As developers, we deal with JSON almost every day: API responses, service logs, configuration files, or payloads sent to the front end. At first it looks simple — until one day you face a payload that is:
- Hundreds of lines long
- Completely unformatted
- And mysteriously breaking your app because of a single missing comma
This article walks through a calmer way to deal with messy JSON, how a formatter and validator help, and a few best practices that make collaboration with other teams smoother.
3 JSON Problems That Cause the Most Headaches
In real projects, JSON issues are rarely about “not knowing the syntax”. What happens more often looks like this.
1. Unformatted JSON
All fields are squeezed into a single long line:
{"user":{"id":123,"name":"Sari","email":"sari@example.com"},"orders":[{"id":1,"total":120000},{"id":2,"total":89000}]}The problems:
- Your eyes get tired quickly, it is hard to scan the structure
- Finding a specific field during debugging is painful
- It is easy to misread nesting levels or data types
2. Invalid JSON
JSON is a strict format. One wrong character can make the entire payload invalid.
Classic example:
{
"name": "Sari"
"email": "sari@example.com"
}The missing comma after "Sari" will often result in a generic error like:
Unexpected string in JSON at position …
Without a validator, hunting down that one character in a long payload can take more time than it should.
3. Inconsistent Structure
In evolving microservices or integrations, JSON structure can drift slowly without clean documentation:
{ "id": 1, "name": "Sari", "email": "sari@example.com" }
{ "id": 2, "fullName": "Budi", "phone": "0812…" }Humans might guess what it means, but for code and data teams:
- It is harder to build consistent mappings
- The risk of subtle parsing or analytics bugs increases
A formatter and validator will not fix structural design issues, but they make them visible much earlier.
A Calmer Workflow for Debugging JSON
Instead of staring at raw code or logs, it is much more efficient to follow a consistent workflow every time you run into “weird” JSON.
1. Start From the Raw Payload
Grab the JSON directly from its source:
- API responses in the network tab
- Application logs
- Request bodies that failed to be processed
Try to copy it as is, including whitespace and strange characters, so you do not accidentally hide the real problem.
2. Format & Indent First
Next step: make it readable.
A good formatter will:
- Add newlines and consistent indentation
- Arrange objects and arrays so nesting levels are clear
- Make it easy to separate keys, values, and larger blocks
Example transformation:
{"user":{"id":123,"name":"Sari","email":"sari@example.com"},"orders":[{"id":1,"total":120000}]}becomes:
{
"user": {
"id": 123,
"name": "Sari",
"email": "sari@example.com"
},
"orders": [
{
"id": 1,
"total": 120000
}
]
}Once the structure is visible, the root cause often starts to reveal itself.
3. Run Validation and Read the Error
After formatting, run the validator:
- If it is valid, you can move on to business logic, mapping, or structural checks.
- If it is invalid, pay attention to the error message and the line/column it points to.
A helpful validator usually highlights things like:
- Unexpected characters
- Unbalanced braces or brackets
- Unclosed quotation marks
With a clean structure, these messages become much easier to act on.
4. Keep the “Clean” Version for Future Reference
Once you have found the issue, do not just forget that payload. Keep a clean version as:
- An API example in documentation
- A test case for unit or integration tests
- Reference material when discussing issues with other developers or QA
A formatter that supports copy-friendly output makes it easy to paste JSON into issue trackers, chats, or spec documents without further editing.
Best Practices for JSON in Collaborative Teams
Beyond debugging, well-structured JSON makes life easier for everyone: front-end, back-end, QA, and data.
Here are a few simple principles that have a big impact.
Do
- Use consistent indentation (2 or 4 spaces) everywhere.
- Choose clear, consistent key names across endpoints and services.
- Store official JSON examples in the repo or documentation, not just in chat threads.
- Validate JSON before committing or sending examples to someone else.
Don’t
- Mix several formats: sometimes objects, sometimes JSON stored as strings.
- Send screenshots of JSON and expect developers to retype them.
- Put comments inside JSON that will be parsed by machines.
- Leave trailing commas that break certain clients or libraries.
Readable JSON is not only for machines — it is for the people who work with it every day.
When Do You Need a Separate JSON Formatter & Validator?
Many editors and IDEs already support JSON out of the box. A dedicated browser tool is still useful when:
- Inspecting payloads from other apps (for example third-party integrations) without touching your IDE workspace.
- Helping non-developers (business, QA, data) who are not comfortable with technical editors.
- Reviewing payloads outside the code context, for example during meetings or when writing documentation.
With a lightweight, easy-to-access tool, this “check it outside first” habit can be adopted by more than just core developers.
Try the JSON Formatter & Validator From SO-NO
At SO-NO, we built a JSON Formatter & Validator as part of our browser-based Developer Tools.
It is designed to:
- Format JSON quickly and safely
- Show clear error messages when the structure is invalid
- Help you copy the cleaned-up result into code, issue trackers, or documentation
If you often receive messy JSON payloads from many different sources, feel free to try our tool:
Use the JSON Formatter & Validator
Hopefully the workflow and best practices in this article make your debugging sessions a little calmer — so you can spend more time solving real business problems instead of chasing a missing comma.