Note: Although the strategies outlined in this article are platform agnostic, we are using concrete examples from Android to highlight their execution.
注意：尽管本文中概述的策略与平台无关，但我们使用的具体例子来自于 的具体例子来强调它们的执行。 的具体例子来强调其执行。
Creating a Performance Framework
In the spring of 2020 we started the journey to improve the performance of Lyft’s mobile applications, initially focusing on app start time (also known as Time to Interact or TTI). There was a great deal of opportunity for improvement in the TTI space at Lyft and we were confident that with a small investment, we would be able to add meaningful impact. The success of this project helped pave the way for further investment in Mobile Performance at Lyft.
Making the jump from a single TTI investment into a holistic plan to improve Mobile Performance at Lyft meant we would have to think beyond a single metric. In doing so, we also wanted to establish key focus areas to avoid “boiling the ocean” through too many avenues of improvement. Leveraging Google’s Android performance documentation, we focused our investment in Mobile Performance into the three metrics with the highest opportunity for improvement.
- Time to interact (app start): Continuing reducing app startup time that started in 2020
- Stability: Reducing the number of crashes and ANRs (App Not Responding) any given user experiences
- Rendering performance: Maintaining a high, buttery smooth frame rate
When evaluating the prioritization of the above metrics, we created a table using rough estimates of each metric’s opportunity size, actionability, and user impact. We gave each area a weight and multiplied them to get a score. The weight of “How Actionable” is de...