# Microservice A software architecture in which an application is built from independent services that communicate through well-defined, lightweight APIs and can be updated, deployed, and scaled separately. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## Thursday Notes on Architecture Trade-offs and High-Performing Teams DevFeed: [Thursday Notes on Architecture Trade-offs and High-Performing Teams]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-09-10T15:00:57Z Content type: opinion Language: en Sources: [Tech World With Milan Newsletter]() Topics: [Architecture & Design](), [Microservice]() Tags: [architecture](), [fault-tolerance](), [security](), [software-architecture](), [split](), [team-topologies](), [transactions]() ### AI overview This issue of Thursday Notes discusses architecture as a search for acceptable trade-offs, including reasons to split or merge services. It also explains how teams cycle through forming, storming, norming, and performing, and how leadership should adapt to each stage. ### Source excerpt Hey, Milan here. ## 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 [...] ## 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. ## APIs & Service Communication: The Essential Guide DevFeed: [APIs & Service Communication: The Essential Guide]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-08T13:03:41Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [API](), [Microservice](), [GraphQL](), [gRPC](), [Kafka](), [WebSocket]() Tags: [api](), [apis](), [graphql](), [http](), [kafka](), [rest]() ### AI overview A tutorial comparing communication patterns for distributed systems. It explains how request-response APIs, event streams, and bidirectional connections affect responsiveness, service coupling, failure propagation, operational complexity, reliability, and scalability, then introduces REST and GraphQL with their main strengths, tradeoffs, and use cases. ### Source excerpt REST, gRPC, Kafka, WebSockets, Pub/Sub, and more. How do they all fit together? ## 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. ## Modular Monoliths: Creating Real Boundaries Before Reaching for Microservices DevFeed: [Modular Monoliths: Creating Real Boundaries Before Reaching for Microservices]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-20T13:48:25Z Content type: article Language: en Sources: [freek.dev - all blogposts]() Topics: [Architecture & Design](), [Microservice](), [Database]() Tags: [apis](), [architecture](), [database](), [design-patterns](), [microservices](), [migration](), [modular](), [monolith](), [tests]() ### AI overview This article examines modular monoliths as a way to establish real architectural boundaries before adopting microservices. It covers module APIs, database ownership, cross-module communication, architecture tests, incremental migration, and signals that may justify a move to microservices. ### Source excerpt A deep dive into modular monoliths, from module APIs and database ownership to cross-module communication, architecture tests, incremental migration, and the signals that justify microservices. Read more ## Data Engineering Weekly #283 DevFeed: [Data Engineering Weekly #283]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-17T02:59:40Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Data Quality](), [Microservice](), [Apache Spark](), [Kafka](), [Feature Engineering](), [audit trail](), [Protocol (disambiguation)]() Tags: [ai](), [anthropic](), [article](), [data-engineering](), [data-quality](), [engineering](), [feature-engineering](), [kafka](), [newsletter](), [pipelines](), [services](), [testing]() ### AI overview Data Engineering Weekly #283 is a newsletter covering data platform fundamentals, multiagent system coordination, payments platform data contracts, financial data quality, declarative data engineering, and cost-efficient export workloads. ### Source excerpt The Weekly Data Engineering Newsletter ## Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS DevFeed: [Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS]() Original publisher: [Read original article]() Author: Nuno Loureiro; Luis Grangeia Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Vulnerabilities](), [Authorization](), [Software as a service](), [Microservice](), [Microservices](), [Front end](), [API](), [App](), [Artificial Intelligence](), [Trustworthy AI]() Tags: [agent](), [ai](), [api](), [application-security](), [applications](), [attacks](), [autonomous](), [awareness](), [blog](), [customer](), [customer-featured](), [cybersecurity](), [devops](), [enterprise](), [frontend](), [interest](), [microservices](), [saas](), [security](), [vulnerabilities](), [vulnerability-insights](), [web]() ### AI overview Evo Continuous Offensive Security (COS) is presented as an autonomous offensive-security system combining AI pentesting, agent red teaming, and dynamic testing. The article reports a real assessment of a multi-tenant enterprise SaaS application that uncovered 33 confirmed vulnerabilities, including tenant-wide compromise and critical authorization flaws. ### Source excerpt A real Evo Continuous Offensive Security assessment uncovered 33 confirmed vulnerabilities in a multi-tenant enterprise SaaS, including tenant-wide compromise and critical authorization flaws. ## AI speeds up code writing, but coordination across distributed services remains a system-design challenge DevFeed: [AI speeds up code writing, but coordination across distributed services remains a system-design challenge]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-02T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Artificial Intelligence](), [distributed-systems](), [backends](), [Microservice](), [migration](), [OpenAPI Specification]() Tags: [ai](), [alignment](), [architecture](), [backend](), [distributed-systems](), [migration](), [openapi](), [services](), [system-design]() ### AI overview The article argues that AI assistants have accelerated implementation work while leaving coordination between teams and services largely unchanged. It presents ownership boundaries, contract stability, migration coordination, and failure semantics as central system-design concerns because failures often occur between otherwise correct components. ### Source excerpt AI made the write path fast and left the coordination path untouched, so failures now live in the gaps between services: two event schema versions across two regions, three teams, and one assumption that someone else handled the migration window. Alignment work, deciding ownership boundaries, contract stability, and failure semantics before anyone opens an IDE, is system design, and it produces concrete network cost when skipped. ## Agentic Engineering: Evolution of Developer Portals DevFeed: [Agentic Engineering: Evolution of Developer Portals]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-07-30T11:25:32Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [AI Development](), [Software Engineering](), [Developer experience](), [DevOps](), [Cloud Native Ecosystem](), [Microservice](), [Kubernetes](), [GitOps](), [CI/CD](), [incident](), [Vulnerabilities]() Tags: [agentic](), [ai](), [ai-agents](), [ci-cd](), [cloud-native](), [developer](), [developer-experience](), [devops](), [engineering](), [kubernetes](), [microservices](), [tech-debt]() ### AI overview The article presents agentic engineering as the next evolution of software engineering, with AI agents autonomously handling code, tests, pull requests, incidents, vulnerabilities, technical debt, and other software development lifecycle activities. It argues that developer portals can help provide the guidance and guardrails needed to manage the complexity created by DevOps, cloud-native systems, microservices, Kubernetes, GitOps, and extensive CI/CD automation. ### Source excerpt Internal developer portals are changing. Read all about Port's next evolution, the agentic engineering platform. ## Add API visualizations in Port with Swagger UI | Port DevFeed: [Add API visualizations in Port with Swagger UI | Port]() Original publisher: [Read original article]() Author: Dudi Elhadad Published: 2026-07-30T10:09:05Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [OpenAPI Specification](), [Markdown](), [Microservice](), [dev-tools]() Tags: [api](), [developer-portal](), [documentation](), [integration](), [markdown](), [openapi-specification](), [software-catalog](), [visualization]() ### AI overview Port adds visualization features to its software catalog and developer portal, including Markdown documentation, OpenAPI specifications rendered with Swagger UI, and embedded URLs. These features centralize documentation, API discovery, monitoring dashboards, and other developer tools in context, reducing search and context switching. ### Source excerpt Here are the visualization features we just added: Markdown documentation visualization, Open API - swagger and embedded URLs. ## A field guide to splitting systems, keeping data correct, and surviving failure in production DevFeed: [A field guide to splitting systems, keeping data correct, and surviving failure in production]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-07-23T10:24:07Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [Microservice](), [Microservices](), [Architecture & Design](), [systems]() Tags: [data](), [microservices](), [patterns](), [production](), [systems]() ### AI overview This field guide covers microservices design patterns for splitting systems, maintaining data correctness, and handling failures in production. ### Source excerpt #164: A field guide to splitting systems, keeping data correct, and surviving failure in production ## Port's internal deployment pipeline DevFeed: [Port's internal deployment pipeline]() Original publisher: [Read original article]() Author: Matan Heled Published: 2026-07-22T12:04:29Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [internal developer portal](), [Deployment](), [GitHub](), [Kubernetes](), [Microservice](), [Amazon Web Services]() Tags: [aws](), [complexity](), [deployment](), [developer-portal](), [github](), [kubernetes](), [platform](), [reduce]() ### AI overview The article explains how Port's platform team built an internal deployment pipeline using Port's developer portal and self-service actions. The approach lets developers trigger deployments directly from the portal while reducing operational tickets and standardizing procedures. ### Source excerpt Port enhances developer operations by simplifying self-service deployment, streamlining pipeline complexity, and improving log visibility. ## You don't have a deployment problem. You have a validation problem. DevFeed: [You don't have a deployment problem. You have a validation problem.]() Original publisher: [Read original article]() Author: Arjun Iyer Published: 2026-07-16T17:00:00Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Deployment](), [Microservice](), [Architecture & Design](), [feature flags]() Tags: [ai-agents](), [coding-agents](), [deployment](), [feature-flags](), [kubernetes](), [microservices](), [pipelines](), [platform-engineering](), [post-contributed](), [production](), [signadot](), [sponsor-signadot](), [sponsored-post-contributed](), [strategy]() ### AI overview The article argues that organizations often have mature deployment tooling but still release changes in batches because they lack confidence in validating individual service changes against the live versions of their dependencies. It presents validation, rather than deployment mechanics, as the main constraint on independent releases and says coding agents are increasing the volume and opacity of batch changes. ### Source excerpt Ask a platform team about their deployment capabilities, and you will usually hear a genuinely impressive story. Progressive rollouts that The post You don't have a deployment problem. You have a validation problem. appeared first on The New Stack. ## When Duplicated Code Is the Better Tradeoff DevFeed: [When Duplicated Code Is the Better Tradeoff]() Original publisher: [Read original article]() Author: Franco Fernando Published: 2026-07-04T06:05:31Z Content type: opinion Language: en Sources: [The Polymathic Engineer]() Topics: [Software Engineering](), [Code](), [distributed-systems](), [Microservice](), [Library]() Tags: [abstraction](), [bugs](), [code](), [components](), [distributed](), [distributed-systems](), [reusable-code](), [services](), [software](), [software-engineering](), [teams]() ### AI overview This article examines when following the DRY principle creates more cost than value. It argues that duplicated code can be reasonable when abstraction adds coordination overhead, tight coupling, or inflexibility, especially across distributed services and teams whose code may evolve differently. ### Source excerpt When duplicated code is the better tradeoff. ## Sea's View on the Future of Agentic Software Development with Codex DevFeed: [Sea's View on the Future of Agentic Software Development with Codex]() Original publisher: [Read original article]() Published: 2026-05-14T20:30:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [codex](), [software-development](), [AI Development](), [Microservice](), [Architecture & Design](), [code productivity](), [cursor]() Tags: [agentic](), [ai](), [ai-agents](), [ai-coding](), [architecture](), [codex](), [development](), [microservices](), [productivity](), [software-development](), [southeast-asia](), [speed](), [workflows]() ### AI overview Sea is rolling out Codex across its developer organisation, viewing agentic AI coding as a structural shift in how engineering teams manage complexity and build software at scale. David Chen describes Codex's contextual understanding of large codebases, its usefulness in microservices environments, and its impact on developer productivity, experimentation speed, and higher-level architectural work. ### Source excerpt Sea Limited's CPO explains why the company is deploying Codex across engineering teams to accelerate AI-native software development in Asia. ## Which engineering metrics actually drive outcomes? DevFeed: [Which engineering metrics actually drive outcomes?]() Original publisher: [Read original article]() Author: John Crowley Published: 2026-05-07T13:05:28Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [dora metrics](), [Architecture & Design](), [Microservice](), [cloud-infrastructure]() Tags: [architecture](), [cloud](), [dora-metrics](), [infrastructure](), [measurement](), [microservices]() ### AI overview The article explains why DORA metrics alone can mislead engineering organizations when applied uniformly across teams. It argues that metrics should be interpreted in the context of each team's responsibilities, services, dependencies, incidents, architecture, and operational history, with different benchmarks defined for different types of teams. ### Source excerpt Metrics without context don't really drive improvement. And can really do more harm than good. ## When CDC Becomes a Crutch: A Philosophy on Data Replication DevFeed: [When CDC Becomes a Crutch: A Philosophy on Data Replication]() Original publisher: [Read original article]() Published: 2026-04-06T00:00:00Z Content type: opinion Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Replication](), [consistency](), [Databases](), [Microservice](), [Kafka]() Tags: [consistency](), [data-replication](), [database](), [distributed](), [replication]() ### AI overview The article argues that Change Data Capture can become a crutch when teams replicate database tables across services without a clear data-ownership or consistency strategy. Although CDC tools make replication easy, maintaining and reconciling divergent replicas remains costly, especially after schema changes, pipeline lag, or ingestion bugs. ### Source excerpt Your orders service needs the user's email to send a confirmation. The payments service needs the user's tier to apply a discount... ## CockroachDB Plans Native X.509 SAN Support for SPIFFE and SPIRE Integration DevFeed: [CockroachDB Plans Native X.509 SAN Support for SPIFFE and SPIRE Integration]() Original publisher: [Read original article]() Author: Sanchit Khanna,Biplav Saraf Published: 2026-03-13T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [Databases](), [SPIFFE](), [SPIRE](), [certificates](), [Zero Trust](), [Security](), [Microservice]() Tags: [architectures](), [auditability](), [aws-iam](), [certificates](), [cockroachdb](), [compliance](), [cryptographic](), [database](), [distributed](), [processor](), [regex](), [security](), [soc](), [soc-2](), [spiffe](), [spire](), [standards](), [teams](), [verification](), [zero-trust]() ### AI overview The article previews planned CockroachDB authentication support for Subject Alternative Name fields in X.509 certificates. It explains how SAN support is intended to enable integration with SPIFFE and SPIRE and support regex-based identity mapping for cloud-native workloads. ### Source excerpt In the evolution of cloud-native security, identity has become the new perimeter. ## gRPC vs. REST: Key Differences, Performance, and Use Cases DevFeed: [gRPC vs. REST: Key Differences, Performance, and Use Cases]() Original publisher: [Read original article]() Author: LASZLO GYORI, SOLUTION ARCHITECT @ TOPTAL Published: 2026-03-10T04:00:00Z Content type: comparison Language: en Sources: [Toptal Blog]() Topics: [Remote Procedure Call (RPC)](), [distributed-systems](), [Microservice]() Tags: [api](), [architecture](), [complexity](), [distributed-systems](), [framework](), [guide](), [http](), [latency](), [microservices](), [performance](), [scalability]() ### AI overview A comparison of gRPC and REST APIs that focuses on performance, operational trade-offs, and selecting an approach for distributed systems and microservices. ### Source excerpt Trying to decide between gRPC and REST? This guide goes beyond surface comparisons to focus on the performance and operational trade-offs that determine which API framework is right for your next build. ## From Static Catalogue to Smart Discovery: Engineering Lightning-Fast Search at DreamSetGo DevFeed: [From Static Catalogue to Smart Discovery: Engineering Lightning-Fast Search at DreamSetGo]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-03-09T13:50:30Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [PostgreSQL](), [Microservice](), [systems](), [elasticsearch]() Tags: [architecture](), [dream-sports](), [elasticsearch](), [engineering](), [open](), [performance](), [postgresql](), [search](), [search-engines](), [sports](), [tech](), [travel]() ### AI overview DreamSetGo's tech team explains how its growing sports-travel catalogue created a need for faster, more intuitive product discovery. The article describes rebuilding the catalogue as a microservice, considering PostgreSQL with Elasticsearch, and choosing PostgreSQL based on architectural and operational trade-offs. ### Source excerpt By the DreamSetGo Tech Team If you're excited about building high-performance systems that power premium sports travel experiences, explore our open roles at DreamSetGo. Why Search Became a Product Imperative Over the past couple of years, DreamSetGo's catalogue didn't just grow, it accelerated. We were introducing access to new tournaments, adding destinations across continents, and were expanding into different hospitality formats. What began as a few structured products quickly evolved into a layered catalogue of packages and experiences. And that's when we started noticing something subtle but important. Adding products was straightforward. Making them discoverable wasn't. Every new experience meant longer descriptions and richer metadata. On paper, this was definitely progress. But from a user perspective, it introduced friction. If a traveller already knew exactly where to click, they could navigate their way through the catalogue. But if they came in with intent like: "Wimbledon finals hospitality" "Hot air balloons in Cappadocia" "Scuba diving in the Andamans" ...there was no fast, intuitive way to show them the most relevant experience. Once we acknowledged this gap, the next step became clear: we needed a search system that was scalable, resilient, and fast enough to keep up with our growth. This blog takes you through how we designed that search, from architectural trade-offs to production optimisations, and why PostgreSQL turned out to be the best choice for this. The Obvious Architecture (That We Didn't Choose) We started revamping our architecture six months back and rebuilt our travel product catalogue as a dedicated microservice, one that would eventually power search, and filter it by date, price and more. At that point, we had roughly 1,000 products. But we weren't building for 1,000. We had a clear vision of significantly expanding the catalogue within the near future. Like most teams, our first instinct was predictable: PostgreSQL -> Source of Truth ## Data-to-Production: Bridging the Gap Between Iceberg and Live Microservices DevFeed: [Data-to-Production: Bridging the Gap Between Iceberg and Live Microservices]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-02-17T11:04:08Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [Apache Iceberg](), [data observability](), [Microservice](), [clickhouse](), [Amazon S3](), [Architecture & Design](), [Apache Spark](), [airflow](), [Back end](), [API]() Tags: [airflow](), [amazon-s3](), [apache-iceberg](), [api](), [architecture](), [clickhouse](), [data](), [data-engineering](), [json](), [microservices](), [spark]() ### AI overview Wix describes Data-to-Production, a platform that activates data from Amazon S3 and Apache Iceberg for backend microservices. The system ingests Iceberg data into ClickHouse and serves it through a type-safe JSON API, using metadata governance and an Airflow and Python ingestion engine. ### Source excerpt At Wix, our Data Warehouse (DWH) is a massive repository of insights. Built on Amazon S3 using Apache Iceberg table formats, and populated by Trino and Spark jobs, it houses petabytes of data--from user segmentation and logs to AI chat analytics. However, storage is only half the battle. The real challenge--and the "holy grail" for many data engineering teams--is Activation : taking that petabyte-scale data and exposing it to backend microservices with millisecond latency, high availability, and... ## How LY Corporation uses ClickHouse to observe one of the largest Kafka deployments on earth DevFeed: [How LY Corporation uses ClickHouse to observe one of the largest Kafka deployments on earth]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-02-09T13:22:47Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Kafka](), [Grafana](), [data](), [Monitoring](), [Messaging](), [Streaming](), [telemetry](), [real-time](), [Microservice](), [site-reliability-engineering]() Tags: [clickhouse](), [data](), [ecosystem](), [infrastructure](), [kafka](), [logging](), [logs](), [messaging](), [monitoring](), [observability](), [performance](), [production](), [real-time](), [scale](), [software](), [streaming](), [sync](), [tech-lead](), [telemetry]() ### AI overview LY Corporation uses ClickHouse to monitor a massive Apache Kafka deployment, processing more than 1 trillion messages and 2.6 PB of I/O daily. ClickHouse ingests 7 million API-log rows per second and enables millisecond-level debugging across more than 4.1 trillion rows in real time, using only 24 servers. ### Source excerpt "ClickHouse plays a really important role in our automated monitoring efforts. We're handling a massive amount of data--about seven million rows per second--but with just 24 servers, we've achieved outstanding cost performance." - Haruki Okada, Senior Softw [Next page]()