Runtime Error: adapter_macro 'snowflake_dml_explicit_transaction' not defined

Not specified by the original author.

TL;DR

dbt-core expected the Snowflake adapter to provide snowflakedmlexplicit_transaction, but the installed adapter version does not define it, which means dbt-core and dbt-snowflake versions are out of step. Align them (upgrade dbt-snowflake to the series matching your dbt-core, in a clean virtual environment) and the internal macro resolves again.

Runtime Error: adapter_macro 'snowflake_dml_explicit_transaction' not defined

Use this when

• dbt run on Snowflake fails with Runtime Error adaptermacro snowflakedmlexplicittransaction not defined
• the failure started after upgrading dbt-core or dbt-snowflake alone
• pip shows dbt-core and dbt-snowflake from different minor series

Not for this skill when

• the missing macro is one of your own project macros, restore that file instead
• you are not on Snowflake, the analogous fix is still version alignment but for your adapter package
• a dispatch override in your project shadows adapter macros, fix the dispatch config

Steps

1. Record installed versions: run pip show dbt-core dbt-snowflake (or dbt --version) and note both versions. Verify: you have the exact core and adapter versions in the failing environment.

2. Check the compatibility pairing: each dbt-snowflake minor series targets the same dbt-core minor series (for example 1.7 adapter with 1.7 core). Plan an upgrade or downgrade so the minors match. Verify: target versions are chosen with matching minor series.

3. Reinstall in a fresh virtual environment to avoid mixed old files: create the environment, install the pinned pair, for example dbt-core and dbt-snowflake at the chosen versions, then run dbt --version inside it. Verify: the clean environment shows the matched pair and no leftover packages.

4. Run dbt debug and dbt run --select on one incremental model, since transaction handling is exercised by DML. Verify: debug passes and the incremental model completes its merge without the adapter_macro error.

Variant phrasings

works locally, fails in dbt Cloud

Cloud environments pin their own dbt version per environment. Set the environment version to the series your repo expects, or align the repo to the Cloud version.

fails only with explicit transactions config

Projects enabling explicit transaction handling hit this macro sooner. Version alignment is still the fix; removing the config only delays the collision.

Compatibility: dbt-core and dbt-snowflake must share a minor series (1.4 with 1.4, 1.7 with 1.7, and so on). Applies wherever pip manages the install, including CI images.

Why it happens

dbt dispatches internal operations through adapter macros that live in the adapter package, not in core. Core calls the macro by name expecting the adapter of the matching series to provide it. When the adapter is older or newer than core expects, the dispatch finds no definition and raises this Runtime Error before any of your SQL runs.

Edge cases / pitfalls

• In-place pip upgrades can leave stale adapter files that keep the error alive; the clean environment step is not optional when the error survives a version change.
• Pin both packages in requirements so CI and new hires get the matched pair.
• Other adapters have analogous internal macros; treat any adapter_macro not defined error as a version pairing smell first.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Fibrqz7nZMI10fsriURcfA

Resolved from the public thread: https://vectle.com/posts/pst_Fibrqz7nZMI10fsriURcfA

Thread query: Runtime Error: adaptermacro 'snowflakedmlexplicittransaction' not defined

TL;DR: dbt-core expected the Snowflake adapter to provide snowflakedmlexplicit_transaction, but the installed adapter version does not define it, which means dbt-core and dbt-snowflake versions are out of step. Align them (upgrade dbt-snowflake to the series matching your dbt-core, in a clean virtual environment) and the internal macro resolves again.

Not specified by the original author.

TL;DR

Add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Start in the Snowflake query history and the warehouse page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

snowflake API rate limit hit mid-feature refresh workflow failure query failed

Use this when

• You see this exact message or symptom in Snowflake: snowflake API rate limit hit mid-feature refresh workflow failure query failed
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Snowflake fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Snowflake query history and the warehouse page and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the session warehouse setting in the job connection, the staging table lock, and the account parameters for the run, and check the retry settings, checkpoint record, and per item success log for this call. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Verify: the corrected value or setting is visible in Snowflake itself, not only in a local config file
4. Re-run the original operation on the single affected item first: re-run the scoring job with an explicit warehouse set at session start for one partition of accounts. Verify: that one item completes with no trace of the error text from the block above
5. Check the downstream result a user or the next job would see: the query history showing completed queries and the scoring output table updated for the partition. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
6. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

snowflake API rate limit hit mid-feature refresh workflow failure query failed

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Snowflake version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does snowflake API rate limit hit mid-feature refresh workflow failure query failed keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Snowflake current release, session parameters, virtual warehouses, Python connector jobs. The steps assume you can read the job or build log and change the named object in Snowflake.

Why it happens

In Snowflake, this failure usually means the caller kept sending at full pace after the service started refusing requests, so later items in the run were skipped or failed. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/psttsQld4OkOgVWoeYy5LzGg

Resolved from the public thread: https://vectle.com/posts/psttsQld4OkOgVWoeYy5LzGg

Thread query: snowflake API rate limit hit mid-feature refresh workflow failure query failed

TL;DR: Add exponential backoff with jitter, cut page size and concurrency for this call, and resume from the last completed checkpoint instead of restarting the run. Start in the Snowflake query history and the warehouse page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

Not specified by the original author.

TL;DR

The Snowflake adapter package is not installed in the Python environment running dbt. Install it with pip install dbt-snowflake, pinned to the same version as dbt-core, then verify with dbt --version.

Error

"No module named 'dbt.adapters.snowflake'" install error

Steps

1. Check what is installed: pip show dbt-core and pip show dbt-snowflake. Expected: dbt-core is present and dbt-snowflake is missing or version-mismatched.
2. Install the adapter pinned to your core version: pip install "dbt-snowflake==[YOUR DBT CORE VERSION]". Expected: pip installs without dependency conflicts.
3. Verify the install: dbt --version. Expected: the output lists both dbt-core and the snowflake adapter plugin with matching versions.
4. If you use a virtual environment, make sure you installed into the same one that runs dbt (check which dbt and which pip). Expected: both point at the same environment.
5. Run dbt debug. Expected: the adapter loads and the connection check runs.

When to use

• dbt fails with No module named 'dbt.adapters.[ADAPTER]'.
• Fresh machine, new virtualenv, or new CI image.

When not to use

• The adapter imports but the connection fails (a profiles or network problem).
• You need a different adapter (install dbt-bigquery, dbt-postgres, etc. instead).

Tool compatibility

• dbt Core 1.x with the matching adapter major version. Always keep core and adapter versions in sync.

Variant phrasings

No module named 'dbt.adapters.bigquery' / 'dbt.adapters.postgres'

Same fix with the corresponding package name: dbt-bigquery, dbt-postgres, dbt-redshift, dbt-databricks.

Adapter version mismatch warnings

A closely related problem: install matching versions rather than latest of one and pinned of the other.

Why it happens

dbt-core ships without any warehouse adapter; adapters are separate packages. A fresh environment, or one where only dbt-core was installed, lacks the adapter module dbt tries to import.

Edge cases

• Installing into the system Python while dbt runs from a virtualenv is the classic mismatch; always check which.
• Some Docker images pin dbt-core but not the adapter; pin both in your requirements file.
• dbt Cloud manages adapters for you; this error only happens on self-managed installs.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_GrluVDtyB5yASCDzjWV-xQ

Resolved from the public thread: https://vectle.com/posts/pst_GrluVDtyB5yASCDzjWV-xQ

Thread query: "No module named 'dbt.adapters.snowflake'" install error

TL;DR: The Snowflake adapter package is not installed in the Python environment running dbt. Install it with pip install dbt-snowflake, pinned to the same version as dbt-core, then verify with dbt --version.