The actual standard for network data exchange is JSON (JavaScript object notation), but it also has disadvantages, and in some cases other formats may be more applicable. This article will compare the advantages and disadvantages of various alternatives, including ease of use and performance.
Note: This article will not introduce implementation details in detail, but if you are a Ruby programmer, please check out this article written by Dhaivat, which introduces ways to implement some serialization formats in Ruby.
According to Wikipedia's definition, serialization is:
The process of converting data structures or object states into formats that can be stored (eg, stored in a file or memory buffer, or transmitted over a network connection link) and later reconstructed in the same or other computer environment.
Suppose you want to collect certain data about a group of people—name, last name, nickname, date of birth, instruments they play. You can easily set up a spreadsheet, define some columns, and place each row as an entry. You can go a step further, the Definition Date of Birth column must be a number, and the Instrument column can be a list of options. It looks like this:
Name
Short name
Date of birth
Nickname
Music Instrument
William Bailey 1962 Axl Rose vocals, piano Saul Hudson 1965 Slash guitar
More or less, what you do there is define a data structure; if you only need the spreadsheet format, you will do it well. The problem is that if you want to exchange this information with a database or website, then the implementation mechanisms of these data structures on these other platforms will be very different even if the underlying semantics are generally the same. You cannot just insert a spreadsheet into a web application unless the application is designed specifically for this. Unless you have some kind of export tool or gateway, you cannot transfer information from the website to the database.
Let's assume that our website already implements these data structures in its internal logic and that it simply cannot handle spreadsheet formats. To solve these problems, you can convert these data structures into a format that is easy to share between different applications, architectures, or other content: you serialize them. By doing this, you can ensure that not only can this data be transferred across platforms, but they can be reconstructed in a reverse process called deserialization. Also, if you exchange back to a spreadsheet from the website, you get semantically the same clone of the original object—that is, the rows that look exactly the same as the one you originally sent.
In short: Serializing data is to find some common format that is easy to share among different applications.
JSON (JavaScript object notation) is a lightweight data exchange format. It is easy to read and write by humans; it is easy to parse and generate by machines.
JSON is the most widely used data serialization format, and it has the following characteristics:
The following is what our previous spreadsheet looks like after serialization in JSON:
<code>[
{
"name": "William",
"last name": "Bailey",
"dob": 1962,
"nickname": "Axl Rose",
"instruments": [
"vocals",
"piano"
]
},
{
"name": "Saul",
"last name": "Hudson",
"dob": 1965,
"nickname": "Slash",
"instruments": [
"guitar"
]
}
]
</code>
BSON, i.e. binary JSON, is a binary code serialization of JSON class documents...it also contains extensions that allow representations of data types that do not belong to the JSON specification.
JSON is a plain text format. Although binary data can be encoded into text, this has some limitations and will make the JSON file very large. BSON is used to deal with these issues.
It has the following characteristics:
It's similar to JSON. But faster and smaller.
MessagePack (also known as msgpack) is another binary format for serialization. Not as famous as BSON, but worth a look.
Its characteristics include:
YAML: YAML is not a markup language. What it is: YAML is a humanized data serialization standard for all programming languages.
Back to the plain text format, YAML is an alternative to JSON:
The following is what our spreadsheet looks like after serialization in YAML:
<code>[
{
"name": "William",
"last name": "Bailey",
"dob": 1962,
"nickname": "Axl Rose",
"instruments": [
"vocals",
"piano"
]
},
{
"name": "Saul",
"last name": "Hudson",
"dob": 1965,
"nickname": "Slash",
"instruments": [
"guitar"
]
}
]
</code>There are many other serialization formats, such as Protocol Buffers (protobuf, also in binary format), which I have omitted (in a rather random way). If you want to know only all possible formats, check out Wikipedia about data serialization format comparisons.
We will deviate a little bit from the topic here. Layered Data Format Version 5 (HDF5) is not really for serialization, but for storage, and it is sweeping over data science and other industries. It is a very fast and universal format that can be used not only to store many data structures, but also as a replacement for relational databases.
To end this episode, let's just mention that if you're using binary formats like BSON and MessagePack to store/exchange a lot of information, you might be tempted to check out HDF5.
It is also worth noting that even for the same format, performance may depend on the serializer and parser you choose.
While it sounds silly, BSON has the advantage of name: people will automatically associate MongoDB-developed formats (BSON) with standard (JSON), and there is no connection between them. Therefore, you can consider other options as well when searching for binary alternatives to JSON.
In fact, MessagePack seems to outperform BSON in every way: it is faster and smaller, and it is even more JSON compatible than BSON. (In fact, if you're already using JSON, MessagePack is almost a plug-and-play optimization.) Maybe as a "reporter" I should be a bit more balanced, but as a developer, there's no doubt about that.
Nevertheless, BSON is the format used by MongoDB to store and represent data, so if you are using this NoSQL database, there is a reason to stick with it.
Of course, serialization is not just about storing binary data. Granted, JSON has a different goal—i.e. “Human Readable.” However, a little attention will reveal that YAML is doing better in this regard.
However, the YAML specification is very large, especially compared to the JSON specification. But it must be said that because it contains more data types and features.
On the other hand, it cannot be ignored that the simplicity of JSON is key to its adoption as other serialization formats. It relies on a widely used language that already exists, JavaScript, and if you know or have been exposed to JS (if you are in the web development industry, you will know about JSON).
So why not use YAML now? In many cases, this is not easy. JSON still has a place in the Web API because you can easily embed JSON code into HTTP requests (for GETs, such as in URLs, and POSTs, such as in sending forms): This format will let you know if the transfer is suddenly interrupted , because the code will automatically render invalid, which may not be the case with YAML and other competing plain text formats. Additionally, you still need to interact with the JSON-based API and legacy code at some point, and maintaining two snippets of code (JSON and YAML methods) for the same purpose (data serialization) is always a painful thing.
But then again, these parts are the same as the argument that pushes us backwards and prevents us from adopting newer, more efficient technologies (e.g. Python 3 instead of Python 2). I once thought for a minute that we programmers and entrepreneurs are innovators, aren’t we?
JSON and YAML are both data serialization formats, but they have some key differences. JSON is a subset of JavaScript and is often used in web applications due to its compatibility with JavaScript. It uses simple syntax and is easy to read and write. However, it lacks some features such as comments and multi-line strings. YAML, on the other hand, is a superset of JSON and has a more humanized syntax. It supports comments and multi-line strings, making it easier to use as a configuration file. However, it is more complex than JSON and is not as widely supported as JSON.
BSON or binary JSON is a binary representation of a JSON class document. It is designed to be efficient in space, and it is also true in compute-intensive scenarios such as network transmission. BSON can store more data types than JSON, including binary and date data types. However, it is not as readable as JSON or YAML and is mainly used to store and retrieve data in MongoDB.
MessagePack is a JSON-like but more efficient binary serialization format. It is compact, fast and supports a variety of data types. It is often used in applications that require high performance, such as real-time streaming applications. However, like BSON, it is not as readable as JSON or YAML.
Yes, there are several other alternatives to JSON, including XML, Protobuf, and Avro. XML is a human-readable markup language that supports complex data structures, but it is more verbose than JSON. Protobuf or Protocol Buffers is a binary serialization format developed by Google, which is compact and fast, but not readable. Avro is a binary serialization format developed by Apache that supports pattern evolution to make it suitable for long-term data storage.
The selection of data serialization format depends on your specific needs. If you need a human-readable and easy to use format, then JSON or YAML may be the best choice. If you need a compact and fast format, then MessagePack or BSON may be more suitable. If you need a format that supports pattern evolution, Avro is probably the best choice. Before making a decision, it is important to understand the pros and cons of each format.
Yes, multiple data serialization formats can be used in the same application. For example, you can use JSON to exchange data between the client and the server and use BSON to store data in MongoDB. However, using multiple formats can increase the complexity of your application, so be sure to weigh the pros and cons carefully.
There are several libraries and tools that can be used to convert data between different serialization formats. For example, you can use the json module in Python to convert data between JSON and Python objects, or use the yaml module to convert data between YAML and Python objects. There are also some online tools, such as json2yaml, that can be used to convert data between JSON and YAML.
The performance impact of using different data serialization formats may vary by use case. Binary formats like BSON and MessagePack are often faster and more compact than text-based formats like JSON and YAML. However, they are less readable than humans, which may make debugging more difficult. The performance of libraries and tools used to serialize and deserialize data must also be considered.
Yes, there are some safety precautions when using data serialization format. For example, if some formats such as JSON and YAML are not cleaned correctly, they can execute arbitrary code, which can lead to security vulnerabilities. Be sure to use trusted libraries and tools to serialize and deserialize data and clean up any user-provided data.
There are many resources online to help you learn more about data serialization formats. You can start by reading official documents in each format, which usually contain tutorials and examples. There are also many tutorials and articles on sites like Stack Overflow and Medium. Finally, you can try different formats in your own project to gain hands-on experience.
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