How TextQuery's SQL Editor Works

Updated

TextQuery's SQL editor is a desktop SQL workspace for imported files: you write queries with autocomplete, formatting, history, and favorites; edit table values inline and commit changes together; narrow rows with filters instead of writing WHERE clauses by hand; and keep multiple queries and tables open in tabs. It fits people who already know SQL and want to query local files without setting up a database server. It is a desktop app for macOS and Windows, not a browser tool.

What you can do in the editor

The editor is built around writing SQL against tables that TextQuery creates from your imported files.

  • SQL autocomplete — suggests table and column names as you type, so you don't have to memorize the schema.
  • Query history and favorites — previously run queries are kept, and you can save the ones you reuse.
  • Query formatter — reformats a query for readability.
  • Multiple selections — edit several parts of a query at once.
  • Tabs — work on multiple queries and tables simultaneously instead of closing one to open another.

Because import doesn't require you to define a schema, writing code, or commands, the editor is the first place you interact with the data after loading a file.

Editing data without leaving the editor

Two features cover the common "I just need to fix a few values" case:

Inline editor

You edit values directly in the table, collect your changes, and commit them in one go. This matters when you want to review a batch of edits before they take effect rather than changing rows one at a time.

Filters

Filters narrow down a table's rows without writing queries manually. Use them when you want to inspect or work on a subset and don't need a reusable query.

A concrete workflow

For example, you have a CSV of orders and want to check totals for one region:

  1. Import the file — TextQuery reads it into a table without schema definition.
  2. Open a tab and write a query against that table; autocomplete fills in column names.
  3. Run it, then save it to favorites if you'll rerun it.
  4. If you only need to eyeball a subset, apply a filter instead of editing the query.
  5. Fix any bad values with the inline editor, collect the changes, and commit them together.
  6. Export the result as CSV, JSON, Excel, or SQL, or create a new table from the query results.

Where it fits and where it doesn't

Choose TextQuery's editor when Look elsewhere when
Your data is in files (.xlsx, .xls, .csv, .tsv, .json, .jsonld, .zip, .gz, .tgz, .tar) You need a shared, multi-user database server
You already know SQL and want no setup You want to query from a browser or phone
You want local analysis of sensitive data You need scheduled or automated pipelines
You want charts (line, bar, area, scatter, pie) in the same app You only need a spreadsheet and no SQL

TextQuery states it neither records nor sends anything related to your usage, which is the basis for querying sensitive data locally. It is available for macOS and Windows, and there is a free version with certain limitations plus a Pro upgrade; the site describes a perpetual license with free updates rather than a subscription. Check the pricing page for current terms before deciding.

Common snags

  • Assuming browser access — this is a desktop app; there is no browser extension version described.
  • Expecting schema setup — import is designed to skip schema definition, so don't look for a table-creation wizard before you can query.
  • Editing without committing — inline edits are collected and committed together, so changes aren't live until you commit.
  • Reaching for SQL when a filter is faster — for quick row narrowing, filters avoid writing a query at all.