glom: Restructuring data, the Python way

Request Type

Thoughts on using the glom library for analyzers

Description

From https://sedimental.org/
Nested data is tricky though. Reaching into deeply structured data can get you some ugly errors. Consider this simple line:

value = target.a['b']['c']

That single line can result in at least four different exceptions, each less helpful than the last:

AttributeError: 'TargetType' object has no attribute 'a'
KeyError: 'b'
TypeError: 'NoneType' object has no attribute 'getitem'
TypeError: list indices must be integers, not str

Clearly, we need our tools to catch up to our nested data.

By way of introduction, let's start off with space-age access, the classic "deep-get":
from glom import glom
target = {'galaxy': {'system': {'planet': 'jupiter'}}}
spec = 'galaxy.system.planet'
output = glom(target, spec)
# output = 'jupiter'

target is our data, be it dict, list, or any other object
spec is what we want output to be

The issue names no files, tests, or entry points; start by reviewing the analyzer implementations and how they currently access nested data. Determine which analyzer workflows would use glom and define a concrete scope and acceptance criteria before implementation; done should include an agreed integration plan and corresponding validation.