YAML vs. JSON: Breaking Down the Key Differences

by KodeKloud
KodeKloud
•
June 14, 2023
•
7 min read

Highlights

Core Distinction: The main difference between YAML and JSON comes down to readability versus widespread programmatic support.
Structural Variances: When comparing JSON vs YAML syntax, YAML relies heavily on clean indentation, while JSON uses explicit brackets and curly braces.
Target Use Cases: Knowing exactly when to use YAML (like in Kubernetes and CI/CD pipelines) versus JSON (like in Web APIs) is a critical skill for DevOps engineers.
Strengths: The primary YAML advantages include native support for comments and aliases, whereas JSON advantages center around universal language support and serialization speed.
Performance: In terms of pure YAML vs JSON performance, JSON generally parses faster natively in languages like JavaScript and Python.

In modern software development, YAML and JSON are often used for configuring applications, setting up infrastructure, and interacting with web application interfaces (Web APIs). While both can be used interchangeably in some specific cases, they are very different tools designed for different purposes.

If you are exploring the YAML vs JSON debate, this article discusses the unique features they bring to the table and their key differences, helping you decide which to use for your next project.

What is JSON?

JavaScript Object Notation (JSON) is a lightweight data format that makes it easy for machines to parse and generate data. It is widely used in web applications requiring fast data transfer. It supports different data types, such as numbers, strings, booleans, or objects.

One of the biggest JSON advantages is that programming languages like JavaScript and Python have built-in support for it out of the box, which makes developing software for data transfer incredibly convenient.

To learn more about how JSON works, check out the FREE JSON Path Test Course.

What is YAML?

YAML is a highly human-readable data-serialization language. It is actually a superset of JSON, which means a valid YAML file can theoretically contain JSON objects. In addition to supporting standard JSON data types, it also supports complex types like dates and timestamps.

Because of the clear YAML advantages regarding readability, it is most often used to configure files across different programming languages, deployment pipelines, and cloud platforms.

Difference Between YAML vs. JSON

Now that you understand what YAML and JSON are - let's look at the key differences between them.

Syntax

When analyzing JSON vs YAML syntax, the most obvious difference is formatting. While YAML uses indentation to structure its fields and objects, JSON strictly uses curly braces and commas.

Below is an example of a YAML object defining a person:

id: 1
name: anonymous
address:
	city: Hanoi
	country: Vietnam

Below is the same object definition using JSON:

{
	"id": 1,
	"name": "anonymous",
	"address": {
		"city": "Hanoi",
		"country": "Vietnam"
	}
}

Using indentation to separate fields and objects makes YAML more readable than JSON.

Data Types

As for data types, JSON supports strings, objects, numbers, booleans, arrays, and null. The primary difference between YAML and JSON here is that YAML provides additional native support for dates and timestamps.

Below is a YAML example of an object with a timestamp value:

userId: 1
userName: anonymous
createdTime: 2023-05-17T00:00:00Z
updatedTime: 2023-05-17T00:00:00Z

Comments

In software development, comments are great for improving the collaboration experience for people working on the same piece of code. Unlike JSON, YAML supports comments, allowing you to explain the responsibility of a line or block of code. This is especially important for complicated YAML files, such as Kubernetes deployments and GitLab CI deployment files.

Below is an example of a YAML file containing comments:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
...
rules:
# EventListeners need to be able to fetch all namespaced resources
- apiGroups: ["triggers.tekton.dev"]
	resources: ["eventlisteners", "triggerbindings", "triggertemplates", "triggers", "interceptors"]
	verbs: ["get", "list", "watch"]
- apiGroups: [""]
# configmaps is needed for updating logging config
	resources: ["configmaps"]
	verbs: ["get", "list", "watch"]
# Permissions to create resources in associated TriggerTemplates
- apiGroups: ["tekton.dev"]
	resources: ["pipelineruns", "pipelineresources", "taskruns"]
	verbs: ["create"]
...

Since there are several permissions that the role needs access to, it's important to have the comments in place so that other people working on the same team can understand the meaning of these permissions. This makes YAML a great asset in a collaborative development environment.

Extensibility

In terms of extensibility, YAML supports object references and aliases, allowing you to reference the same object multiple times within a document. JSON does not have built-in support for references or aliases, making it less flexible when you want to reuse your code. Below is an example of YAML code that uses an alias:

definitions:
	steps:
	- step: &unit-integration-test
		name: Unit and Integration Test
		script:
			- pytest tests/unit
			- pytest tests/integration
pipelines:
	branches:
	develop:
		- step: *unit-integration-test
	main:
		- step: *unit-integration-test

In this deployment pipeline, the execution scripts for step “unit-integration-test” are only defined once. You can trigger the “unit-integration-test” step in different code branches without redefining the execution scripts by referencing the defined step using “*unit-integration-test”.

Programming Languages Built-In Support

Programming languages such as JavaScript and Python have built-in support for JSON. This allows you to read and generate data in JSON format easily. Conversely, there is no major programming language that has built-in support for YAML; you almost always have to import a third-party library.

This difference in native integration directly impacts YAML vs JSON performance. Because JSON is natively supported and has a lighter structural footprint, it parses and serializes much faster than YAML, making it the superior choice for high-speed application development.

Ease of Learning and Adoption

Although YAML is more readable, it requires a steeper learning curve than JSON because of several reasons:

YAML offers more functionalities and data type support than JSON
YAML is not natively supported in software programming languages, so you have to use a library or create a new one to work with YAML

In terms of adoption, YAML and JSON are suitable for different use cases.

YAML is widely adopted for configuring infrastructure and applying infrastructure as code, thanks to its readability.
JSON, on the other hand, is often used in web API due to its great support for serialization data.

JSON vs. YAML: Which Do I Use?

Both JSON and YAML have their own pros and cons, as you have learned in the sections above. Depending on your specific needs, the answer to the question will be different.

Since JSON is great at serialization and some programming languages have built-in support for it, choose it if you need to:

Work with the web APIs
Store temporary data
Integrate with another system
Logging and data analytics

Because of a high number of features and design for readability, choose YAML if you need to:

Create configuration files
Apply CI/CD deployment pipelines
Apply infrastructure as code
Create human-readable data representation

FAQ

Why Does Kubernetes Use YAML Instead of JSON?

Kubernetes uses YAML over JSON because of several reasons:

YAML offers greater readability
YAML supports more expressive data structures, allowing you to create nested and complicated configurations
YAML also supports comments which help to improve the collaboration between members working on the same configuration files.

Why Is JSON Preferred in APIs?

Below are the reasons why JSON is more popular and preferred in APIs development:

It is light-weight and more compact than YAML, which allows for data stored in JSON format to be transmitted faster over HTTP request
It is natively supported in some programming languages like JavaScript or Python and has a number of libraries to work with
It is compatible with web browsers since it was built to work with JavaScript.

Understanding how to use YAML and JSON is a must for DevOps engineers. Check out the different areas where the two are utilized in our DevOps Learning Path:

Conclusion

In summary, YAML provides better readability and is better suited for configuration in infrastructure and deployment pipelines. JSON, on the other hand, is better at serialization and better suited for storing data in web APIs.

FAQs

Q1: What is the biggest difference between YAML and JSON?

The biggest difference is their intended use case: YAML is optimized for human readability and configuration files, while JSON is optimized for data serialization and machine-to-machine communication over APIs.

Q2: Why does Kubernetes use YAML instead of JSON?

Kubernetes prefers YAML because of its massive YAML advantages: it offers greater readability, supports expressive nested data structures, and allows for comments, which are vital for collaborative infrastructure teams.

Q3: In the debate of JSON vs YAML syntax, which is easier to write?

YAML is generally easier and faster for humans to write because it does not require you to keep track of closing brackets, curly braces, and trailing commas like JSON does.

Q4: Why are the JSON advantages preferred in Web APIs?

JSON is lightweight, compact, and natively supported in almost every programming language (especially JavaScript). This native support means data transmitted in JSON format is processed much faster over HTTP requests.

Q5: How does YAML vs JSON performance compare?

JSON is significantly faster to parse and generate than YAML. YAML's complex feature set (like aliases and explicit data typing) requires more computational overhead to parse, which is why it is rarely used for high-speed data transfers.