基准测试结果

下面是在Python和Perl上比较YAML和JSON加载时间的基准测试结果

JSON要快得多，但牺牲了一些可读性和注释等特性

测试方法

在一台快速机器上连续运行100次，平均秒数 数据集是一个3.44MB的JSON文件，包含从维基百科抓取的电影数据 https://raw.githubusercontent.com/prust/wikipedia-movie-data/master/movies.json 链接来源:https://github.com/jdorfman/awesome-json-datasets

结果

Python 3.8.3 timeit
    JSON:            0.108
    YAML CLoader:    3.684
    YAML:           29.763

Perl 5.26.2 Benchmark::cmpthese
    JSON XS:         0.107
    YAML XS:         0.574
    YAML Syck:       1.050

Perl 5.26.2 Dumbbench (Brian D Foy, excludes outliers)
    JSON XS:         0.102
    YAML XS:         0.514
    YAML Syck:       1.027

我发现YAML更方便使用:括号更少，""等等。虽然YAML中有标签的烦恼…但总有人能掌握窍门。

在性能/资源方面，我不认为两者之间有很大的差异。

此外，我们讨论的是配置文件，所以我不期望编码/解码活动的频率很高，不是吗?

如果你不需要YAML有而JSON没有的任何特性，我更喜欢JSON，因为它非常简单，并且得到广泛支持(有很多语言的库)。YAML更复杂，支持更少。我不认为解析速度或内存使用会有太大的不同，而且可能不是程序性能的主要部分。

差异:

YAML, depending on how you use it, can be more readable than JSON JSON is often faster and is probably still interoperable with more systems It's possible to write a "good enough" JSON parser very quickly Duplicate keys, which are potentially valid JSON, are definitely invalid YAML. YAML has a ton of features, including comments and relational anchors. YAML syntax is accordingly quite complex, and can be hard to understand. It is possible to write recursive structures in yaml: {a: &b [*b]}, which will loop infinitely in some converters. Even with circular detection, a "yaml bomb" is still possible (see xml bomb). Because there are no references, it is impossible to serialize complex structures with object references in JSON. YAML serialization can therefore be more efficient. In some coding environments, the use of YAML can allow an attacker to execute arbitrary code.

观察:

Python programmers are generally big fans of YAML, because of the use of indentation, rather than bracketed syntax, to indicate levels. Many programmers consider the attachment of "meaning" to indentation a poor choice. If the data format will be leaving an application's environment, parsed within a UI, or sent in a messaging layer, JSON might be a better choice. YAML can be used, directly, for complex tasks like grammar definitions, and is often a better choice than inventing a new language.

基准测试结果

下面是在Python和Perl上比较YAML和JSON加载时间的基准测试结果

JSON要快得多，但牺牲了一些可读性和注释等特性

测试方法

在一台快速机器上连续运行100次，平均秒数 数据集是一个3.44MB的JSON文件，包含从维基百科抓取的电影数据 https://raw.githubusercontent.com/prust/wikipedia-movie-data/master/movies.json 链接来源:https://github.com/jdorfman/awesome-json-datasets

结果

Python 3.8.3 timeit
    JSON:            0.108
    YAML CLoader:    3.684
    YAML:           29.763

Perl 5.26.2 Benchmark::cmpthese
    JSON XS:         0.107
    YAML XS:         0.574
    YAML Syck:       1.050

Perl 5.26.2 Dumbbench (Brian D Foy, excludes outliers)
    JSON XS:         0.102
    YAML XS:         0.514
    YAML Syck:       1.027

JSON编码六种数据类型:对象(映射)、数组、字符串数字、布尔值和Null。对于机器来说，它非常容易解析，并且提供很少的灵活性。说明书大约有一页半。

YAML allows the encoding of arbitrary Python data and other crazy crap (which leads to vulnerabilities when decoding it). It is hard to parse because it offers so much flexibility. The specification for YAML was 86 pages, the last time I checked. YAML syntax is obviously influenced by Python, but maybe they should have been a little more influenced by the Python philosophy on a few points: e.g. “there should be one—and preferably only one—obvious way to do it” and “simple is better than complex.”

与JSON相比，YAML的主要优点是更容易阅读和编辑，这使它成为配置文件的自然选择。

最近，我倾向于使用TOML作为配置文件。它不像YAML那样漂亮或灵活，但对机器和人类来说都更容易解析。该语法(几乎)是INI语法的超集，但它解析为类似json的数据结构，只添加了一种额外类型:日期类型。