CRISP:微服务架构的关键路径分析

CRISP: Critical Path Analysis for Microservice Architectures

Uber’s backend is an exemplar of microservice architecture. Each microservice is a small, individually deployable program performing a specific business logic (operation). The microservice architecture is a type of distributed computing system, which is suitable for independent deployments and scaling of software programs, and so is widely used across modern service-oriented industries. Uber has a few thousand microservices interacting with one another via remote procedure calls (RPC).

Uber的后端是微服务架构的一个典范。每个微服务都是一个可单独部署的小程序，执行一个特定的业务逻辑（操作）。微服务架构是一种分布式计算系统，适用于软件程序的独立部署和扩展，因此在现代面向服务的行业中被广泛使用。Uber有几千个微服务，通过远程过程调用（RPC）相互作用。

A service request arriving at an entry point (aka end-point) to the Uber backend systems undergoes multiple “hops” through numerous microservice operations before being fully serviced. The life of a request results in complex microservice interactions. These interactions are deeply nested, asynchronous, and invoke numerous other downstream operations. As a result of this complexity, it is very hard to identify which underlying service(s) contribute to the overall end-to-end latency experienced by a top-level request. Answering this question is critical in many situations, for example:

一个到达Uber后台系统入口点（又称端点）的服务请求，在被完全服务之前，要经过许多微服务操作的多次 "跳转"。一个请求的生命周期会导致复杂的微服务互动。这些互动是深度嵌套的，异步的，并调用许多其他下游操作。由于这种复杂性，很难确定哪些底层服务对顶级请求所经历的整体端到端延迟有贡献。在许多情况下，回答这个问题是至关重要的，例如。

Identifying optimization opportunities for a top-level microservice
识别顶层微服务的优化机会
Identifying common bottleneck operations affecting many services
识别影响许多服务的共同瓶颈业务
Setting appropriate time-to-live values for downstream RPC calls
为下游的RPC调用设置适当的生存时间值
Diagnosing outages and error conditions
诊断停电和错误状况
Capacity planning and reduction
能力规划和减少

While latency is one of the metrics of interest, other metrics such as time-to-live, error rates, etc., also fall in the scope.

虽然延迟是感兴趣的指标之一，但其他指标，如生存时间、错误率等，也属于这个范围。

We have developed a tool, CRISP (named taking letters from critical and span), to pinpoint and quantify underlying services that impact the overall latency...