Web Performance Pitfalls

Abstract

Web performance is widely studied in terms of load times, numbers of objects, object sizes, and total page sizes. However, for all these metrics, there are various definitions, data sources, and measurement tools. These often lead to different results and almost all studies do not provide sufficient details about the definition of metrics and the data sources they use. This hinders reproducibility as well as comparability of the results. This paper revisits the various definitions and quantifies their impact on performance results. To do so we assess Web metrics across a large variety of Web pages. Amazingly, even for such “obvious” metrics as load times, differences can be huge. For example, for more than 50% of the pages, the load times vary by more than 19.1% and for 10% by more than 47% depending on the exact definition of load time. Among the main culprits for such difference are the in-/exclusion of initial redirects and the choice of data source, e.g., Resource Timings API or HTTP Archive (HAR) files. Even “simpler” metrics such as the number of objects per page have a huge variance. For the Alexa 1000, we observed a difference of more than 67 objects for 10% of the pages with a median of 7 objects. This highlights the importance of precisely specifying all metrics including how and from which data source they are computed.