engineering posts
How We Optimized Dragonfly to Get 30x Throughput with BullMQ
In this blog post, we navigate the journey of optimizing Dragonfly for BullMQ, achieving an exceptional 30x throughput increase. This endeavor highlights our deep commitment to the community and demonstrates our dedication to expanding and adapting Dragonfly to the open-source ecosystem.
Building E-Commerce Applications with Dragonfly
In this blog post, we discover how Dragonfly boosts e-commerce platforms with efficient caching, personalized content, and managing high-traffic events. Learn about Dragonfly's capabilities for ensuring smooth operations during peak times.
Building a Real-Time Ad Server with Dragonfly
In this blog post, we build a real-time ad server application using Bun, ElysiaJS, and Dragonfly, showcasing seamless integration and exceptional developer experience. Dive into hands-on examples and enhance your understanding of these cutting-edge technologies.
Running BullMQ with Dragonfly
In this post, we explore the seamless integration of Dragonfly as a drop-in replacement for Redis as the backing in-memory store of BullMQ, a robust background job processing library for Node.js.
Developing with Dragonfly: Solve Caching Problems
In the Developing with Dragonfly series of blog posts, we will explore different techniques and best practices for developing applications with Dragonfly. In this blog post, we explore the 3 common caching problems and best practices to mitigate them.
Developing with Dragonfly: Cache-Aside
In the Developing with Dragonfly series of blog posts, we will explore different techniques and best practices for developing applications with Dragonfly. We start with one of the most common usages: Cache-Aside.
Fearlessly Handling BigKeys with Dragonfly
Wrestling with BigKeys is hardly an issue when using Dragonfly, the groundbreaking in-memory data store. You can use BigKeys in Dragonfly if they are necessary to your server applications, fearlessly.
Running the Feast Feature Store with Dragonfly
In this blog post, we explore the seamless integration of Dragonfly as a drop-in replacement for Redis in Feast—an acclaimed feature storage and server project widely recognized in the machine learning domain. With just some simple steps, we unveil the outstanding compatibility of Dragonfly as an online store solution for Feast, ensuring a smooth transition without compromising existing functionalities.
The Unbearable Lightness of Horizontal Scaling
This post explores the limitations of horizontal scaling in terms of cluster reliability, load distribution, and cloud over-commitment. It also outlines design decisions that were made to allow Dragonfly, a drop-in Redis replacement, to scale vertically in order to handle heavy workloads and large data volumes on a single instance. By adopting Dragonfly's vertical scaling capabilities, organizations can achieve improved performance, cost savings, and operational efficiency in their distributed systems.
Zero Downtime Migration from Redis to Dragonfly using Redis Sentinel
This blog post discusses zero-downtime migration from Redis to Dragonfly using Redis Sentinel. It covers the benefits of Dragonfly, migration techniques, and the role of Redis Sentinel in ensuring seamless migration without service interruption. The post provides steps to configure Dragonfly as a replica and promote it using Sentinel.
Monitoring In-memory datastores
This blog post covers monitoring in-memory datastores, focusing on Dragonfly. Learn how to use Prometheus and Grafana to monitor Dragonfly metrics and visualize them on a dashboard. Explore memory consumption, client-side metrics, server metrics and more.
Leveraging the power of Lua scripting with Dragonfly
This blog post discusses the limitations of Lua scripting in Redis and introduces Dragonfly as a drop-in replacement for Redis. Dragonfly addresses the challenges of long-running scripts and scalability, offering a vertically scalable, multi-threaded, and asynchronous architecture that improves performance and efficiency for Lua scripting in Redis.
We’re Ready for You Now: Dragonfly In-Memory DB Now Supports Replication for High Availability
Dragonfly, an in-memory database that can be a drop-in Redis replacement, now supports replication for high availability in its version 1.0 release.
Migrating from a Redis Cluster to Dragonfly on a single node
In this blog post, you will learn how to migrate data from a Redis Cluster to a single-node Dragonfly instance. We will use a sample application to demonstrate the migration process and cover everything step by step.
Rate Limiting with Dragonfly
Dragonfly offers a rate limiting API via the CL.THROTTLE command. This post outlines the rate limit algorithm background and how you can use it in your application.
Building a Background Processing Pipeline with Dragonfly
In this blog post, you will learn how to use Redis Lists to build a background processing pipeline with Dragonfly.
Top 5 Reasons Why Your Redis Instance Might Fail
In this article, we will explain the main reasons why your Redis instance might fail, and provide advice to avoid this.
Redis vs. Dragonfly Scalability and Performance
A thorough benchmark comparison of throughput, latency, and memory utilization between Redis and Dragonfly.
Balanced vs Unbalanced
Balance is essential in life. When our focus is limited to improving a single aspect of our life, we weaken the whole system.
Dragonfly Cache Design
I talked in my previous post about Redis eviction policies. In this post, I would like to describe the design behind Dragonfly cache.
Redis Analysis - Part 2: Simplicity
Let’s talk about the simplicity of Redis. Redis was initially designed as a simple store, and it seems that its APIs achieved this goal.
Redis Analysis - Part 1: Threading model
Following my previous post, we are going start with the “hottest potato” - single-threaded vs multi-threaded argument.
A prelude to analysis of Redis memory-store
During the last 13 years, Redis has become a truly ubiquitous memory store that has won the hearts of numerous dev-ops and software engineers.
Dragonfly is fully compatible with the Redis ecosystem and requires no code changes to implement.