of Roy Fielding's RESTful dissertation, but API designers have stuck to the principles of stateless architecture, resource-based endpoints, and HTTP commands to an impressive degree. That's all starting to change, now that we've radically recontextualized the way we use ... ## AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity DevFeed: [AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-09-16T19:13:53Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [Monitoring](), [reliability](), [Microservices](), [Data pipelines](), [Artificial Intelligence](), [Job]() Tags: [ai](), [bug](), [data-pipelines](), [jobs](), [microservices](), [monitoring](), [reliability]() ### AI overview Spotify describes how rapid change, content-processing weaknesses, capacity limits, and a scheduling bug contributed to delays in publishing episodes. It reports adding end-to-end monitoring, fixing the scheduler, lowering batch-job priority, and increasing capacity. ### Source excerpt Quality and reliability have always been a point of pride for Spotify. We run an extraordinarily complex... The post AI Changed How Spotify Builds. What We Learned (and Fixed) About Quality at Higher Velocity appeared first on Spotify Engineering. ## Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time DevFeed: [Behind the Scenes: How the OpenTelemetry Plugin Maps Your Microservices in Real-Time]() Original publisher: [Read original article]() Author: Egor Klimov Published: 2026-09-16T12:34:47Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [OpenTelemetry](), [Microservices](), [observability](), [telemetry](), [Traces](), [ide]() Tags: [all-things-web](), [backstage](), [environment-variables](), [goland](), [ide](), [idea](), [intellij-idea](), [intellij-platform](), [jetbrains](), [microservices](), [observability](), [opentelemetry](), [performance-optimization](), [plugin-development](), [plugin-highlights](), [plugins](), [pycharm](), [research](), [rider](), [telemetry](), [traces](), [webstorm]() ### AI overview This article explains how the JetBrains OpenTelemetry Plugin generates a service map from runtime telemetry. It describes using logs, metrics, and especially standardized trace spans to visualize how microservices communicate, along with the plugin's lightweight local OpenTelemetry backend. ### Source excerpt We've all been there: you join a new project, and the first thing you ask for is the architecture diagram. You're handed a diagram that looks great, but after a week of debugging, you realize it's six months out of date. Service A hasn't talked to Service B since the spring, and there's a new [...] ## Spring Office Hours Podcast: S5E23 - Java 27 Release Party with Billy Korando DevFeed: [Spring Office Hours Podcast: S5E23 - Java 27 Release Party with Billy Korando]() Original publisher: [Read original article]() Author: danvega Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Spring]() Topics: [Java](), [java-27](), [JDK 27]() Tags: [batch](), [boot](), [cloud](), [developer](), [ecosystem](), [event-driven](), [java](), [java-27](), [live](), [live-stream](), [microservices](), [podcast](), [reactive](), [release](), [serverless](), [spring](), [web-applications]() ### AI overview A Spring Office Hours podcast episode discusses Java 27 with Billy Korando and highlights related launch updates and selected JDK enhancement proposals. ### Source excerpt Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun are joined by Kansas City JUG organizer and Java Developer Advocate, Billy Korando. In this episode, we celebrate the release of Java 27. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform. Show Notes JDK 27 Java 27 Launch Stream JEP 534: Compact Object Headers by Default JEP 523: Make G1 the Default Garbage Collector in All Environments JEP 533: Structured Concurrency (Seventh Preview) JEP 531: Lazy Constants (Third Preview) JEP 536: JFR In-Process Data Redaction Transitioning Java to more frequent security updates Project Babylon S5E22 - Live from KCDC Billy Korando on LinkedIn The show: springofficehours.io - episodes, schedule, community Spring Developer on YouTube - join us live every Monday Dan Vega DaShaun Carter The Spring Blog - the news we cover each week ## This Week in Spring - September 15th, 2026 DevFeed: [This Week in Spring - September 15th, 2026]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Spring AI](), [Java](), [Security](), [OAuth 2.0](), [Spring Boot](), [JavaFX](), [IntelliJ IDEA](), [debugging]() Tags: [batch](), [boot](), [cloud](), [debugging](), [event-driven](), [intellij-idea](), [java](), [learn](), [microservices](), [oauth-2-0](), [reactive](), [security](), [serverless](), [spring](), [spring-ai](), [web-applications]() ### AI overview A September 15, 2026 roundup of Spring-related developer content, covering Java memory management, Spring AI chat-memory summarization, JavaFX application security with Spring Security and OAuth 2.0, OAuth 2.1 with Spring Authorization Server, Spring and Gemini video understanding, Java AI libraries, Netflix's Java and Spring story, Spring Batch, and Spring Boot debugging. ### Source excerpt Hi, Spring fans! Welcome to another rip-roarin' installment of This Week in Spring! I'm writing this to you from sun-kissed San Francisco, CA, sipping some coffee and watching the bay from my breakfast nook. What a wonderful day! A wonderful day in which to learn about the latest and greatest in Spring, even! Let's dive right in! I really loved this presentation by Oracle's Ron Pressler on the principles of memory management in Java More Craig Walls Spring AI goodness! Here's a nice recipe on summarizing chat memory Over the last few weeks, I've done some content on securing JavaFX applications with Spring Security and OAuth 2.0. That work has landed, and our friends at JFX-central.com have taken that video and the resulting code and transcribed the content into this lovely tutorial - check it out! I loved this post on securing modern applications with OAuth 2.1 and Spring Authorization Server This is a really cool video on understanding video with Spring and Gemini I just learned about this amazing new Java library called Quixotic.ai (what a name! LOL) that provides all sorts of cool stuff that might make your Java-based AI workloads even better. I wonder if there are amazing integration possibilities for Spring AI, too... In last week's A Bootiful Podcast, I was delighted to sit down and chat with Netflix's Paul Bakker on their Java and Spring story, scaling the system, and more. Huh! There's a new Spring Batch IntelliJ IDEA plugin, but search me for what's new! No release notes. Either way, get it while it's hot! Speaking of Spring Batch, there's a nice post here on scaling to millions of rows with Spring Batch that just dropped Speaking of Spring and IntelliJ, there's a nice article over on Baeldung on debugging Spring Boot-based workloads with the Spring debugger ## Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB DevFeed: [Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-14T13:48:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Cache](), [Caching](), [migration](), [sql-server](), [Latency](), [Prometheus]() Tags: [a-b-testing](), [agoda-price-cache-dragonflydb](), [architecture-design](), [cache](), [caching](), [development](), [devops](), [distributed-cache](), [distributed-data](), [distributed-systems](), [latency](), [microservices](), [migration](), [news](), [prometheus](), [redis](), [sql-server]() ### AI overview Agoda migrated its 1.5 TB hotel Price Cache from 72 Microsoft SQL Server shards to DragonflyDB. The staged migration used workload testing, dual reads, parity metrics, and gradual traffic shifting; Agoda reported substantially lower P99 read latency. ### Source excerpt Agoda migrated its 1.5 TB hotel Price Cache from 72 SQL Server shards to DragonflyDB to handle growing read and write volumes. The migration used staged dual reads, parity validation, gradual traffic shifting, and decentralized failover detection. Agoda reports an approximately eightfold reduction in P99 read latency, with two DragonflyDB clusters providing high availability. By Leela Kumili ## Multi-Agent Architecture and Microservices: What Still Works and Where It Breaks DevFeed: [Multi-Agent Architecture and Microservices: What Still Works and Where It Breaks]() Original publisher: [Read original article]() Author: Byte-Sized Design Published: 2026-09-12T17:56:36Z Content type: opinion Language: en Sources: [Byte-Sized Design]() Topics: [Microservice](), [Architecture & Design]() Tags: [agent](), [architecture](), [microservices](), [multi-agent]() ### AI overview An opinion article comparing multi-agent architecture with microservices. It discusses what still works, three places the approach breaks, and one current heuristic. ### Source excerpt The parts that still work, the three places it breaks, and one heuristic for right now ## When to Split a Single AI Agent into Multiple Agents DevFeed: [When to Split a Single AI Agent into Multiple Agents]() Original publisher: [Read original article]() Author: The T-Shaped Dev Published: 2026-09-12T05:19:14Z Content type: opinion Language: en Sources: [The T-Shaped Dev]() Topics: [Artificial Intelligence](), [Architecture & Design](), [Microservices]() Tags: [agent](), [ai](), [ai-agents](), [microservices](), [multi-agent]() ### AI overview The article argues that multi-agent systems can be worthwhile when a single agent suffers from expanding tool choices, conflicting system instructions, and excessive blast radius. It recommends starting with one agent and using measurable signs to determine when to split into specialized agents managed by a supervisor. ### Source excerpt When one agent should become many, when it shouldn't, and the supervisor pattern that makes the split work. (8 min) ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Red Hat edge platforms: Choosing the right one for your use case DevFeed: [Red Hat edge platforms: Choosing the right one for your use case]() Original publisher: [Read original article]() Author: Daniel Froehlich Published: 2026-09-11T13:01:48Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Edge](), [Containers](), [Kubernetes](), [Architecture & Design](), [Hardware](), [Availability](), [Microservice]() Tags: [containers](), [deployment](), [edge](), [edge-computing](), [hardware](), [high-availability](), [kubernetes](), [management-tools](), [microservices](), [red-hat](), [virtual-machines]() ### AI overview This article introduces a series about choosing Red Hat edge platforms for specific deployment needs. It compares Red Hat Enterprise Linux and Red Hat OpenShift across workload types, platform sizes, hardware requirements, Kubernetes use cases, availability, storage, and management needs. ### Source excerpt Choosing the right platform for an edge deployment is one of the most consequential decisions an organization makes--and one of the most confusing. The options range from a single-board computer running a handful of containers to a full Kubernetes cluster with high availability, software-defined storage, and centralized management. Pick too small, and you hit a wall when requirements grow. The post Red Hat edge platforms: Choosing the right one for your use case appeared first on Red Hat Developer. ## Presentation: How To Run on Three Clouds at Once, and When Not To DevFeed: [Presentation: How To Run on Three Clouds at Once, and When Not To]() Original publisher: [Read original article]() Author: Ross McFarlane, Kevin Holditch Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [networking](), [Databases](), [Kubernetes]() Tags: [amazon](), [architecture](), [architecture-design](), [availability](), [aws](), [cloud](), [cloud-architecture](), [cloud-computing](), [cloud-networking](), [cockroach-labs](), [cockroachdb](), [containers](), [databases](), [devops](), [disaster-recovery](), [engineering](), [europe](), [financial-applications](), [fintech](), [form3-multicloud-architecture](), [infoq](), [kubernetes](), [microservices](), [networking](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [resilience](), [transcripts](), [us]() ### AI overview A presentation on Form3's move from one cloud to a triple-active multi-cloud architecture, covering cross-cloud networking, distributed databases, Kubernetes operators, and regional disaster-recovery expectations. ### Source excerpt Ross McFarlane and Kevin Holditch discuss Form3's evolution from a single-cloud setup to a triple active multi-cloud architecture. They share key engineering strategies for cross-cloud networking, distributed databases with CockroachDB and NATS, custom Kubernetes operators, and navigating distinct regional disaster recovery expectations across the UK, Europe, and US financial markets. By Ross McFarlane, Kevin Holditch ## A Bootiful Podcast: Netflix's Paul Bakker DevFeed: [A Bootiful Podcast: Netflix's Paul Bakker]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Concurrency](), [Concurrent Programming](), [Tooling]() Tags: [ai](), [batch](), [boot](), [cloud](), [complexity](), [concurrency](), [event-driven](), [java](), [microservices](), [netflix](), [podcast](), [reactive](), [serverless](), [spring](), [tooling](), [web-applications]() ### AI overview A podcast interview with Netflix's Paul Bakker on scaling Java, adopting newer JDKs to reduce costs, and using virtual threads and structured concurrency instead of reactive complexity. It also previews AI-powered tooling and Project Valhalla's future. ### Source excerpt Hi, Spring fans! This week's interview is a must-watch if you care about where Java is headed next. This week, I talk to Netflix's Paull Bakker! We dig into Netflix's real-world playbook for scaling Java, cutting costs with newer JDKs, and replacing reactive complexity with virtual threads and structured concurrency. Plus, there's a sneak peek at the exciting future of Java, from AI-powered tooling to the upcoming Valhalla era. ## NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC DevFeed: [NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC]() Original publisher: [Read original article]() Author: NVIDIA Writers Published: 2026-09-09T16:00:42Z Content type: release Language: en Sources: [NVIDIA Blog]() Topics: [AI Development](), [GPU](), [SDKs](), [Microservice]() Tags: [ai](), [artificial-intelligence](), [compliance](), [events](), [gpu](), [holoscan-for-media](), [image-to-video](), [media](), [media-and-entertainment](), [microservices](), [news](), [nvidia](), [nvidia-nim](), [pro-graphics](), [real-time](), [sdks](), [streaming](), [synthetic](), [text-to-video](), [video]() ### AI overview NVIDIA announced an expansion of NVIDIA AI for Media at IBC 2026, including GPU-accelerated SDKs and NIM microservices for media workflows. The article highlights Synthetic Video Detector integrations for assessing whether video footage may be AI-generated and for compliance review. ### Source excerpt At the IBC conference, running Sept. 11-14 in Amsterdam, the creative, technology and business communities are coming together to turn ideas into action and discuss innovations across the media and entertainment industries. More than 44,000 attendees from 170+ countries are gathering to explore 1,300+ exhibitions in 14+ halls and outdoor spaces, with over 600 speakers [...] ## Spring Tools 5.4.0 released DevFeed: [Spring Tools 5.4.0 released]() Original publisher: [Read original article]() Author: martinlippert Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Spring]() Topics: [releases](), [Claude](), [Model Context Protocol](), [Visual Studio Code]() Tags: [batch](), [boot](), [claude](), [claude-code](), [cloud](), [event-driven](), [java](), [mcp](), [microservices](), [performance](), [reactive](), [release](), [release-notes](), [serverless](), [spring](), [tools](), [updates](), [validation](), [web-applications]() ### AI overview Spring Tools 5.4.0 has been released for several development environments, with new validations and quick fixes, Claude Code and MCP enhancements, and stability and performance improvements. ### Source excerpt On behalf of the team and everyone who has contributed, I am pleased to announce the 5.4.0 release of the Spring Tools for Visual Studio Code, Cursor, Eclipse, Theia - and Claude Code. Hightlights New Validations & Quick Fixes: Added validations and quick fixes to convert to @ApplicationModuleListener, @SpringJUnitConfig, @RestController, and specific @Scope annotations Claude Code / MCP Enhancements: Enabled the Claude Code plugin to render a project's logical structure Stability & Performance: Significant speedups around various quick fixes and repository-based version validation + improved reliability while indexing source code Updates to the Spring Tools for Eclipse distribution updated to the latest Eclipse 2026-09 release (new and noteworthy) Detailed changes can be found in the release notes: https://github.com/spring-projects/spring-tools/releases/tag/5.4.0.RELEASE Downloads To download the distribution for Eclipse and find links to the marketplace entries for Visual Studio Code, Cursor, and Theia, please go visit: Spring Tools: https://spring.io/tools/ Next up is the 5.5.0 release, currently scheduled for mid December 2026. ## Spring Office Hours Podcast: S5E22 - Live from KCDC DevFeed: [Spring Office Hours Podcast: S5E22 - Live from KCDC]() Original publisher: [Read original article]() Author: danvega Published: 2026-09-09T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Spring AI](), [Java](), [JDK 27]() Tags: [ai](), [batch](), [boot](), [cloud](), [conference](), [event-driven](), [events](), [java](), [jdk-27](), [live-stream](), [microservices](), [podcast](), [reactive](), [serverless](), [spring](), [web-applications]() ### AI overview Spring Office Hours Podcast episode S5E22 features Dan Vega and DaShaun Carter broadcasting from the Kansas City Developer Conference, with conference highlights, attendee and speaker conversations, and news from the Spring and Java ecosystems. ### Source excerpt Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun broadcast live from the Kansas City Developer Conference, one of the largest community driven developer events in the country. Expect conference highlights, hallway track conversations with speakers and attendees, and the latest news from the Spring and Java worlds. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform. Show Notes KCDC Spring AI Recipes Spring Modulith Spring AI Community Project Babylon JDK 27 The show: springofficehours.io - episodes, schedule, community Spring Developer on YouTube - join us live every Monday Dan Vega DaShaun Carter The Spring Blog - the news we cover each week ## Built for Reliability: How American Express Processes Payments at Scale DevFeed: [Built for Reliability: How American Express Processes Payments at Scale]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-08T18:31:03Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [Architecture & Design](), [Microservice](), [Microservices](), [Network](), [Server]() Tags: [account-balance](), [architecture](), [bank](), [card](), [cell-based-architecture](), [microservices](), [network](), [payments](), [scale](), [server](), [services]() ### AI overview The article explains how American Express processes payments through a cell-based architecture. Transactions move through independent processing units and microservices, with isolation designed to limit disruption when services fail. ### Source excerpt In this article, we will try to understand how the transaction runs through such a cell-based architecture and how the payments are processed even when some services are failing. ## This Week in Spring - September 8th, 2026 DevFeed: [This Week in Spring - September 8th, 2026]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [Artificial Intelligence](), [Large Language Model](), [Electron](), [App](), [IntelliJ IDEA]() Tags: [ai](), [applications](), [article](), [batch](), [boot](), [cloud](), [event-driven](), [idea](), [intellij](), [intellij-idea](), [java](), [linux](), [llm](), [llms](), [microservices](), [oauth](), [reactive](), [serverless](), [spring](), [spring-ai](), [spring-boot](), [video](), [web-applications](), [windows]() ### AI overview This weekly Spring roundup highlights Spring AI content about running LLMs in the JVM and locally, building a Spring AI starter for agents.md, and improving tool use. It also covers Spring Boot and JavaFX desktop applications, native images, Spring Security OAuth clients, secure application images, external configuration, Spring Cloud AWS, and a Spring Boot Analyzer. ### Source excerpt Bonjour a tout le monde! Welcome to another rip-roarin' installment of This Week in Spring! It's a fabulous and fun day here in Paris, France, as I wait to board the train to Amsterdam for the IntelliJ IDEA conference! It's going to be amazing. We've got another incredible week's roundup to dive into, so let's do it! I feel like some of the best content over the past several months in this weekly roundup has been Craig Walls' Spring AI Recipes section. Fantastic stuff! This latest one looks at running an LLM in-JVM This is a nice article on using vertical slices in Spring Boot on Solodev.sk, by Dominik. Well done! Another amazing installment from Craig Walls, this one looking at running against local LLMs My friend and colleague DaShaun Carter talks about his Spring AI starter for agents.md Craig also has this lovely post on efficient tool use in Spring AI Last week, I did two videos on using Spring Boot, JavaFX, GraalVM native images, and Spring Security (and PKCE) to build native, lightning-fast, dynamic, efficient, reusable desktop applications that run well on Mac, Windows, and Linux, and look amazing, while taking small fractions of the RAM of a similar Electron-based application. Here's the first one, showing how to use Spring Boot and JavaFX together, so that you get the component model, event dispatch subsystem, internationalization, lifecycle management, and, of course, the entire and very rich ecosystem of Spring components and can use them to manage JavaFX components, too. We also look at native image compilation. Here's the second video, which looks at using Spring Security's OAuth client in the context of a desktop application, which can not, by definition, hold a client secret. In last week's installment of A Bootiful Podcast, I was delighted to chat with BellSoft's Catherine Edelveis about trusted and secure images for your Spring Boot applications This is a nice post on managing external configurations with Spring Cloud Config A nice recap of some of ## Five Years of Kafka at Razorpay's UPI Switch DevFeed: [Five Years of Kafka at Razorpay's UPI Switch]() Original publisher: [Read original article]() Author: Kshitij Nawandar Published: 2026-09-07T09:09:58Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Kafka](), [Architecture & Design](), [event driven](), [Amazon Simple Queue Service (SQS)](), [distributed-systems](), [Microservices](), [Amazon Web Services]() Tags: [aws](), [aws-sns](), [distributed-systems](), [event-driven](), [kafka](), [microservices](), [razorpay](), [redshift](), [sns](), [sqs]() ### AI overview Razorpay describes five years of evolution in its UPI Switch, from a monolith using AWS SQS to Kafka-based infrastructure. The article covers architectural decisions, operational challenges, and optimization work affecting payment-processing performance, reliability, and scale. ### Source excerpt Preface The UPI Switch at Razorpay has evolved significantly in the five years since we started building it. The Switch is the platform that enables real-time payment processing with NPCI. When the team began, it was little more than an idea. Today it powers more than 70% of Razorpay's total UPI volume. Because UPI is inherently asynchronous, a messaging system sits at the heart of the Switch and has a direct impact on performance, reliability, and scale. What began as a straightforward queue became the core of the system, shaping how every new feature was designed and delivered. This post covers that evolution: the decisions that enabled growth, the ones that slowed us down, the operational issues that forced us to rethink our assumptions, and the optimizations that ultimately stabilized our Kafka-based infrastructure. This is the story of what we got right, what we got wrong, and how we eventually built something stable enough to grow on. The First Version: Monolith and SQS When we began building the UPI Switch, we weren't thinking about massive scale, distributed systems, or elegant event routing. So we built Switch v1 as a monolith. No microservices, no distributed orchestration: just one solid block of code doing everything. That was the right call. We needed to move fast, experiment, and learn, and we followed the Keep It Simple, Stupid (KISS) principle deliberately. For messaging, we picked AWS SQS: reliable, managed, and low on cognitive load. We didn't need ordering guarantees at the time, so a standard queue worked fine. We started with just two queues, and this setup held its ground. It handled a peak of 400 TPS during the IPL. The limitations showed up as the ecosystem grew. A single event, like a successful payment, needed to fan out into multiple workflows: Update NPCI with an API call Send callbacks to merchants about payment status Push structured data into our warehouse (AWS Redshift) To handle this, we started bolting on AWS SNS plus SQS for fan-ou ## The Real Python Podcast - Episode #310: Performance Engineering: Profiling and Making Apps Fast by Default DevFeed: [The Real Python Podcast - Episode #310: Performance Engineering: Profiling and Making Apps Fast by Default]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-04T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [Developer experience](), [Frameworks](), [Microservice](), [Large Language Model]() Tags: [engineering](), [frameworks](), [llms](), [microservices](), [performance](), [podcast](), [profiling](), [python]() ### AI overview This Real Python Podcast episode discusses performance engineering for Python applications with Den Odell, author of "Fast by Default: Practical Performance Engineering." It covers performance budgets, profiling, data-driven decisions, planning for performance, and maintaining speed as systems and user bases grow. ### Source excerpt How do you plan for the performance of your Python applications? What does a performance budget entail, and where should you spend your resources? This week on the show, we speak with Den Odell about his new book "Fast by Default: Practical Performance Engineering." ## A Bootiful Podcast: BellSoft's Catherine Edelveis on hardened runtime images, container security, and more DevFeed: [A Bootiful Podcast: BellSoft's Catherine Edelveis on hardened runtime images, container security, and more]() Original publisher: [Read original article]() Author: joshlong Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Spring]() Topics: [container-security]() Tags: [batch](), [boot](), [cloud](), [container-security](), [event-driven](), [java](), [microservices](), [podcast](), [reactive](), [security](), [serverless](), [spring](), [spring-boot](), [web-applications]() ### AI overview A podcast conversation about using buildpacks and hardened images to ship Spring Boot applications with stronger security, cleaner defaults, and less Dockerfile work. ### Source excerpt Hi, Spring fans! I chat with BellSoft's Catherine Edelweiss about using buildpacks and hardened images to ship Spring Boot apps with stronger security, cleaner defaults, and far less Dockerfile pain. #BellSoft #Docker #Java #JRE #SpringBoot ## AWS Launches Graviton5-Based EC2 R9g and R9gd Memory-Optimized Instances DevFeed: [AWS Launches Graviton5-Based EC2 R9g and R9gd Memory-Optimized Instances]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-01T18:42:59Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Amazon Web Services](), [Amazon EC2](), [Arm](), [cpu](), [Hardware](), [Nitro System](), [virtualization](), [Nitro Isolation Engine](), [Database](), [Caching](), [Kubernetes](), [Formal verification]() Tags: [amazon-ec2](), [amazon-eks](), [arm](), [aws](), [c-plus-plus](), [caching](), [cloud](), [database](), [docker](), [enterprise](), [formal-verification](), [go](), [hardware](), [java](), [microservices](), [nitro-isolation-engine](), [nitro-system](), [node-js](), [nvme](), [processors](), [python](), [time]() ### AI overview AWS has generally released the Amazon EC2 R9g and R9gd instances, powered by AWS Graviton5 processors. These Arm-based, memory-optimized instances offer higher per-vCPU performance, faster memory, larger cache capacity, and increased networking and Amazon EBS bandwidth for database, caching, analytics, container, and microservices workloads. R9gd adds local NVMe SSD storage for low-latency local capacity. Both families use the AWS Nitro System and Nitro Isolation Engine, with AWS citing formal verification for the isolation properties. ### Source excerpt AWS has made its Amazon EC2 R9g and R9gd instances generally available, introducing memory-optimized systems powered by AWS Graviton5 processors. The new Arm-based instances target database, caching, analytics, container, and microservices workloads that require high memory bandwidth and improved per-vCPU performance. Compared with Graviton4-based R8g instances, AWS states that R9g can deliver up to 25% The post AWS Launches Graviton5-Based EC2 R9g and R9gd Memory-Optimized Instances appeared first on StorageReview.com. ## Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science DevFeed: [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:30:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI research agents](), [OpenSSH]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [bionemo](), [claude](), [code](), [developer-tools-techniques](), [drug-discovery](), [gpu](), [microservices](), [nim](), [nvidia](), [science](), [simulation-modeling-design](), [workflow]() ### AI overview A tutorial for running NVIDIA BioNeMo NIM microservices with Claude Science to perform protein-structure prediction using multiple-sequence alignment and multiple folding models. ### Source excerpt Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.... ## Propagating User Identity Chains Across JVM Service Calls DevFeed: [Propagating User Identity Chains Across JVM Service Calls]() Original publisher: [Read original article]() Author: Peter Firmstone Published: 2026-08-23T00:00:00Z Content type: article Language: en Sources: [Nicolas Fränkel]() Topics: [Security](), [Java](), [Authorization](), [Protocol (disambiguation)]() Tags: [authorization](), [dirtychai](), [java](), [jgdms](), [jini](), [jvm](), [microservices](), [protocol](), [security](), [technical](), [tls](), [tokens]() ### AI overview This article explains how a chain of user identities crosses service calls on the wire. It distinguishes TLS machine authentication from in-band user claims, describes receiver-side limits and principal handling, and notes that standard Java 24 lacks the required authorization support without DirtyChai on both ends. ### Source excerpt Alice calls the order service. The order service calls the ledger on her behalf. At the second hop, the ledger has to decide whose authority the debit is being made under. Most stacks answer badly. Forward Alice's bearer token verbatim, and the ledger cannot tell her from the service that relayed it. Drop the token and the ledger sees a machine, with no record that a human started the chain. Neither option lets the ledger authorize the combination. ## When Microservice Decomposition Is the Wrong Default DevFeed: [When Microservice Decomposition Is the Wrong Default]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-08-21T07:00:00Z Content type: opinion Language: en Sources: [Philipp Lehmann]() Topics: [Microservice](), [Architecture & Design](), [Microservices](), [FastAPI]() Tags: [api](), [architecture](), [circuit](), [docker](), [microservices](), [migration](), [modular-monolith](), [monolith](), [openapi](), [tracing]() ### AI overview The article argues that splitting a small system into microservices should not be the default. It recommends keeping responsibilities in a modular monolith unless differences in scaling needs, organizational boundaries, or failure isolation justify a network boundary, whose costs include contracts, compatibility management, retries, timeouts, circuit breaking, and distributed tracing. ### Source excerpt import-linter forbidden contract as a module boundary: a FastAPI modular monolith instead of early microservices, and when a network split pays off. [Next page]()