Quantitative analysis on startup time is performed via the cProfile tool. The performance of Quod Libet is affected by the number of song files Quod Libet needs to load. To ameliorate this, the program may need to introduce multithreading and perhaps even perform searchesĭifferent configurations were tested to quantitatively assess the scalability of Quod Libet. Matching against every song in a 1 million song collection can become a very expensive task, despite its If the regex pattern is moderately complex, matching may take a non-trivialĪmount of time. Quod Libet provides a regex search utility. Taken to only work with subsets of the total list whenever possible. Beyond this point, the program needs to rely more on the filesystem, and care should be Quod Libet uses Song objects which point to files, but about 1 million elements the collection of song objects itself Possible, otherwise they should be decoupled from the UI so that they don’t cause the interface to freeze. These expensive tasks should be avoided if ![]() ForĮxample, sorting songs in alphabetical order may have a noticeable delay. ![]() 100,000 Songsīy the time a collection contains 100,000 songs or more, tasks more complex than O(n) will become problematic. Program must apply the partial representation techniques discussed later in this essay. ![]() Would experience this as latency and stuttering when scrolling through their music collection. UI Components can’t be created for every songīy this point, instantiating a UI component to represent each song would begin to slow down the interface. Instead, audio data needs to be loaded from storage on demand. 1,000 SongsĮven music collections as small as 1,000 songs can take up gigabytes of space. Introduce their own challenges, which place requirements on the architecture. To test the scalability of Quod Libet, we imported a range of files into the Quod Libet library and measured the performance.Īs a music collection grows, the performance challenges inherent to working with it accumulate. While Apple Music allows a maximum of 100 thousand songs in a library 2.īased on these findings, we can conclude that a use case of 100s of thousands or even millions of songs is plausible.Īs a music collection grows, the performance challenges inherent to working with it accumulate.ĭifferent scales introduce their own challenges, which place requirements on the architecture. Key scalability challengesīased on one study from 2011, the average iTunes user has 7160.Ĭompetitor apps like Spotify allow their user to have 10 thousand songs for in each playlist, Finally, we will look at how Quod Libet achieves the performance it does, and what couldīe changed to further improve performance. Next, we will measure how well the current version Quod First, we will examine some of the keyĬhallenges standing in the way of this type of scalability. ![]() It makes sense for us to evaluate Quod Libet’s scalability along the same axis it evaluates itself, so this article willįocus primarily on how the program scales to very large music collections. Quod Libet markets itself on its scalability, specifically promoting its suitability for massive music collections. Unlike some, Quod Libet will scale to libraries with tens of thousands of songs. Software projects, sometimes it can even be a direct prerequisite for success. Scalability is an important quality attribute for many
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