Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category: Text Processing
Input types: textarea, select, checkbox
Output type: json
Sample coverage: 4
API ready: Yes

Overview

Fixed-Width Column Parser converts aligned text records into structured JSON using an explicit schema or inferred field boundaries. Choose character or UTF-8 byte positions, optionally treat the first row as headers, trim values, and review parsed rows with anomaly information.

When to use

Parse legacy fixed-width reports into named fields for ETL workflows.
Use a known schema when field start and end positions are documented.
Inspect aligned text with inferred columns and identify extra or inconsistent data.

How it works

1Paste the fixed-width records into the Text Input field.
2Select Schema to use Schema JSON, or select Infer to derive boundaries from aligned whitespace.
3Choose character positions or UTF-8 byte positions for field boundaries.
4Optionally mark the first row as a header and trim field values, then review the structured JSON result and anomaly report.

Use cases

Convert fixed-width operational reports into structured records for data preparation.
Parse text exports with documented field ranges and named schemas.
Validate aligned records by detecting trailing data and other parsing anomalies.

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Who it's for

Best for

Parse content in the browser.
Workflows where Parse Mode matters, such as Schema or Infer.
The “Parse an explicit fixed-width schema” workflow, done in one pass.

Not ideal for

Fully automated batch jobs — this page is built for interactive use; use the API endpoint for scripts.
Content in other formats — this tool expects content.

Edge cases & common failures

Very large files
Large files take longer and can hit browser memory limits — try a smaller sample first.
Skipping the output check
Compare the first rows or pages of the output with the input before using it in production.
Files from unusual exporters
Some files use format variants that do not convert cleanly — if the output looks wrong, re-save the file from the app that created it and try again.

Examples

1. Parse a student score report with an explicit schema

Data analyst

Background

A data analyst receives a padded text report containing student IDs, names, and scores.

Problem

The fields have documented character ranges, but the report must be converted into structured rows while ignoring padding spaces.

How to use

Paste the report, select Schema, mark the first row as a header, keep character positions, and enable Trim field values.

[{"name":"id","start":0,"end":5},{"name":"name","start":5,"end":15},{"name":"score","start":15,"end":20}]

Outcome

The parser resolves the supplied schema, returns trimmed rows for the records, and reports zero anomalies for the correctly aligned input.

2. Infer columns and detect trailing data

ETL developer

Background

An ETL developer receives a small fixed-width text export whose column boundaries are not documented.

Problem

The developer needs to infer the CODE and VALUE fields and identify a record containing data beyond the expected layout.

How to use

FAQ

What input does the parser accept?

It accepts fixed-width text records pasted into the Text Input field.

Can I define the columns manually?

Yes. Select Schema and provide JSON fields with names plus start and end positions.

Can the tool infer column boundaries?

Yes. Select Infer to derive boundaries from aligned whitespace in the text.

Are positions measured in characters or bytes?

You can choose Characters or UTF-8 bytes with the Position Mode setting.

What does the output contain?

The JSON result includes the resolved or inferred schema, parsed rows, a preview, and an anomaly report.

Fixed-Width Column Parser

Run this tool

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Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Overview

When to use

How it works

Use cases

Compare alternatives

Who it's for

Edge cases & common failures

Examples

1. Parse a student score report with an explicit schema

2. Infer columns and detect trailing data

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