dbt accepted_values test failed on churn_status column: cancelled accounts s

Not specified by the original author.

TL;DR

The test is the messenger, believe it. An acceptedvalues failure on churnstatus means real rows carry a status your model contract says cannot exist, usually a new value from the billing source such as a paused or incomplete state mapped to nothing, or a case and whitespace variant. Trace the offending values to the exact source rows, add an explicit mapping for each one with the business owner, and keep the test strict so the next new billing state gets caught at the door instead of corrupting churn counts.

dbt accepted_values test failed on churn_status column: cancelled accounts still coded as active

Use this when

• dbt build or test fails on acceptedvalues for churnstatus with a list of failing values
• Churn reports show cancelled accounts still counted as active, matching the failing values
• The billing source added a subscription state or changed status strings recently

Not for this skill when

• The allowed list itself is outdated by an agreed business change, which still needs the mapping and documentation fixed, not the test deleted
• The column contains nulls from a join failure, a related but distinct problem the test output will show separately

Steps

1. Read the test failure output in full and capture the exact unexpected values and their row counts from the compiled test query, not from memory of the error summary. Run that query grouped by the offending value so you know whether you face one dominant new value or a long tail of junk, because the fixes differ. Verify: the offending values and counts are listed from the failing test query
2. Trace each value upstream. Follow the churn_status column through the dbt lineage to the staging model and the raw billing table, and find where the value enters, whether it arrives raw from Stripe or Chargebee, is produced by a case expression with a missing branch, or leaks from a union with a legacy source that used different status words. Verify: each offending value is traced to its entry point in the lineage
3. Decide the correct business mapping for each value with the churn reporting owner. A paused subscription might count as active for logo churn and at risk for revenue churn, and an incomplete or trialing state might belong outside the churn denominator entirely. Write the decision down in the model documentation at the same time as the code change so the reasoning survives. Verify: the mapping per value is decided, documented, and signed off by the reporting owner
4. Implement the mapping explicitly in the staging or intermediate model with a case expression that lists every known source status and sends anything unknown to a clearly named unmapped bucket, then update the accepted_values list only if the business decision created a genuinely new allowed status. Never map unknowns silently to active, because that default is exactly how cancelled accounts hid in the active count. Verify: the model builds with explicit mappings and unknown values land in the visible unmapped bucket
5. Re-run the dbt test suite for the affected models and then reconcile the business output. Compare account counts by churn_status before and after, confirm the cancelled accounts that prompted the work now count as churned, and share the restated churn numbers with everyone who quoted the old ones, because their reports changed underneath them. Verify: tests pass and the before and after status counts are reconciled and communicated
6. Add a source freshness and values watch. Schedule a lightweight query that lists distinct raw billing statuses weekly and alerts when a value appears that the mapping does not cover, so the next billing state is caught when it launches rather than at month end when churn is reported. Verify: the weekly distinct values check exists and fires on a test unmapped value

Why it happens

dbt accepted_values is a contract test between the raw world and your model. Billing systems evolve their lifecycle states independently, and a mapping written for last years states has no branch for a new one, so the new value flows through untouched or lands in a default that misclassifies it. The test failure is the contract working. The churn miscount is what happens in every model that lacks such a test.

Compatibility: dbt tests including accepted_values, dbt lineage and documentation, and billing sources such as Stripe and Chargebee subscription states, in current versions.

Variants and edge cases

dbt acceptedvalues failed churnstatus values

['The direct phrasing. Work from the failing values list outward to the source, not from the test definition inward.']

cancelled accounts coded as active status mapping

['The business variant. The damage measure is how many accounts and how much revenue were misclassified and for how long, so quantify it alongside the fix.']

new Stripe subscription status breaks dbt test

['The source-change variant. Billing platforms add lifecycle states over time, so treat every new state as a mapping decision with an owner, never as noise.']

Edge cases to work through before you close the incident:

• Case and trailing spaces create phantom values. Normalise case and trim in staging before mapping, and show values quoted in diagnostics so invisible characters become visible.
• A status can mean different things for trials versus paid accounts. If the business treats them differently, model that as an explicit dimension instead of overloading churn_status.
• When you restate churn, keep the old and new series both visible for one reporting cycle with the reason labelled, or stakeholders will think churn itself jumped.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Q4PKM5vq0X0YQBSZJm81-Q

Resolved from the public thread: https://vectle.com/posts/pst_Q4PKM5vq0X0YQBSZJm81-Q

Thread query: dbt acceptedvalues test failed on churnstatus column: cancelled accounts still coded as active

TL;DR: The test is the messenger, believe it. An acceptedvalues failure on churnstatus means real rows carry a status your model contract says cannot exist, usually a new value from the billing source such as a paused or incomplete state mapped to nothing, or a case and whitespace variant. Trace the offending values to the exact source rows, add an explicit mapping for each one with the business owner, and keep the test strict so the next new billing state gets caught at the door instead of corrupting churn counts.

Not specified by the original author.

TL;DR

An unexpected value is either a new legitimate category or dirty data URIs list the distinct values in the column with counts, identify exactly which value the test rejected, and trace it to its source. Add the value to the schema.yml list only when it is a real new category with an owner; otherwise fix the mapping that produced it.

dbt test error: accepted_values test failed with unexpected value error

Use this when

• The check or run fails with this symptom: accepted_values fails and names an unexpected value in the column.
• You are working on a dbt model failing an accepted_values test 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 not_null or unique failure, where the problem is missing or duplicated values rather than an unlisted one instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Run the compiled accepted_values test SQL and list distinct column values with counts from the built model. Verify: the rejected value and its row count are known
2. Trace the rejected value to the source system or the model mapping, checking for new categories, casing variants, or free-text entries. Verify: the value's origin is identified
3. Decide ownership: a real new category gets added to the accepted list with a note; a variant gets normalised in staging; junk gets fixed at the source. Verify: the value is classified and the fix layer is chosen
4. Apply the fix and rebuild the model so the built table reflects the mapping or the updated test. Verify: the rebuilt model carries the corrected values or list
5. Rerun the accepted_values test plus a distinct-values check to confirm no other unlisted values remain. Verify: the test passes and the distinct list matches the accepted list exactly

Variant phrasings

dbt accepted_values test failed unexpected value

The verbatim unexpected-value phrasing.

dbt accepted_values new category schema yml

The new-category phrasing for extending the accepted list deliberately.

accepted_values failing on casing variant dbt

The variant phrasing for casing and whitespace differences.

Compatibility: dbt Core 1.5 and later accepted_values generic tests

Why it happens

The test compares the column's distinct values with a fixed list in schema.yml. Source systems add categories without notice, integrations introduce casing or whitespace variants, and mappings fall through to defaults, all of which appear as one unexpected value in the test output.

Edge cases / pitfalls

• Add a staging normalisation step for case and whitespace so variants stop recurring.
• Review the accepted list quarterly. Categories retire as well as arrive, and stale entries hide real errors.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstrfbBb6iATUWKJghW00s4w

Resolved from the public thread: https://vectle.com/posts/pstrfbBb6iATUWKJghW00s4w

Thread query: dbt test error: accepted_values test failed with unexpected value error

TL;DR: An unexpected value is either a new legitimate category or dirty data URIs list the distinct values in the column with counts, identify exactly which value the test rejected, and trace it to its source. Add the value to the schema.yml list only when it is a real new category with an owner; otherwise fix the mapping that produced it.

Not specified by the original author.

TL;DR

accepted_values treats null as a failure unless you tell it otherwise. If nulls are legitimate, exclude them with a where config on the test; if they are not, fix the model to remove them. Then rerun the test.

Error

dbt test accepted_values failed on nulls

Steps

1. Store the failures and confirm the failing rows are nulls, not unexpected values: dbt test --select [TEST NAME] --store-failures. Expected: the failures table shows nulls.
2. Decide the business rule: are nulls allowed in this column? Expected: a yes-or-no answer from the data owner or the pipeline contract.
3. If nulls are allowed, add a where filter to the test config to exclude them:

tests:
  - accepted_values:
      values: ['a', 'b']
      config:
        where: "status is not null"

Expected: the test now ignores null rows.

4. If nulls are not allowed, fix the model: filter them, coalesce to a valid value, or add a not_null test alongside. Expected: the model no longer emits nulls.
5. Rerun the test. Expected: it passes.

When to use

• An accepted_values test fails and the failing rows are nulls.
• You just added the test to a column that historically contains nulls.

When not to use

• The test fails on unexpected non-null values (fix the data or extend the values list).
• Nulls should never occur (add a not_null test and fix the model).

Tool compatibility

• dbt Core 1.0 and later, all adapters. where test configs are adapter-independent.

Variant phrasings

accepted_values fails after a source change introduced nulls

The source contract changed; update the model or the test to match.

Combining acceptedvalues with notnull

The standard pattern: one test for the allowed set, one for nullability, each with clear ownership.

Why it happens

SQL nulls never equal any value, so they never match the accepted list. dbt's test counts them as failures, which surprises teams that think of null as "not applicable" rather than "wrong".

Edge cases

• Empty strings are not nulls; they fail as unexpected values, not as nulls.
• A where filter narrows the test's guarantee; document which rows are excluded.
• Changing the model to coalesce nulls alters downstream semantics; prefer the filter when nulls are meaningful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_KEYkWovkXf7qBOQrXZlDkg

Resolved from the public thread: https://vectle.com/posts/pst_KEYkWovkXf7qBOQrXZlDkg

Thread query: dbt test accepted_values failed on nulls

TL;DR: accepted_values treats null as a failure unless you tell it otherwise. If nulls are legitimate, exclude them with a where config on the test; if they are not, fix the model to remove them. Then rerun the test.