Effective latency optimization in complex distributed systems hinges on a simple principle: you can’t improve what you can’t measure. Having a comprehensive, multi-layered measurement baseline that offers a granular, end-to-end view of the request path helps isolate whether delays originate at the client, the network, the CDN, the load balancer, or deep within origin services. At DevRev, we track how much latency each hop from the end user to the origin services contributes, and this allows us to consistently refine our system and deliver the best value to our customers. Today, we’ve accumulated over 10 billion latency records across different APIs, giving us unparalleled visibility into performance.
By analyzing these latency datasets, we’ve uncovered patterns that aren’t always visible in small-scale tests like hidden queuing delays under burst traffic, cross-region routing inefficiencies, or subtle inconsistencies introduced by third-party dependencies. More importantly, we’ve learned that latency issues often don’t come from a single bottleneck, but from the compounded effect of several small inefficiencies across layers. In this blog, we delve into the lessons we learned while uncovering blind spots in improving end-to-end latency across DevRev API traffic.
I’d argue that there’s a corporate ideology that privileges throughput over latency and often it is a struggle to get latency taken seriously. Try submitting a ticket to, say, Adobe or Microsoft about how it takes 5 seconds for a keystroke to register in one of their products and see how much they care.
Effective latency optimization in complex distributed systems hinges on a simple principle: you can’t improve what you can’t measure. Having a comprehensive, multi-layered measurement baseline that offers a granular, end-to-end view of the request path helps isolate whether delays originate at the client, the network, the CDN, the load balancer, or deep within origin services. At DevRev, we track how much latency each hop from the end user to the origin services contributes, and this allows us to consistently refine our system and deliver the best value to our customers. Today, we’ve accumulated over 10 billion latency records across different APIs, giving us unparalleled visibility into performance.
By analyzing these latency datasets, we’ve uncovered patterns that aren’t always visible in small-scale tests like hidden queuing delays under burst traffic, cross-region routing inefficiencies, or subtle inconsistencies introduced by third-party dependencies. More importantly, we’ve learned that latency issues often don’t come from a single bottleneck, but from the compounded effect of several small inefficiencies across layers. In this blog, we delve into the lessons we learned while uncovering blind spots in improving end-to-end latency across DevRev API traffic.
I’d argue that there’s a corporate ideology that privileges throughput over latency and often it is a struggle to get latency taken seriously. Try submitting a ticket to, say, Adobe or Microsoft about how it takes 5 seconds for a keystroke to register in one of their products and see how much they care.