JSON Formatting Is a Debugging Skill

When you're staring at a 50KB blob of minified JSON, scrolling endlessly through an unbroken river of brackets and commas, it's easy to miss what you're looking for. The data is valid. Your code parses it fine. But you can't see it.

Formatting JSON is not about making it pretty. It's about making it useful for human debugging.

The Structure Problem

Consider this API response:

{"users":[{"id":1,"name":"Alice","email":"[email protected]","settings":{"theme":"dark","notifications":true}},{"id":2,"name":"Bob","email":"[email protected]","settings":{"theme":"light","notifications":false}}]}

Now formatted:

{
  "users": [
    {
      "id": 1,
      "name": "Alice",
      "email": "[email protected]",
      "settings": {
        "theme": "dark",
        "notifications": true
      }
    },
    {
      "id": 2,
      "name": "Bob",
      "email": "[email protected]",
      "settings": {
        "theme": "light",
        "notifications": false
      }
    }
  ]
}

The data is identical. But in the formatted version, the structure is visible at a glance. You can see there are two users. You can see each has the same fields. You can see the nesting depth of settings.

This isn't about aesthetics—it's about making the shape of data perceivable.

When Formatting Reveals Bugs

Some bugs are invisible in minified JSON but obvious when formatted:

Inconsistent structure: One object has an extra field, or two arrays have different lengths when they shouldn't. Formatted JSON lets you spot these at a glance.

Missing data: A null value or empty array might be meaningful. In minified JSON, "items":[] blends into the noise. Formatted, it stands out.

Type inconsistencies: Is that "price":"100" or "price":100? The quotes matter. Formatting with syntax highlighting makes type visible.

Nesting errors: An object closed too early or too late. A malformed structure that parsers reject. Proper indentation shows the hierarchy your data should have.

Beyond Pretty Printing

Good JSON formatting tools do more than add whitespace:

Syntax validation: Is this even valid JSON? Many tools will tell you before trying to format, and point to where parsing failed.

Sorting keys: Alphabetically ordering object keys makes comparison easier. Did the same API response change between requests? Sorted keys make the diff clean.

Minification: Sometimes you need the opposite—removing whitespace for production APIs or storage efficiency.

Tree navigation: Large JSON documents need more than formatting—they need collapsible sections and search.

The Debugging Workflow

When debugging JSON-based systems, I typically:

1. Capture the raw JSON. From the network tab, logs, or API response.

2. Format it. Even valid JSON is unreadable when minified.

3. Validate it. Is it actually valid? Parse errors often point to the real bug.

4. Navigate to the relevant section. For large payloads, collapsing irrelevant sections helps focus.

5. Compare with expected structure. Either mentally or with a diff tool.

Half of my API debugging is just getting the data into a readable form. The bug often becomes obvious once I can actually see what I'm working with.

JSON Weirdness

JSON looks simple but has surprises:

No trailing commas: [1, 2, 3,] is invalid. This trips up people coming from JS.

No comments: JSON has no comment syntax. // comment or /* comment */ will cause parse errors.

Keys must be quoted: {name: "Alice"} is invalid. Needs {"name": "Alice"}.

No undefined: JSON has null, but not undefined. They're different in JavaScript, and the distinction matters during serialization.

Number precision: Very large or very precise numbers might lose accuracy when parsed, as JSON numbers are typically consumed as floating point.

A good formatter will catch these issues and point you to the problem, rather than just failing mysteriously.

The Right Tool for the Job

For quick formatting—a single response you need to read right now—a browser-based tool is fastest. Paste, format, done.

For repeated work—debugging an API you work with daily—consider browser extensions or CLI tools that integrate with your workflow.

For validation and transformation—processing JSON as part of a pipeline—reach for programming language libraries that give you full control.

But the starting point is almost always the same: make the data readable, and the problem becomes visible.