how to fix duplicate primary key failure error on incremental model run

TL;DR

Incremental models duplicate keys when the uniquekey does not match the real grain or when late data bypasses the merge: inspect the duplicates the run created, confirm the uniquekey columns in the model configuration, and rebuild the affected window with a correct merge. A full refresh proves the logic; fixing only the symptom rows leaves the next run to duplicate again.

how to fix duplicate primary key failure error on incremental model run

Use this when

• The check or run fails with this symptom: the incremental run creates duplicate rows for the same primary key.
• You are working on an incremental dbt model producing duplicate primary keys and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• Duplicates in a full-refresh table with no incremental logic, which points at joins or sources instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. List the duplicated keys created by the latest run and the timestamps of the duplicate rows. Verify: the duplicated keys and their run timing are known
2. Read the model's unique_key and incremental strategy in its configuration and compare them with the table's real grain. Verify: the configured key and strategy are explicit
3. Check the incremental filter for a lookback gap that lets corrected or late rows append instead of merge on some runs. Verify: the filter behaviour for late rows is understood
4. Correct the unique_key or strategy, then rebuild the affected date window with the incremental logic, not just a delete of bad rows. Verify: the rebuilt window holds one row per key
5. Run the model twice in a row and confirm the second run changes no row counts, proving idempotency. Verify: two consecutive runs produce stable counts

Variant phrasings

duplicate primary key incremental model dbt

The verbatim incremental phrasing.

dbt incremental unique_key duplicates late data

The late-data phrasing for lookback and merge gaps.

incremental model not idempotent duplicates on rerun

The idempotency phrasing for repeated runs.

Compatibility: dbt incremental models with unique_key on Snowflake, BigQuery, and Postgres

Why it happens

Incremental merges rely on the declared unique_key and the run's filter window. A key that omits a grain column, a strategy that appends under some conditions, or late-arriving corrections outside the lookback all insert second copies that only a uniqueness check will catch.

Edge cases / pitfalls

• Always pair incremental models with a unique test on the declared key. The test is the alarm for this class.
• Keep the lookback window larger than the source's real correction horizon, measured, not guessed.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ux7Iesqe4Xzy2CpkBkT8ZQ