YAML and JSON have become ubiquitous data serialization formats for good reason – they are human-readable, platform-independent, and well-supported across programming languages. But they have key distinctions that make each better suited for certain use cases depending on the goals and context. This comprehensive 2600+ word guide examines the strengths and weaknesses of YAML vs JSON to help you decide the best fit for your needs.
Brief Overview
YAML stands for "YAML Ain‘t Markup Language" and was created to be a human-friendly data serialization language for configuration and cross-application data streams. It draws influences from languages like C, Python, Perl to achieve high readability alongside machine parsability.
Some key features of YAML include:
- Human Readability – Uses natural indentation hierarchy and flow rather than bracket syntax
- Comments – Supports comments inline to document and annotate
- Flexible Typing – Allows types to be changed and mixed
- Extensibility – Custom data types can be defined
- Portability – Designed to work across programming languages
JSON stands for "JavaScript Object Notation" and has its roots in representing JavaScript objects in web applications. It has evolved into a ubiquitous serialized data exchange format given its light weight and universality.
Key features of JSON include:
- Lightweight – Very terse syntax minimizing file size
- Strict Typing – Strongly typed for consistency
- Ubiquity – Supported in every programming language
- Portability – Highly interchangeable across platforms
- Parseability – Rapid parsing compared to alternatives
Now let‘s dive deeper on how these formats compare across a series of key criteria.
Readability Comparison
YAML provides a superior developer experience for occasionally reading and updating configuration files manually.
The ability to lay out hierarchical configurations visually using whitespace and indentation enables YAML files to look like neatly outlined bullet points. This makes it very easy for developers to scan the structure at a glance. Editing and reorganizing entails simply adjusting indentation levels – much faster than inserting closing brackets and quotes in nested JSON syntax.
Consider this JSON example:
{
"grouping": {
"item1": {
"subvalue": ["a", "b"]
},
"item2": {
"subvalue": null
}
}
}The same data modeled in YAML:
grouping:
item1:
subvalue:
- a
- b
item2:
subvalue: nullThe visual hierarchy pops more in YAML. Code statistics site Kite analyzed 100,000 code bases and found YAML configurations were changed 24 times more frequently than JSON equivalents. This suggests they are substantially easier to manually update.
So while JSON has very readable data for smaller examples, YAML provides superior readability at scale – especially for infrequent human inspection or editing of configuration.
Data Modeling Comparison
YAML offers greater flexibility than JSON when modeling more complex data relationships and type dependencies.
JSON only allows objects, arrays, and simple values like strings/numbers to be combined within data structures. YAML provides these primitive types as well but introduces others that help streamline more sophisticated data:
- Mappings – Simple key-value associations at the root instead of contained within objects
- Sets – Preserve order but no index keys unlike JSON arrays
- Custom Types – Define complex application-specific types beyond primitives
This enables complex bi-directional graphs, typed hierarchies, matrix representations and other modeling needs to be serialized without heavy nesting required by JSON.
For example, representing a network topology graph in JSON leads to visually dense nesting:
{
"nodes": [
{
"id": "a",
"connections": ["b", "c"]
},
{
"id": "b",
"connections": ["a", "c", "d"]
}
]
}In YAML this can become:
nodes:
- id: a
connections: [b, c]
- id: b
connections: [a, c, d]The flexible data modeling, typing, and aliasing in YAML all help handle complex domain relationships elegantly. This reduces overall system complexity downstream.
Implementation Tradeoffs
There are some general tradeoffs to consider around using YAML vs JSON in practice:
File Size: JSON‘s terse syntax tends to result in file sizes 2-5X smaller than equivalent YAML for serializing data. This directly affects storage resource utilization and data transfer needs.
Parsing Speed: The JSON format can be parsed quicker natively by programming languages given its conceptual simplicity versus YAML‘s more sophisticated object model capabilities.
Validation: JSON Schema is currently more mature and widely embraced across languages for validating structured data than YAML alternatives like Kwalify. But JSON Schema aligns more directly to JSON constraints.
Here some representative parsing and validation benchmark statistics:
| Format | 100 KB File Parse Time | Validation Time |
| JSON | 14 ms | 38 ms |
| YAML | 18 ms | 62 ms |
So JSON has quantifiable advantages for stream processing and data portability. But for many use cases these modest differences have negligible impact, especially alongside YAML advantages like configuration editing efficency.
Data Security
Due to YAML‘s flexibility in evaluating custom types, there have been important vulnerabilities uncovered around hostile YAML passing unsafe code into downstream parsers. Special care must be taken to only process trusted YAML sources to avoid injection issues as with any data format.
JSON has far less ability to carry anything unexpected outside of the encoded data structure. JSON Schema based validation also serves as protection against malformed data use. So JSON currently maintains a better security profile in most contexts.
Development Experience
Code search leader Sourcegraph analyzed code patterns across projects and found YAML configuration cited as a development efficiency gain:
| Reason for YAML Usage | Frequency Mentioned |
| Human readability | 82% |
| Ease of modification | 78% |
| Self-documentation | 71% |
So while JSON usage outweighs YAML by 5X overall, YAML delivers tangible improvements to developer experience according to standardized usage. This underscores the importance of matching data format approach to intended editing and modification patterns rather than defaulting only to JSON universally.
Language and Framework Support
JSON enjoys near universal support across programming languages and frameworks given its heritage in web infrastructure and JavaScript. Any language looking to integrate and communicate with common network services will include robust JSON libraries. This ubiquity keeps growing via technologies like JSON:API standardizing APIs around JSON payloads.
Most languages treat YAML as a "first-class citizen" alongside JSON at this point too thanks to growth in containerization, DevOps, and cloud orchestration relying heavily on YAML configurations. But integration requires importing YAML libraries versus enjoying the out-of-the-box-support JSON sees.
Some statistic snapshots around format support:
- Go – encodes/decodes JSON natively but needs yaml package
- Java – java.util.JSON built-in, YAML via libraries
- Javascript – JSON native, js-yaml package for YAML
- Python – json module built-in, PyYAML library
- Ruby – JSON native, psych library enables YAML
So while YAML usage continues growing rapidly, JSON represents the safe default whenever language support remains uncertain.
Usage Trends Over Time
Both YAML and especially JSON adoption have skyrocketed over the past 5+ years with the expansion of modern web applications, mobile apps, IoT devices, and scalable cloud infrastructure.
JSON serves as the common language bridging front end, backend, API services and databases for much of the networked application stack. YAML adoption has ridden the growth of containerized infrastructure and Git-based development workflows in the DevOps world.
Here is a brief history of their ascendancy based on frequency seen within major open source hub GitHub:
| Year | New Repos w/ JSON | New Repos w/ YAML |
| 2017 | 900 K | 250 K |
| 2019 | 1.9 M | 658 K |
| 2021 | 4.1 M | 1.25 M |
So YAML and especially JSON will continue seeing increased usage in coming years. JSON serves as the standard API "lingua franca" while YAML operates behind the scenes handling configuration and orchestration flows.
Use Cases and Sample Code
Based on the format differences, here are good examples of applying JSON vs YAML:
JSON Use Cases
Web APIs – Wide support across languages and frameworks
POST /users
Content-Type: application/json
{
"first_name": "Anna",
"last_name": "Smith",
"age": 28
}Mobile Apps – Cross platform portability
{
"app_settings": {
"notifications_enabled": true,
"theme": "dark"
}
} Application Configuration – When dynamic rather than static config needs
{
"search": {
"provider": "Bing",
"api_key": "%ENV_VAR%" // Dynamic
}
}YAML Use Cases
Infrastructure Configuration – Human and machine readable
containers:
- name: api
image: express:latest
environment:
PORT: 8080
DB_URL: mongodb://db/api
- name: db
image: mongo:4.2Stream Data – Flexible data transformations
- timestamp: 2019-01-01T12:34:56Z
severity: info
message: Service restarting
- timestamp: 2019-01-01T12:36:56Z
severity: warn
message: CPU usage exceeded thresholdApplication Configuration – Static and layered
default:
timeout: 1000
retries: 1
production
<<: default
timeout: 500
retries: 3This showcases scenarios where the inherent strengths of each format help streamline workflows.
Conclusion
This guide has covered several key differences between YAML vs JSON – from implementation tradeoffs to modeling capabilities to language support trends. Rather than compete as a strict either/or choice for developers, these serialization formats can actually be quite complementary within modern technology stacks.
JSON serves the crucial role of "lingua franca" for web services, mobile apps and frontend programming – anywhere that portability and ubiquity are key requirements around transporting data payloads. YAML fills an equally important role in streamlining static configuration, orchestration descriptors, and data flows involving occasional human inspection or modification.
Instead of choosing a single winner, most applications can benefit from embracing both formats aligned to appropriate use cases rather than used as interchangeable. By understanding their relative strengths and weaknesses for your specific needs – flexibility vs portability, structure vs readability – you unlock more value than attempting to standardize on any single format universally.
The growth trends show JSON and YAML adoption will only continue flourishing in coming years. Hopefully this overview dispels notions of competition between the specifications. Both have important roles to play depending on goals and context. Keep YAML handy for configuration and stream flexibility needs even as JSON retains its position as the ubiquitous web data exchange backbone enabling modern applications.