Author: Joe Zhou

Ad-serving systems frequently need to accommodate millions, or even billions, of requests daily, constructing and delivering personalized ads within just a few milliseconds. Beyond handling the substantial daily volume of requests, these platforms must be capable of managing spiky traffic with sudden surges in user activity as well.

In this blog post, we will construct a real-time ad cache server utilizing the cutting-edge technologies of Bun, ElysiaJS, and Dragonfly. Not just for the enjoyment of exploring new tools, but also to leverage their exceptional developer experience and performance capabilities. Let's take a brief look at the technologies we will be using in this post:

In today's digital age, leaderboards have become an integral part of many applications, providing a dynamic way to display user scores and rankings. To build gamification features for any application (i.e., games, educational platforms), leaderboards serve as a powerful tool to engage and motivate users. In this blog post, we're going to delve into the process of building a practical and realistic leaderboard system.

Our journey will involve leveraging the capabilities of Dragonfly, a highly efficient drop-in replacement for Redis, known for its ultra-high throughput and multi-threaded shared-nothing architecture. Specifically, we'll be utilizing two of Dragonfly's data types: Sorted-Set and Hash. These data structures are perfect for handling real-time data and ranking systems, making them ideal for our leaderboards.

In the high-octane world of e-commerce applications, both response speed and data accuracy are crucial. Customers expect seamless access to searched items, past orders, recently viewed products, and personalized recommendations. In the meantime, these applications often experience fluctuating traffic, especially during peak periods like the Christmas season or Black Friday. High-traffic events furthermore introduce significant challenges, requiring rapid response and precise data handling. Addressing these variations often demands a scalable and robust in-memory data storage solution.

Dragonfly, an ultra-performant in-memory data store, utilizes a multi-threaded, shared-nothing architecture that pushes hardware to its limits, supporting up to 4 million ops/sec and 1TB of memory on a single instance. This can drastically reduce operational complexities while providing a high-performance solution for e-commerce applications. For even more demanding scenarios, Dragonfly also offers cluster mode on top of the stunning single-node performance. This adaptability makes Dragonfly an ideal choice for e-commerce platforms that contend with unpredictable and varied traffic patterns.

In the realm of web application development (or any other applications involving backend servers), the quest for optimal performance often intersects with the challenge of managing data retrieval efficiently. Imagine a scenario where a web application is tasked with fetching a large amount of data from its main database: user profiles, blog posts, product details, transaction history, and more. These queries to the primary database will inevitably introduce significant latency, resulting in a poor user experience and potentially pushing the database to its limits.

A common solution is to add a cache that can relieve the primary database of much of the load. This cache mechanism operates as a strategic intermediary, positioned between the intricate network of services constituting your application and the primary database itself. Its fundamental purpose is to store a duplicate copy of the most frequently requested data or data anticipated to be accessed imminently.