Faizan Rao’s Post

📢 Are you facing performance issues with BullMQ? Discover how Dragonfly overcomes the Redis bottlenecks in BullMQ in our latest video. Dive deep into the world of caching and understand how to boost your performance and speed. 👉 https://hubs.la/Q02w055d0

#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.

Unlock the power of real-time interactions in your applications with Redis Pub/Sub! Learn how this game-changing mechanism can revolutionize your projects. Check out my latest blog post to dive deeper:

NATS JetStream is not just streaming but also a proper KV data store with many features beyond just Put, Get and Delete, and can hold it's own when compared to the reference that is Redis in many use cases. Another great video by Jeremy Saenz going through all the KV features currently available (and some of the upcoming 2.11 features) in NATS.

🚀 Scaling Real-Time Apps with Redis Pub/Sub & Socket.io Recently, I had the opportunity to scale our real-time applications using Redis Pub/Sub with Socket.io, and it’s been a game-changer. Here’s how Redis Pub/Sub helped us: 🔄 Cross-Server Messaging: Redis enabled seamless communication across multiple server instances, ensuring all users receive real-time data, no matter which server they connect to. 📡 Efficient Broadcasting: Redis allowed us to broadcast events like messages and notifications in real time across all servers. 📈 High Scalability: We can now scale horizontally by adding more servers as needed, without worrying about isolating socket connections. 🛡️ Resiliency: Even if one server goes down, Redis continues to relay messages across the remaining instances, keeping everything running smoothly. The setup was straightforward: 1️⃣ Clients connect to different server instances using Socket.io. 2️⃣ Servers communicate through Redis Pub/Sub, distributing messages across all connected clients. 3️⃣ Scaling became as simple as adding more servers while Redis handled real-time message broadcasting. While there are alternatives like NATS, Kafka, RabbitMQ, and ZeroMQ, for our needs, Redis Pub/Sub struck the perfect balance between ease of use, scalability, and performance.

Check out this in-depth recap of Riyu Wang’s presentation at FlinkForward 2023, where the Senior Development Engineer from Alibaba Cloud Intelligence discussed Using Apache Paimon + StarRocks for High-speed Batch and Streaming Lakehouse Analysis. The article explores the development history, main scenarios, technical principles, performance tests, and future planning of Apache Paimon + StarRocks. Dive into the full article to explore these aspects in detail: https://lnkd.in/gW4Jb2MF #DataAnalytics #DataEngineering 

DiceDB's throughput is not 5x, but 10x of Redis, and it can do 2 million ops/sec peak on a 24-core machine while Redis' peak was 200k 🔥 DiceDB is also better in p50 and p90 latency numbers - essentially 5x better in median latency. If you want to dig deeper into the nuances and steps to reproduce the numbers, check dicedb.io/benchmarks and blogs on the website. github.com/dicedb/dice #softwareengineering #DiceDB #DatabaseInternals

🌊 #apachepaimon and #starrocks nailing #olap on object storage; a true Streaming Lakehouse 🤘 ------- With the joint efforts of StarRocks kernel optimization and Apache Paimon, the StarRocks + Apache Paimon Lakehouse analysis capability is 15 times the query performance of the previous version. 👆 Although this article is from last year; so if you think this is exciting, do keep an eye on the new support coming for optimizing via Deletion Vectors. 👉 https://lnkd.in/dNXn8ip3 #datalake #lakehouse #olap #dataengineering

https://lnkd.in/dHTYqJ6T [ main site: https://lnkd.in/d3mcNX-C ] << ...WarpStream is an Apache Kafka® compatible data streaming platform built directly on top of object storage: no inter-AZ bandwidth costs, no disks to manage, and infinitely scalable, all within your VPC... >>

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