What is the purpose of collections.ChainMap?

Question

In Python 3.3 a ChainMap class was added to the collections module:

A ChainMap class is provided for quickly linking a number of mappings so they can be treated as a single unit. It is often much faster than creating a new dictionary and running multiple update() calls.

Example:

>>> from collections import ChainMap >>> x = {'a': 1, 'b': 2} >>> y = {'b': 10, 'c': 11} >>> z = ChainMap(y, x) >>> for k, v in z.items():         print(k, v) a 1 c 11 b 10 

It was motivated by this issue and made public by this one (no PEP was created).

As far as I understand, it is an alternative to having an extra dictionary and maintaining it with update()s.

The questions are:

• What use cases does ChainMap cover?
• Are there any real world examples of ChainMap?
• Is it used in third-party libraries that switched to python3?

Bonus question: is there a way to use it on Python2.x?

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I've heard about it in Transforming Code into Beautiful, Idiomatic Python PyCon talk by Raymond Hettinger and I'd like to add it to my toolkit, but I lack in understanding when should I use it.

Answer 1

I like @b4hand's examples, and indeed I have used in the past ChainMap-like structures (but not ChainMap itself) for the two purposes he mentions: multi-layered configuration overrides, and variable stack/scope emulation.

I'd like to point out two other motivations/advantages/differences of ChainMap, compared to using a dict-update loop, thus only storing the "final" version":

1. More information: since a ChainMap structure is "layered", it supports answering question like: Am I getting the "default" value, or an overridden one? What is the original ("default") value? At what level did the value get overridden (borrowing @b4hand's config example: user-config or command-line-overrides)? Using a simple dict, the information needed for answering these questions is already lost.

2. Speed tradeoff: suppose you have N layers and at most M keys in each, constructing a ChainMap takes O(N) and each lookup O(N) worst-case[*], while construction of a dict using an update-loop takes O(NM) and each lookup O(1). This means that if you construct often and only perform a few lookups each time, or if M is big, ChainMap's lazy-construction approach works in your favor.

[*] The analysis in (2) assumes dict-access is O(1), when in fact it is O(1) on average, and O(M) worst case. See more details here.