Forge Json

Flatten Large JSON Datasets

Flatten large JSON datasets carefully by sampling first, controlling array expansion, and keeping the output usable for CSV, BI, or analysis.

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Try examples:

→ Flatten or nest keys in this JSON structure and flatten nested object.

{
"user": {
"name": "Alice",
"address": {
"city": "NYC",
"zip": "10001"
}
}
}
Output✓
1{
2  "user.name": "Alice",
3  "user.address.city": "NYC",
4  "user.address.zip": "10001"
5}

Love the result?

Use this exact pipeline in your app, backend, or LLM workflow.

No setup needed. Works with curl, Node, Python.

Uses example data. For edited input, copy from the playground.

Read integration guide

Works with:

  • JSON datasets
  • Analytics event exports
  • Log batches
  • BI and CSV prep

Example: input → output

Flatten large JSON datasets

Large JSON datasets need a different flattening strategy than small examples. A tiny payload can be flattened directly. A dataset with thousands of records, nested arrays, and inconsistent fields needs sampling, column control, and cleanup before export.

This page uses the same engine as the JSON flattener, with guidance for analytics exports, event logs, API result sets, and BI-bound datasets.

Dataset flattening workflow

  1. Flatten a representative sample first.
  2. Count the output columns and inspect array expansion.
  3. Decide whether child arrays should stay arrays, become indexed paths, or become separate rows.
  4. Normalize inconsistent records before final export.
  5. Convert the flat dataset to CSV only after the shape is readable.

For file-size-focused guidance, use Flatten Large JSON Files. For spreadsheet output, use Flatten JSON for CSV.

Large dataset example

Input:

[
  {
    "event": "signup",
    "user": {
      "id": "u_1",
      "traits": {
        "plan": "free",
        "country": "US"
      }
    },
    "context": {
      "campaign": {
        "source": "newsletter"
      }
    }
  }
]

Flattened output:

[
  {
    "event": "signup",
    "user.id": "u_1",
    "user.traits.plan": "free",
    "user.traits.country": "US",
    "context.campaign.source": "newsletter"
  }
]

That output is useful for BI and CSV workflows because each nested path becomes a candidate column.

Why datasets get messy

Datasets usually contain drift. One record has user.traits.country, another has user.country, and a third has no country at all. Flattening exposes the drift, but normalizing fixes it. Use Normalize JSON when columns need to line up across every row.

Arrays are the other big issue. Indexed array paths are fine for short fixed lists, but large datasets often need child arrays split into separate rows.

Common large dataset mistakes

  • Flattening the full dataset before checking a sample.
  • Creating thousands of columns from variable-length arrays.
  • Ignoring missing fields until the CSV has empty columns.
  • Keeping noisy metadata that should be removed before BI import.
  • Assuming flat JSON is still the right shape for API writes.

Frequently asked questions

How should I flatten a large JSON dataset?+

Start with a representative sample, inspect the output columns, control array expansion, normalize inconsistent records, then export the flat dataset.

Is large dataset flattening the same as large file flattening?+

They overlap, but dataset flattening focuses on record shape, arrays, columns, and BI usability. Large file flattening also focuses on browser memory and file size.

Should I flatten arrays in datasets?+

Only when arrays are small and predictable. Large variable arrays usually need separate rows instead of indexed columns.

Related tools

Read more on the blog

Advanced usage (optional)

Flatten / Nest

v1.0.0
Structure
objectarrayreversible

Description

Flatten / Nest

Convert between nested and flat object structures. Flatten deep objects into single-level key-value pairs, or nest flat keys back into hierarchical objects. Supports multiple key formats: delimiter-separated, camelCase, snake_case, PascalCase, and kebab-case.

Modes

Flatten

Convert nested objects into flat keys. Each nested path becomes part of the key name.

{ "user": { "name": "Alice" } }  →  { "user.name": "Alice" }

Nest

Convert flat keys back into nested objects by splitting on the delimiter or case boundaries.

{ "user.name": "Alice" }  →  { "user": { "name": "Alice" } }

Key Formats

Delimiter (default)

Join/split key segments with a character (default: .).

  • Flatten: user + name → user.name
  • Nest: user.name → user / name

camelCase

Join/split on uppercase letter boundaries.

  • Flatten: user + address + city → userAddressCity
  • Nest: userAddressCity → user / address / city

snake_case

Join/split on underscores.

  • Flatten: user + address + city → user_address_city
  • Nest: user_address_city → user / address / city

PascalCase

Same as camelCase but with uppercase first letter.

  • Flatten: user + name → UserName

kebab-case

Join/split on hyphens.

  • Flatten: user + name → user-name

Configuration

FieldTypeDefaultDescription
Modeenumflattenflatten or nest
Key Formatenumdelimiterdelimiter, camelCase, snake_case, PascalCase, or kebab-case
Delimiterstring.Character(s) used to join/split key segments (only for delimiter format)
Max Depthnumber0Maximum nesting depth to flatten (0 = unlimited)
Target Pathspath-picker[]Scope operation to specific paths only (empty = apply everywhere)

Use Cases

API Integration

  • Flatten for forms: Convert nested API responses to flat form field names
  • Nest for APIs: Convert flat form data back to nested API request bodies
  • Format conversion: Transform between dot-notation and camelCase conventions

Database Operations

  • MongoDB flattening: Flatten nested documents for tabular export
  • SQL mapping: Convert hierarchical JSON to flat column names for SQL insertion
  • Schema migration: Convert between naming conventions (snake_case ↔ camelCase)

Configuration Management

  • Environment variables: Flatten config objects to dot-notation for .env files
  • Depth limiting: Flatten only the first level while preserving deep structures

Configuration

NameTypeDefaultDescription
Modeenumflattenflatten: convert nested objects to flat keys. nest: convert flat keys back to nested objects. flatten nest
Key Formatenumdelimiterdelimiter: use a character to join/split. Others: camelCase, snake_case, PascalCase, kebab-case. delimiter camelCase snake_case PascalCase kebab-case
Delimiterstring.Character(s) used to join/split key segments (default is dot)
Max Depthnumber0Maximum nesting depth to flatten (0 = unlimited)
Target Pathspath-picker[]Scope operation to specific paths only (empty = apply everywhere)

Examples

AI Prompt
Flatten or nest keys in this JSON structure and flatten nested object.
{
"user": {
"name": "Alice",
"address": {
"city": "NYC",
"zip": "10001"
}
}
}
Output✓
1{
2  "user.name": "Alice",
3  "user.address.city": "NYC",
4  "user.address.zip": "10001"
5}
Config
Mode
flatten
Delimiter
.
Max Depth
0
Target Paths
all

API Usage

POST /api/v1/utilities/structure.flatten-nest
Example:
curl -X POST https://your-domain.com/api/v1/utilities/structure.flatten-nest \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"inputs":{"primary":{"user":{"name":"Alice","address":{"city":"NYC","zip":"10001"}}}},"config":{"mode":"flatten","delimiter":".","maxDepth":0,"targetPaths":[]}}'
Response
1{
2  "user.name": "Alice",
3  "user.address.city": "NYC",
4  "user.address.zip": "10001"
5}