# load Published articles for load. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Researchers link the RubyHack campaign to alleged OpenAI-connected agents, while RubyGems disputes key claims DevFeed: [Researchers link the RubyHack campaign to alleged OpenAI-connected agents, while RubyGems disputes key claims]() Original publisher: [Read original article]() Author: amaksimovv (CodeScoring) Published: 2026-09-16T15:08:49Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [rubygems](), [Ruby](), [OpenAI](), [legacy](), [API]() Tags: [api](), [appsec](), [cdn](), [gemstuffer](), [legacy](), [load](), [openai](), [ruby](), [rubydoc](), [rubygems](), [rubyhack](), [tag-070ecaaf0eda](), [tag-64251c106897](), [yard]() ### AI overview Researchers from Nightingale Collective linked the RubyHack campaign in RubyGems to alleged internal OpenAI agents. The article describes malicious code executed during RubyDoc documentation builds, data collection from British municipal websites, attempts to obtain RubyGems API keys, and a caching flaw involving legacy keys. RubyGems said it could not independently identify the package creators and found no evidence that чужие keys were successfully obtained. ### Source excerpt 11 сентября исследователи Nightingale Collective опубликовали разбор RubyHack и связали майскую кампанию в RubyGems с внутренними агентами OpenAI. По их версии, скрипты внутри пакетов запускали код в инфраструктуре RubyDoc, собирали открытые данные с сайтов британских муниципалитетов и публиковали результаты обратно в реестр. В нескольких образцах исследователи также обнаружили попытки получить чужие API-ключи RubyGems. Команда RubyGems не смогла независимо установить, кем именно были созданы пакеты, и не нашла доказательств успешного получения чужих ключей. Во время майской кампании она отозвала вредоносные пакеты и на четыре дня остановила регистрацию новых пользователей. Одной из точек входа стала обычная сборка документации. В пакет добавляли .yardopts - файл параметров генератора YARD - с указанием загрузить Ruby-скрипт. Когда для пакета запрашивали документацию на RubyDoc, сборщик обрабатывал этот файл и запускал вложенный код. Такое поведение соответствует механике самого YARD: параметр --load загружает указанный Ruby-файл перед выполнением команды. Запущенный скрипт обращался к муниципальным сайтам, сохранял ответы, собирал из них новый .gem-архив и отправлял его в RubyGems со встроенным ключом. В результате реестр использовался как канал передачи и хранения собранных данных. Установка такого пакета множеством разработчиков для работы этой схемы не требовалась: достаточно было исполнения кода в сервисе сборки документации. Отдельные пакеты обращались к старому endpoint выдачи API-ключей. В нём действовала ошибка кэширования: при определённых условиях CDN мог сохранить ответ с legacy-ключом и до часа отдавать его следующим запросам на том же узле. RubyGems исправил проблему 9 июля, очистил кэш, отозвал legacy-ключи и затем отключил старый endpoint. Читать далее ## How We Built Automated Capacity Testing for Kafka Consumers DevFeed: [How We Built Automated Capacity Testing for Kafka Consumers]() Original publisher: [Read original article]() Author: Kaan Karakaya Published: 2026-09-14T09:46:34Z Content type: tutorial Language: en Sources: [Booking.com Development - Medium]() Topics: [Kafka](), [Testing](), [HTTP]() Tags: [automated](), [capacity](), [health-checks](), [kafka](), [load](), [load-balancer](), [parallelism](), [partition](), [recovery](), [scale](), [site-reliability-engineer](), [sre](), [testing]() ### AI overview This article describes Booking.com's extension of an existing capacity-testing platform for Kafka consumers. It explains how changing partition assignment can provide a controlled, measurable way to test consumer throughput and whether remaining consumers can absorb reassigned work after an instance or failure domain disappears. ### Source excerpt Photo by GuerrillaBuzz on Unsplash Kafka makes it easy to distribute work across consumer instances. It is much harder to prove, safely and repeatedly, how those instances behave when the distribution changes and one of them has to carry more than its usual share. For teams that run Kafka at scale, this is a practical reliability question: how much load can a consumer instance actually handle? We had automated capacity testing for HTTP services, but Kafka consumers were still tested with manual drills. Those drills could tell us something, but they were disruptive, difficult to reproduce, and risky precisely when the system was close to its limit. We wanted a controlled way to answer three questions: What is the maximum sustainable throughput of a consumer instance? If an instance or failure domain disappears, can the remaining consumers absorb the reassigned work? Are we overprovisioning resources because we do not know the real limit? The result was an extension to our capacity-testing platform that turns Kafka partition assignment into a safe, measurable load-control mechanism. Why HTTP capacity testing did not translate Our existing platform was designed for request-response services behind a load balancer. A scheduled test selects one instance, routes an increasing share of traffic to it, runs health checks after each step, and records the highest ratio the instance can sustain. After the test, traffic returns to its normal distribution and the result is reported to the service owner. Kafka has no equivalent traffic knob. Consumers pull records, and the unit of parallelism is the partition. Within a consumer group, each partition is owned by one consumer at a time. If a topic has 12 partitions and four equally loaded instances, each instance owns about three. When one instance disappears, a rebalance gives the survivors more partitions -- and the extra work arrives as a step change, not as a smooth increase from a load balancer. The key translation: for an HTTP ## Linux server performance: Is disk I/O slowing your application? DevFeed: [Linux server performance: Is disk I/O slowing your application?]() Original publisher: [Read original article]() Author: Hayden James Published: 2026-09-12T16:11:54Z Content type: tutorial Language: en Sources: [Hayden James]() Topics: [Linux](), [IO](), [Server](), [Command-line interface](), [Processes](), [NVMe]() Tags: [blog](), [command-line](), [cpu](), [linux](), [load](), [mysql](), [nvme](), [performance](), [processes](), [request](), [server](), [ssd](), [storage](), [sysadmins](), [terminal]() ### AI overview This tutorial explains how disk I/O bottlenecks can cause high Linux load averages even when CPU usage is low. It shows how to use top, atop, and iotop on a LEMP web server, including per-core I/O wait measurements, to identify storage-related application slowdowns. ### Source excerpt If your Linux server is bogged down by disk I/O, your first step may often be to use the top command in the terminal to check load averages. Continue reading... ## Performance Progression of Percona Server for MySQL 8.4 DevFeed: [Performance Progression of Percona Server for MySQL 8.4]() Original publisher: [Read original article]() Author: Bogdan Degtyariov Published: 2026-08-27T13:07:20Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [MySQL](), [benchmarking](), [Benchmark](), [Scalability](), [configuration](), [cpu](), [Hardware]() Tags: [2026](), [benchmark](), [benchmarking](), [benchmarks](), [concurrency](), [configuration](), [cpu](), [data](), [database](), [hardware](), [intel](), [latency](), [load](), [measurement](), [mysql](), [percona](), [percona-server-for-mysql](), [performance](), [processors](), [run]() ### AI overview This article investigates read/write performance changes across Percona Server for MySQL 8.4 versions released in 2026. It describes benchmark scope, database and buffer-size combinations, configuration, and CPU-frequency controls, while excluding features, security patches, latency percentiles, and resource-utilization measurements. ### Source excerpt 1. Purpose and scope This performance investigation aims to look into the read/write performance of Percona Server for MySQL 8.4 and how it changed between versions released in 2026: 8.4.8-8 released on 12 March 2026 8.4.10-10 released on 30 June 2026 8.4.11-11 released on 20 August 2026 We want to see if there are improvements ... Continued The post Performance Progression of Percona Server for MySQL 8.4 appeared first on Percona. ## Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows DevFeed: [Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows]() Original publisher: [Read original article]() Author: Harichandan Pulagam Published: 2026-08-24T20:18:12Z Content type: article Language: en Sources: [Wealthfront]() Topics: [Amazon Redshift](), [dashboards](), [Data Quality]() Tags: [amazon-redshift](), [batch](), [dashboards](), [data-quality](), [latency](), [load](), [performance](), [redshift](), [space](), [wealthfront-engineering]() ### AI overview This Wealthfront engineering post examines how Redshift tables grew to nearly 10 times the size of their useful data because deleted rows remained on disk as ghost rows. It describes the resulting read and write latency and the write strategies adopted to control table size. ### Source excerpt Amazon Redshift is a core part of our analytics platform, powering dashboards, data quality checks, ad-hoc analytical workloads, and downstream reporting on a shared cluster. Because everything runs on the same cluster, the size and health of our tables directly affects every workload. At Wealthfront, data drives every decision we make, which means any performance... Read more ## Harness RT Agents Detect Resilience Risks and Generate Tests for CD Pipelines and Kubernetes Workloads DevFeed: [Harness RT Agents Detect Resilience Risks and Generate Tests for CD Pipelines and Kubernetes Workloads]() Original publisher: [Read original article]() Author: Uma Mukkara Published: 2026-08-24T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Resilience](), [Testing](), [Continuous Delivery (CD)](), [Kubernetes](), [dashboards]() Tags: [agentic](), [agents](), [blog](), [chaos](), [configuration](), [continuous-delivery](), [dashboards](), [deployment](), [experiments](), [infrastructure](), [insights](), [kubernetes](), [load](), [pipelines](), [product](), [resilience](), [services](), [teams](), [testing](), [tests]() ### AI overview Harness announces an update to Resilience Testing called RT Agents. The agents analyze CD pipelines and Kubernetes workloads for resilience risks, recommend the testing needed to confirm those risks, and can generate and run chaos experiments or load tests and interpret the results. ### Source excerpt RT Agents detect resilience risk in your CD pipelines and Kubernetes workloads, then generate and run chaos experiments or load tests to confirm it. | Blog ## Linux Server Health Checks: 10 Metrics Every Sysadmin Should Monitor DevFeed: [Linux Server Health Checks: 10 Metrics Every Sysadmin Should Monitor]() Original publisher: [Read original article]() Author: Sujitha Paduchuri, ManageEngine Published: 2026-07-06T12:13:41Z Content type: tutorial Language: en Sources: [Hayden James]() Topics: [Linux](), [Monitoring](), [Linux performance](), [health checks]() Tags: [blog](), [cpu](), [disk-space](), [guests](), [health-checks](), [linux](), [linux-performance](), [load](), [memory](), [metrics](), [monitoring](), [observability](), [performance](), [performance-monitoring](), [server](), [sysadmins]() ### AI overview A practical guide to monitoring Linux server health through metrics that can reveal resource problems earlier than broad CPU, memory, or disk-usage figures. It discusses iowait, load average, active swap activity, and inode exhaustion, along with warning signs and follow-up investigations. ### Source excerpt Servers give you warnings before they fail. Most sysadmins performing Linux server monitoring miss them because they're watching the wrong numbers. The metrics that actually matter are one level deeper: iowait instead of CPU percentage, active swap paging instead of memory usage, inode counts instead of just disk space. Continue reading... ## How to Manage an AI Agent's Context Window DevFeed: [How to Manage an AI Agent's Context Window]() Original publisher: [Read original article]() Author: Fran Soto Published: 2026-05-24T06:00:55Z Content type: opinion Language: en Sources: [Strategize Your Career]() Topics: [context]() Tags: [agent](), [context](), [load](), [workflows]() ### AI overview The article argues that larger context windows do not solve poor workflows. Reliable agent use depends on deciding what context to load, forget, and persist. ### Source excerpt Bigger context windows won't fix bad workflows. The real skill is deciding what an agent should load, forget, and persist. ## Request-Based Autoscaling Is Now Generally Available on App Platform DevFeed: [Request-Based Autoscaling Is Now Generally Available on App Platform]() Original publisher: [Read original article]() Author: Greeshma Pillai Published: 2026-05-22T18:02:26Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [autoscaling](), [Digital Ocean](), [App](), [Latency](), [Containers]() Tags: [autoscaling](), [capacity](), [container](), [containers](), [cpu](), [digitalocean](), [http](), [load](), [performance](), [product-launch](), [product-updates](), [production](), [real-time]() ### AI overview DigitalOcean App Platform now generally supports request-based autoscaling for shared and dedicated CPU instances. Apps can scale horizontally using live HTTP requests per second and P95 response latency, with containers scaling up when thresholds are exceeded and down when load falls. ### Source excerpt Traffic doesn't spike on a schedule. A product launch, a viral moment, or a flash sale can send request volume through the roof in seconds, long before your CPU metrics catch up. That gap is where performance suffers. Today, we're excited to announce that request-based autoscaling on DigitalOcean App Platform is now generally available. Your apps can now automatically scale based on live HTTP traffic signals (requests per second and P95 response latency) so your infrastructure reacts to what's actually happening, not what happened minutes ago. Now Available for Shared and Dedicated CPU Instances Until now, autoscaling on App Platform required a dedicated CPU plan. That meant a good portion of App Platform users (anyone running on shared CPU instances) had no path to automatic horizontal scaling at all. That changes today. Request-based autoscaling works on both shared and dedicated CPU instances. Whether you're running an early-stage project on a shared plan or a high-throughput production service on dedicated resources, you can now configure autoscaling to match your traffic--no plan upgrade required. Faster, More Responsive Scaling CPU-based autoscaling is reactive by nature. CPU is a lagging indicator: your containers have to be visibly struggling before the scaler knows there's a problem, and by then, your users are already waiting. Request-based autoscaling acts on the signals that actually reflect user experience: Requests per second per instance: how many requests each container is handling right now P95 request latency: the response time that 95% of your users are seeing When traffic rises and either threshold is exceeded, new containers spin up immediately. When load drops and all metrics fall back below their targets, the scaler brings containers back down. You get the capacity headroom you need, faster, and pay only for what you use. You can also combine request-based and CPU-based metrics on dedicated plans. The autoscaler scales up when any configured th ## Walking Through an Unplanned Failover: SQL Server Availability Groups on Kubernetes DevFeed: [Walking Through an Unplanned Failover: SQL Server Availability Groups on Kubernetes]() Original publisher: [Read original article]() Author: Anthony Nocentino Published: 2026-04-19T15:00:00Z Content type: tutorial Language: en Sources: [Kubernetes on Anthony Nocentino's Blog]() Topics: [sql-server](), [Availability](), [Kubernetes](), [Databases]() Tags: [automatic](), [availability-groups](), [container](), [containers](), [crash](), [database](), [failover](), [kubernetes](), [load](), [open-source](), [operator](), [recovery](), [sql-server](), [test]() ### AI overview This walkthrough tests unplanned SQL Server Availability Group failover on Kubernetes by force-deleting the current primary pod under idle and sustained HammerDB TPC-C load. Across six force-deletes, the operator automatically promoted new primaries and restored all three replicas, with the article examining detection, recovery behavior, logs, and timing. ### Source excerpt In my planned failover walkthrough, I showed what happens when you deliberately move the primary role to another replica. That's the easy case. Now I want to show what happens when the primary pod just disappears unexpectedly, like during a node failure or a container crash. No graceful shutdown, no demotion, just gone. I ran two test scenarios, each cycling the primary role across all three pods by force-deleting the current primary three times in a row. First, a 5GB TPC-C database idle. Then, that same 5GB database under sustained HammerDB TPC-C load. Six force-deletes total, six successful automatic failovers. I'll walk through the error log from the promoted replica, the operator's detection and recovery behavior, and the full timing data. ## Survey finds technology leaders expect AI workloads to strain existing infrastructure DevFeed: [Survey finds technology leaders expect AI workloads to strain existing infrastructure]() Original publisher: [Read original article]() Author: Becca Weng Published: 2026-02-17T00:00:00Z Content type: opinion Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [AI Infrastructure](), [systems](), [Cloud]() Tags: [ai](), [ai-infrastructure](), [architecture](), [engineering](), [global](), [load](), [scale](), [survey]() ### AI overview This article examines how AI agents, copilots, and background processes create continuous, machine-driven workloads that differ from human traffic. It cites a Cockroach Labs and Wakefield Research survey of 1,125 senior technology leaders, 83% of whom expect their infrastructure to fail under AI pressure within two years. ### Source excerpt 83% of technology leaders believe their infrastructure will fail under AI pressure within two years. That number is not meant to provoke alarm. It's simply a fact that reflects the forward-looking judgment of 1,125 global engineering and technology leaders who are already seeing stress where their systems were never designed to carry it. ## How Fever Made Real-Time Partner Reporting Reliable Under Load with Tinybird DevFeed: [How Fever Made Real-Time Partner Reporting Reliable Under Load with Tinybird]() Original publisher: [Read original article]() Author: Tinybird Published: 2026-02-02T15:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [real-time]() Tags: [customer-stories](), [load](), [partner](), [real-time](), [reporting]() ### AI overview A case study describes how Fever replaced a fragile real-time ingestion pipeline with Tinybird to make partner reporting reliable under load. ### Source excerpt Learn how one of the world's largest live entertainment platforms replaced a fragile real-time ingestion pipeline with Tinybird, eliminating the inverse scaling problem where better business meant worse reporting. ## On Metastable Failures and Interactions Between Systems DevFeed: [On Metastable Failures and Interactions Between Systems]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2025-12-24T21:26:49Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [systems](), [retry](), [clients](), [servers](), [Latency]() Tags: [clients](), [fault-tolerance](), [latency](), [load](), [metastabiilty](), [other-thoughts](), [performance](), [retry](), [server](), [signal](), [state](), [systems](), [timeout]() ### AI overview The article explains metastable failures as self-sustaining performance failures caused by positive feedback loops. It uses retry storms to show how overload, latency, timeouts, and client retries can reinforce one another, and examines how ambiguous timeout signals make recovery more difficult. ### Source excerpt I've written about metastable failures before. The topic has been picked up by a few different teams since the, all analyzing metastable failures more, while I apparently has been slacking off... Anyway, Metastable failures are self-sustaining performance failures that arise in systems due to a positive feedback loop triggered by an initial problem. This positive [...] ## Major compute price reduction on Neon DevFeed: [Major compute price reduction on Neon]() Original publisher: [Read original article]() Author: Nikita Shamgunov Published: 2025-11-03T19:23:07Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [autoscaling](), [databricks](), [cloud-infrastructure](), [Supabase]() Tags: [autoscaling](), [company](), [compute](), [cost](), [cost-savings](), [databases](), [databricks](), [durability](), [infrastructure](), [load](), [serverless](), [snapshots]() ### AI overview Neon announces compute price reductions of up to 25% across its plans, including lower rates for the Launch and Scale Plans. The article also reviews earlier reductions to storage, minimum spend, enterprise features, and Free Plan compute, then compares costs with Aurora Serverless v2 and Supabase in selected usage scenarios. ### Source excerpt Databases are often one of the biggest infrastructure expenses for any company. From day one, Neon's mission has been to make databases radically more efficient through separation of storage and compute, allowing instant autoscaling and better unit economics. Now, with Neon runni... ## Using the OSI Model for Effective Production Issue Debugging DevFeed: [Using the OSI Model for Effective Production Issue Debugging]() Original publisher: [Read original article]() Author: Kamal Kumar Published: 2025-08-28T17:37:45Z Content type: tutorial Language: en Sources: [Housing.com]() Topics: [debugging](), [networking](), [Network](), [Routing (disambiguation)](), [Server](), [Authentication](), [Encryption](), [configuration](), [business logic](), [Framework]() Tags: [analysis](), [apis](), [authentication](), [configuration](), [debugging](), [devops](), [errors](), [firewalls](), [load](), [mapping](), [network](), [osi-model](), [production-debugging](), [production-issue](), [rca](), [root-cause-analysis](), [routing](), [servers](), [sre](), [tcp](), [troubleshooting](), [udp](), [web]() ### AI overview This tutorial explains how to use the seven-layer OSI model to structure root cause analysis for production alerts. It recommends checking lower layers for network and gateway errors, and higher layers for application and client errors, including connectivity, routing, logs, APIs, authentication, and configuration. ### Source excerpt In production environments, debugging alerts can sometimes feel like finding a needle in a haystack. Over the years, I've found the OSI (Open Systems Interconnection) model to be a reliable guide during Root Cause Analysis (RCA) of production issues. What is the OSI Model? The OSI model is a conceptual framework that standardizes the functions of a telecommunication or computing system into seven layers: Physical Layer -- Hardware, cables, switches Data Link Layer -- MAC addresses, switches, network topology Network Layer -- IP addressing, routing Transport Layer -- TCP/UDP, ports, session reliability Session Layer -- Session management, authentication Presentation Layer -- Data translation, encryption Application Layer -- APIs, web servers, applications How I Use OSI Layers in RCA: When I debug production alerts, I follow different approaches depending on the type of error: Network / Gateway Errors (e.g., 502, 504): These errors usually indicate communication issues between services. I start from the bottom layers (Physical -> Network -> Transport) to check connectivity, firewalls, routing, or load balancers. Application / Client Errors (e.g., 500, 503, 404): These errors generally originate from the application or business logic. I start from the top layers (Application -> Presentation -> Session) to check service logs, APIs, authentication issues, or configuration problems. Why this approach works: Following the OSI model provides a structured, layer-by-layer method for troubleshooting, ensuring that we don't miss low-level network issues or high-level application errors. It helps reduce mean time to resolution (MTTR) and improves the quality of RCA reports. Takeaway: The OSI model is not just a theoretical concept -- it's a practical tool that can guide engineers through complex production debugging. Next time you face a tricky alert, try mapping it to the OSI layers, and you might find the root cause faster than you think. Using the OSI Model for Effective Production Issue ## How a global gaming platform cut latency and CPU load with smarter observability DevFeed: [How a global gaming platform cut latency and CPU load with smarter observability]() Original publisher: [Read original article]() Author: Alexander Lavoie Published: 2025-06-16T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [observability](), [Monitoring](), [cpu](), [dashboards](), [Grafana](), [Latency](), [real-time]() Tags: [cpu](), [cut-cpu-load-online-gaming](), [dashboards](), [enhancing-gaming-experience-with-observability](), [game-platform-optimization](), [grafana](), [improve-online-gaming-performance](), [infrastructure](), [latency](), [load](), [metrics](), [monitoring](), [observability](), [observability-in-gaming](), [observability-strategies-in-gaming](), [observability-tools-for-gaming](), [performance](), [performance-boost-gaming-platform](), [real-time](), [real-time-gaming-platform-monitoring](), [redpanda-observability-solutions](), [reduce-latency-gaming-platform](), [setting-up-redpanda-observability](), [thought-leadership](), [upgrade]() ### AI overview Redpanda describes how observability helped Chess.com diagnose excessive CPU usage caused by inefficient and overly frequent compaction on compressed data. After upgrading to Redpanda version 25.1 with improved compaction controls, the team monitored the rollout in real time and reduced CPU utilization without downtime. ### Source excerpt Learn how Redpanda's game-changing observability cut latency and CPU load for the world's leading online chess platform. ## How to run load tests in real-time data systems DevFeed: [How to run load tests in real-time data systems]() Original publisher: [Read original article]() Author: Ana Guerrero, Iago Enríquez Published: 2025-03-07T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [systems](), [data]() Tags: [how-to](), [load](), [production](), [real-time](), [scalable-analytics-architecture](), [systems]() ### AI overview A tutorial on running load tests for real-time data systems, noting that they fail differently from traditional applications and discussing how to avoid disrupting production. ### Source excerpt Load tests on real-time data systems fail differently than traditional apps. Here's how to run them without breaking production. ## Heroku Investigates Puma Bug Causing High Response Times with Router 2.0 DevFeed: [Heroku Investigates Puma Bug Causing High Response Times with Router 2.0]() Original publisher: [Read original article]() Author: Elizabeth Cox Published: 2024-11-21T18:13:05Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Ruby](), [Rails](), [HTTP](), [servers]() Tags: [applications](), [article](), [bug](), [cloud-infrastructure](), [dashboards](), [dynos](), [engineering](), [heroku](), [http](), [improvements](), [legacy](), [load](), [performance](), [request](), [router](), [router-2-0](), [ruby](), [server](), [technical](), [update](), [upgrade]() ### AI overview Heroku investigates a Puma bug that caused elevated response times for a small subset of applications after enabling Router 2.0. The article explains the investigation, reproduction conditions, mitigation guidance, and the subsequent Puma 7.0 fix. ### Source excerpt Update: Puma 7.0 was released with a fix for the bug described in this article. We recommend Ruby applications upgrade to Puma 7.0.4 or higher. This week, Heroku made Router 2.0 generally available, bringing features like HTTP/2, performance improvements and reliability enhancements out of the beta program! Throughout the Router 2.0 beta, our engineering team [...] The post Pumas, Routers & Keepalives--Oh my! appeared first on Heroku. ## Ep. 5: Efficient Concurrency in Go: Managing GoRoutines and Load Shedding DevFeed: [Ep. 5: Efficient Concurrency in Go: Managing GoRoutines and Load Shedding]() Original publisher: [Read original article]() Published: 2024-06-11T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Concurrency](), [Go Language](), [Concurrent Programming](), [HTTP](), [servers]() Tags: [best-practices](), [concurrency](), [go](), [goroutines](), [http](), [http-server](), [kubernetes-clusters](), [load](), [managing-goroutines](), [networking](), [programming](), [server]() ### AI overview This video tutorial explains how to manage goroutines in Go using parent-child relationships, clean shutdown APIs, and load shedding. It focuses on preventing orphaned goroutines, protecting data consistency during shutdowns, and gracefully handling HTTP server traffic. ### Source excerpt Introduction: In this installment, Bill delves into the concept of load shedding in Go, explaining its importance in managing GoRoutines and ensuring clean shutdowns. How to manage GoRoutines using a parent-child relationship model to prevent orphan GoRoutines. The role of load shedding in maintaining clean and orderly shutdowns, particularly using the HTTP package in Go. Strategies for implementing GoRoutines in Go to handle concurrent tasks efficiently without risking data corruption during shutdowns. ## Unintended Consequences of IPv6 SLAAC DevFeed: [Unintended Consequences of IPv6 SLAAC]() Original publisher: [Read original article]() Published: 2024-04-16T06:45:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Network](), [Network design](), [Caching](), [Hardware]() Tags: [cache](), [cpu](), [hardware](), [ipv6](), [load](), [network](), [network-design](), [performance](), [tcp]() ### AI overview The article explains how IPv6 SLAAC privacy address rotation, long-running TCP sessions, and device reconnections can increase neighbor-discovery cache usage. When hardware forwarding devices exhaust that cache, packets may be handled by the CPU or dropped, reducing performance. It outlines tradeoffs involving interface limits, RFC 7217 identifiers, and DHCPv6 address allocation. ### Source excerpt One of my friends is running a large IPv6 network and has already experienced a shortage of IPv6 neighbor cache on some of his switches. Digging deeper into the root causes, he discovered: In my larger environments, I see significant neighbor table cache entries, especially on network segments with hosts that make many long-term connections. These hosts have 10 to 20 addresses that maintain state over days or weeks to accomplish their processes. What's going on? A perfect storm of numerous unrelated annoyances: Read more ... ## Performance tips for Neon Postgres DevFeed: [Performance tips for Neon Postgres]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2024-04-04T16:56:28Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Database](), [Serverless](), [autoscaling](), [Optimization](), [API]() Tags: [api](), [autoscaling](), [best-practices](), [databases](), [load](), [optimization](), [performance](), [postgres](), [scale](), [tips]() ### AI overview This article presents performance tips for Neon Postgres, covering connection pooling, resource sizing, autoscaling, and query optimization. It explains how to investigate slow queries using query statistics and EXPLAIN (ANALYZE, BUFFERS). ### Source excerpt Neon is serverless Postgres. Neon databases elastically scale up and down according to load--including scaling to zero--and database operations are greatly simplified via robust API support and database branching capabilities. But just like every Postgres, to get the most out of Ne... ## How Tinybird scales a real-time data platform to handle billions of events DevFeed: [How Tinybird scales a real-time data platform to handle billions of events]() Original publisher: [Read original article]() Author: Javi Santana Published: 2024-02-16T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [data](), [real-time]() Tags: [data](), [events](), [load](), [platform](), [real-time](), [scalable-analytics-architecture](), [scale]() ### AI overview This article describes the infrastructure decisions Tinybird uses to scale a real-time data platform to handle billions of events while keeping queries fast under load. ### Source excerpt How Tinybird scales to handle billions of events without breaking. The infrastructure decisions that keep queries fast under load. ## New GraphQL observability and insights in GraphOS Studio DevFeed: [New GraphQL observability and insights in GraphOS Studio]() Original publisher: [Read original article]() Author: Parul Schroff Published: 2023-10-31T16:11:06Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [observability](), [dashboards](), [API Platform](), [SIEM, Security, Observability]() Tags: [announcement](), [api](), [api-platform](), [apollo](), [apollo-federation](), [architecture](), [graphos](), [graphql](), [improvements](), [insights](), [list](), [load](), [observability](), [platform](), [release](), [search]() ### AI overview Apollo announces a new insights dashboard in GraphOS Studio for scalable GraphQL observability. The release consolidates operation and field insights, adds search and filtering, and improves list loading for large graphs. ### Source excerpt As API platform teams continue to back Apollo Federation as the standard for GraphQL architecture at scale, graphs have rapidly become more sophisticated -- expanding across innumerable teams, services, clients, and business units. As sophistication increases, so too does the need for powerful and user-friendly observability, tailored to the needs of graph owners. ## Real-Time Data Ingestion Patterns for Analytics at Scale DevFeed: [Real-Time Data Ingestion Patterns for Analytics at Scale]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2023-08-18T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [real-time](), [data]() Tags: [data](), [load](), [patterns](), [real-time](), [scalable-analytics-architecture](), [scale]() ### AI overview The article discusses patterns for real-time data ingestion at scale and warns that incorrect approaches can cause data loss under load. ### Source excerpt Real-time data ingestion at scale requires specific patterns. Get this wrong and you'll lose data under load. Here's what works. [Next page]()