Abhay Pandey’s Post

Today I Learned – Redis connections growing unbounded. In our production deployments, we utilize gunicorn and gevent with great success. Our benchmarking showed this setup was once the fastest many years ago. The Problem: The increase in active Redis connections may not be in your Celery workers or Celery Beat, but rather in your WSGI (gunicorn) process. Despite gevent providing green threads, Celery can create a new connection pool for each task, leading to a rise in connection count. The Solution: To address this issue, ensure Celery recognizes the thread-safe environment by setting result_backend_thread_safe = True. For Django users, add CELERY_RESULT_BACKEND_THREAD_SAFE = True in settings.py. This adjustment should enable proper re-use of Redis connections, resolving the problem. Hope this insight proves beneficial!

Cached the frequently accessed searches to avoid database queries. Use Redis or whatever cache driver. Clear the particular cache key value by using Observers or other approach of choice on model update.

Injecting Chaos: Easy Techniques for Simulating Network Issues in Redis Clusters https://lnkd.in/dDMFKW7s While comprehensive chaos testing tools offer a wide range of features, sometimes you just need a quick and easy solution for a specific scenario. This article focuses on a targeted approach: simulating network issues between Redis client and Redis Cluster in simple steps. These methods are ideal when you don't require a complex setup and want to focus on testing a particular aspect of your Redis cluster's behavior under simulated network issues. Set-Up This article assumes that you already have a Redis cluster and the client code for sending traffic to the cluster is set up and ready to use. If not, you can refer to the following steps:

Day 237 of the #365GenAIChallenge! 📅 Today is Redis' turn! Redis is an in-memory data structure store widely used for caching, real-time analytics, and high-performance databases. It supports versatile data types like strings, hashes, lists, and sets, and offers ultra-fast read/write operations, making it ideal for low-latency applications such as session management, message queues, and leaderboards.

Redis handles tens of thousands of requests per second using just ONE thread. When Redis needs to read data, it uses a system call that can get blocked if the data isn't readily available. This is like waiting for a slow elevator—it holds everything up! Redis leverages I/O multiplexing to monitor its sockets. It's like having a lookout who signals when the elevator arrives, so Redis only makes the call when it's guaranteed data availability. - By ensuring data is available before reading, Redis eliminates unnecessary delays. - Avoiding multi-threading complexities like lock contention keeps Redis lean and efficient. P.S: Follow me, Prafful Agarwal, for more insights.

🚀 Effortless Automation with Redis! Just implemented a smart seat management system using Redis key expiration events. 🎯 With a simple setup: ✅ Listen for expired seat locks in real-time. ✅ Automatically release seats when users don't confirm in time. ✅ Enhance system efficiency without constant polling. Redis + Node.js = A powerful duo for scalable event-driven systems! 💡 #Redis #NodeJS #Automation #BackendDevelopment #Scalability ➡️Checkout my article on this:

📈 Increase 10x API Read speed with cache using Redis 🔍 Caches significantly reduce the load on databases and decrease latency by storing data that doesn't change often and is accessed frequently. In this episode of Implementing, I show you how to use Redis as a caching system and how I introduced it into the Magic Link-based authentication app to cache user data 😊 Let me know your thoughts! 💭 https://lnkd.in/dNvdDqad

How Aerospike eats Redis use cases for lunch! In-Memory database solutions are awesome until you get stuck with the bill. It's no wonder why Redis users are migrating to Aerospike to achieve massive performance at scale on significantly reduced hardware footprints. Isn't it about time you demanded more from your database... Not a believer, sounds too good to be true... I get it, I really do. Give me 15 mins of your time and your apprehension will melt faster than the cheese on this burger. #performanceatscale #dataondisk #whoscalesbetterthanusnobody #keepthecacheinyourpocket

#silentlesson "Why is Redis Fast?" Ever wondered what makes Redis such a speedy option for in-memory data storage? The key lies in its utilization of RAM-based storage, offering rapid access that outperforms disk storage by a significant margin. To ensure durability, Redis supports disk snapshots and append-only file logging, combining the speed of RAM with the persistence of disk storage. However, recovery from disk can be slower in case of failure, highlighting a tradeoff between speed and data security. For improved resilience, Redis replication comes into play, enabling instant failover to a synchronized replica in memory without the need for rehydration. This setup ensures both speed and rapid recovery in case of system failures. Redis adopts an event-driven, single-threaded model for its core operations, minimizing context switching and synchronization issues common in multi-threaded systems. By using non-blocking I/O for asynchronous connection handling, Redis can efficiently manage numerous client connections with minimal overhead. Moreover, Redis leverages pipelining to enhance throughput, allowing clients to pipeline commands without waiting for individual responses. This optimization reduces network round trips, thereby boosting overall performance. With Redis now incorporating multi-threaded I/O for network communication in version 6.0, the question arises: how should we configure Redis to fully harness the CPU cores of modern hardware for optimal performance in a production environment? Let's discuss and explore the possibilities together! #datastructures #databases #technology #redis #techdiscussion

How to Monitor Valkey/Redis with Percona Monitoring and Management Ever wanted to monitor Valkey/Redis like a pro? Dive into this guide by @Percona where they unravel the secrets of adding external exporters to Percona Monitoring and Management (PMM). VictoriaMetrics does the grunt work, making friends with Prometheus to keep you in the loop. Whether it's Valkey, Redis, or another tech love of yours, the principles remain the same. Think of PMM as the Swiss Army knife for your DB needs. It uses Prometheus exporters, so you've got versatility at your fingertips. Ready to level up your monitoring game? Check the detailed walkthrough here: https://lnkd.in/e2hceE6a Credit to the awesome Percona team for this gem! https://lnkd.in/eu6XmMEW