# valkey Published articles for valkey. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ZDI-26-833: Valkey clusterCreatePublishMsgBlock Numeric Truncation Error Remote Code Execution Vulnerability DevFeed: [ZDI-26-833: Valkey clusterCreatePublishMsgBlock Numeric Truncation Error Remote Code Execution Vulnerability]() Original publisher: [Read original article]() Published: 2026-10-28T05:00:00Z Content type: news Language: en Sources: [Zero Day Initiative]() Topics: [valkey]() Tags: [2026](), [account](), [allows](), [authentication](), [buffer](), [can](), [code](), [code-execution](), [context](), [error](), [execution](), [exploit](), [installations](), [remote](), [remote-code-execution](), [remote-code-execution-vulnerability](), [required](), [this](), [valkey](), [vulnerability]() ### AI overview Authenticated remote attackers can execute code as the Valkey service account by exploiting insufficient validation of parameters handled by the PUBLISH message handler. The flaw can cause an incorrect size calculation before buffer allocation. Valkey has issued an update to correct the vulnerability; the supplied excerpt does not identify affected versions or describe the update. ### Source excerpt This vulnerability allows remote attackers to execute arbitrary code on affected installations of Valkey. Authentication is required to exploit this vulnerability. The ZDI has assigned a CVSS rating of 8.8. ## Olson and Söderqvist Discuss Valkey's Evolution and Future Past Caching Use Cases at OSS EU DevFeed: [Olson and Söderqvist Discuss Valkey's Evolution and Future Past Caching Use Cases at OSS EU]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-10-09T09:09:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [valkey](), [Redis](), [Dependency management](), [Amazon ElastiCache]() Tags: [automation](), [change-data-capture](), [cluster](), [connection-pooling](), [development](), [distributed-cache](), [durability](), [news](), [redis](), [replication](), [security](), [storage](), [valkey](), [valkey-evolution]() ### AI overview The article reports on Valkey's development since its fork from Redis, covering throughput and memory-efficiency improvements, cluster features, and the GLIDE client library. It also discusses possible future directions, including synchronous replication and tiered storage, alongside release automation and security work. ### Source excerpt Valkey is a growing project, launched as a fork of Redis over two years ago, supported by twelve maintainers and nine committee members from various companies. Recent developments include enhanced throughput, improved cluster functionality, and plans for features like synchronous replication and tiered storage. The project also emphasises security and release efficiency through automation. By Olimpiu Pop ## Valkey Memory Optimization, Version by Version and Encoding: How Many Bytes Did Each Release Actually Save? DevFeed: [Valkey Memory Optimization, Version by Version and Encoding: How Many Bytes Did Each Release Actually Save?]() Original publisher: [Read original article]() Author: Percona Team Published: 2026-09-29T15:33:33Z Content type: article Language: en Sources: [Percona]() Topics: [valkey](), [Encoding](), [Compression](), [version](), [CPU Cache]() Tags: [benchmark](), [caching](), [cpu-cache](), [memory-optimization](), [valkey]() ### AI overview A benchmark and source-code analysis compares memory use across Valkey versions 7.2.14 through 9.1.1. In the tested string-heavy workload, average key size fell from 102.48 bytes in 7.2.14 to 64.02 bytes in 9.1.1, a 37.5% reduction. The article attributes major changes to inline key storage and a redesigned hash table, while noting the results do not apply to every Valkey environment. ### Source excerpt Introduction This RAM/Memory reduction is most useful for Valkey 9.1 environments that are key string heavy. They are used extensively in caching, sessions/authentication, real-time apps, ecomm, Ad tech/martech, and gaming. These scenarios see a reduction of 37.5%, which has been attained in our testing environments versus version 7.2.. The biggest jump in the reduction came ... Continued The post Valkey Memory Optimization, Version by Version and Encoding: How Many Bytes Did Each Release Actually Save? appeared first on Percona. ## Implement a correctness-safe Bloom filter lookup with Amazon ElastiCache for Valkey and Amazon Aurora PostgreSQL DevFeed: [Implement a correctness-safe Bloom filter lookup with Amazon ElastiCache for Valkey and Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Chintan Agrawal Published: 2026-09-17T16:01:43Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [bloom-filter](), [valkey](), [Amazon Aurora](), [Cache](), [PostgreSQL](), [consistency](), [Latency]() Tags: [advanced-300](), [amazon-aurora](), [amazon-elasticache](), [architecture](), [bloom-filter](), [cache](), [consistency](), [latency](), [performance](), [postgresql](), [postgresql-compatible](), [technical-how-to](), [valkey]() ### AI overview This post explains a three-tier membership lookup that combines a Bloom filter in Amazon ElastiCache for Valkey, an exact-match cache, and Amazon Aurora PostgreSQL as the relational source of truth. The design provides fast negative checks and preserves correctness when false positives would affect business outcomes. ### Source excerpt This post shows how to compose a Bloom filter with an exact-match cache and a relational source of truth into a three-tier, correctness-safe membership lookup using Amazon ElastiCache for Valkey and Amazon Aurora PostgreSQL, serving sub-millisecond decisions at peak throughput without false-positive risk. ## Dragonfly 2.0 adds Redis and Valkey compatibility and reduces processing overhead DevFeed: [Dragonfly 2.0 adds Redis and Valkey compatibility and reduces processing overhead]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-17T10:02:19Z Content type: release Language: en Sources: [Phoronix]() Topics: [in-memory database](), [Memcached](), [Redis](), [data]() Tags: [compatibility](), [desktop-linux](), [in-memory-database](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [memcached](), [open-source-graphics](), [overhead](), [phoronix](), [phoronix-test-suite](), [redis](), [release](), [serialization](), [ubuntu-benchmarks](), [ubuntu-hardware](), [valkey](), [version]() ### AI overview Dragonfly 2.0 is a new release of the in-memory database that expands Redis and Valkey compatibility, adds Valkey 9 RDB loading support, and reduces connection processing and memory accounting overhead. ### Source excerpt Released on Wednesday was a new version of the Dragonfly database that continues to promote itself as "the world's most efficient in-memory data store" and a modern replacement to the likes of Redis and Memcached while being fully compatible with their APIs... ## AlmaLinux Day: Germany 2026 -- Full Schedule Announced DevFeed: [AlmaLinux Day: Germany 2026 -- Full Schedule Announced]() Original publisher: [Read original article]() Author: Benny Vasquez Chair; Board Published: 2026-03-18T00:00:00Z Content type: release Language: en Sources: [AlmaLinux]() Topics: [systems](), [cloud-infrastructure](), [Open Source](), [Security](), [Hardware](), [Scalability](), [valkey]() Tags: [2026](), [events](), [germany](), [hardware](), [infrastructure](), [open-source](), [scaling](), [security](), [valkey]() ### AI overview An announcement for AlmaLinux Day: Germany 2026, a full-day event featuring talks and a panel on AlmaLinux infrastructure, open-source use, high-performance computing, security, AMD EPYC performance, Valkey, and the project's future. The event takes place during CloudFest 2026 in Rust, Germany. ### Source excerpt We're just 9 days out from AlmaLinux Day: Germany 2026, and the full schedule is here! On Thursday, March 26, we'll be taking over the Convento room at Europa-Park in Rust, Germany for a full day of talks, a panel, and the kind of hallway conversations that make these events so worthwhile. AlmaLinux Day: Germany happens during the final day of CloudFest 2026, so you'll also have access to the legendary CloudFest BierFest to close out the evening. ## Heroku Key-Value Store Now Supports Valkey 8.1 with JSON and Bloom Modules DevFeed: [Heroku Key-Value Store Now Supports Valkey 8.1 with JSON and Bloom Modules]() Original publisher: [Read original article]() Author: Jonathan Brown Published: 2025-07-02T15:00:03Z Content type: release Language: en Sources: [Heroku]() Topics: [valkey](), [Heroku](), [Redis]() Tags: [active-memory](), [applications](), [benchmark](), [benchmarks](), [bloom](), [caching](), [cases](), [clients](), [command](), [core](), [efficiency](), [general-availability](), [heroku](), [heroku-key-value-store](), [json](), [key-value-store](), [modules](), [news](), [performance](), [redis](), [valkey]() ### AI overview Heroku Key-Value Store now offers Valkey 8.1, with Valkey Bloom and ValkeyJSON modules. The excerpt attributes lower latency and higher throughput to a more memory-efficient hash table, I/O threading optimizations, and active memory defragmentation; the hash table also reduces memory use per key. It says existing use cases should benefit mostly without changes. To upgrade, the stated command upgrades mini instances immediately; on premium and larger plans it prepares maintenance, which must then be run. The excerpt introduces internal comparisons with Valkey 7.2 but cuts off before giving benchmark results. ### Source excerpt We're thrilled to announce the general availability of Valkey v8.1 in Redis OSS compatible Heroku Key-Value Store. This isn't just an incremental update; it's a significant leap forward, bringing enhanced performance and greater efficiency. To add to this excitement, we're bringing powerful new module capabilities to v8.1, with Valkey Bloom and ValkeyJSON. For years, Heroku [...] The post Heroku Key-Value Store Now Supports Valkey 8.1 with JSON and Bloom Modules appeared first on Heroku. ## Scaling Nextdoor's Datastores: Part 3 DevFeed: [Scaling Nextdoor's Datastores: Part 3]() Original publisher: [Read original article]() Author: Ronak Shah Published: 2025-03-19T15:08:43Z Content type: article Language: en Sources: [Nextdoor]() Topics: [Cache](), [Database](), [schema-evolution](), [Object-relational mapping](), [Django](), [Python](), [Redis](), [valkey]() Tags: [blog](), [cache](), [cache-invalidation](), [caching](), [compatibility](), [database](), [django](), [lua](), [python](), [redis](), [schema](), [serialization-format](), [thundering-herd](), [valkey]() ### AI overview Part 3 of Nextdoor's datastore-scaling series explains how applications serialize database objects for Redis or Valkey look-aside caches. It describes compatibility problems caused by runtime, package, and schema changes, including cache misses and thundering-herd effects during migrations. ### Source excerpt In this part of the Scaling Nextdoor's Datastores blog series, we'll explore how the Core-Services team at Nextdoor serializes database data for caching while ensuring forward and backward compatibility between the cache and application code. In part 1 of this series we discussed how ORMs, object-relational mapping frameworks, help abstract away database specific schemas and queries from application code. Developers simply utilize objects in their application's language to access database data. Here's a simple example of using Python's Django ORM to define a model: from django.db import models class Users(models.Model): first_name = models.CharField(max_length=30) last_name = models.CharField(max_length=30) The associated SQL create table would look like: CREATE TABLE users ( "id" bigint NOT NULL PRIMARY KEY GENERATED BY DEFAULT AS IDENTITY, "first_name" varchar(30) NOT NULL, "last_name" varchar(30) NOT NULL ); Developers would then access database data like this: user_id = 123 user = User.objects.get(id=user_id) print(user.first_name)Object Byte Serialization for Caching An issue arises when adding a look-aside cache such as Redis/Valkey to an application: How do you store what you got from the database in the cache? A common solution to caching complex objects, such as those from ORMs, is object byte serialization. This process converts language objects into bytes before storing them in the cache. When reading from the cache the process is done in reverse where the byte data is turned into language objects. For instance in Python this is often done with the pickle package. The interaction between the application, database, and the cache looks like this: Look-Aside Cacheimport pickle # Try getting from cache ('None' if not in cache) user_bytes = cache.get("user_123") if user_bytes is not None: # Read bytes using pickle user = pickle.loads(user_bytes) else: # Fetch from database user = User.objects.get(id=123) # Convert to bytes user_bytes = pickle.dumps(user) # Sto ## Chainguard Images April 2024: Secure, reliable, feature-rich DevFeed: [Chainguard Images April 2024: Secure, reliable, feature-rich]() Original publisher: [Read original article]() Published: 2024-05-09T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard images](), [supply-chain-security](), [container images](), [Security](), [Open Source](), [FFmpeg (Fast Forward Moving Picture Experts Group)](), [MATLAB](), [Kubernetes]() Tags: [chainguard](), [chainguard-images](), [compliance](), [container-images](), [ffmpeg](), [fips](), [fips-compliance](), [harbor](), [kubernetes](), [open-source](), [release](), [rstudio](), [secure-container-image](), [security](), [software-supply-chain](), [software-supply-chain-security](), [tesseract](), [valkey](), [vulnerability]() ### AI overview Chainguard announces approximately 60 new Chainguard Images in its April 2024 release. The hardened, minimal images focus on software supply chain security, reliability, and reduced CVE exposure, with FIPS variants available for some images. Featured additions include rstudio, Harbor, FFmpeg, Tesseract, and Valkey. ### Source excerpt Explore the latest features and security enhancements in Chainguard Images (April 2024 release). Strengthen your software supply chain.