Faker unique proxy failed error UniquenessException: Got duplicated values aft

Not specified by the original author.

TL;DR

Fix this in Faker by acting on the exact message first: widen the value pool or lower the requested count, call clear on the unique proxy before a fresh batch, and drop the unique proxy for high volume fields where the pool is smaller than the number of rows requested. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

UniquenessException: Got duplicated values after retries

Use this when

• The operation in Faker fails with this exact message or symptom: UniquenessException: Got duplicated values after retries.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Faker, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: widen the value pool or lower the requested count, call clear on the unique proxy before a fresh batch, and drop the unique proxy for high volume fields where the pool is smaller than the number of rows requested. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Faker on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in Faker so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

The unique proxy can only return as many distinct values as the underlying provider pool holds, and it raises UniquenessException after exhausting retries when more unique values are requested than exist.

Variant phrasings

UniquenessException: Got duplicated values after retries

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - Faker unique proxy failed error "UniquenessException: Got duplicated values" after retries

The how-to phrasing for the same failure in Faker. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Faker troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Faker at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_MCFDn39eVSxFrjt9WU9cgQ

Resolved from the public thread: https://vectle.com/posts/pst_MCFDn39eVSxFrjt9WU9cgQ

Thread query: Faker unique proxy failed error "UniquenessException: Got duplicated values" after retries

TL;DR: Fix this in Faker by acting on the exact message first: widen the value pool or lower the requested count, call clear on the unique proxy before a fresh batch, and drop the unique proxy for high volume fields where the pool is smaller than the number of rows requested. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in Faker by acting on the exact message first: compare the number of rows requested with the size of the domain, for a column such as a two letter status or a three value tier there are only that many unique values possible, so drop the unique requirement for that column, widen the domain with a suffix, or reduce the row count, and keep unique only on columns whose pool is larger than the table. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

unique values exhausted for small domain - synthetic column fails

Use this when

• The operation in Faker fails with this exact message or symptom: unique values exhausted for small domain - synthetic column fails.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Faker, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: compare the number of rows requested with the size of the domain, for a column such as a two letter status or a three value tier there are only that many unique values possible, so drop the unique requirement for that column, widen the domain with a suffix, or reduce the row count, and keep unique only on columns whose pool is larger than the table. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Faker on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in Faker so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

The unique proxy cannot invent values outside the provider pool, and once every value in a small domain has been used it has nothing left to return no matter how many retries it makes.

Variant phrasings

unique values exhausted for small domain - synthetic column fails

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - how to fix Faker error unique values exhausted for small domain synthetic column fails

The how-to phrasing for the same failure in Faker. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Faker troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Faker at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_5fU3wxik3R1QlOqGvek-uw

Resolved from the public thread: https://vectle.com/posts/pst_5fU3wxik3R1QlOqGvek-uw

Thread query: how to fix Faker error unique values exhausted for small domain synthetic column fails

TL;DR: Fix this in Faker by acting on the exact message first: compare the number of rows requested with the size of the domain, for a column such as a two letter status or a three value tier there are only that many unique values possible, so drop the unique requirement for that column, widen the domain with a suffix, or reduce the row count, and keep unique only on columns whose pool is larger than the table. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the count

Not specified by the original author.

TL;DR

Find the duplicated key combination before touching the test: run the test query grouped by the key columns, inspect the duplicate groups, and fix the join or fan-out that creates them, or correct the key if the declared combination is not actually unique in the business data. Deleting the test only hides a real grain problem.

dbt test failed: unique_combination_of_columns failed with duplicate keys error

Use this when

• The check or run fails with this symptom: the uniquecombinationof_columns test fails and lists duplicate keys.
• You are working on a dbt model whose declared unique key combination is failing 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

• A model that is intentionally at a finer grain than the test assumes, where the test definition needs correction, not the data.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Open the compiled test SQL in target/compiled and run it manually to list the exact duplicated combinations with their counts. Verify: you have the concrete key values and counts, not just a fail count
2. For one duplicated combination, trace both rows back through the model SQL and look for a join that fans out, such as joining to a history table without picking the current record. Verify: the fan-out join or missing filter is identified
3. Check whether an upstream source started sending late or corrected records that the incremental logic appends instead of merging. Verify: the duplicate source is confirmed in the raw table
4. Fix the model: deduplicate with a qualify or row_number window on the load timestamp, add the missing current-record filter, or merge on the key in an incremental model. Verify: the model rebuild produces one row per key combination
5. Rerun dbt test for that model plus its downstream models. Verify: the uniquecombinationof_columns test passes and downstream tests stay green

Variant phrasings

dbt uniquecombinationof_columns duplicate keys

The short phrasing focused on listing the duplicated combinations.

dbt test unique combination failed how to debug

The debugging phrasing, centred on running the compiled test SQL by hand.

duplicate keys in dbt model incremental run

The incremental-model variant, where corrected records are appended instead of merged.

Compatibility: dbt Core 1.5 and later and dbt Cloud with uniquecombinationof_columns generic tests

Why it happens

The test groups the model by the declared columns and fails on any group with more than one row. Duplicates usually come from join fan-out, slowly changing dimensions joined without a current flag, or incremental appends of corrected records, so the same business key legitimately appears twice in the output.

Edge cases / pitfalls

• Do not deduplicate blindly with distinct: it can hide a fan-out that also inflates measures.
• If the key includes a nullable column, null groups can look like duplicates. Check null handling in the key columns.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_c6BbGFkAihORpXLa30NSSA

Resolved from the public thread: https://vectle.com/posts/pst_c6BbGFkAihORpXLa30NSSA

Thread query: dbt test failed: uniquecombinationof_columns failed with duplicate keys error

TL;DR: Find the duplicated key combination before touching the test: run the test query grouped by the key columns, inspect the duplicate groups, and fix the join or fan-out that creates them, or correct the key if the declared combination is not actually unique in the business data. Deleting the test only hides a real grain problem.