Qurban Ullah’s Post

Techniques for Chaos Testing Your Redis Cluster https://lnkd.in/dvHMR7ga For large-scale, distributed systems, chaos testing becomes an essential tool. It helps uncover potential failure points and strengthen overall system resilience. This article delves into practical and straightforward methods for injecting chaos into your Redis cluster, enabling you to proactively identify and address weaknesses before they cause real-world disruptions. Set Up Create a Redis cluster

Low latency needed? It’s all about information theory! Avoid network hops if you can - your users maybe half the way around the world, and without a CDN it willl result in a bad experience (for short-lived info that has already been retrieved). Cache is king…! Go for a multi-layered caching system: 1. Local cache (nothing beats it) - you will get nano second performance 2. Memory cache (e.g. Redis) - let any local cache misses fallback to Redis for millisecond performance (but will depend on network speed) 3. Key / value store cache (e.g. Dynamo DB or Mongo), maybe 3x slower than Redis, but in human terms still fast 4. Go to slower SSD - fine for “most things” where latency is not critical… (2 to 4 depend on network speed and performance - i.e. distance travelled) …but nanosecond performance is where you really want to be (very fast, non-stale, local memory access) #cache #lowlatency #optimisationisfun!

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:

𝐈'𝐦 𝐪𝐮𝐢𝐭𝐞 𝐩𝐫𝐨𝐮𝐝 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐨𝐧𝐞! 𝐓𝐡𝐞 𝐚𝐭𝐭𝐚𝐜𝐡𝐞𝐝 𝐢𝐦𝐚𝐠𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐬𝐩𝐚𝐫𝐤𝐥𝐞 𝐲𝐨𝐮𝐫 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭 :) Since Redis 7.2.5, we've introduced significant latency reduction and throughput increment for many common commands and usage patterns. We see performance improvements in over 20 commands of Redis, some of which with a broad usage and impact. 𝐖𝐡𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐜𝐨𝐦𝐢𝐧𝐠 𝐟𝐫𝐨𝐦? There are 18 performance optimizations on M01 and M02 that strictly focus on reducing CPU cycles, reducing memory allocations, and optimizing data access patterns of commonly used code. As an example, if you're using ZADD or ZRANGE commands you can expect at least 30% median and tail latency drops, much of it related to the adoption by Redis of the fast_float library (https://lnkd.in/d3TpnQa2) by Daniel Lemire. And the interesting bit, is that while we were adding this to Redis we found an issue on fast_float that we've solved (https://lnkd.in/d66d3mUW). For me, this represents our way of working - always striving to leave every project better than we found it. In short, Redis is now more CPU efficient and cache friendly. 𝐀𝐧𝐝 𝐰𝐞'𝐫𝐞 𝐧𝐨𝐭 𝐬𝐭𝐨𝐩𝐩𝐢𝐧𝐠! 𝐄𝐱𝐩𝐞𝐜𝐭 𝐦𝐨𝐫𝐞 𝐟𝐫𝐨𝐦 𝐮𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐢𝐧𝐠 𝐦𝐨𝐧𝐭𝐡𝐬! Read more about it in https://lnkd.in/dvAvZGzu, and download to give it a try. #redis #performance #latency

❔ Why Redis? Redis has emerged as an industry standard & cornerstone for applications of all scales. Known for its caching, in-memory speed and diverse data structures. ⚡: Its ability to function as a robust pub/sub and Streams, facilitating real-time communication between applications, underscores its versatility. 🚀: As a high-performance cache, Redis dramatically accelerates application response times by reducing database load. 🛠: Pipelining, which enables the execution of multiple commands in a single roundtrip, further amplifies Redis' performance. And for more Atomicity - Transactions. Beyond these, Redis also has HyperLogLogs for approximate set cardinality estimation, Bloom filters for probabilistic set membership testing, and Geo hashing for efficient geospatial queries. According to me, the true power of Redis lies within the creative combinations of these features to address complex application requirements in Development. I recently enhanced my proficiency in Redis through the RU102PY certification. #redis #softwaredevelopment

The change to the Redis license earlier this year has sent a lot of engineering organizations looking for an alternative. While it’s understandable that this breach of trust has rubbed the community the wrong way, there’s more to it than licensing, it's about evaluating the best alternative! Discover how the Dragonfly license compares to that of Redis, and maybe more interestingly, how the technology is superior to Redis as well. 👉 https://hubs.ly/Q02KhrMs0 #Cache #Community #DataInfra #DataPlatform #Innovation #KeyDB #OpenSource #Redis #Valkey

#RedisExplainer: How to utilize XDUMP for stream entry serialization In Redis, the XDUMP command is a powerful tool for stream entry serialization. It allows developers to serialize and deserialize data efficiently. By utilizing XDUMP, you can streamline data storage and retrieval processes, making your application more efficient. Remember, understanding the correct implementation of XDUMP is crucial for effective Redis usage.

Boost Your Application Performance with Redis Cache 🚀 In the journey of developing robust applications, there comes a time when performance doesn't meet expectations. Various solutions can address these challenges, but one effective approach is implementing a caching technique to accelerate database calls. This is where Redis Cache steps in. 🛠️ Redis Cache significantly enhances performance by reducing the number of network calls required to access data from the database. By storing frequently accessed data in memory, Redis allows for rapid retrieval, minimizing latency and improving overall responsiveness. Implementing Redis Cache can be a game-changer for your application, providing the performance boost needed to meet user expectations and deliver a seamless experience. #Redis #Caching #PerformanceOptimization #Database #WebDevelopment #TechSolutions

🚀 Enhancing Application Performance with Redis In my recent experience working on a high-scale data processing system, Redis has been a crucial tool for optimizing both speed and efficiency. 🔄 Task Queue Management: We leveraged Redis to manage incoming tasks through its in-memory queuing system, allowing us to process and distribute tasks across multiple workers in parallel. This drastically improved throughput and reduced bottlenecks. ⚡ Low Latency Caching: Redis served as a fast, in-memory cache for frequently accessed data, minimizing delays and providing rapid access without hitting the primary database for every request. 📊 Distributed Systems Scaling: Using Redis for inter-process communication between distributed nodes, we were able to scale the system efficiently and handle a huge volume of data conversions in real-time. Redis has been a game-changer in building a robust and scalable system capable of handling demanding workloads with ease. It’s a must-have tool when speed and performance are at the core of your application’s needs. #Redis #Scalability #PerformanceOptimization #DistributedSystems #SoftwareEngineering #TechStack

Increase 10x API Read speed with cache using Redis