dbt-dry-run 0.1.6

Dry run dbt projects

dbt-dry-run

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.

Quickstart

Installation

The dry runner can be installed via pip:

pip install dbt-dry-run

Running

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 as you normally would.

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 with:

dbt-dry-run <PROFILE>

By default it will search for profiles.yml in ~/.dbt/ and use the default target specified. It will also look for the manifest.yml in the current working directory. Just like in the dbt CLI you can override these defaults:

python -m dbt_dry_run default  --profiles-dir /my_org_dbt/profiles/ --target local --manifest-path target/manifest.json

Reporting Failures

The dry runner will exit 0 if there are no failures. If there are failures it will exit 1

Capabilities and Limitations

Things this can catch

The dry run can catch anything the BigQuery planner can identify before the query has run. Which includes:

1. Typos in SQL keywords: selec instead of select
2. Typos in columns names: orders.produts instead of orders.products
3. Problems with incompatible data types: Trying to execute "4" + 4
4. Incompatible schema changes to models: Removing a column from a view that is referenced by a downstream model explicitly
5. Incompatible schema changes to sources: Third party modifies schema of source tables without your knowledge
6. 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

Things this can't catch

There are certain cases where a syntactically valid query can fail due to the data in the tables:

1. Queries that run but do not return intended/correct result. This is checked using tests
2. NULL values in ARRAY_AGG (See IGNORE_NULLS bullet point)
3. Bad query performance that makes it too complex/expensive to run

Things still to do...

Implementing the dry runner required re-implementing some areas of dbt. Mainly how the adapter sets up connections and credentials with the BigQuery client, we have only implemented the methods of how we connect to our warehouse so if you don't use OAUTH or service account JSON files then this won't be able to read profiles.yml correctly.

The implementation of seeds is incomplete as well 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.

Snapshots are also not yet supported.

License

Copyright 2022 Auto Trader Limited

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.