Toolz functions interoperate because they consume and produce only a small set of common, core data structures. Each toolz function consumes just iterables, dictionaries, and functions and each toolz function produces just iterables, dictionaries, and functions. This standardized interface enables us to compose several general purpose functions to solve custom problems.

Standard interfaces enable us to use many tools together, even if those tools were not designed with each other in mind. We call this “using together” composition.

Standard Interface

This is best explained by two examples; the automobile industry and LEGOs.


Automobile pieces are not widely composable because they do not adhere to a standard interface. You can’t connect a Porsche engine to the body of a Volkswagen Beetle but include the safety features of your favorite luxury car. As a result when something breaks you need to find a specialist who understands exactly your collection of components and, depending on the popularity of your model, replacement parts may be difficult to find. While the customization provides a number of efficiencies important for automobiles, it limits the ability of downstream tinkerers. This ability for future developers to tinker is paramount in good software design.


Contrast this with Lego toys. With Lego you can connect a rocket engine and skis to a rowboat. This is a perfectly natural thing to do because every piece adheres to a simple interface - those simple and regular 5mm circular bumps. This freedom to connect pieces at will lets children unleash their imagination in such varied ways (like going arctic shark hunting with a rocket-ski-boat).

The abstractions in programming make it far more like Lego than like building cars. This breaks down a little when we start to be constrained by performance or memory issues but this affects only a very small fraction of applications. Most of the time we have the freedom to operate in the Lego model if we choose to give up customization and embrace simple core standards.

Other Standard Interfaces

The Toolz project builds off of a standard interface – this choice is not unique. Other standard interfaces exist and provide immeasurable benefit to their application areas.

The NumPy array serves as a foundational object for numeric and scientific computing within Python. The ability of any project to consume and produce NumPy arrays is largely responsible for the broad success of the various SciPy projects. We see similar development today with the Pandas DataFrame.

The UNIX toolset relies on files and streams of text.

JSON emerged as the standard interface for communication over the web. The virtues of standardization become glaringly apparent when we contrast JSON with its predecessor, XML. XML was designed to be extensible/customizable, allowing each application to design its own interface. This resulted in a sea of difficult to understand custom data languages that failed to develop a common analytic and data processing infrastructure. In contrast JSON is very restrictive and allows only a fixed set of data structures, namely lists, dictionaries, numbers, strings. Fortunately this set is common to most modern languages and so JSON is extremely widely supported, perhaps falling second only to CSV.

Standard interfaces permeate physical reality as well. Examples range from supra-national currencies to drill bits and electrical circuitry. In all cases the interoperation that results becomes a defining and invaluable feature of each solution.