Mastodon v4.3.10 veröffentlicht.
• Abhängigkeiten aktualisiert.
• Datenbank-Backups vor Updates empfohlen.
• `charlock_holmes` Gem-Build-Problem mit `gcc` möglich.

Mastodon v4.3.10 veröffentlicht.
• Abhängigkeiten aktualisiert.
• Datenbank-Backups vor Updates empfohlen.
• `charlock_holmes` Gem-Build-Problem mit `gcc` möglich.
First working Redis with post-quantum mTLS using Falcon (NIST finalist) — running in a hardened Alpine container with OpenSSL 3.3.4 + oqs-provider.
Falcon keys + certs generated inside the image, Redis launched via --tls-port, and PONGs confirmed via PQ mTLS.
GitHub: https://github.com/zenthracore/zen.redis
Docker: https://hub.docker.com/r/zenthracore/zen.redis
This might be the first public Redis instance running on PQ crypto.
@bookwyrm Installed, running (not thanks to #redis, #socket and python
https://bookwyrm.projetretro.io/user/shalien
now gonna find all the book in the house to scan them
GitHub - tidwall/cache-benchmarks:快取軟體基準測試
➤ 各種快取軟體效能的全面比較
✤ https://github.com/tidwall/cache-benchmarks
這個 GitHub 倉庫 `tidwall/cache-benchmarks` 提供了 Memcache、Redis、Valkey、Dragonfly 和 Garnet 等快取軟體的基準測試結果。測試在 AWS c8g.8xlarge 伺服器上進行,透過 `memtier_benchmark` 工具,針對不同的管道大小(1, 10, 25, 50)測量了吞吐量、延遲(50%、90%、99%、99.9%、99.99% 百分位數以及最大值)和 CPU 週期。每個基準測試都有 31 次運行,取中位數作為繪圖的依據。測試環境設定了多線程,並將 CPU 核心劃分給基準測試工具和快取伺服器。整個測試需要約兩週時間完成。
+ 這些基準測試對於選擇適合自己專案的快取方案非常有幫助,可以瞭解不同快取軟體在不同配置下的表現。
+ 測試結果
#效能測試 #快取 #Redis #Memcache
向量集合介紹:主要指令與概念 - YouTube
➤ Redis向量集合:顛覆傳統的向量搜尋方法
✤ https://www.youtube.com/watch?v=kVApsFUeuEA
這段YouTube影片將解釋Redis新數據類型「向量集合」與傳統向量資料庫/索引的根本差異,並展示一些基本的指令和概念。影片旨在幫助觀眾理解向量集合的獨特性質和應用方式。
+ 終於有人解釋向量集合跟傳統向量資料庫的區別了,之前一直搞不清楚!
+ 期待能看到更多關於Redis向量集合實際應用案例的影片。
#數據庫 #Redis #向量搜尋 #向量資料庫
Ah yes, the "groundbreaking" revelation that #Redis can handle #vector #sets, brought to you by a #YouTube video that thinks it's 2025 already. Because nothing screams cutting-edge #tech like a #tutorial that doubles as a #privacy #policy recital.
https://www.youtube.com/watch?v=kVApsFUeuEA #Groundbreaking #HackerNews #ngated
HNSW as abstract data structure: video intro to Redis vector sets
After messing around with fixing #harborregistry (I got locked out somehow), I got #authentik running on HA #postgres and #redis on my 2 node #kubernetes cluster on #talos. Next up is to get probably set up cloudflare bot protections and then set up a simple app. #Fediverse apps are incoming!
#selfhosting
How White-Label WordPress #Reseller #Hosting Supercharges Your #Digital #Agency’s Growth
Instant Setup with 1-Click #WordPress Deployments
Launch & Iterate Faster with 1-Click #Staging, Push, & #Cloning
Pre-Built with Your Stack—#Themes & #Plugins Preloaded
Bulletproof #Backup & #Restore System
Blazing WordPress Performance with #LiteSpeed + #Redis
Zero-Downtime Site #Migrations: We Do the Heavy Lifting
Server #Update / #Upgrade Day
- update my #Linux server OS #OpenSUSE Leap 15.6
- upgrade #Nextcloud from 30.0.7 to 30.0.12 and then 31.0.6
- upgrade #Redis for Nextcloud from 7 to 8
- learn that I have to dump/restore to upgrade #PostgreSQL and upgrade from 12 to 17
- upgrade #Traefik from 2.10.7 to 3.4.3
- upgrade #Vaultwarden to newest version
- deactivated #Quassel and #Jupyter since I didn't use them for at least 4 years
- clean up old #Docker images and containers to free some disk space
Mastodon v4.4.0 veröffentlicht.
• Min. Redis-Version auf 6.2 erhöht.
• Min. PostgreSQL-Version auf 13 erhöht.
• “Follower, die du kennst”-Widget hinzugefügt.
• Unterstützung für Redis Namespaces entfernt.
Memcached vs #Redis #Cache This article provides a detailed comparison of #Memcached vs Redis cache.
What is Memcached?
Memcached is a high-performance, distributed memory caching system designed to speed up dynamic web applications by reducing the database load. Key Features
In-memory storage: Stores data in RAM, making it extremely fast.
Key-value store: Caches data using a simple string-based key-value ...
Continued https://blog.radwebhosting.com/memcached-vs-redis-cache/?utm_source=mastodon&utm_medium=social&utm_campaign=mastodon.social #selfhosting #selfhosted #opensource
La bibliothèque https://github.com/ZhuoZhuoCrayon/throttled-py permet mettre en place du rate limiting sur vos API #Python ; ou sur l'appel de fonctions vers un backend.
Les algorithmes classiques de #throttling sont proposés, et l'état peut être stocké en mémoire ou sur #Redis (pour un usage multi-serveurs).
Et pour en savoir plus sur le sujet, ce site explique visuellement ces différents concepts : https://smudge.ai/blog/ratelimit-algorithms.
Why is Redis just the absolute best glue between processes? Surely Linux/Unix has something just as good? But in my experience, there's literally nothing better for communicating between processes.
Don't get me wrong, I love Redis, but I just wonder why the best solution ended up being a memory based database and not something in the operating system itself.