# Redis Published articles for Redis. 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. ## Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent DevFeed: [Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent]() Original publisher: [Read original article]() Author: TNS Staff Published: 2026-09-15T16:21:45Z Content type: article Language: en Sources: [The New Stack]() Topics: [SRE](), [incident](), [Azure](), [observability](), [DevOps](), [Redis]() Tags: [agent](), [ai-agents](), [ai-operations](), [azure](), [code-review](), [deployment](), [devops](), [incident](), [microsoft-azure](), [monitoring](), [observability](), [post](), [redis](), [sponsor-microsoft-azure](), [sponsored](), [sponsored-post](), [sre](), [telemetry]() ### AI overview The article describes Azure SRE Agent as a system that analyzes telemetry, correlates deployment and monitoring data, investigates incidents, identifies root causes, recommends or prepares fixes, and supports mitigation and other operational tasks under human approval. It cites examples involving Microsoft service teams and InEight, including a recommendation to scale Redis. ### Source excerpt What if engineers could spend their time building and optimizing systems rather than maintaining them? It's 3 a.m., and the The post Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent appeared first on The New Stack. ## Why your Redis is slow: it is single threaded and you sent it KEYS DevFeed: [Why your Redis is slow: it is single threaded and you sent it KEYS]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Redis](), [Latency](), [Concurrency]() Tags: [blocking](), [caching](), [command](), [latency](), [observability](), [performance](), [redis](), [single-threaded]() ### AI overview The article explains that Redis executes commands one at a time on a single thread, so expensive commands can block every client and cause latency spikes across all operations. It identifies KEYS as a key offender and also discusses large DEL operations, unbounded LRANGE, SMEMBERS on large sets, and FLUSHALL. Redis 6 threaded I/O helps with socket handling but does not change single-threaded command execution. ### Source excerpt Redis executes commands one at a time on a single thread, so any command that takes 200ms makes every other client wait 200ms regardless of how trivial their request was. KEYS scans the entire keyspace and is the obvious offender, but DEL on a large collection, unbounded LRANGE, SMEMBERS on a big set and FLUSHALL are all linear in the size of the data and block identically. The tell is a latency spike across all operations at once while CPU and network look normal. ## 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 ## Security updates for Monday DevFeed: [Security updates for Monday]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-14T13:18:14Z Content type: news Language: en Sources: [LWN.net]() Topics: [Security](), [Debian](), [Fedora](), [Firefox](), [Git](), [nginx](), [Python](), [cURL](), [Elixir](), [F#](), [Redis](), [Rust]() Tags: [curl](), [debian](), [elixir](), [firefox](), [git](), [nginx](), [python](), [redis](), [rust](), [security](), [updates]() ### AI overview Security updates were issued across AlmaLinux, Debian, Fedora, Gentoo, Mageia, Oracle, and SUSE. The affected software includes operating-system components, browsers, developer tools, programming-language packages, servers, libraries, and cloud-related utilities. ### Source excerpt Security updates have been issued by AlmaLinux (389-ds-base, apr-util, coreutils, freerdp, git-lfs, glib2, gstreamer1-plugins-base, kernel, libkcapi, nginx, nodejs:22, nodejs:24, osbuild-composer, perl-YAML-Syck, postgresql16-postgis, ruby, ruby4.0, ruby:3.3, and vim), Debian (jbig2dec, kamailio, nginx, spip, and xorg-server), Fedora (baresip, bind, bluez, bubblewrap, chirp, chromium, cockpit, composer, corosync, darktable, dokuwiki, elixir, exiv2, expat, firefox, freerdp, freerdp2, gdk-pixbuf2, gegl04, golang-x-perf, grpcurl, kernel, kernel-headers, libevent, libmongocrypt, libpcap, libre, libsoup3, memcached, mingw-expat, mingw-openexr, mongo-c-driver, mrtg, nagios-plugins, nsd, nss, openssl, openvpn, PackageKit, pdns-recursor, perl-Net-OAuth, perl-XML-Bare, php-pecl-mongodb2, python-asteval, python-pip, rclone, rest, rust-hickory-net, rust-hickory-proto, rust-hickory-resolver, rust-ppmd-rust, rust-webbrowser, srt, syncthing, tar, tkimg, and valkey), Gentoo (Chromium, Google Chrome, Microsoft Edge, Opera, Vivaldi and Ruby), Mageia (bind, ffmpeg, glibc, java-17-openjdk, java-21-openjdk, librabbitmq, perl-Catalyst-Plugin-Static-Simple, perl-Imager, tor, and xz), Oracle (389-ds:1.4, ansible-core, apr-util, coreutils, freerdp, git-lfs, glib2, gstreamer1-plugins-base, gzip, httpd:2.4, image-builder, java-21-openjdk, kernel, mrtg, nginx, osbuild-composer, perl-DBI, postgresql16-postgis, python-lxml, python3.12-lxml, redis:6, and vim), SUSE (389-ds, ansible-core, ansible-creator, azure-storage-azcopy, cargo-audit, chromedriver, chromium, clamav, containerized-data-importer1.65, containerized-data-importer1.66, curl, dracut, ffmpeg-4, google-guest-agent, google-osconfig-agent, helm, java-1_8_0-ibm, jupyter-nbconvert, kernel, libpng16, libusb-1_0, libvirt, multipath-tools, NetworkManager, opensc, openssl-3, perl-Authen-SASL, perl-HTML-FormHandler, perl-Mojolicious, perl-Protocol-HTTP2, python-jwcrypto, python-sqlparse, python-tornado6, python313-geopy, python313-modelscope ## Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more DevFeed: [Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more]() Original publisher: [Read original article]() Author: Bosmat Tuvel Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Data structures](), [Data Management](), [Open Source]() Tags: [data-management](), [improvements](), [memory](), [new-features](), [operations](), [performance](), [redis](), [streams](), [tech](), [time-series]() ### AI overview Redis 8.10 in Redis Open Source introduces compact hashes, incremental backup and restore, JSONPath syntax extensions, more flexible Stream consumption, new Set cardinality operations, atomic movement of multiple List elements, and enhanced Time Series capabilities. The release also improves memory efficiency, throughput, and operational reliability at scale. ### Source excerpt Redis 8.10 in Redis Open Source is now available, delivering improvements that make Redis more memory efficient, expressive, and easier to operate at scale. Highlights include compact hashes with up to 50% lower memory usage and 2x higher hash loadin... ## 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. ## Put Redis data and engineering guidance to work in ChatGPT Work DevFeed: [Put Redis data and engineering guidance to work in ChatGPT Work]() Original publisher: [Read original article]() Author: Olga Lopaci Published: 2026-09-11T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Developer Tools](), [AI Chat](), [AI Bots]() Tags: [agent-skills](), [chatgpt](), [codex](), [engineering](), [observability](), [rag](), [redis](), [review](), [security](), [skills](), [tech](), [work]() ### AI overview Redis launched a development plugin for ChatGPT Work and Codex that supplies current Redis guidance for writing, reviewing, and troubleshooting code. It also describes connecting Redis data to ChatGPT Work's Data agent for plain-language exploration and investigation. ### Source excerpt Redis has launched a development plugin that brings current Redis engineering guidance into ChatGPT Work and Codex. It helps teams write, review, and troubleshoot Redis code without switching between documentation and development tools. Alongside Ope... ## Choosing among write-through, write-behind, and write-around caching strategies DevFeed: [Choosing among write-through, write-behind, and write-around caching strategies]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Databases](), [Redis](), [consistency](), [Latency]() Tags: [cache](), [caching](), [consistency](), [database](), [latency](), [orders](), [postgres](), [redis](), [strategies](), [system-design](), [write](), [write-behind](), [write-through]() ### AI overview This article compares write-through, write-behind, and write-around caching strategies. It explains their trade-offs among database latency, cache hit rates, consistency, and the risk of losing acknowledged writes when a process fails. ### Source excerpt The three write strategies differ in what happens between the cache write and the database write, and that gap is where correctness lives. Write-through updates both before acknowledging, so it is safe and pays the full database latency. Write-behind acknowledges after the cache write and flushes later, which is fast and can lose acknowledged writes if the process dies. Write-around skips the cache entirely on write, which avoids caching data nobody reads and costs a miss on the next read. ## Caching Responses for Repeated Questions in Production LLM Applications DevFeed: [Caching Responses for Repeated Questions in Production LLM Applications]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-10T21:34:26Z Content type: article Language: en Sources: [Daily Dose of Data Science]() Topics: [Caching](), [Redis](), [Embeddings](), [Large Language Model](), [Inference]() Tags: [cache](), [caching](), [embeddings](), [inference](), [llm](), [redis]() ### AI overview Production LLM applications can reduce repeated model calls by caching generated responses and matching new questions to prior questions using embeddings. The article discusses Redis LangCache, including similarity thresholds, expiration, data isolation, monitoring, and an example where a cache hit was roughly 6x faster than direct inference. ### Source excerpt What production agents must preserve between turns, and what they must reset. ## Security updates for Thursday DevFeed: [Security updates for Thursday]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-10T13:14:35Z Content type: news Language: en Sources: [LWN.net]() Topics: [Security, Privacy and Abuse Prevention](), [.NET](), [Boot Process, passwd, systemd](), [Grafana]() Tags: [ansible](), [grafana](), [kernel](), [linux](), [net](), [opentelemetry](), [oracle](), [prometheus](), [python](), [redis](), [rust](), [security](), [systemd](), [ubuntu](), [updates]() ### AI overview A roundup of security updates issued by several Linux distributions for a range of packages. ### Source excerpt Security updates have been issued by AlmaLinux (389-ds-base, ansible-core, buildah, expat, glib2, gpsd, gpsd-minimal, gzip, kernel, kernel-rt, opentelemetry-collector, osbuild-composer, perl-DBI, python-lxml, python3.12-lxml, qt5-qtbase, thunderbird, valkey, vim, and xz), Debian (pyasn1), Fedora (darktable, freeipa, freerdp2, gdk-pixbuf2, GitPython, libsoup3, openssl, perl-Net-DNS, rust-ppmd-rust, samba, and valkey), Mageia (ceph, firefox, nss, perl-DBI, thunderbird, and wget), Oracle (389-ds-base, buildah, expat, git-lfs, glib2, glibc, gpsd, gpsd-minimal, grafana-pcp, kernel, libssh, nginx, perl-GD, python3.14-cryptography, redis:7, skopeo, thunderbird, valkey, xmlrpc-c, and xz), Slackware (xz), SUSE (bzip2, cpio, curl, dracut, fuse-overlayfs, golang-github-vpenso-prometheus_slurm_exporter, helm, java-1_8_0-ibm, kbfs, kernel, kernel-devel, libopenslide-devel, libsoup, libssh2_org, libusb-1_0, libvirt, libzypp, zypper, mcphost, multipath-tools, NetworkManager, opensc, openssl-3, perl-Net-DNS, python-aiohttp, python-Authlib, python-pip, python-sqlparse, python313-dnspython, python313-idna, rpcbind, sssd, strongswan, systemd, tomcat11, ucode-intel, and wget), and Ubuntu (dotnet8, dotnet10, ffmpeg, flatpak, netty, and perl). ## Security updates for Wednesday DevFeed: [Security updates for Wednesday]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-09T13:04:46Z Content type: news Language: en Sources: [LWN.net]() Topics: [Debian](), [cURL](), [Chromium](), [systemd](), [Terraform](), [.NET]() Tags: [chromium](), [cli](), [debian](), [google](), [intel](), [java](), [kernel](), [opentelemetry](), [python](), [redis](), [security](), [systemd](), [terraform](), [updates]() ### AI overview Security updates have been issued across AlmaLinux, Debian, Fedora, Mageia, Red Hat, SUSE, and Ubuntu for a broad set of packages, including kernels, systemd, Chromium, curl, Python, Java, Terraform provider components, and other system and developer tools. ### Source excerpt Security updates have been issued by AlmaLinux (expat, glib2, microcode_ctl, mrtg, pam, redis, thunderbird, and valkey), Debian (fort-validator, gst-plugins-base1.0, kernel, and slurm-wlm), Fedora (complyctl, libevent, openvpn, and tar), Mageia (dovecot and spice-vdagent), Red Hat (ignition, opentelemetry-collector, and osbuild-composer), SUSE (amazon-ssm-agent, aws-nitro-enclaves-cli, bzip2, cadvisor, chromium, curl, distribution-registry, emacs, freeciv, fuse-overlayfs, gh, google-guest-agent, GraphicsMagick, hauler, insighttoolkit-devel, java-17-openjdk, libidn, libusb-1_0, libvirt, libvncserver, libzypp, zypper, lkl, lxd, multipath-tools, NetworkManager, perl-Net-DNS, perl-URI, python, python-authlib, python-sqlparse, python-tornado, python3, rpcbind, supergfxctl, systemd, terraform-provider-null, ucode-intel, wget, wireshark, and xen), and Ubuntu (curl, ffmpeg, glibc, hsqldb1.8.0, imagemagick, perl, and vim). ## Negative caching protects databases from repeated lookups for nonexistent keys DevFeed: [Negative caching protects databases from repeated lookups for nonexistent keys]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-09T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Cache](), [Database](), [Security](), [Latency]() Tags: [cache](), [cache-penetration](), [caching](), [database](), [negative-caching](), [protection](), [redis](), [reliability](), [security](), [ttl]() ### AI overview Negative caching prevents repeated database queries for nonexistent keys by storing a distinguishable marker for negative results. The article explains how partner integrations, scrapers, stale clients, migrations, or attackers can exploit this gap and recommends using a shorter TTL for negative entries. ### Source excerpt A cache that stores only found values gives you no protection against lookups for things that do not exist, and a miss on a nonexistent key costs the full origin query every single time. If the key is user supplied, an attacker can generate unlimited unique misses and bypass the cache entirely, which is cache penetration. Caching the negative result fixes it, with a shorter TTL than positive entries because a value appearing is a much more likely event than one disappearing. ## Delivering Real-Time Personalization with Databricks and Redis DevFeed: [Delivering Real-Time Personalization with Databricks and Redis]() Original publisher: [Read original article]() Author: Philip Laussermair, Anant Pingle Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [data]() Tags: [analytics](), [apache-flink](), [batch](), [data](), [databricks](), [e-commerce](), [performance](), [personalization](), [real-time](), [redis](), [spark](), [streaming](), [tech]() ### AI overview The article explains how Databricks Real-Time Mode and Redis support low-latency personalization by continuously processing event streams and serving fresh results quickly. ### Source excerpt Why real-time matters A customer is browsing an e-commerce site. They search for running shoes, open a product, read reviews, and add an item to the cart. Every one of those actions is a signal about what they want right now. If the homepage they lan... ## 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. ## Why Gusto hired Evil Martians for Sidekiq infrastructure DevFeed: [Why Gusto hired Evil Martians for Sidekiq infrastructure]() Original publisher: [Read original article]() Author: Irina Nazarova (inazarova@evilmartians.com) Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Evil Martians]() Topics: [Sidekiq](), [Rails](), [Redis](), [Ruby]() Tags: [ai](), [case-study](), [engineering](), [infrastructure](), [rails](), [redis](), [ruby](), [scale](), [sidekiq]() ### AI overview This case study examines why Gusto hired Evil Martians to work on the Sidekiq and Redis infrastructure supporting background jobs in its large Rails monolith. It discusses the operational demands of running payroll-related jobs at scale and the tradeoffs involved when a company uses consultants instead of waiting for a perfect hire. ### Source excerpt Gusto runs payroll for 500,000+ businesses on one of the largest Rails monoliths anywhere. Why a team this strong hired Evil Martians for Sidekiq at scale, what running background jobs at that size actually takes, and when to stop waiting for the perfect hire. ## 8 Redis Use Cases Engineers Should Know DevFeed: [8 Redis Use Cases Engineers Should Know]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-09-01T12:54:52Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Redis](), [Data structures](), [Caching](), [real-time](), [Server](), [systems]() Tags: [caching](), [data-structure](), [real-time](), [redis](), [server](), [use-cases]() ### AI overview This article explains that Redis is more than a caching system. It describes how Redis data structures support real-time application patterns such as counters, sessions, queues, leaderboards, rate limiting, coordination, event logs, notifications, and expiring state. ### Source excerpt Most engineers know Redis for caching. But that's only the beginning... ## Hyperloop B: the coroutine engine behind Appwrite 2.0 DevFeed: [Hyperloop B: the coroutine engine behind Appwrite 2.0]() Original publisher: [Read original article]() Author: Luke B. Silver Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Coroutines](), [Concurrency](), [PHP](), [Benchmark]() Tags: [architectures](), [backend](), [benchmark](), [concurrency](), [coroutine](), [coroutines](), [cpu](), [memory](), [php](), [platform](), [process](), [redis]() ### AI overview The article explains Hyperloop B, a coroutine-based engine behind Appwrite 2.0. It describes how yielding during I/O lets one process serve many concurrent requests, reporting 7x higher requests per second and 6x lower memory use on an I/O-bound benchmark. ### Source excerpt Hyperloop B is the Swoole coroutine engine behind Appwrite 2.0. Here is what it is, the concurrency problem it solves, and the 7x I/O throughput it buys. ## OAuth token refresh has a race condition. Fix it with a conditional write, not a distributed lock. DevFeed: [OAuth token refresh has a race condition. Fix it with a conditional write, not a distributed lock.]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-31T00:00:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [OAuth](), [race-condition](), [Authorization](), [Auth0](), [Redis]() Tags: [auth0](), [authorization](), [concurrent](), [oauth](), [race-condition](), [redis](), [the-result]() ### AI overview This article explains how concurrent OAuth token refreshes can cause two distinct failures: stale tokens from lost updates and user disconnection when refresh-token rotation treats concurrent reuse as replay. It recommends layered defenses, including conditional writes, and explains why a Redis lock does not address both problems. ### Source excerpt Concurrent refreshes don't just fail. They can disconnect the user entirely. Here are four layers of defense, cheapest first, and why the Redis lock everyone reaches for isn't the one keeping you safe. ## Security Advisory: CVE-2026-81934 DevFeed: [Security Advisory: CVE-2026-81934]() Original publisher: [Read original article]() Author: Riaz Lakhani Published: 2026-08-28T00:00:00Z Content type: news Language: en Sources: [Redis Blog]() Topics: [vulnerability](), [releases]() Tags: [authentication](), [cloud](), [redis](), [releases](), [security](), [tech](), [tls](), [vulnerability]() ### AI overview Redis remediated CVE-2026-81934, a high-severity use-after-free flaw in TLS pending-data processing that could allow authenticated attackers to execute remote code under specific conditions. The advisory lists fixed Redis releases and recommends upgrading, restricting network access, and enforcing least-privilege authentication controls. ### Source excerpt Update 9/1/2026: Following further review, the public CVE record for CVE-2026-81934 was updated to reflect a revised CVSS score of 7.5 (High). What happened? Redis identified and remediated a use-after-free vulnerability in TLS pending-data processi... ## How Redis Builds AI-Native Engineering Teams DevFeed: [How Redis Builds AI-Native Engineering Teams]() Original publisher: [Read original article]() Author: Gregor Ojstersek Published: 2026-08-27T12:35:46Z Content type: opinion Language: en Sources: [Engineering Leadership]() Topics: [Redis](), [Artificial Intelligence]() Tags: [ai](), [engineering](), [insights](), [redis](), [teams]() ### AI overview The article presents insights from a conversation with Redis Distinguished Engineer Eric Sammer about how Redis builds AI-native engineering teams. The supplied evidence does not provide specific practices or findings beyond that subject. ### Source excerpt Insights from my conversation with Eric Sammer, Distinguished Engineer at Redis. ## Investigating Redis Client Failures After a Node Loss at hh.ru DevFeed: [Investigating Redis Client Failures After a Node Loss at hh.ru]() Original publisher: [Read original article]() Author: nicholasgribanov (hh.ru) Published: 2026-08-26T05:30:49Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Redis]() Tags: [consul](), [downtime](), [java](), [jedis](), [redis](), [sre](), [tag-1d4e49052118](), [tag-68e701e78517](), [tag-b0a411324cb6](), [tag-b92bf5906bbd](), [tag-dfac9042ce7b](), [timeout]() ### AI overview A Platform team at hh.ru investigates incidents in its internal Redis client, including a 57-minute outage after a Redis node was lost. The team used controlled-load testing, Jedis updates, timeout and pool experiments, new metrics, and logs to trace the failures to a code issue and improve client stability. ### Source excerpt Пару лет назад мы устроили настоящее расследование серии инцидентов в поисках скрытого дефекта нашего Redis-клиента. Команда воспроизводила сбои под контролируемой нагрузкой, проверяла одну гипотезу за другой, обновляла Jedis, экспериментировала с таймаутами и размерами пулов -- но ничего не помогало. А помогли новые метрики и логи, настойчивость команды, ночные эксперименты и готовность разбирать поведение системы до последнего соединения. Получилась история с неожиданными поворотами, ложными следами и одной лишней строчкой кода в роли главного подозреваемого -- а её итогом стал Redis-клиент, который оказался устойчивее, чем был до начала расследования. Меня зовут Коля Грибанов, я тимлид команды "Платформа" в hh.ru. В статье расскажу, почему потеря одной ноды Redis вызывала шторм из десятков тысяч соединений, и как мы шаг за шагом искали причину инцидентов. Читать далее ## How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency DevFeed: [How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-24T13:30:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Latency](), [Low Latency](), [Databases](), [PostgreSQL](), [clickhouse](), [Redis](), [real-time](), [Back end](), [recommendation systems](), [Artificial Intelligence]() Tags: [backend](), [clickhouse](), [data](), [databases](), [engineering](), [latency](), [low-latency](), [postgres](), [postgresql](), [real-time](), [recommendation-systems](), [redis](), [user-stories]() ### AI overview Sprig outgrew PostgreSQL as its AI-powered product research platform scaled to more than 1.3 trillion events, 75 billion attributes, and high-volume real-time processing. The article describes its database challenges and the path toward lower-latency data infrastructure involving PostgreSQL, ClickHouse, and Redis. ### Source excerpt With ScyllaDB, a small engineering team could focus on building their product instead of battling their databases. ## Efficient Bulk Hash Insertion with Redis 8.10's HIMPORT DevFeed: [Efficient Bulk Hash Insertion with Redis 8.10's HIMPORT]() Original publisher: [Read original article]() Author: David Maier Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Redis](), [Ruby](), [Code](), [Command-line interface]() Tags: [article](), [cli](), [code](), [make](), [redis](), [ruby](), [tech]() ### AI overview This article explains how to use Redis 8.10's HIMPORT command with redis-rb to perform more efficient bulk hash insertion in Ruby. It covers connection setup, prepared field sets, automatic preparation, reconnection behavior, the demo CLI, and pipelined imports. ### Source excerpt I'm not a Ruby developer, and my code samples might make that clear. However, there are two reasons I'm using Ruby for the examples in this article: A new release of redis-rb. Its support for a new Redis command: HIMPORT. Getting started Establish... [Next page]()