dbt is a tool that helps manage data transformations using templated SQL queries. These SQL queries are executed against a target data warehouse. It doesn't check the validity of SQL queries before it executes your project. This dry runner uses BigQuery's dry run capability to allow you to check that SQL queries are valid before trying to execute them.
See the blog post for more information on how the dry runner works.
The dry runner can be installed via pip:
pip install dbt-dry-run
The dry runner has a single command called dbt-dry-run in order for it to run you must first compile a dbt manifest
using dbt compile.
How much of the project should I compile?
It is best practice to compile the entire dbt project when supplying a manifest for dry run. The dry run loops through your project in the DAG order (staging -> intermediate -> mart) based on `ref` and predicts the schema of each model as it progresses. If you dry run `marts` but have not compiled `staging` then it cannot determine if `marts` will run as it does not know the predicted schema of the upstream models and you will see `NotCompiledException` in the dry run output.Then on the same machine (So that the dry runner has access to your dbt project source and the
manifest.yml) you can run the dry-runner in the same directory as our dbt_project.yml:
dbt-dry-run
Like dbt it will search for profiles.yml in ~/.dbt/ and use the default target specified. Just like in the dbt CLI
you can override these defaults:
dbt-dry-run default --project-dir /my_org_dbt/ --profiles-dir /my_org_dbt/profiles/ --target local
The full CLI help is shown below, anything prefixed with [dbt] can be used in the same way as a normal dbt parameter:
❯ dbt-dry-run --help
Usage: dbt-dry-run [OPTIONS]
Options:
--profiles-dir TEXT [dbt] Where to search for `profiles.yml`
[default: /Users/connor.charles/.dbt]
--project-dir TEXT [dbt] Where to search for `dbt_project.yml`
[default: /Users/connor.charles/Code/dbt-
dry-run]
--vars TEXT [dbt] CLI Variables to pass to dbt
[default: {}]
--target TEXT [dbt] Target profile
--target-path TEXT [dbt] Target path
--verbose / --no-verbose Output verbose error messages [default: no-
verbose]
--report-path TEXT Json path to dump report to
--skip-not-compiled Whether or not the dry run should ignore
models that are not compiled. This has
several caveats that make this not a
recommended option. The dbt manifest should
generally be compiled with `--select *` to
ensure good coverage
--full-refresh [dbt] Full refresh
--extra-check-columns-metadata-key TEXT
An extra metadata key that can be used in
place of `dry_run.check_columns` for
verifying column metadata has been specified
correctly. `dry_run.check_columns` will
always take precedence. The metadata key
should be of boolean type or it will be cast
to a boolean to be 'True/Falsey`
--version
--install-completion [bash|zsh|fish|powershell|pwsh]
Install completion for the specified shell.
--show-completion [bash|zsh|fish|powershell|pwsh]
Show completion for the specified shell, to
copy it or customize the installation.
--help Show this message and exit.
If the result is successful it will output the number of models that were tested like so:
Dry running 3 models
DRY RUN SUCCESS!
The process will also return exit code 0
If there are failures it will print a summary table of the nodes that failed:
Dry running 3 models
Node model.test_models_with_invalid_sql.second_layer failed with exception:
400 POST https://bigquery.googleapis.com/...: Column d in USING clause not found on left side of join at [6:88]
(job ID: 5e336f32-273d-480a-b8bb-cdf4fca66a98)
Total 1 failures:
1 : model.test_models_with_invalid_sql.second_layer : BadRequest : ERROR
DRY RUN FAILURE!`
The process will also return exit code 1
The dry runner can also be configured to inspect your metadata YAML and assert that the predicted schema of your dbt
projects data warehouse matches what is documented in the metadata. To enable this for your models specify the key
dry_run.check_columns: true. The dry runner will then fail if the model's documentation does not match. You can also
specify a custom extra key to enable check_columns by setting the CLI argument --extra-check-columns-metadata-key.
For example the full metadata for this model:
models:
- name: badly_documented_model
description: This model is missing some columns in its docs
meta:
dry_run.check_columns: true
columns:
- name: a
description: This is in the model
- name: b
description: This is in the model
# - name: c
# description: Forgot to document c
- name: d
description: This shouldn't be hereThis model is badly documented as the predicted schema is 3 columns a,b,c the dry runner will therefore output the
following error and fail your CI/CD checks:
Dry running X models
Node model.test_column_linting.badly_documented_model failed linting with rule violations:
UNDOCUMENTED_COLUMNS : Column not documented in metadata: 'c'
EXTRA_DOCUMENTED_COLUMNS : Extra column in metadata: 'd'
Total 1 failures:
1 : model.test_column_linting.badly_documented_model : LINTING : ERROR
DRY RUN FAILURE!
Currently, these rules can cause linting failures:
- UNDOCUMENTED_COLUMNS: The predicted schema of the model will have extra columns that have not been documented in the YAML
- EXTRA_DOCUMENTED_COLUMNS: The predicted schema of the model does not have this column that was specified in the metadata
The dbt package dbt-external-tables gives dbt support for staging and managing external tables. These sources do not produce any compiled sql in the manifest, so it is not possible for the dry runner to predict their schema. Therefore, you must specify the resulting schema manually in the metadata of the source.
However, if the columns schema is already defined under the name in the yaml config, you do not need to specify dry_run_columns under external. The dry runner will use the columns schema if dry_run_columns is not specified. This avoids duplicated schema definitions.
For example if you were import data from a gcs bucket:
version: 2
sources:
- name: source_dataset
tables:
- name: event
description: "Some events bucket. If external is populated then the dry runner will assume it is using `dbt-external-tables`"
external:
location: 'gs://bucket/path/*'
format: csv
dry_run_columns:
- name: string_field
data_type: STRING
description: "Specify each column in the yaml for external sources"
- name: record_array_field[]
data_type: RECORD[]
description: "For struct/record fields specify the `data_type` as `RECORD`"
- name: record_array_field.foo
data_type: NUMERIC
description: "For record attributes use the dot notation"
- name: integer_array
data_type: NUMERIC[]
description: "For repeated fields suffix data_type with []"The dry runner cannot predict the schema, therefore, it is up to you to accurately describe the schema in the YAML otherwise you may get false positive/negative results from the dry run.
If you specify ---report-path a JSON file will be outputted regardless of dry run success/failure with detailed
information of each node's predicted schema or error message if it has failed:
{
"success": false,
"node_count": 3,
"failure_count": 1,
"failed_node_ids": [
"model.test_models_with_invalid_sql.second_layer"
],
"nodes": [
{
"unique_id": "seed.test_models_with_invalid_sql.my_seed",
"success": true,
"status": "SUCCESS",
"error_message": null,
"table": {
"fields": [
...
]
}
},
{
"unique_id": "model.test_models_with_invalid_sql.first_layer",
"success": true,
"status": "SUCCESS",
"error_message": null,
"table": {
"fields": [
...
]
}
},
{
"unique_id": "model.test_models_with_invalid_sql.second_layer",
"success": false,
"status": "FAILURE",
"error_message": "BadRequest",
"table": null
}
]
}The dry run can catch anything the BigQuery planner can identify before the query has run. Which includes:
- Typos in SQL keywords:
selecinstead ofselect - Typos in columns names:
orders.produtsinstead oforders.products - Problems with incompatible data types: Trying to execute "4" + 4
- Incompatible schema changes to models: Removing a column from a view that is referenced by a downstream model explicitly
- Incompatible schema changes to sources: Third party modifies schema of source tables without your knowledge
- Permission errors: The dry runner should run under the same service account your production job runs under. This allows you to catch problems with table/project permissions as dry run queries need table read permissions just like the real query
- Incorrect configuration of snapshots: For example a typo in the
unique_keyconfig. Orcheck_colswhich do not exist in the snapshot
There are certain cases where a syntactically valid query can fail due to the data in the tables:
- Queries that run but do not return intended/correct result. This is checked using tests
NULLvalues inARRAY_AGG(See IGNORE_NULLS bullet point)- Bad query performance that makes it too complex/expensive to run
The implementation of seeds is incomplete as we don't use them very much in our own dbt projects. The dry runner
will just use the datatypes that agate infers from the CSV files. It will ignore any type overrides you add in the YAML.
If you see anything else that you think it should catch don't hesitate to raise an issue!
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