by_analogy.rst

glom by Analogy

glom is pure Python, and you don't need to know anything but Python to use it effectively.

Still, most everyone who encounters glom for the first time finds analogies to tools they already know. Whether SQL, list comprehensions, or HTML templates, there seems to be no end to the similarities. Many of them intentional!

While glom is none of those tools, and none of those tools are glom, a little comparison doesn't hurt. This document collects analogies to help guide understanding along.

Similarity to list comprehensions

One of the key inspirations for glom was the humble list comprehension, one of my favorite Python features.

List comprehensions make your code look like its output, and that goes a long way in readability. glom itself does list processing with square brackets like [lambda x: x % 2], which actually makes it more like a list comp and the old filter() function.

glom's list processing differs in two ways:

• Required use of a callable or other glom spec, to enable deferred processing.
• Ability to return :data:`~glom.SKIP`, which can exclude items from a list.

Similarity to templating (Jinja, Django, Mustache)

glom is a lot like templating engines, including modern formatters like gofmt, but with all the format affordances distilled out. glom doesn't just work on HTML, XML, JSON, or even just strings.

glom works on objects, including functions, dicts, and all other primitives. In fact, it would be safe to call glom an "object templating" system.

A lot of insights for glom came (and continue to come) from writing ashes.

Similarity to SQL and GraphQL

In some ways, glom is a Python query language for Python objects. But thanks to its restructuring capabilities, it's much more than SQL or GraphQL.

With SQL the primary abstraction is an table, or table-like resultset. With GraphQL, the analogous answer to this is, of course, the graph.

glom goes further, not only offering the Python object tree as a graph, but also allowing you to change the shape of the data, restructuring it while fetching and transforming values, which GraphQL only minimally supports, and SQL barely supports at all. Table targets get you table outputs.

Similiarity to validation (jsonschema, schema, cerberus)

glom is a generalized form of intake libraries including validation. We definitely took schema becoming successful as a sign that others shared our appetite for succinct, declarative Python datastructure manipulation.

More importantly, these libraries seem to excel at structuring and parsing data, and don't solve much on the other end. Translating valid, structured objects like database models to JSON serializable objects is glom's forté.

Similarity to jq

:doc:`The CLI <cli>` that glom packs is very similar in function to jq, except it uses Python as its query language, instead of making its own. Most importantly glom gives you a programmatic way forward.

Similarity to XPath/XSLT

These hallowed technologies of yore, they were way ahead of the game in many ways. glom intentionally avoids their purity and verbosity, while trying to take as much inspiration as possible from their function.

Others

Beyond what's listed above, several other packages and language features exist in glom's ballpark, including:

• Specter (for Clojure)
• Lenses (for Haskell)
• Dig (for Ruby Hashmaps)

If you know of other useful comparisons, let us know!