Developer Tools

Extract SQL Column Names

Extract SQL column names from pasted SELECT lists, INSERT column lists, and UPDATE column lists locally without running queries.

Runs locally in your browser. No database connection, schema lookup, query execution, upload, storage, or logging.

0 / 30,000 characters. No upload, storage, or account required.

Extracted items
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Unique items
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Duplicates removed
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Extracted SQL column names

SQL column extraction workflows

Use this extractor when SQL needs a quick browser-local column inventory before mapping data, reviewing reports, or documenting migrations.

Understand query shape

Pair column extraction with Extract SQL Table Names and Extract SQL Join Clauses.

Check filter logic

Use Extract SQL WHERE Clauses when business rules or report scopes need review.

Prepare mapping output

Clean extracted names with Sort Lines, Remove Duplicate Lines, or CSV Column Extractor.

Quick answer

Extract SQL Column Names is a browser-based extract SQL column names for checking copied developer data such as payloads, URLs, encoded values, identifiers, snippets, or logs without sending the input to a server.

Primary task
extract SQL column names
Processing
Runs on copied snippets in your browser; no account or saved input.
Workflow
Developer Data Toolkit

What this tool does

Extract SQL Column Names scans pasted SQL snippets as text, pulls column-like identifiers from SELECT, INSERT, and UPDATE column sections, and returns a deduped copy-ready list with source-line context.

Common use cases

Extract column names from pasted SQL SELECT, INSERT, and UPDATE snippets locally for mapping, report QA, migration notes, and schema-review handoffs without executing SQL.

Use Extract SQL Column Names when you are working with copied API payloads, logs, encoded values, config snippets, identifiers, or debugging data and need a quick browser-local check before pasting the result into docs, tickets, tests, or another developer tool.

Debug a copied sampleUse a sanitized payload, URL, config value, identifier, stylesheet, markup snippet, or log excerpt when you need a fast check before opening a heavier IDE, schema, or API client.
Prepare a handoff noteTurn messy developer data into a clearer example for tickets, documentation, code review comments, test fixtures, or support notes without exposing secrets.
Chain a safe transformationMove to a related formatter, validator, encoder, decoder, converter, or generator only after the current output still needs another compatible developer-data step.

How to use it

  1. Paste SQL SELECT, INSERT, UPDATE, migration, or code-review snippets that include visible column lists.
  2. Choose whether duplicate columns should be removed and whether extracted identifiers should be normalized.
  3. Review the column inventory, then copy it for report QA, mapping sheets, migration notes, schema review, or documentation cleanup.

Example workflow

Copy a small payload or encoded value from an API response, request URL, log line, or config file. Run the focused check here, confirm the output is readable, then continue with related developer data tools such as validation, formatting, decoding, timestamp conversion, or CSV/JSON conversion.

Privacy note

Extract SQL Column Names runs locally in your browser. It does not connect to databases, parse schemas, execute SQL, upload, store, or log pasted text. Redact sensitive identifiers and production examples before sharing output.

FAQ

These answers focus on copied-snippet workflows, safe sample data, validation boundaries, and what to check before moving output into code, APIs, docs, or tickets.

3 focused answers for this page

Does this read my database schema?

No. It only scans pasted SQL text in your browser and does not connect to databases, inspect schemas, execute queries, upload, store, or log input.

Which columns can it find?

It extracts visible identifiers from common SELECT lists, INSERT column lists, and UPDATE column-list snippets. Complex generated SQL should still be reviewed manually.

When is this useful?

Use it for report QA, field mapping, migration planning, data dictionary cleanup, and code-review handoffs before relying on a query change.

Next steps

Use these routes only when you need a related task or a wider comparison.