What are the differences between YAML and JSON, specifically considering the following things?
Performance (encode/decode time)
Memory consumption
Expression clarity
Library availability, ease of use (I prefer C)
I was planning to use one of these two in our embedded system to store configure files.
Related:
Should I use YAML or JSON to store my Perl data?
Technically YAML is a superset of JSON. This means that, in theory at least, a YAML parser can understand JSON, but not necessarily the other way around.
See the official specs, in the section entitled "YAML: Relation to JSON".
In general, there are certain things I like about YAML that are not available in JSON.
As #jdupont pointed out, YAML is visually easier to look at. In fact the YAML homepage is itself valid YAML, yet it is easy for a human to read.
YAML has the ability to reference other items within a YAML file using "anchors." Thus it can handle relational information as one might find in a MySQL database.
YAML is more robust about embedding other serialization formats such as JSON or XML within a YAML file.
In practice neither of these last two points will likely matter for things that you or I do, but in the long term, I think YAML will be a more robust and viable data serialization format.
Right now, AJAX and other web technologies tend to use JSON. YAML is currently being used more for offline data processes. For example, it is included by default in the C-based OpenCV computer vision package, whereas JSON is not.
You will find C libraries for both JSON and YAML. YAML's libraries tend to be newer, but I have had no trouble with them in the past. See for example Yaml-cpp.
Differences:
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.
Observations:
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.
Bypassing esoteric theory
This answers the title, not the details as most just read the title from a search result on google like me so I felt it was necessary to explain from a web developer perspective.
YAML uses space indentation, which is familiar territory for Python developers.
JavaScript developers love JSON because it is a subset of JavaScript and can be directly interpreted and written inside JavaScript, along with using a shorthand way to declare JSON, requiring no double quotes in keys when using typical variable names without spaces.
There are a plethora of parsers that work very well in all languages for both YAML and JSON.
YAML's space format can be much easier to look at in many cases because the formatting requires a more human-readable approach.
YAML's form while being more compact and easier to look at can be deceptively difficult to hand edit if you don't have space formatting visible in your editor. Tabs are not spaces so that further confuses if you don't have an editor to interpret your keystrokes into spaces.
JSON is much faster to serialize and deserialize because of significantly less features than YAML to check for, which enables smaller and lighter code to process JSON.
A common misconception is that YAML needs less punctuation and is more compact than JSON but this is completely false. Whitespace is invisible so it seems like there are less characters, but if you count the actual whitespace which is necessary to be there for YAML to be interpreted properly along with proper indentation, you will find YAML actually requires more characters than JSON. JSON doesn't use whitespace to represent hierarchy or grouping and can be easily flattened with unnecessary whitespace removed for more compact transport.
The Elephant in the room: The Internet itself
JavaScript so clearly dominates the web by a huge margin and JavaScript developers prefer using JSON as the data format overwhelmingly along with popular web APIs so it becomes difficult to argue using YAML over JSON when doing web programming in the general sense as you will likely be outvoted in a team environment. In fact, the majority of web programmers aren't even aware YAML exists, let alone consider using it.
If you are doing any web programming, JSON is the default way to go because no translation step is needed when working with JavaScript so then you must come up with a better argument to use YAML over JSON in that case.
This question is 6 years old, but strangely, none of the answers really addresses all four points (speed, memory, expressiveness, portability).
Speed
Obviously this is implementation-dependent, but because JSON is so widely used, and so easy to implement, it has tended to receive greater native support, and hence speed. Considering that YAML does everything that JSON does, plus a truckload more, it's likely that of any comparable implementations of both, the JSON one will be quicker.
However, given that a YAML file can be slightly smaller than its JSON counterpart (due to fewer " and , characters), it's possible that a highly optimised YAML parser might be quicker in exceptional circumstances.
Memory
Basically the same argument applies. It's hard to see why a YAML parser would ever be more memory efficient than a JSON parser, if they're representing the same data structure.
Expressiveness
As noted by others, Python programmers tend towards preferring YAML, JavaScript programmers towards JSON. I'll make these observations:
It's easy to memorise the entire syntax of JSON, and hence be very confident about understanding the meaning of any JSON file. YAML is not truly understandable by any human. The number of subtleties and edge cases is extreme.
Because few parsers implement the entire spec, it's even harder to be certain about the meaning of a given expression in a given context.
The lack of comments in JSON is, in practice, a real pain.
Portability
It's hard to imagine a modern language without a JSON library. It's also hard to imagine a JSON parser implementing anything less than the full spec. YAML has widespread support, but is less ubiquitous than JSON, and each parser implements a different subset. Hence YAML files are less interoperable than you might think.
Summary
JSON is the winner for performance (if relevant) and interoperability. YAML is better for human-maintained files. HJSON is a decent compromise although with much reduced portability. JSON5 is a more reasonable compromise, with well-defined syntax.
GIT and YAML
The other answers are good. Read those first. But I'll add one other reason to use YAML sometimes: git.
Increasingly, many programming projects use git repositories for distribution and archival. And, while a git repo's history can equally store JSON and YAML files, the "diff" method used for tracking and displaying changes to a file is line-oriented. Since YAML is forced to be line-oriented, any small changes in a YAML file are easier to see by a human.
It is true, of course, that JSON files can be "made pretty" by sorting the strings/keys and adding indentation. But this is not the default and I'm lazy.
Personally, I generally use JSON for system-to-system interaction. I often use YAML for config files, static files, and tracked files. (I also generally avoid adding YAML relational anchors. Life is too short to hunt down loops.)
Also, if speed and space are really a concern, I don't use either. You might want to look at BSON.
I find YAML to be easier on the eyes: less parenthesis, "" etc. Although there is the annoyance of tabs in YAML... but one gets the hang of it.
In terms of performance/resources, I wouldn't expect big differences between the two.
Futhermore, we are talking about configuration files and so I wouldn't expect a high frequency of encode/decode activity, no?
Technically YAML offers a lot more than JSON (YAML v1.2 is a superset of JSON):
comments
anchors and inheritance - example of 3 identical items:
item1: &anchor_name
name: Test
title: Test title
item2: *anchor_name
item3:
<<: *anchor_name
# You may add extra stuff.
...
Most of the time people will not use those extra features and the main difference is that YAML uses indentation whilst JSON uses brackets. This makes YAML more concise and readable (for the trained eye).
Which one to choose?
YAML extra features and concise notation makes it a good choice for configuration files (non-user provided files).
JSON limited features, wide support, and faster parsing makes it a great choice for interoperability and user provided data.
If you don't need any features which YAML has and JSON doesn't, I would prefer JSON because it is very simple and is widely supported (has a lot of libraries in many languages). YAML is more complex and has less support. I don't think the parsing speed or memory use will be very much different, and maybe not a big part of your program's performance.
Benchmark results
Below are the results of a benchmark to compare YAML vs JSON loading times, on Python and Perl
JSON is much faster, at the expense of some readability, and features such as comments
Test method
100 sequential runs on a fast machine, average number of seconds
The dataset was a 3.44MB JSON file, containing movie data scraped from Wikipedia
https://raw.githubusercontent.com/prust/wikipedia-movie-data/master/movies.json
Linked to from: https://github.com/jdorfman/awesome-json-datasets
Results
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
From: Arnaud Lauret Book “The Design of Web APIs.” :
The JSON data format
JSON is a text data format based on how the JavaScript programming language describes data but is, despite its name, completely language-independent (see https://www.json.org/). Using JSON, you can describe objects containing unordered name/value pairs and also arrays or lists containing ordered values, as shown in this figure.
An object is delimited by curly braces ({}). A name is a quoted string ("name") and is sep- arated from its value by a colon (:). A value can be a string like "value", a number like 1.23, a Boolean (true or false), the null value null, an object, or an array. An array is delimited by brackets ([]), and its values are separated by commas (,).
The JSON format is easily parsed using any programming language. It is also relatively easy to read and write. It is widely adopted for many uses such as databases, configura- tion files, and, of course, APIs.
YAML
YAML (YAML Ain’t Markup Language) is a human-friendly, data serialization format. Like JSON, YAML (http://yaml.org) is a key/value data format. The figure shows a comparison of the two.
Note the following points:
There are no double quotes (" ") around property names and values in YAML.
JSON’s structural curly braces ({}) and commas (,) are replaced by newlines and
indentation in YAML.
Array brackets ([]) and commas (,) are replaced by dashes (-) and newlines in
YAML.
Unlike JSON, YAML allows comments beginning with a hash mark (#).
It is relatively easy to convert one of those formats into the other. Be forewarned though, you will lose comments when converting a YAML document to JSON.
Since this question now features prominently when searching for YAML and JSON, it's worth noting one rarely-cited difference between the two: license. JSON purports to have a license which JSON users must adhere to (including the legally-ambiguous "shall be used for Good, not Evil"). YAML carries no such license claim, and that might be an important difference (to your lawyer, if not to you).
Sometimes you don't have to decide for one over the other.
In Go, for example, you can have both at the same time:
type Person struct {
Name string `json:"name" yaml:"name"`
Age int `json:"age" yaml:"age"`
}
I find both YAML and JSON to be very effective. The only two things that really dictate when one is used over the other for me is one, what the language is used most popularly with. For example, if I'm using Java, Javascript, I'll use JSON. For Java, I'll use their own objects, which are pretty much JSON but lacking in some features, and convert it to JSON if I need to or make it in JSON in the first place. I do that because that's a common thing in Java and makes it easier for other Java developers to modify my code. The second thing is whether I'm using it for the program to remember attributes, or if the program is receiving instructions in the form of a config file, in this case I'll use YAML, because it's very easily human read, has nice looking syntax, and is very easy to modify, even if you have no idea how YAML works. Then, the program will read it and convert it to JSON, or whatever is preferred for that language.
In the end, it honestly doesn't matter. Both JSON and YAML are easily read by any experienced programmer.
If you are concerned about better parsing speed then storing the data in JSON is the option. I had to parse the data from a location where the file was subject to modification from other users and hence I used YAML as it provides better readability compared to JSON.
And you can also add comments in the YAML file which can't be done in a JSON file.
JSON encodes six data types: Objects (mappings), Arrays, Strings Numbers, Booleans and Null. It is extremely easy for a machine to parse and provides very little flexibility. The specification is about a page and a half.
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.”
The main benefit of YAML over JSON is that it’s easier for humans to read and edit, which makes it a natural choice for configuration files.
These days, I’m leaning towards TOML for configuration files. It’s not as pretty or as flexible as YAML, but it’s easier both for machines and humans to parse. The syntax is (almost) a superset of INI syntax, but it parses out to JSON-like data structures, adding only one additional type: the date type.
I need to parse a large (4gb) xml file in ruby, preferably with nokogiri. I've seen a lot of code exampled using
File.open(path)
but this takes too much time in my case. Is there an option to read the xml node by node in order to prevent loading the file at ones. Or what would be the fastest way to parse such a large file.
Best,
Phil
You can try using Nokogiri::XML::SAX
The basic way a SAX style parser works is by creating a parser,
telling the parser about the events we’re interested in, then giving
the parser some XML to process. The parser will notify you when it
encounters events your said you would like to know about.
I do this kind of work with LibXML http://xml4r.github.io/libxml-ruby/ (require 'xml') and its LibXML::XML::Reader API. It's simpler than SAX and allows you to make almost everything. REXML includes a similar API also, but it's quite buggy. Stream APIs like the one I mention or SAX shouldn't have any problem with huge files. I have not tested Nokogiri.
you may like to try this out - https://github.com/amolpujari/reading-huge-xml
HugeXML.read xml, elements_lookup do |element|
# => element{ :name, :value, :attributes}
end
I also tried using ox
I recently migrated from rails 2 to Rails 3 and thus got the new regular expression engine that comes by default in ruby 1.9.2.
I had heard a lot of good things about this regex engine. However, a portion of my app that relies heavily on regex has become very slow.
This is what I want to achieve: I need to check a string for some specific keywords. Once I hit a keyword, I need to modify the string to add a link to some site based on the keyword that matched. A string might contain more than one such keywords, and I need to check the string for thousands of keywords. All this needs to happen in a matter of minutes, and everything was working fine with the logic in ruby 1.8.7.
Earlier it used to get done in a matter of seconds, and now it takes hours. I compared today running both simultaneously, and the ruby 1.8.7 got done in 2 seconds, whereas the 1.9.2 one took 1.5 hours! There is obviously something wrong.
My regular expressions looks like:
/.*\b(sometext)\b/i
Questions:
Do I need to phrase by regex differently, or is there some other trick to speed up the matching process in ruby 1.9.2?
Worst case, is there a way to use the ruby 1.8.7 regex engine with ruby 1.9.2?
You can drop the .* from your regex completely. All it does is match the entire string and then backtrack until your search string is found. Remove it and see if it's still as slow.
It may not be the regex engine, but the fact that 1.9.x has String encoding built-in and will default to UTF-8 (I think). Try forcing the encoding on your input string to US-ASCII.
source_string.force_encoding("US-ASCII")
Performing thousands of regexes on UTF-8, which is computationally expensive (comparatively) is likely to be a great deal slower.
This may or may not work. I haven't tested it, but it springs to mind, before the regex engine does, when we're talking about speed differences on this magnitude.
How big are your input strings? o_O
I'd also profile your algorithm to try and identify where the bottlenecks are. You may be surprised.
Just as a recommendation for dealing with multiple regex lookups:
Check into the Regexp.union method, or use regex '|' to or your expressions into groups. The engine is fast, but only you know how to best look for things, so it relies on you to set it up for success.
For example, you can search for multiple targets various ways:
if string[/\btarget1\b/] || string[/\btarget2\b/] ...
or
if string[/\b(?:target1|target2)\b/] ...
You can build that or'd list of targets however you want, but it will be faster than separate searches.
Use Ruby's Benchmark module to prove your work. :-)
And, sometimes it is useful to think outside the Ruby box. Consider using a database to do your searches. Set up your data correctly and a DBM can be incredibly fast.
This is the general goal I am trying to achieve:
My VB.NET program will generate some Lists that may contain booleans, integers, strings, or more lists. I want the program to output a "file" which basically contains such data. It is important that the file cannot be read by humans Okay actually, fine, human-readable data wouldn't be bad.
Afterward, I want my Ruby program to take such file and read the contents. The Lists become arrays, and integers, booleans and strings are read alright with Ruby. I just want to be able to read the file, I might not need to write it using Ruby.
In .Net you'd use a BinaryWriter, if you're using IronRuby you'd then use a BinaryReader. If you're not using IronRuby, then perhaps...
contents = open(path_to_binary_file, "rb") {|io| io.read }
Why do you not want it to be human readable? I hope it's not for security reasons...
use JSON you can use the json.net nuget package.