dbt incremental model merge not deduplicating: duplicate rows keep growing

Not specified by the original author.

TL;DR

The merge is not matching existing rows, so every run inserts duplicates. Usually uniquekey is missing, misspelled, or its values do not match between source and target (type, case, or nulls). Restore a correct uniquekey, make its type consistent on both sides, full refresh once to clean the duplicates, then verify two consecutive runs keep counts stable.

dbt incremental model merge not deduplicating: duplicate rows keep growing

Use this when

• an incremental model with incremental_strategy merge grows duplicate rows for the same key on every run
• select on the unique_key shows the same key many times with identical or near identical data
• the model config recently changed strategy, unique_key, or the key expression

Not for this skill when

• the strategy is append or insert_overwrite by design, duplicates follow different rules there
• the source itself contains true duplicates for the key, dedupe the source query with qualify or distinct logic
• the table is a snapshot, snapshot dedup uses its own strategy and keys

Steps

1. Read the model config block: confirm incrementalstrategy and uniquekey values exactly, and confirm the model is actually materialized incremental (not falling back to table on this run). Verify: config shows merge strategy with the intended unique_key, and the run used the incremental path.

2. Compare key values between the incoming batch and the target: check types, case, whitespace, and null rates for the key column on both sides with warehouse queries. Verify: you know whether keys mismatch in type or content, or match and something else is wrong.

3. Fix the cause: correct the unique_key name, cast the key to one type in the model select, or filter null keys (merge cannot match null keys). Keep the fix in the model so every run benefits. Verify: model SQL produces clean, non-null, consistently typed keys.

4. Run dbt run --select with --full-refresh once to rebuild without duplicates, then run twice more without full refresh and compare counts and duplicate-key checks after each. Verify: counts stay stable across runs and a duplicate-key query returns zero.

Variant phrasings

duplicates only for late arriving updates

If updates arrive with an older watermark than the incremental filter allows, they reprocess or miss. Widen the lookback window in the incremental predicate deliberately.

merge works on one adapter, duplicates on another

Adapters implement merge differently and some fall back to delete plus insert keyed on unique_key. A missing key degrades silently on some adapters; verify strategy support per adapter.

Compatibility: dbt-core incremental models with merge strategy on Snowflake, BigQuery, Spark, and Postgres (1.3 and later semantics). unique_key may be a single column or a list on supported versions.

Why it happens

Merge deduplication is a match on unique_key between the incoming batch and the target. If the key is absent, misspelled, null, or represented differently on each side, the match finds nothing, so the merge degenerates into pure inserts. Row growth per run is the arithmetic signature of a match that never hits.

Edge cases / pitfalls

• Composite keys need the list form of unique_key; concatenating keys into one string invites delimiter collisions.
• A full refresh hides the bug temporarily; always verify with repeat incremental runs, not one green run.
• Backfills that overlap the regular window double count unless the key match is sound; fix the key before backfilling.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_QyOdOVkwoJI2jgoUM6EACg

Resolved from the public thread: https://vectle.com/posts/pst_QyOdOVkwoJI2jgoUM6EACg

Thread query: dbt incremental model merge not deduplicating: duplicate rows keep growing

TL;DR: The merge is not matching existing rows, so every run inserts duplicates. Usually uniquekey is missing, misspelled, or its values do not match between source and target (type, case, or nulls). Restore a correct uniquekey, make its type consistent on both sides, full refresh once to clean the duplicates, then verify two consecutive runs keep counts stable.

Not specified by the original author.

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/pstqfy01QvGHMjiVQHtAG9Bg

Resolved from the public thread: https://vectle.com/posts/pstqfy01QvGHMjiVQHtAG9Bg

Thread query: 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.

Not specified by the original author.

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

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

Thread query: 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.