all-or-nothing
방식으로 동작.
This discussion covers two important concurrency patterns in Programming(e.g. Kotlin):
While they serve different purposes:
Both patterns are valuable tools in building robust concurrent systems with Kotlin.
Interview with a Server Engineer Working on Live Streaming Applications.
Here's how they optimized their microservices architecture:
They implemented a single-flight pattern at the aggregator level where requests expecting the same result share one response using the same key. This means:
This implementation resulted in significant performance improvements:
They further optimized the system using Rueidis
, which provides server-assisted client-side caching in Go.
Their architecture flows as follows:
AWS CloudFront -> Aggregator -> Service
The system benefits from multiple optimization layers.
This multi-layered approach significantly reduces Redis load:
The combination of single-flight at both CloudFront and Aggregator levels, plus client-side caching, has resulted in a substantial reduction in service requests and overall Redis load, creating a more efficient and performant system.
They noted a critical consideration when implementing single-flight, particularly in Go:
As a backend engineering, I find this discussion particularly relevant since it addresses a common business scenario we often encounter. When multiple clients requests the same resource simultaneously (e.g. fetching user profiles or processing payment transactions), we need to carefully handle these concurrent requests to optimize system resources and maintain data consistency.
What fascinates me is how Golang has built-in support for this pattern through their singleflight
package. It shows that this concurrency pattern is so commonly needed that they decided to provide it at the language ecosystem level. This makes me wonder why other languages haven't adopted similar built-in solutions.
In my experience with Kotlin and Spring Boot, we often have to implement this pattern ourselves or rely on caching mechanisms. While these solutions work, having a standardized approach like Go's would be beneficial for consistency across different projects and team.
While this pattern might not be 100% applicable in distributed environments with multiple instances, having it as part of the ecosystem provides valuable insights into solving concurrency challenges. Without exploring Go or studying various concurrency patterns, I might never have discovered this elegant approach to handling duplicate requests.
Have you encountered similar concurrency challenges in your project? How did you handle them?