移动性能 @ Lyft
In Q2 of 2021, Lyft served 17.1 million active riders through our suite of mobile applications. At this scale, every crash, frozen frame, or hiccup can translate to thousands of hours of wasted time and frustration. Given the potential to impact millions of Lyft’s customers, we doubled our efforts in 2020 to improve our applications’ speed and stability by launching an internal mobile performance initiative.
在2021年第二季度，Lyft通过我们的移动应用程序套件为1710万活跃乘客提供服务。在这种规模下，每一个崩溃、冻结的框架或打嗝可以转化为数千小时的时间浪费和挫折。鉴于有可能影响到数百万Lyft的客户，我们在2020年加倍努力，通过启动内部移动性能倡议来提高我们应用程序的速度和稳定性。
This is the first in a series of stories detailing our journey to materially improve the speed and stability of our applications. We hope that this can be used by other companies as a reference and inspiration for their own investments in this space.
这是一系列故事中的第一个，详细介绍了我们实质性地提高应用程序的速度和稳定性的历程。我们希望这可以被其他公司用作参考，并为他们自己在这个领域的投资提供灵感。
In this post, we will explore both how and why we started investing in mobile performance at Lyft, and we will dig into our team’s philosophy — everything from how we select projects to how we evaluate success.
在这篇文章中，我们将探讨我们在Lyft开始投资移动性能的方式和原因，我们将挖掘我们团队的理念--从我们如何选择项目到我们如何评估成功。
Defining Performance
定义绩效
Performance within the context of software development has historically referred solely to the speed of an application’s experiences (i.e., app start, and overall “smoothness” of the experience). At Lyft we see the investment in performance as an effort to remove unexpected deviations in functionality or latency.
在软件开发的背景下，性能历来只指应用程序的体验速度（即应用程序的启动和整体体验的 "流畅度"）。在Lyft，我们认为对性能的投资是为了消除功能或延迟方面的意外偏差。
Mobile Performance = Speed (app launch, smoothness of experience) + Stability (crash rate, bugs, etc.)
移动性能=速度（应用启动、体验的流畅性）+稳定性（崩溃率、错误等）。
The Early Days
早期的日子
Before we kick-started our mobile performance initiative in Q1 of 2020, product teams primarily used an app crash metric to indicate performance. We capture app crashes on the client and ingest them into our analytics pipeline. If the crash rate exceeds a threshold, it triggers alarms, and feature dev...