collections.ChainMap 的目的是什么? [英] What is the purpose of collections.ChainMap?

问题描述

在 Python 3.3 中添加了 ChainMap 类到 collections 模块:

<块引用>

提供了一个 ChainMap 类，用于快速链接多个映射因此它们可以被视为一个单元.它通常比创建一个新字典并运行多个 update() 调用.

示例:

<预><代码>>>>从集合导入 ChainMap>>>x = {'a':1，'b':2}>>>y = {'b': 10, 'c': 11}>>>z = ChainMap(y, x)>>>对于 k, v 在 z.items():打印(k，v)111乙 10

它的动机是这个问题并由这个(没有创建PEP).

据我所知，这是拥有额外字典并使用 维护它的替代方法update()s.

问题是:

• ChainMap 涵盖哪些用例?
• 是否有任何 ChainMap 的真实示例?
• 是否在切换到python3的第三方库中使用?

额外问题:有没有办法在 Python2.x 上使用它?

<小时>

我在 将代码转换为漂亮的惯用 Python 中听说过它Raymond Hettinger 的 PyCon 演讲，我想将它添加到我的工具包中，但我不知道什么时候应该使用它.

解决方案

我喜欢@b4hand 的例子，事实上我在过去使用过类似 ChainMap 的结构(但不是 ChainMap 本身)用于他提到的两个目的:多-分层配置覆盖和变量堆栈/范围仿真.

我想指出 ChainMap 的另外两个动机/优势/差异，与使用 dict-update 循环相比，因此只存储最终"版本:

1. 更多信息:由于 ChainMap 结构是分层"的，因此它支持回答如下问题:我得到的是默认"值还是被覆盖的值?原始(默认")值是多少?该值在什么级别被覆盖(借用@b4hand 的配置示例:user-config 或 command-line-overrides)?使用简单的字典，回答这些问题所需的信息已经丢失.

2. 速度权衡:假设您有 N 层，每个层最多 M 个键，构建 ChainMap 需要 O(N) 和每次查找 O(N) 最坏情况 [*]，而使用更新循环构建 dict 需要 O(NM) 和每次查找 O(1).这意味着如果您经常构建并且每次只执行几次查找，或者如果 M 很大，ChainMap 的惰性构建方法对您有利.

[*] (2) 中的分析假设 dict-access 是 O(1)，而实际上它平均是 O(1)，并且 <代码>O(M) 最坏的情况.在此处查看更多详情.

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?

---

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.

解决方案

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.

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