JSON to Table Viewer
Render any JSON array as a data grid — flatten nested objects, sort columns, filter rows live and export to CSV without leaving the page.
Render any JSON array as a data grid — flatten nested objects, sort columns, filter rows live and export to CSV without leaving the page.
Your rendered data grid will appear here.
Standard JSON converters fail when encountering nested objects. Layerbit's Grid Engine automatically flattens multi-level objects (e.g., user.address.city) and securely stringifies internal arrays, ensuring zero data loss during conversion.
body array and an optional header.Flatten massive nested JSON responses to quickly identify missing fields, verify data types, or spot anomalies across complex data structures.
Visualize document-based database exports (like MongoDB or DynamoDB) in a readable grid format before sharing them with non-technical stakeholders.
Paste structured JSON application logs into the grid to easily filter by severity levels, timestamp ranges, or specific error codes instantly.
Test the grid rendering engine instantly. Click the templates below to load sample payloads and watch how the parser handles flat arrays, nested objects, and API headers.
The parser utilizes a recursive flattening algorithm. If it encounters a nested object, it collapses it into a single column using dot notation (e.g., a location object containing a city property becomes a column named location.city).
Because the grid operates entirely client-side, there are no artificial server limits or payload size caps. The only limitation is the amount of RAM available on your local machine to process and render the DOM elements.
Yes. The CSV export feature intelligently respects your active search filter. If you search for a specific term and click Export CSV, only the rows currently visible on your screen will be downloaded.
Formatted JSON is the right shape for reading one record carefully. It is the wrong shape for comparing forty of them, because each record's fields are separated vertically by every other field — so answering "which of these users has no email address?" means scrolling rather than glancing down a column.
A table inverts that. Fields become columns, records become rows, and gaps, outliers and type inconsistencies become visible as visual patterns instead of things you have to hold in memory. That is the whole value: not prettier output, but a layout where anomalies find you rather than the reverse.
API responses are rarely uniform — optional fields appear on some objects and not others. The table is built from the union of all keys across every record, so a field present on only two of fifty rows still gets a column, with the gaps visible rather than silently dropped.
A column where most values are numbers and three are quoted strings is a serialisation bug that is nearly invisible in indented JSON and obvious in a column. The same goes for a mix of null, "" and missing — three different states that mean different things.
Tables are flat and JSON is not, so a nested object or array inside a field has to be represented rather than expanded. Deeply nested payloads are better explored with the collapsible tree view; tables are for arrays of broadly flat records.
Worth being explicit about the lossy parts, because they matter when you copy data out of a table and into something else:
null, or the value was an empty string. Those are three different facts and a table shows them identically.None of that makes the table wrong — it makes it a view. Use it to find the record or field you care about, then go back to the JSON for the authoritative detail.
The JSON most worth viewing as a table is production API output: user records, order lines, audit events. That is exactly the data that should not be pasted into a service you cannot inspect. Parsing and rendering here happen entirely in the page — verifiable in your browser's Network tab, and functional with the network disconnected.