Find Fuzzy Duplicates in YAML Files Online

Find near-duplicate rows in YAML files using Levenshtein edit distance or Jaro-Winkler similarity — all in your browser. Set your own threshold and download the matched pairs as CSV — no upload required.

How to find Fuzzy Duplicates in YAML files

Drop your file onto the upload area. Files with more than 5,000 rows show a notice, because only the first 5,000 rows are compared.
Choose the column to check for near-duplicates, usually a name, company or address column. The first column is selected by default.
Pick a similarity method. Levenshtein counts character edits, with a maximum distance of 1 to 4 (default 2). Jaro-Winkler gives a 0 to 1 score, with a minimum similarity slider from 0.70 to 0.99 (default 0.85).
Click Find Fuzzy Duplicates. The tool lists matching pairs of rows with their values and score.
Review the pairs and click Download Pairs CSV. The file is a list of pairs to check, not a cleaned copy of your data.

Your file is processed locally in your browser and is never uploaded. The free limit is 50 MB per file; larger files work if your device has the memory for them.

Worked example

An accounts payable team merged supplier lists from two systems and suspects some vendors were entered twice with small spelling differences.

Input (YAML)

- vendor_id: V-001
  vendor_name: Northgate Plumbing
  city: Leeds
- vendor_id: V-002
  vendor_name: Northgate Plumbing Ltd
  city: Leeds
- vendor_id: V-003
  vendor_name: Brightwater Electrical
  city: York
- vendor_id: V-004
  vendor_name: Brightwater Electric
  city: York
- vendor_id: V-005
  vendor_name: Harlow & Sons
  city: Hull
- vendor_id: V-006
  vendor_name: Northgate Plumbng
  city: Leeds

Settings

Column: vendor_name
Method: Jaro-Winkler
Min similarity: 0.85

Result

row_a	row_b	val_a	val_b	similarity
1	6	Northgate Plumbing	Northgate Plumbng	0.9889
3	4	Brightwater Electrical	Brightwater Electric	0.9818
1	2	Northgate Plumbing	Northgate Plumbing Ltd	0.9636
2	6	Northgate Plumbing Ltd	Northgate Plumbng	0.9545

row_a and row_b are 1-based row positions in the file, and the most similar pairs come first. The three Northgate rows produce three pairs, because every pair is scored separately. Harlow & Sons matches nothing. With Levenshtein at distance 2 instead, only the typo (distance 1) and Electrical/Electric (distance 2) would be listed. Adding " Ltd" is 4 edits.

Frequently Asked Questions

Does the tool remove the near-duplicates for me?

No. It lists pairs of similar rows with their row numbers and score. You decide which rows to keep, then remove the others yourself or with Filter.

Are exact duplicates included in the pairs?

No. Levenshtein pairs must be at least 1 edit apart, and Jaro-Winkler pairs must not be identical. Use Find Duplicates or Remove Duplicates for exact matches.

Which method should I choose?

Levenshtein suits short values and typos, such as codes or single words. Jaro-Winkler suits names and company names, because it rewards a shared start. For example "Robert" and "Robrt" score about 0.96.

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