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{}JSONKits

Nested JSON to Flat Converter

Flatten deeply nested JSON objects into dot-notation key-value pairs. Perfect for data analysis, CSV export, and simplifying complex structures.

Input

json
loading editor…

Paste your nested JSON here...
or drop a file anywhere in this pane

Output

json

Flat JSON output will appear here...
press ⌘↵ to flatten

What Is JSON Flattening?

JSON flattening converts deeply nested objects into a flat structure using dot notation for keys. For example, {user: {name: "Alice"}} becomes {user.name: "Alice"}. This is essential for data analysis, machine learning, and database imports.

Flattening is useful for:

  • Data Analysis: Converting nested JSON to flat tables for spreadsheet analysis.
  • CSV Export: Preparing nested data for CSV conversion.
  • Database Ingestion: Flattening JSON for relational database inserts.
  • Log Analysis: Simplifying complex log entries for querying.

Nested JSON vs. Flat JSON

FeatureNested JSONFlat JSON
ReadabilityClear hierarchy.All keys at one level.
Data AnalysisHarder to tabulate.Easy to analyze and sort.
CSV CompatibilityNot directly convertible.Directly convertible to CSV.
Key LengthShort, hierarchical keys.Longer dotted keys.
Primary UseAPIs, configs.Data processing, analytics.

How to Flatten JSON Online

  1. Paste nested JSON: Copy your multi-level JSON into the input editor.
  2. Click Flatten: Convert to dot-notation flat structure.
  3. Review flat keys: All nested paths become single-level dotted keys.
  4. Reverse if needed: Use bidirectional mode to restore nesting.

Frequently Asked Questions

How are arrays handled in flattening?

Array elements are indexed with bracket notation, e.g., items[0].name. This preserves array ordering.

Can I flatten deeply nested objects?

Yes. There is no depth limit. The tool recursively flattens all levels.

Is this tool safe?

Yes. Processing happens 100% in your browser. No data is uploaded.

Real-World Examples

JSON flattening is used when preparing API responses for CSV export, transforming MongoDB documents for relational database migration, flattening nested configuration for environment variable mapping, and simplifying complex JSON logs for analysis in tools like Excel or Google Sheets.

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