# Join on approximate matches Join / fuzzy approximate string match join Page: ## Status Runs as a SQL step. arcform carries it as a `sql:` step today. ## The DuckDB step match names by string similarity It reads the `orders` and `customers` tables the proofs share. Create them first: ```sql CREATE TABLE orders AS SELECT * FROM (VALUES (1, 'Alice Ng', 'north', DATE '2026-01-03', 120.00, 'widget', 'a|b', '{"channel":"web","items":[1,2]}'), (2, 'alice ng', 'north', DATE '2026-01-05', 80.50, 'gadget', 'b', '{"channel":"shop","items":[3]}'), (3, 'Bob Okafor', 'south', DATE '2026-01-05', 300.00, 'widget', 'a|c', '{"channel":"web","items":[4,5,6]}'), (4, 'Carla Díaz', 'east', DATE '2026-01-09', NULL, 'gizmo', NULL, '{"channel":"web","items":[]}'), (5, 'Bob Okafor', 'south', DATE '2026-01-12', 45.25, 'gadget', 'c', '{"channel":"phone","items":[7]}'), (6, 'Dev Patel', 'west', DATE '2026-01-12', 510.00, 'widget', 'a', '{"channel":"web","items":[8,9]}'), (7, 'Carla Diaz', 'east', DATE '2026-01-20', 99.99, 'gizmo', 'b|c', '{"channel":"shop","items":[10]}'), (8, 'Dev Patel', 'west', DATE '2026-02-02', 510.00, 'widget', 'a', '{"channel":"web","items":[8,9]}'), (9, 'Eve Larsen', NULL, DATE '2026-02-03', 15.00, 'gadget', NULL, '{"channel":"web","items":[11]}'), (10, 'Alice Ng', 'north', DATE '2026-02-10', 220.00, 'gizmo', 'a|b|c', '{"channel":"phone","items":[12,13]}') ) t(order_id, customer, region, order_date, amount, product, tags, meta); CREATE TABLE customers AS SELECT * FROM (VALUES ('Alice Ng', 'retail', DATE '2025-06-01', 52.52, 13.40), ('Bob Okafor', 'wholesale', DATE '2025-09-15', 48.85, 2.35), ('Carla Diaz', 'retail', DATE '2025-11-30', 40.42, -3.70), ('Frank Moore', 'retail', DATE '2026-01-01', 51.51, -0.13) ) t(customer, segment, joined, lat, lon); ``` Then run the statement: ```sql CREATE TEMP TABLE result AS SELECT DISTINCT o.customer AS as_written, c.customer AS matched, round(jaro_winkler_similarity(lower(o.customer), lower(c.customer)), 3) AS score FROM orders o JOIN customers c ON jaro_winkler_similarity(lower(strip_accents(o.customer)), lower(c.customer)) >= 0.92 ORDER BY score; SELECT * FROM result ORDER BY ALL; SELECT CASE WHEN ((SELECT count(*) = 6 AND count(*) FILTER (WHERE as_written IN ('Dev Patel', 'Eve Larsen')) = 0 AND max(matched) FILTER (WHERE as_written = 'Bob Okafor') = 'Bob Okafor' AND max(matched) FILTER (WHERE as_written = 'Carla Díaz') = 'Carla Diaz' FROM result)) THEN 'as expected' ELSE error('a misspelt name did not match, or a stranger did') END AS verdict; ``` It needs nothing installed beyond DuckDB itself. ## Variants - Generate candidate pairs: Limit comparisons to pairs of records that share a blocking key before scoring them. - Join on similar keys: Join two tables where key values are similar enough by a string similarity measure and threshold. - [Find near-duplicate rows](https://meridian.online/operations/join-on-approximate-matches/find-near-duplicate-rows): Compare the rows of one table with each other and flag or drop those whose keys nearly match. - Score the similarity of two strings: Add a column with the similarity or distance between two string values. ## What other tools call it Meridian's classification; an independent blind rating agreed on 86.2% of 312 rows (exact class, version 2). - Alteryx Designer: [`Fuzzy Match`](https://help.alteryx.com/current/en/designer/tools/join/fuzzy-match-tool.html) (read 2026-09-27) - Duckle: [`Candidate Pairs (Blocking)`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/frontend/src/workflow-ui/palette-data.ts) (read 2026-09-25) - Duckle: [`Fuzzy Deduplicate`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/website/docs/components.html) (read 2026-09-25) - Duckle: [`Record Linkage`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/frontend/src/workflow-ui/palette-data.ts) (read 2026-09-25) - Duckle: [`Record Match`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/website/docs/components.html) (read 2026-09-25) - Duckle: [`Text Similarity`](https://github.com/slothflowlabs/duckle/blob/f85f8960edfb6efbf4cb89d58a7b32e16df0e867/website/docs/components.html) (read 2026-09-25) - Apache Hop: [`Fuzzy match`](https://hop.apache.org/manual/latest/pipeline/transforms/fuzzymatch.html) (read 2026-09-27) - Pentaho Data Integration: [`Fuzzy match`](https://docs.pentaho.com/pdia-data-integration/9.3-data-integration/pdi-transformation-steps-reference-overview) (read 2026-09-27) - Power Query M: [`Table.FuzzyJoin`](https://learn.microsoft.com/en-us/powerquery-m/table-functions#transformation) (read 2026-09-27) - Power Query M: [`Table.FuzzyNestedJoin`](https://learn.microsoft.com/en-us/powerquery-m/table-functions#transformation) (read 2026-09-27) - Talend: [`tBlockedFuzzyJoin`](https://help.qlik.com/talend/en-US/components/8.0/fuzzy-matching/fuzzy-matching-component) (read 2026-09-27) - Talend: [`tFuzzyJoin`](https://help.qlik.com/talend/en-US/components/8.0/fuzzy-matching/fuzzy-matching-component) (read 2026-09-27) - Talend: [`tFuzzyMatch`](https://help.qlik.com/talend/en-US/components/8.0/fuzzy-matching/fuzzy-matching-component) (read 2026-09-27) - Talend: [`tFuzzyUniqRow`](https://help.qlik.com/talend/en-US/components/8.0/fuzzy-matching/fuzzy-matching-component) (read 2026-09-27) - Talend: [`tMatchPairing`](https://help.qlik.com/talend/en-US/components/8.0/matching-with-machine-learning/matching-with-machine-learning-component) (read 2026-09-27) - Talend: [`tRecordMatching`](https://help.qlik.com/talend/en-US/components/8.0/data-matching/data-matching-component) (read 2026-09-27)