# distributed-systems Published articles for distributed-systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Local and distributed cache coherence: stale data caused by missed invalidation messages DevFeed: [Local and distributed cache coherence: stale data caused by missed invalidation messages]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-17T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Redis](), [data](), [Network]() Tags: [cache](), [cache-coherence](), [caching](), [consistency](), [data](), [distributed-cache](), [distributed-systems](), [jvm](), [network](), [redis](), [ttl]() ### AI overview The article explains that adding an in-process cache in front of a distributed cache can improve latency but creates independently stale copies across service instances. It focuses on Redis pub/sub invalidation, which provides no persistence, acknowledgement, retry, or replay, allowing instances to miss invalidation messages and serve stale data until their TTL expires. ### Source excerpt An in-process cache in front of a distributed cache removes a network hop and adds one independent copy of the data per instance, each of which can be stale on its own schedule. Invalidation is normally broadcast over pub/sub, which is fire and forget, so any instance that is restarting, garbage collecting or briefly disconnected simply misses the message and serves stale data until its TTL expires. That TTL is not a performance setting, it is the maximum duration of incorrectness. ## Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads DevFeed: [Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-16T14:42:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [dropbox](), [Artificial Intelligence](), [API](), [Orchestration](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [ai-ml-data-engineering](), [apache](), [apis](), [architecture](), [architecture-design](), [asynchronous-architecture](), [backend](), [caching](), [data-pipelines](), [development](), [distributed-systems](), [dropbox](), [dropbox-riviera-ai-platform](), [embeddings](), [enterprise-content-management](), [large-language-models](), [ml-data-engineering](), [model-context-protocol-mcp](), [news](), [orchestration](), [platform-engineering](), [plugins](), [rag](), [tika]() ### AI overview Dropbox has expanded Riviera from an internal file-preview service into a content-processing platform supporting more than 300 file formats and over 100 transformation capabilities. The platform supports Dropbox products including Search, Replay, Sign, and Dash, and provides APIs for asynchronous document conversion, media transcription, and structured metadata extraction for AI and RAG workflows. ### Source excerpt Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows. By Leela Kumili ## App Engine Flex Language Shootout DevFeed: [App Engine Flex Language Shootout]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-04-17T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Hackathon](), [Back end](), [Elixir](), [distributed-systems](), [Open Source]() Tags: [backend](), [distributed-systems](), [elixir](), [engineering](), [hackathon](), [infrastructure](), [news](), [open-source]() ### AI overview A first-person account of a Khan Academy hackathon project exploring Google App Engine Flex and comparing programming-language options. The author describes Khan Academy's existing Python backend, interest in gaining experience with Flex, and personal interest in Elixir, while noting that a language switch was not imminent. ### Source excerpt By Amos Latteier This is the second time I've been to Silicon Valley. Some years ago - never ... Read more ## How end-to-end SLO monitoring detected a livestream failure that component dashboards missed DevFeed: [How end-to-end SLO monitoring detected a livestream failure that component dashboards missed]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-15T16:31:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [dashboards](), [observability](), [incident](), [Monitoring](), [Web](), [client](), [Network]() Tags: [dashboards](), [distributed-systems](), [incident](), [monitoring](), [observability](), [security](), [server](), [site-reliability-engineer](), [software-engineering](), [web]() ### AI overview The article examines a June 8, 2026 incident in which a third-party client-side security script fetched from a provider CDN added URL validation that the video provider did not pass, causing black screens for newly loaded web clients. Most component-level dashboards remained green, while end-to-end service-level objective monitoring detected the broken livestream experience and paged the owning teams within five minutes. ### Source excerpt Karol Gil | Reliability Platform (Poland) On June 8, 2026, newly loaded web clients began showing black screens instead of livestream video. For our platform, that's a serious problem: it's pretty hard to sell Pokémon cards that no one can see. It turned out that a third-party script we use for client-side security monitoring wasn't bundled with our release, but was rather fetched live from the provider's CDN. When the provider updated the script all new web clients fetched it, and it included an additional URL validation which our video provider didn't pass. The result? Black screens for users of the affected web clients, with most internal dashboards staying green. 3,000 users were impacted in the first 30 minutes of the incident. One system did catch it. Our end-to-end service-level objective (E2E SLO) monitoring was already in production and paged the owning teams within five minutes. Here's what it saw. The real problem Most of our dashboards stayed green because they monitor component-level health: a server, an endpoint, a specific function. These are all useful, but can all be healthy while the actual user experience is completely broken. This problem gets worse the more external dependencies there are, or the more sophisticated an experience you want to deliver. In complex, integrated product experiences like ours, a "small" problem can have an outsize impact on the user experience. Measuring this requires a different approach to observability, namely, to model the user journey across multiple surfaces that must be true for a customer to have a good experience. So how do we measure this in a complex distributed application? Joining a livestream is not one thing Joining a livestream sounds like one action, but the user expects at least three things: Video to be playing Auction details to be shown Chat to be visible and up to da Each of those can succeed or fail completely independently of the other two. Our video depends on third-party providers and CDN netwo ## 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 ## Cache invalidation is a distributed systems problem DevFeed: [Cache invalidation is a distributed systems problem]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-13T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Cache](), [Caching](), [distributed-systems]() Tags: [cache](), [cache-invalidation](), [caching](), [consistency](), [distributed](), [distributed-systems](), [ordering](), [race-conditions](), [redis](), [ttl]() ### AI overview Cache invalidation coordinates updates between a database and a cache without a shared transaction. The article explains how operation ordering can let a concurrent reader repopulate stale data and recommends writing to the database before invalidating the cache, followed by a second invalidation to close the remaining race. ### Source excerpt A cache and a database are two stores that must agree, which makes every invalidation a distributed transaction without a coordinator. The ordering matters more than the mechanism: invalidating before the database write leaves a window where a concurrent reader repopulates the cache with the old value and it stays wrong until the TTL expires. Deleting the key rather than writing the new value removes a whole class of ordering bug, because two concurrent deletes commute and two concurrent writes do not. ## 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 ## CNCF and SlashData Report Highlights China's Cloud Native Momentum as AI Moves to Inference DevFeed: [CNCF and SlashData Report Highlights China's Cloud Native Momentum as AI Moves to Inference]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:50:55Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Cloud Native Ecosystem](), [Inference](), [AI Platform](), [model-deployment](), [distributed-systems](), [Back end]() Tags: [ai](), [ai-infrastructure](), [announcements](), [backend](), [china](), [cloud](), [cloud-native-ecosystem](), [developers](), [distributed-systems](), [inference](), [production](), [research]() ### AI overview CNCF and SlashData report that cloud native adoption in China is growing, including among IIoT and younger backend developers. The research describes cloud native infrastructure as supporting AI teams' transition from experimentation and training to production serving and distributed inference. ### Source excerpt New research finds China's IIoT developers (48%) outpace the global average (42%) in cloud native adoption as AI infrastructure matures Key Highlights: SHANGHAI - KubeCon + CloudNativeCon + OpenInfra Summit + PyTorch Conference China --Sept. 8,... ## 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 ## How Fixed Intervals Synchronize Distributed Clients and How Jitter Helps DevFeed: [How Fixed Intervals Synchronize Distributed Clients and How Jitter Helps]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-07T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [reliability](), [distributed-systems](), [systems](), [Network](), [Cache](), [Redis]() Tags: [aws](), [cache](), [code](), [distributed-systems](), [jitter](), [network](), [reliability](), [resilience](), [retries](), [thundering-herd]() ### AI overview Fixed retry intervals, TTLs, restarts, reconnects, and other recurring events can cause independent clients in distributed systems to synchronize, producing repeated load spikes. Adding randomness, or jitter, helps break that alignment; the article cites AWS measurements indicating that full jitter improves total work and completion time under contention. ### Source excerpt Distributed systems synchronise themselves without being told to: every client that retries on a fixed interval, restarts after a deploy, or reconnects after a network blip ends up firing at the same moment as every other client. The resulting spike is a self-inflicted denial of service that no single component is responsible for. Adding randomness to any repeating interval breaks the alignment, and it is usually one line of code against a failure mode that otherwise requires capacity you never needed. ## EP224: MCP vs RAG vs AI Agents DevFeed: [EP224: MCP vs RAG vs AI Agents]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-05T15:30:58Z Content type: article Language: en Sources: [ByteByteGo]() Topics: [AI Agent](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [distributed-systems](), [Publish-subscribe pattern](), [Replication]() Tags: [ai-agent](), [distributed-systems](), [mcp](), [messaging](), [rag](), [replication]() ### AI overview This article compares MCP, RAG, and AI agents. It explains MCP as a standard protocol for connecting AI models to external tools and data, RAG as a way to retrieve current information from external sources, and AI agents as systems that perform tasks and make decisions autonomously. It also summarizes several distributed-systems patterns, including replication, sharding, publish-subscribe, circuit breakers, retries, and leader election. ### Source excerpt An AI agent is kind of an AI system where the agent performs the task autonomously and takes the decisions. ## Cache stampede: how one expired key takes down the database DevFeed: [Cache stampede: how one expired key takes down the database]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-05T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Cache](), [Caching](), [Database](), [consistent hashing](), [Redis]() Tags: [cache-stampede](), [caching](), [connection-pool](), [consistent-hashing](), [distributed-systems](), [jitter](), [query](), [redis](), [reliability](), [thundering-herd](), [traffic](), [ttl]() ### AI overview This article explains how a cache stampede occurs when a hot key expires and many requests simultaneously recompute the same value against the database. It recommends TTL jitter to prevent synchronized expirations and request coalescing so only one caller recomputes while others wait or serve stale data. It also discusses cache warming and consistent hashing for broader cache-failure scenarios. ### Source excerpt A cache TTL is a scheduled simultaneous failure: every request being served from one key misses at the same instant and goes to the origin together. If the recompute takes two seconds, every request arriving during those two seconds also misses, so the pileup grows faster than it drains. Jitter on the TTL stops keys expiring in lockstep, and request coalescing so only one caller recomputes while the rest wait or serve stale is what stops a single expensive key from saturating the database. ## The invisible heartbeat of our networks DevFeed: [The invisible heartbeat of our networks]() Original publisher: [Read original article]() Author: Luca Cicchelli Published: 2026-08-31T23:03:47Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Networks](), [distributed-systems](), [Critical Infrastructure](), [Security](), [5G](), [Cybersecurity](), [cloud-infrastructure](), [AI Infrastructure]() Tags: [5g](), [ai](), [ai-infrastructure](), [critical-infrastructure](), [cybersecurity](), [data](), [distributed-systems](), [engineering](), [gnss](), [gps](), [guest-post](), [hardware](), [network](), [networking](), [networks](), [outage](), [outages](), [post](), [servers](), [systems](), [tech-matters](), [time](), [transactions]() ### AI overview The article examines how accurate time synchronization underpins modern networks and distributed systems. It uses the Telstra outage to show how misaligned time servers can disrupt communications and rail services, and discusses resilient time sources, terrestrial backups, local atomic clocks, and synchronization requirements for finance, 5G, cloud, and AI infrastructure. ### Source excerpt Guest Post: The recent Telstra outage highlighted a critical but often overlooked dependency in modern networks: Accurate time synchronization. ## The Essential Guide to Multi-Agent Systems DevFeed: [The Essential Guide to Multi-Agent Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-31T11:38:47Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [AI Agent](), [distributed-systems](), [systems]() Tags: [ai-agent](), [architecture](), [consistency](), [distributed-systems](), [multi-agent-systems](), [observability](), [state]() ### AI overview This guide explains what multi-agent systems are, how they differ from workflow engines and generic distributed systems, and why coordination, communication, shared state, consistency, and observability shape their architecture. ### Source excerpt Building one AI agent is hard. But what changes when you start adding more? ## Coding Challenge #134 - Agentic Engineering Graph DevFeed: [Coding Challenge #134 - Agentic Engineering Graph]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-08-29T08:01:12Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [agentic-coding](), [Orchestration](), [coding](), [MCP Server](), [Langgraph](), [MCP](), [Chaos Engineering](), [ide]() Tags: [agent](), [agentic](), [agentic-coding](), [agentic-engineering](), [ci](), [code](), [distributed-systems](), [langgraph](), [news](), [orchestration](), [review](), [sonarqube](), [systems]() ### AI overview A coding challenge guides readers through building an agentic engineering graph: a small orchestration system that runs an autonomous coding loop of planning, coding, testing, review, and retry. It uses scripts and files for dispatch and persisted state, adds a deterministic Sonar static-analysis quality gate, and then replaces it with an AI-augmented review node using the SonarQube MCP Server. ### Source excerpt This challenge is to build your own agentic engineering graph. ## Where Kafka Fits in Modern Systems DevFeed: [Where Kafka Fits in Modern Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-25T12:02:09Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Kafka](), [Event-Streaming](), [systems](), [distributed-systems](), [data]() Tags: [architecture](), [consumer](), [distributed-systems](), [durability](), [event-streaming](), [kafka](), [parallelism](), [partition](), [pipelines](), [real-time](), [systems]() ### AI overview This article explains when Apache Kafka is appropriate for distributed systems. It presents Kafka as a durable event log whose replayability, consumer fan-out, and partition-based parallelism support real-time data pipelines and shared event-driven architectures, while warning that Kafka can add unnecessary operational complexity for simpler asynchronous workflows. ### Source excerpt When should you actually use Kafka? And when is it just unnecessary complexity? ## Control trace volume with OpenTelemetry tail-based sampling DevFeed: [Control trace volume with OpenTelemetry tail-based sampling]() Original publisher: [Read original article]() Author: Bill Meyer; Eddie Cai Published: 2026-08-21T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [OpenTelemetry](), [Traces](), [tracing](), [observability](), [Application Performance Management (APM)](), [configuration](), [Latency]() Tags: [apm](), [architecture](), [distributed-systems](), [how-to](), [latency](), [learn](), [metrics](), [observability](), [opentelemetry](), [rideshare](), [synthetic](), [traces]() ### AI overview A guide to using OpenTelemetry tail-based sampling to reduce exported trace volume while retaining errors, slow requests, and other diagnostically valuable traces. It explains the difference between head- and tail-based sampling, the role of Span Metrics, and the collector architecture required to evaluate complete traces. ### Source excerpt Learn how to configure tail-based sampling in the OpenTelemetry Collector to drop noisy traces, keep the ones that matter, and control APM costs. ## \[Podcast\] DNS cold start DevFeed: [\[Podcast\] DNS cold start]() Original publisher: [Read original article]() Author: George Michaelson Published: 2026-08-19T20:30:00Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Caching](), [Cache](), [distributed-systems](), [Network](), [cpu](), [Computer science]() Tags: [cache](), [caching](), [cpu](), [data](), [distributed-systems](), [dns](), [network](), [podcast](), [tech-matters]() ### AI overview A podcast discussion examines DNS behavior during a cold start, when a resolver has no cached data. It explains how caching reduces network delays and explores DNS's hierarchical dependencies, comparing the startup challenge with black-start power generation. ### Source excerpt Geoff Huston discusses the unexpected behaviours of the DNS when you perform a query after 'cold start' with no cached data. ## Designing for failure: Building resilient systems on AWS DevFeed: [Designing for failure: Building resilient systems on AWS]() Original publisher: [Read original article]() Author: Dhvani Vora Published: 2026-08-13T20:03:28Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [incident](), [Incident response]() Tags: [amazon-ec2](), [aws](), [aws-architecture-blog-resilience](), [aws-management-tools-blog-aws-resilience-hub](), [best-practices](), [cassandra](), [distributed-systems](), [hardware](), [incident](), [intermediate-200](), [kafka](), [observability](), [resilience](), [systems]() ### AI overview An AWS resilience guide examines a correlated EC2 hardware failure that took down three colocated Cassandra nodes. It covers placement groups, alarms, automated recovery, and observability patterns for distributed data systems. ### Source excerpt Learn how to prevent correlated hardware failures in distributed systems on Amazon EC2. This post walks through real incident response patterns, including Partition Placement Groups, composite alarms, automated recovery with Auto Scaling, and observability best practices. ## ClickStack and Hud bring runtime intelligence to AI-powered development DevFeed: [ClickStack and Hud bring runtime intelligence to AI-powered development]() Original publisher: [Read original article]() Author: May Walter, Hud.io Published: 2026-08-13T12:53:04Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI-assisted coding](), [observability](), [Model Context Protocol](), [OpenTelemetry](), [telemetry](), [Artificial Intelligence](), [Instrumentation](), [Deployment](), [Incident response](), [distributed-systems](), [clickhouse](), [log management]() Tags: [agents](), [ai](), [clickhouse](), [code](), [coding](), [deployment](), [developers](), [development](), [distributed-systems](), [engineering](), [incident](), [incident-response](), [infrastructure](), [instrumentation](), [integration](), [mcp](), [metrics](), [monitoring](), [observability](), [open]() ### AI overview ClickHouse and Hud announce an integration that combines ClickStack service-level observability with Hud's function-level runtime context for AI-assisted software development. Shared trace IDs and MCP servers help coding agents assess risky changes before deployment, monitor releases, and investigate incidents using production context. ### Source excerpt ClickStack and Hud now share trace IDs, pairing service-level observability with function-level runtime forensics so coding agents can assess risky changes before they ship, catch regressions right after deploy, and fix them with real production context. ## Graceful Kubernetes Shutdowns Help Protect In-Flight Orders DevFeed: [Graceful Kubernetes Shutdowns Help Protect In-Flight Orders]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-08-12T14:03:07Z Content type: tutorial Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Kubernetes](), [distributed-systems](), [Cloud]() Tags: [cloud](), [cloud-computing](), [devops](), [distributed-systems](), [infrastructure](), [kubernetes](), [messaging](), [operations](), [scale](), [software-engineering]() ### AI overview This article explains how Kubernetes scaling, rolling updates, and cluster maintenance can interrupt in-flight requests and messages. It introduces graceful shutdown patterns in application code and infrastructure to reduce message loss and improve reliability for large-scale order processing. ### Source excerpt Why graceful shutdowns matter when millions of orders are in flight. by: Samuel Hapgood, Software Engineer III Quick Bytes At McDonald's scale, rare failures in distributed systems can impact real customer and crew experiences Graceful shutdown patterns in Kubernetes -- across application code and infrastructure -- help protect in-flight work during scaling and deployments With the right shutdown strategies, teams can reduce message loss, improve reliability, and ensure orders make it to the kitchen Imagine you're placing a mobile order at McDonald's during a busy lunch rush. You hit "submit," and the kitchen never gets the order. Now you're left waiting -- no food, no updates, and growing frustration. At first glance, it seems like an easy problem to avoid. How hard can it be to just keep track of the order? But in modern, large-scale distributed systems -- especially those that must scale continuously -- that's not always straightforward. Behind the scenes, platforms like Kubernetes (which orchestrate containerized applications in the cloud) are continuously rescheduling pods as part of normal operations such as scaling applications, performing rolling updates, and maintaining cluster health. During these transitions, in-flight requests or background messages can be interrupted in subtle ways. A pod might shut down before finishing a task, a request might be canceled mid-flight, or a message might be acknowledged by a receiving service, but never actually processed. Even with strong messaging platforms and best practices in place, application shutdowns can still interrupt in-flight work if they are not handled carefully. These are edge cases -- but at McDonald's scale, edge cases happen often enough to matter. In the US alone, our platforms support roughly one million orders per hour on a dynamically scaled cluster of hundreds of nodes. At that volume, reliability becomes critical to ensuring a seamless experience for customers, crews, and restaurant operations. Prevent ## ScyllaDB Customer Experience Spotlight: Susie Solis DevFeed: [ScyllaDB Customer Experience Spotlight: Susie Solis]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-10T14:28:35Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Databases](), [distributed-systems](), [cloud-infrastructure](), [Kubernetes](), [Terraform](), [Latency](), [Linux]() Tags: [blog](), [cloud-infrastructure](), [community](), [customer](), [cx-profiles](), [database](), [distributed-systems](), [kubernetes](), [latency](), [linux](), [on-call](), [production](), [streaming](), [support](), [terraform]() ### AI overview Susie Solis, a ScyllaDB Technical Support Engineer, discusses her background in technical support, cloud infrastructure, and distributed systems, along with a Kubernetes and Terraform project built to reproduce environments and troubleshoot customer issues. She also describes ScyllaDB's ability to sustain high-throughput production workloads and low latency during major streaming events. ### Source excerpt Meet Susie Solis, a Technical Support Engineer on the Customer Experience team here at ScyllaDB. ## 34 Amazon Research Awards Build on Trainium recipients announced DevFeed: [34 Amazon Research Awards Build on Trainium recipients announced]() Original publisher: [Read original article]() Author: Amazon Research Awards team Published: 2026-08-05T15:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [Artificial Intelligence](), [responsible-ai](), [AWS AI chips](), [AI Research](), [Optimization](), [Inference](), [moe](), [Large Language Model](), [distributed-systems](), [synthetic-data]() Tags: [academic-ai-funding](), [ai](), [ai-research](), [ai-research-grants](), [ai-safety-and-alignment](), [amazon-research-awards](), [ara](), [aws-ai-chips](), [aws-trainium](), [build-on-trainium](), [data](), [deep-learning](), [distributed-systems](), [inference](), [internal-ara-program-updates](), [llm](), [machine-learning-research](), [mixture-of-experts](), [optimization](), [research](), [responsible-ai](), [synthetic](), [synthetic-data-generation]() ### AI overview Amazon announces 34 recipients of its Build on Trainium program, a $110 million credit initiative supporting AI research and university education. The awards fund work in areas including Responsible AI, language models, synthetic data, distributed systems, model architectures, libraries, and optimization on AWS Trainium. ### Source excerpt Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI. ## On building scalable control planes DevFeed: [On building scalable control planes]() Original publisher: [Read original article]() Author: werner@allthingsdistributed.com (Dr. Werner Vogels) Published: 2026-08-04T14:00:00Z Content type: article Language: en Sources: [All Things Distributed]() Topics: [distributed-systems](), [Amazon Web Services](), [Amazon EC2](), [DSQL](), [Cloud]() Tags: [aws](), [cloud](), [distributed-systems](), [dsql](), [infrastructure](), [kubernetes](), [posts](), [servers](), [ssh]() ### AI overview This article explores the engineering of scalable control planes through Zak van der Merwe's experience building them at AWS, first for EC2 and later for DSQL. It explains how control planes record intended state and reconcile it with actual state, bringing together difficult distributed-systems problems and influencing whether services can survive growth. ### Source excerpt Zak van der Merwe has spent his entire career at AWS building control planes. First for EC2 and now for DSQL. On the surface, the control plane looks quite boring: it records what should exist and reconciles that with what actually does. Nobody leaves school dreaming of building one, but Zak will be the first to tell you that if you like solving hard problems in distributed systems, there are few better places to be. It's where many of those hard problems converge, and where the decisions you make determine whether a service survives its own growth. [Next page]()