Frequently Asked Questions about Homebrew Formulae Analytics

What kind of data is presented in this source?

This source provides analytics data for various software packages, referred to as "formulae," available through the Homebrew package manager. The data includes statistics on how many times each formula has been downloaded or accessed within the last 30, 90, and 365 days. Additionally, it offers a brief description of each formula, its official homepage URL, and the method by which Homebrew fetches the package (typically "homebrew" or "repo_direct" for formulae from specific taps). Some formulae also include information about their definition file path within their respective repositories and their current version.

What does the "formula" type indicate?

In the context of Homebrew, a "formula" is a Ruby script that describes how to download, build, and install a particular piece of software. The prevalence of the "formula" type in this data suggests that it primarily tracks popular software packages available through Homebrew.

What does "stats" refer to in the context of these formulae?

The "stats" section for each formula details the download or access counts over different timeframes: 30 days, 90 days, and 365 days. These numbers indicate the recent popularity and usage of each software package within the Homebrew ecosystem. Higher numbers generally suggest a more widely used or frequently updated tool.

What is the significance of the different timeframes (30d, 90d, 365d) for the statistics?

The different timeframes provide insight into the usage trends of each formula. The 30-day statistics reflect the most recent activity, while the 90-day and 365-day figures offer a broader view of longer-term popularity and adoption. Comparing these numbers can reveal if a formula's usage is increasing, decreasing, or remaining relatively stable over time.

What is the significance of the "fetch" field?

The "fetch" field indicates how Homebrew retrieves the formula definition. fetch: "homebrew" typically means the formula is part of the core Homebrew repository. fetch: "repo_direct" signifies that the formula comes from a specific "tap," which is a third-party repository of Homebrew formulae, and the definition is fetched directly from that tap's repository (e.g., GitHub). Other fetch types might indicate different methods of obtaining the formula definition.

What can be inferred from the "description" and "homepage" fields for each formula?

The "description" field provides a concise summary of the software package's purpose and functionality. The "homepage" field links to the official website or repository of the software, offering users a place to find more detailed information, documentation, and potentially support resources for the tool.

What is the purpose of the "tap" field in some entries?

The "tap" field, present in some formula entries, indicates that the formula is not part of the main Homebrew/homebrew-core repository but resides in a separate, third-party repository or "tap." This allows users to access a wider range of software beyond the core set maintained by the Homebrew team. The tap is usually identified by the owner and the repository name (e.g., "jandedobbeleer/oh-my-posh").

What is the relationship between "definition" and "file_paths" fields?

The "definition" field provides a direct link to the Ruby file (.rb) within the tap's repository that defines how Homebrew should install and manage that specific formula. The "file_paths" field lists the relevant files associated with the formula's definition, often just the main .rb file itself. This information is useful for those who want to understand the installation process or contribute to the formula.

Are there any noticeable trends or patterns in the popularity of the listed formulae?

Yes, several trends are apparent. Programming languages like Python (in multiple versions), general-purpose tools like coreutils, and databases such as MySQL and PostgreSQL consistently show high download statistics across all timeframes, indicating their broad and sustained use. Development-related tools, command-line utilities, and widely adopted libraries also tend to be popular. The data also reveals the emergence of newer tools (like uv and ruff for Python) and the continued use of older, established software. The presence of multiple versions of the same software (e.g., different PHP or Python versions, or different versions of OpenJDK) highlights the need for users to manage specific environment requirements.

How can this data be used?

This data provides valuable insights into the popularity and trends of software usage within the Homebrew community. It can be used for various purposes, such as:

• Identifying popular tools and technologies: By looking at the download statistics, one can see which software packages are most widely used.
• Tracking software trends: Monitoring these statistics over time could reveal emerging or declining trends in software adoption.
• Understanding the Homebrew ecosystem: The data showcases the breadth of software available through Homebrew and the role of core formulas versus community taps.
• Informing package maintenance and prioritization: Homebrew maintainers could use this data to prioritize updates or address issues for the most popular packages.
• Research and analysis: This type of data can be used for academic or industry research on software adoption and developer tool preferences.