dbt_project_evaluator

This package highlights areas of a dbt project that are misaligned with dbt Labs' best practices. Specifically, this package tests for:

1. Modeling - your dbt DAG for modeling best practices
2. Testing - your models for testing best practices
3. Documentation - your models for documentation best practices
4. Structure - your dbt project for file structure and naming best practices
5. Performance - your model materializations for performance best practices

In addition to tests, this package creates the model int_all_dag_relationships which holds information about your DAG in a tabular format and can be queried using SQL in your Warehouse.

Currently, the following adapters are supported:

• BigQuery
• Databricks/Spark
• PostgreSQL
• Redshift
• Snowflake
• DuckDB

Using This Package

Cloning via dbt Package Hub

Check dbt Hub for the latest installation instructions, or read the docs for more information on installing packages.

Additional setup for Databricks/Spark/DuckDB/Redshift

In your dbt_project.yml, add the following config:

# dbt_project.yml

dispatch:
  - macro_namespace: dbt
    search_order: ['dbt_project_evaluator', 'dbt']

This is required because the project currently overrides a small number of dbt core macros in order to ensure the project can run across the listed adapters. The overridden macros are in the cross_db_shim directory.

How It Works

This package will:

1. Parse your graph object and write it into your warehouse as a series of models (see models/marts/core)
2. Create another series of models that each represent one type of misalignment in your project (below you can find a full list of each misalignment and its accompanying model)
3. Test those models to alert you to the presence of the misalignment

Once you've installed the package, all you have to do is run a dbt build --select package:dbt_project_evaluator!

Each test warning indicates the presence of a type of misalignment. To troubleshoot a misalignment:

1. Locate the related documentation below
2. Query the associated model to find the specific instances of the issue within your project or set up an on-run-end hook to display the rules violations in the dbt logs (see displaying violations in the logs)
3. Either fix the issue(s) or customize the package to exclude them

---

Package Documentation

Rules

• Modeling
  • Direct Join to Source
  • Downstream Models Dependent on Source
  • Hard Coded References
  • Model Fanout
  • Multiple Sources Joined
  • Rejoining of Upstream Concepts
  • Root Models
  • Source Fanout
  • Staging Models Dependent on Downstream Models
  • Staging Models Dependent on Other Staging Models
  • Unused Sources
• Testing
  • Missing Primary Key Tests
  • Test Coverage
• Documentation
  • Documentation Coverage
  • Undocumented Models
• Structure
  • Model Naming Conventions
  • Model Directories
  • Source Directories
  • Test Directories
  • Resource YML Naming Conventions
  • Multiple Resource Types in Resource YML
• Performance
  • Chained View Dependencies
  • Exposure Parents Materializations

Customization

• Disabling Models
• Overriding Variables
• Configuring exceptions to the rules

Running this package as a CI check

Querying the DAG with SQL

Limitations

• BigQuery and Databricks

Contributing

---

Rules

Modeling

Direct Join to Source

fct_direct_join_to_source (source) shows each parent/child relationship where a model has a reference to both a model and a source.

Example

int_model_4 is pulling in both a model and a source.

Reason to Flag

We highly recommend having a one-to-one relationship between sources and their corresponding staging model, and not having any other model reading from the source. Those staging models are then the ones read from by the other downstream models.

This allows renaming your columns and doing minor transformation on your source data only once and being consistent across all the models that will consume the source data.

How to Remediate

In our example, we would want to:

1. create a staging model for our source data if it doesn't exist already
2. and join this staging model to other ones to create our downstream transformation instead of using the source

After refactoring your downstream model to select from the staging layer, your DAG should look like this:

Downstream Models Dependent on Source

fct_marts_or_intermediate_dependent_on_source (source) shows each downstream model (marts or intermediate) that depends directly on a source node.

Example

fct_model_9, a marts model, builds from source_1.table_5 a source.

Reason to Flag

We very strongly believe that a staging model is the atomic unit of data modeling. Each staging model bears a one-to-one relationship with the source data table it represents. It has the same granularity, but the columns have been renamed, recast, or usefully reconsidered into a consistent format. With that in mind, if a marts or intermediate type model joins directly to a {{ source() }} node, there likely is a missing model that needs to be added.

How to Remediate

Add the reference to the appropriate staging model to maintain an abstraction layer between your raw data and your downstream data artifacts.

After refactoring your downstream model to select from the staging layer, your DAG should look like this:

Hard Coded References

fct_hard_coded_references (source) shows each instance where a model contains hard coded reference(s).

Example

fct_orders uses hard coded direct relation references (my_db.my_schema.orders and my_schema.customers).

# fct_orders.sql

with orders as (
    select * from my_db.my_schema.orders
),
customers as (
    select * from my_schema.customers
)
select
    orders.order_id,
    customers.name
from orders
left join customers on
	orders.customer_id = customers.id

Reason to Flag

Always use the ref function when selecting from another model and the source function when selecting from raw data, rather than using the direct relation reference (e.g. my_schema.my_table). Direct relation references are determined via regex mapping here.

The ref and source functions are part of what makes dbt so powerful! Using these functions allows dbt to infer dependencies (and check that you haven't created any circular dependencies), properly generate your DAG, and ensure that models are built in the correct order. This also ensures that your current model selects from upstream tables and views in the same environment that you're working in.

How to Remediate

For each hard coded reference:

• if the hard coded reference is to a model, update the sql to instead use the ref function
• if the hard coded reference is to raw data, create any needed sources and update the sql to instead use the source function

For the above example, our updated fct_orders.sql file would look like:

# fct_orders.sql

with orders as (
    select * from {{ ref('orders') }}
),
customers as (
    select * from {{ ref('customers') }}
)
select
    orders.order_id,
    customers.name
from orders
left join customers on
	orders.customer_id = customers.id

Model Fanout

fct_model_fanout (source) shows all parents with more than 3 direct leaf children. You can set your own threshold for model fanout by overriding the models_fanout_threshold variable. See overriding variables section.

Example

fct_model has three direct leaf children.

Reason to Flag

This might indicate some transformations should move to the BI layer, or a common business transformations should be moved upstream.

Exceptions

Some BI tools are better than others at joining and data exploration. For example, with Looker you could end your DAG after marts (i.e. fcts & dims) and join those artifacts together (with a little know how and setup time) to make your reports. For others, like Tableau, model fanouts might be more beneficial, as this tool prefers big tables over joins, so predefining some reports is usually more performant.

To exclude specific cases, check out the instructions in Configuring exceptions to the rules.

How to Remediate

Queries and transformations can move around between dbt and the BI tool, so how do we try to stay effortful in what we decide to put where?

You can think of dbt as our assembly line which produces expected outputs every time.

You can think of the BI layer as the place where we take the items produced from our assembly line to customize them in order to meet our stakeholder's needs.

Your dbt project needs a defined end point! Until the metrics server comes to fruition, you cannot possibly predefine every query or quandary your team might have. So decide as a team where that line is and maintain it.

Multiple Sources Joined

fct_multiple_sources_joined (source) shows each instance where a model references more than one source.

Example

model_1 references two source tables.

Reason to Flag

We very strongly believe that a staging model is the atomic unit of data modeling. Each staging model bears a one-to-one relationship with the source data table it represents. It has the same granularity, but the columns have been renamed, recast, or usefully reconsidered into a consistent format. With that in mind, two {{ source() }} declarations in one staging model likely means we are not being composable enough and there are individual building blocks which could be broken out into their respective models.

Exceptions

Sometimes companies have a bunch of identical sources across systems. When these identical sources will only ever be used collectively, you should union them once and create a staging layer on the combined result.

To exclude specific cases, check out the instructions in Configuring exceptions to the rules.

How to Remediate

In this example specifically, those raw sources, source_1.table_1 and source_1.table_2 should each have their own staging model (stg_model_1 and stg_model_2), as transitional steps, which will then be combined into a new int_model_2. Alternatively, you could keep stg_model_2 and add base__ models as transitional steps.

To fix this, try out the codegen package! With this package you can dynamically generate the SQL for a staging (what they call base) model, which you will use to populate stg_model_1 and stg_model_2 directly from the source data. Create a new model int_model_2. Afterwards, within int_model_2, update your {{ source() }} macros to {{ ref() }} macros and point them to your newly built staging models. If you had type casting, field aliasing, or other simple improvements made in your original stg_model_2 SQL, then attempt to move that logic back to the new staging models instead. This will help colocate those transformations and avoid duplicate code, so that all downstream models can leverage the same set of transformations.

Post-refactor, your DAG should look like this:

or if you want to use base_ models and keep stg_model_2 as is:

Rejoining of Upstream Concepts

fct_rejoining_of_upstream_concepts (source) contains all cases where one of the parent's direct children is ALSO the direct child of ANOTHER one of the parent's direct children. Only includes cases where the model "in between" the parent and child has NO other downstream dependencies.

Example

stg_model_1, int_model_4, and int_model_5 create a "loop" in the DAG. int_model_4 has no other downstream dependencies other than int_model_5.

Reason to Flag

This could happen for a variety of reasons: Accidentally duplicating some business concepts in multiple data flows, hesitance to touch (and break) someone else’s model, or perhaps trying to snowflake out or modularize everything without awareness of what will help build time.

As a general rule, snowflaking out models in a thoughtful manner allows for concurrency, but in this example nothing downstream can run until int_model_4 finishes, so it is not saving any time in parallel processing by being its own model. Since both int_model_4 and int_model_5 depend solely on stg_model_1, there is likely a better way to write the SQL within one model (int_model_5) and simplify the DAG, potentially at the expense of more rows of SQL within the model.

Exceptions

The one major exception to this would be when using a function from dbt_utils package, such as star or get_column_values, (or similar functions / packages) that require a relation as an argument input. If the shape of the data in the output of stg_model_1 is not the same as what you need for the input to the function within int_model_5, then you will indeed need int_model_4 to create that relation, in which case, leave it.

To exclude specific cases, check out the instructions in Configuring exceptions to the rules.

How to Remediate

Barring jinja/macro/relation exceptions we mention directly above, to resolve this, simply bring the SQL contents from int_model_4 into a CTE within int_model_5, and swap all {{ ref('int_model_4') }} references to the new CTE(s).

Post-refactor, your DAG should look like this:

Root Models

fct_root_models (source) shows each model with 0 direct parents, meaning that the model cannot be traced back to a declared source or model in the dbt project.

Example

model_4 has no direct parents

Reason to Flag

This likely means that the model (model_4 below) contains raw table references, either to a raw data source, or another model in the project without using the {{ source() }} or {{ ref() }} functions, respectively. This means that dbt is unable to interpret the correct lineage of this model, and could result in mis-timed execution and/or circular references depending on the model’s upstream dependencies.

Exceptions

This behavior may be observed in the case of a manually defined reference table that does not have any dependencies. A good example of this is a dim_calendar table that is generated by the {{ dbt_utils.date_spine() }} macro — this SQL logic is completely self contained, and does not require any external data sources to execute.

To exclude specific cases, check out the instructions in Configuring exceptions to the rules.

How to Remediate

Start by mapping any table references in the FROM clause of the model definition to the models or raw tables that they draw from, and replace those references with the {{ ref() }} if the dependency is another dbt model, or the {{ source() }} function if the table is a raw data source (this may require the declaration of a new source table). Then, visualize this model in the DAG, and refactor as appropriate according to best practices.

Source Fanout

fct_source_fanout (source) shows each instance where a source is the direct parent of multiple resources in the DAG.

Example

source.table_1 has more than one direct child model.

Reason to Flag

Each source node should be referenced by a single model that performs basic operations, such as renaming, recasting, and other light transformations to maintain consistency through out the project. The role of this staging model is to mirror the raw data but align it with project conventions. The staging model should act as a source of truth and a buffer- any model which depends on the data from a given source should reference the cleaned data in the staging model as opposed to referencing the source directly. This approach keeps the code DRY (any light transformations that need to be done on the raw data are performed only once). Minimizing references to the raw data will also make it easier to update the project should the format of the raw data change.

Exceptions

NoSQL databases or heavily nested data sources often have so much info json packed into a table that you need to break one raw data source into multiple base models.

To exclude specific cases, check out the instructions in Configuring exceptions to the rules.

How to Remediate

Create a staging model which references the source and cleans the raw data (e.g. renaming, recasting). Any models referencing the source directly should be refactored to point towards the staging model instead.

After refactoring the above example, the DAG would look something like this:

Staging Models Dependent on Downstream Models

fct_staging_dependent_on_marts_or_intermediate (source) shows each staging model that depends on an intermediate or marts model, as defined by the naming conventions and folder paths specified in your project variables.

Example

stg_model_5, a staging model, builds from fct_model_9 a marts model.

Reason to Flag

This likely represents a misnamed file. According to dbt best practices, staging models should only select from source nodes. Dependence on downstream models indicates that this model may need to be either renamed, or reconfigured to only select from source nodes.

How to Remediate

Rename the file in the child column to use to appropriate prefix, or change the models lineage by pointing the staging model to the appropriate {{ source() }}.

After updating the model to use the appropriate {{ source() }} function, your graph should look like this:

Staging Models Dependent on Other Staging Models

fct_staging_dependent_on_staging (source) shows each parent/child relationship where models in the staging layer are dependent on each other.

Example

stg_model_2 is a parent of stg_model_4.

Reason to Flag

This may indicate a change in naming is necessary, or that the child model should instead reference a source.

How to Remediate

You should either change the model type of the child (maybe to an intermediate or marts model) or change the child's lineage instead reference the appropriate {{ source() }}.

In our example, we might realize that stg_model_4 is actually an intermediate model. We should move this file to the appropriate intermediate directory and update the file name to int_model_4.

Unused Sources

fct_unused_sources (source) shows each source with 0 children.

Example

source.table_4 isn't being referenced.

Reason to Flag

This represents either a source that you have defined in YML but never brought into a model or a model that was deprecated and the corresponding rows in the source block of the YML file were not deleted at the same time. This simply represents the buildup of cruft in the project that doesn’t need to be there.

How to Remediate

Navigate to the sources.yml file (or whatever your company has called the file) that corresponds to the unused source. Within the YML file, remove the unused table name, along with descriptions or any other nested information.

version: 2

sources:
  - name: some_source
    database: raw
    tables:
      - name: table_1
      - name: table_2
      - name: table_3
      - name: table_4  # <-- remove this line

Testing

Missing Primary Key Tests

fct_missing_primary_key_tests (source) lists every model that does not meet the minimum testing requirement of testing primary keys. Any model that does not have either

1. a not_null test and a unique test applied to a single column OR
2. a dbt_utils.unique_combination_of_columns test applied to a set of columns

will be flagged by this model.

Reason to Flag

Tests are assertions you make about your models and other resources in your dbt project (e.g. sources, seeds and snapshots). Defining tests is a great way to confirm that your code is working correctly, and helps prevent regressions when your code changes. Models without proper tests on their grain are a risk to the reliability and scalability of your project.

How to Remediate

Apply a uniqueness test and a not null test to the column that represents the grain of your model in its schema entry. For models that are unique across a combination of columns, we recommend adding a surrogate key column to your model, then applying these tests to that new model. See the surrogate_key macro from dbt_utils for more info! Alternatively, you can use the dbt_utils.unique_combination_of_columns test from dbt_utils. Check out the overriding variables section to read more about configuring other primary key tests for your project!

Additional tests can be configured by applying a generic test in the model's .yml entry or by creating a singular test in the tests directory of you project.

Test Coverage

fct_test_coverage (source) contains metrics pertaining to project-wide test coverage. Specifically, this models measures:

1. test_coverage_pct: the percentage of your models that have minimum 1 test applied.
2. test_to_model_ratio: the ratio of the number of tests in your dbt project to the number of models in your dbt project
3. < model_type >_test_coverage_pct: the percentage of each of your model types that have minimum 1 test applied.

This model will raise a warn error on a dbt build or dbt test if the test_coverage_pct is less than 100%. You can set your own threshold by overriding the test_coverage_target variable. You can adjust your own model types by overriding the model_types variable. See overriding variables section.

Reason to Flag

We recommend that every model in your dbt project has tests applied to ensure the accuracy of your data transformations.

How to Remediate

Apply a generic test in the model's .yml entry, or create a singular test in the tests directory of you project.

As explained above, we recommend at a minimum, every model should have not_null and unique tests set up on a primary key.

Documentation

Documentation Coverage

fct_documentation_coverage (source) calculates the percent of enabled models in the project that have a configured description.

This model will raise a warn error on a dbt build or dbt test if the documentation_coverage_pct is less than 100%. You can set your own threshold by overriding the documentation_coverage_target variable. See overriding variables section.

Reason to Flag

Good documentation for your dbt models will help downstream consumers discover and understand the datasets which you curate for them. The documentation for your project includes model code, a DAG of your project, any tests you've added to a column, and more.

How to Remediate

Apply a text description in the model's .yml entry, or create a docs block in a markdown file, and use the {{ doc() }} function in the model's .yml entry.

Tip: We recommend that every model in your dbt project has at minimum a model-level description. This ensures that each model's purpose is clear to other developers and stakeholders when viewing the dbt docs site.

Undocumented Models

fct_undocumented_models (source) lists every model with no description configured.

Reason to Flag

Good documentation for your dbt models will help downstream consumers discover and understand the datasets which you curate for them. The documentation for your project includes model code, a DAG of your project, any tests you've added to a column, and more.

How to Remediate

Apply a text description in the model's .yml entry, or create a docs block in a markdown file, and use the {{ doc() }} function in the model's .yml entry.

Tip: We recommend that every model in your dbt project has at minimum a model-level description. This ensures that each model's purpose is clear to other developers and stakeholders when viewing the dbt docs site. Missing documentation should be addressed first for marts models, then for the rest of your project, to ensure that stakeholders in the organization can understand the data which is surfaced to them.

Structure

Model Naming Conventions

fct_model_naming_conventions (source) shows all cases where a model does NOT have the appropriate prefix.

Example

Consider model_8 which is nested in the marts subdirectory:

├── dbt_project.yml
└── models
    ├── marts
        └── model_8.sql

This model should be renamed to either fct_model_8 or dim_model_8.

Reason to Flag

Without appropriate naming conventions, a user querying the data warehouse might incorrectly assume the model type of a given relation. In order to explicitly name the model type in the data warehouse, we recommend appropriately prefixing your models in dbt.

Model Type	Appropriate Prefixes
Staging	stg_
Intermediate	int_
Marts	fct_ or dim_
Other	rpt_

How to Remediate

For each model flagged, ensure the model type is defined and the model name is prefixed appropriately.

Model Directories

fct_model_directories (source) shows all cases where a model is NOT in the appropriate subdirectory:

• For staging models: The files should be nested in the staging folder of a subfolder that matches their source parent's name.
• For non-staging models: The files should be nested closest to the folder name that matches their model type.

Example

Consider stg_model_3 which is a staging model for source_2.table_3:

But, stg_model_3.sql is inappropriately nested in the subdirectory source_1:

├── dbt_project.yml
└── models
    ├── marts
    └── staging
        └── source_1
            ├── stg_model_3.sql

This file should be moved into the subdirectory source_2:

├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── source_1
        └── source_2
            ├── stg_model_3.sql

Consider dim_model_7 which is a marts model but is inappropriately nested closest to the subdirectory intermediate:

├── dbt_project.yml
└── models
    └── marts
        └── intermediate
            ├── dim_model_7.sql

This file should be moved closest to the subdirectory marts:

├── dbt_project.yml
└── models
    └── marts
        ├── dim_model_7.sql

Consider int_model_4 which is an intermediate model but is inappropriately nested closest to the subdirectory marts:

├── dbt_project.yml
└── models
    └── marts
        ├── int_model_4.sql

This file should be moved closest to the subdirectory intermediate:

├── dbt_project.yml
└── models
    └── marts
        └── intermediate
            ├── int_model_4.sql

Reason to Flag

Because we often work with multiple data sources, in our staging directory, we create one subdirectory per source.

├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── braintree
        └── stripe

Each staging directory contains:

• One staging model for each raw source table
• One .yml file which contains source definitions, tests, and documentation (see Source Directories)
• One .yml file which contains tests & documentation for models in the same directory (see Test Directories)

This provides for clear repository organization, so that analytics engineers can quickly and easily find the information they need.

We might create additional folders for intermediate models but each file should always be nested closest to the folder name that matches their model type.

├── dbt_project.yml
└── models
    └── marts
        └── fct_model_6.sql
        └── intermediate
            └── int_model_5.sql

How to Remediate

For each resource flagged, move the file from the current_file_path to change_file_path_to.

Source Directories

fct_source_directories (source) shows all cases where a source definition is NOT in the appropriate subdirectory:

Example

Consider source_2.table_3 which is a source_2 source but it had been defined inappropriately in a source.yml file nested in the subdirectory source_1:

├── dbt_project.yml
└── models
    ├── marts
    └── staging
        └── source_1
            ├── source.yml

This definition should be moved into a source.yml file nested in the subdirectory source_2:

├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── source_1
        └── source_2
            ├── source.yml

Reason to Flag

Because we often work with multiple data sources, in our staging directory, we create one subdirectory per source.

├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── braintree
        └── stripe

Each staging directory contains:

• One staging model for each raw source table (see Model Directories)
• One .yml file which contains source definitions, tests, and documentation
• One .yml file which contains tests & documentation for models in the same directory (see Test Directories)

This provides for clear repository organization, so that analytics engineers can quickly and easily find the information they need.

How to Remediate

For each source flagged, move the file from the current_file_path to change_file_path_to.

Test Directories

fct_test_directories (source) shows all cases where model tests are NOT in the same subdirectory as the corresponding model.

Example

int_model_4 is located within marts/. However, tests for int_model_4 are configured in staging/staging.yml:

├── dbt_project.yml
└── models
    └── marts
        ├── int_model_4.sql
    └── staging
        ├── staging.yml

A new yml file should be created in marts/ which contains all tests and documentation for int_model_4, and for the rest of the models in located in the marts/ directory:

├── dbt_project.yml
└── models
    └── marts
        ├── int_model_4.sql
        ├── marts.yml
    └── staging
        ├── staging.yml

Reason to Flag

Each subdirectory in models/ should contain one .yml file that includes the tests and documentation for all models within the given subdirectory. Keeping your repository organized in this way ensures that folks can quickly access the information they need.

How to Remediate

Move flagged tests from the yml file under current_test_directory to the yml file under change_test_directory_to (create a new yml file if one does not exist).

Resource YML Naming Conventions

fct_resource_yml_naming_conventions (source) shows all cases where resource yml files do not match the recommended naming conventions for these files. These files should:

• Start with a leading underscore _ to sort them alphabetically first in their subdirectory
• Be named for the resource type it contains. For example, a yml file that has model schema entries should be named _models.yml
  • Optionally, use the name of the parent subdirectory to disambiguate these files on large projects i.e. models/marts/finance/_finance__models.yml
• Be plural! _models.yml, not _model.yml

Acceptable names would follow either:

• _[resource_type]s.yml
• _[directory name]__[resource_type]s.yml

Example

Both yml files have model configurations, but have the same generic name, making it impossible to know what the files do without inspecting their contents.

├── dbt_project.yml
└── models
    └── marts
        ├── dim_customers.sql
        ├── schema.yml
    └── staging
        ├── schema.yml

Reason to Flag

Each subdirectory in your project should contain one .yml file per resource type in that directory that includes the tests, documentation, and configuration for all corresponding resources.

How to Remediate

Rename your yml files to be more clear about their purpose! After remediation, the above yml file might look like this:

├── dbt_project.yml
└── models
    └── marts
        ├── _marts__models.yml
        ├── dim_customers.sql
    └── staging
        ├── _models_.yml

Multiple Resource Types in Resource YML

fct_resource_yml_files_with_multiple_resource_types (source) shows all cases where a resource yml file contains more than one resource type. Each resource yml file should only contain one resource type for easier discovery.

Example

Consider this project:

├── dbt_project.yml
└── models
    └── staging
        ├── schema.yml
        ├── stg_model_1.sql

The contents of schema.yml contain both a source definition and a model entry:

version: 2

source:
  - name: my_source 
    database: raw
    schema: data
    tables:
      - table_1


models: 
  - name: stg_model_1
    description: stage data from raw.data.table_1

Reason to Flag

Each subdirectory in your project should contain one .yml file per resource type in that directory that includes the tests, documentation, and configuration for all corresponding resources.

How to Remediate

Split that original yml into two appropriately named files to disambiguate those entries and make them more discoverable.

├── dbt_project.yml └── models └── staging ├── _sources.yml ├── _models.yml ├── stg_model_1.sql

</details>

### Performance
#### Chained View Dependencies

`fct_chained_views_dependencies` ([source](models/marts/performance/fct_chained_views_dependencies.sql)) contains models that are dependent on chains of "non-physically-materialized" models (views and ephemerals), highlighting potential cases for improving performance by switching the materialization of model(s) within the chain to table or incremental. 

This model will raise a `warn` error on a `dbt build` or `dbt test` if the `distance` between a given `parent` and `child` is greater than or equal to 4.
You can set your own threshold for chained views by overriding the `chained_views_threshold` variable. [See overriding variables section.](#overriding-variables)

<details>
<summary><b>Example</b></summary>

`table_1` depends on a chain of 4 views (`view_1`, `view_2`, `view_3`, and `view_4`).

<img width="500" alt="dag of chain of 4 views, then a table" src="https://user-images.githubusercontent.com/53586774/176299679-39028eb1-f9e3-492a-bdb7-b72d9d7958b7.png">
</details>

<details>
<summary><b>Reason to Flag</b></summary>

You may experience a long runtime for a model when it is build on top of a long chain of "non-physically-materialized" models (views and ephemerals). In the example above, nothing is really computed until you get to `table_1`. At which point, it is going to run the query within `view_4`, which will then have to run the query within `view_3`, which will then have the run the query within `view_2`, which will then have to run the query within `view_1`. These will all be running at the same time, which creates a long runtime for `table_1`. 
</details>

<details>
<summary><b>How to Remediate</b></summary>

We can reduce this compilation time by changing the materialization strategy of some key upstream models to table or incremental to keep a minimum amount of compute in memory and preventing nesting of views. If, for example, we change the materialization of `view_4` from a view to a table, `table_1` will have a shorter runtime as it will have less compilation to do. 

The best practice to determine top candidates for changing materialization from `view` to `table`:
- if a view is used downstream my *many* models, change materialization to table
- if a view has more complex calculations (window functions, joins between *many* tables, etc.), change materialization to table
</details>

#### Exposure Parents Materializations

`fct_exposure_parents_materializations` ([source](models/marts/performance/fct_exposure_parents_materializations.sql)) highlights instances where the resources referenced by exposures are either:

1. a `source`
2. a `model` that does not use the `table` or `incremental` materialization

<details>
<summary><b>Example</b></summary>
<img width="500" alt="An example exposure with a table parent (fct_model_6) and an ephemeral parent (dim_model_7)" src="https://user-images.githubusercontent.com/73915542/178068955-742e2c87-4385-48f9-b9fb-94a1cbc8079a.png">

In this case, the parents of `exposure_1` are not both materialized as tables -- `dim_model_7` is ephemeral, while `fct_model_6` is a table. This model would return a record for the `dim_model_7 --> exposure_1` relationship. 
</details>

<details>
<summary><b>Reason to Flag</b></summary>

Exposures should depend on the business logic you encoded into your dbt project (e.g. models or metrics) rather than raw untransformed sources. Additionally, models that are referenced by an exposure are likely to be used heavily in downstream systems, and therefore need to be performant when queried.

</details>

<details>
<summary><b>How to Remediate</b></summary>

If you have a source parent of an exposure, you should incorporate that raw data into your project in some way, then update the exposure to point to that model. 
If necessary, update the `materialized` configuration on the models returned in `fct_exposure_parents_materializations` to either `table` or `incremental`. This can be done in individual model files using a config block, or for groups of models in your `dbt_project.yml` file. See the docs on [model configurations](https://docs.getdbt.com/reference/model-configs) for more info!
</details>

-----
## Customization
### Disabling Models

If there is a particular model or set of models that you *do not want this package to execute*, you can
disable these models as you would any other model in your `dbt_project.yml` file

```yml
# dbt_project.yml

models:
  dbt_project_evaluator:
    marts:
      tests:
        # disable entire test coverage suite
        +enabled: false
      dag:
        # disable single DAG model
        fct_model_fanout:
          +enabled: false

Overriding Variables

Currently, this package uses different variables to adapt the models to your objectives and naming conventions. They can all be updated directly in dbt_project.yml

Testing and Documentation Variables

variable	description	default
test_coverage_target	the minimum acceptable test coverage percentage	100%
documentation_coverage_target	the minimum acceptable documentation coverage percentage	100%
primary_key_test_macros	the set(s) of dbt tests used to check validity of a primary key	"dbt.test_unique", "dbt.test_not_null"], ["dbt_utils.test_unique_combination_of_columns"

Usage notes for primary_key_test_macros:

The primary_key_test_macros variable determines how the fct_missing_primary_key_tests (source) model evaluates whether the models in your project are properly tested for their grain. This variable is a list and each entry must be a list of test names in project_name.test_macro_name format.

For each entry in the parent list, the logic in int_model_test_summary will evaluate whether each model has all of the tests in that entry applied. If a model meets the criteria of any of the entries in the parent list, it will be considered a pass. The default behavior for this package will check for whether each model has either:

1. Both the not_null and unique tests applied to a single column OR
2. The dbt_utils.unique_combination_of_columns applied to the model.

Each set of test(s) that define a primary key requirement must be grouped together in a sub-list to ensure they are evaluated together (e.g. [dbt.test_unique, dbt.test_not_null] ).

While it's not explicitly tested in this package, we strongly encourage adding a not_null test on each of the columns listed in the dbt_utils.unique_combination_of_columns tests.

# dbt_project.yml
# set your test and doc coverage to 75% instead
# use the dbt_constraints.test_primary_key test to check for validity of your primary keys

vars:
  dbt_project_evaluator:
    documentation_coverage_target: 75
    test_coverage_target: 75
    primary_key_test_macros: [["dbt_constraints.test_primary_key"]]
    

DAG Variables

variable	description	default
models_fanout_threshold	threshold for unacceptable model fanout for fct_model_fanout	3 models

# dbt_project.yml
# set your model fanout threshold to 10 instead of 3

vars:
  dbt_project_evaluator:
    models_fanout_threshold: 10

Naming Convention Variables

variable	description	default
model_types	a list of the different types of models that define the layers of your dbt project	staging, intermediate, marts, other
staging_folder_name	the name of the folder that contains your staging models	staging
intermediate_folder_name	the name of the folder that contains your intermediate models	intermediate
marts_folder_name	the name of the folder that contains your marts models	marts
staging_prefixes	the list of acceptable prefixes for your staging models	stg_
intermediate_prefixes	the list of acceptable prefixes for your intermediate models	int_
marts_prefixes	the list of acceptable prefixes for your marts models	fct_, dim_
other_prefixes	the list of acceptable prefixes for your other models	rpt_

The model_types, <model_type>_folder_name, and <model_type>_prefixes variables allow the package to check if models in the different layers are in the correct folders and have a correct prefix in their name. The default model types are the ones we recommend in our dbt Labs Style Guide. If your model types are different, you can update the model_types variable and create new variables for <model_type>_folder_name and/or <model_type>_prefixes.

# dbt_project.yml
# add an additional model type "util"

vars:
  dbt_project_evaluator:
    model_types: ['staging', 'intermediate', 'marts', 'other', 'util']
    util_folder_name: 'util'
    util_prefixes: ['util_']

Performance Variables

variable	description	default
chained_views_threshold	threshold for unacceptable length of chain of views for fct_chained_views_dependencies	4
insert_batch_size	number of records inserted per batch when unpacking the graph into models	10000

# dbt_project.yml

vars:
  dbt_project_evaluator:
    # set your chained views threshold to 8 instead of 4
    chained_views_threshold: 8
    # update the number of records inserted from the graph from 10,000 to 500 to reduce query size
    insert_batch_size: 500

Warehouse Specific Variables

variable	description	default
max_depth_dag	limits the number of looped CTEs when computing the DAG end-to-end for BigQuery and Databricks/Spark compatibility	9

Changing max_depth_dag number to a higher one might prevent the package from running properly on BigQuery and Databricks/Spark.

Configuring exceptions to the rules

While the rules defined in this package are considered best practices, we realize that there might be exceptions to those rules and people might want to exclude given results to get passing tests despite not following all the recommendations.

An example would be excluding all models with names matching with stg_..._unioned from fct_multiple_sources_joined as we might want to union 2 different tables representing the same data in some of our staging models and we don't want the test to fail for those models.

The package offers the ability to define a seed called dbt_project_evaluator_exceptions.csv to list those exceptions we don't want to be reported. This seed must contain the following columns:

• fct_name: the name of the fact table for which we want to define exceptions (Please note that it is not possible to exclude specific models for all the coverage tests, but there are variables available to configure those to the particular users' needs)
• column_name: the column name from fct_name we will be looking at to define exceptions
• id_to_exclude: the values (or like pattern) we want to exclude for column_name
• comment: a field where people can document why a given exception is legitimate

The following section describes the steps to follow to configure exceptions.

1. Create a new seed

With our previous example, the seed dbt_project_evaluator_exceptions.csv would look like:

fct_name,column_name,id_to_exclude,comment
fct_multiple_sources_joined,child,stg_%_unioned,Models called _unioned can union multiple sources

which looks like the following when loaded in the warehouse

fct_name	column_name	id_to_exclude	comment
fct_multiple_sources_joined	child	stg_%_unioned	Models called _unioned can union multiple sources

2. Deactivate the seed from the original package

Only a single seed can exist with a given name. When using a custom one, we need to deactivate the blank one from the package by adding the following to our dbt_project.yml

# dbt_project.yml

seeds:
  dbt_project_evaluator:
    dbt_project_evaluator_exceptions:
      +enabled: false

3. Run the seed and the package

We then run both the seed and the package by executing the following command:

dbt build --select package:dbt_project_evaluator dbt_project_evaluator_exceptions

Displaying violations in the logs

This package provides a macro that can be executed via an on-run-end hook to display the package results in the logs in addition to storing those in the Data Warehouse.

To use it, you can add the following line in your dbt_project.yml:

on-run-end: "{{ dbt_project_evaluator.print_dbt_project_evaluator_issues() }}"

In the case that you are storing the tables with the package results in a schema or database different from the default ones from your profile, the following parameters are available for print_dbt_project_evaluator_issues():

• schema_project_evaluator: the schema where the tables are stored
• db_project_evaluator: the database where the tables are stored

dbt_project.yml

---

Running this package as a CI check

Once you have addressed all current misalignments in your project (either by fixing them or configuring exceptions), you can use this package as a CI check to ensure code changes don't introduce new misalignments. The setup will vary based on whether you are using dbt Cloud or dbt Core, but the general steps are as follows:

1. Override test severity with an environment variable

By default the tests in this package are configured with "warn" severity, we can override that for our CI jobs with an environment variable:

1. Create an environment variable to define the appropriate severity for each environment. In dbt Cloud, for example, we can easily create an environment variable DBT_PROJECT_EVALUATOR_SEVERITY that is set to "error" for the Continuous Integration environment and "warn" for all other environments:

Note: It is also possible to use an environment variable for dbt Core, but the actual implementation will depend on how dbt is orchestrated.

2. Update you project.yml file to override the default severity for all tests in this package:

# dbt_project.yml

tests:
  dbt_project_evaluator:
    +severity: "{{ env_var('DBT_PROJECT_EVALUATOR_SEVERITY', 'warn') }}"

Note: you could follow a similar process to disable the models in this package for your production environment

# dbt_project.yml

models:
  dbt_project_evaluator:
    +enabled: "{{ env_var('ENABLE_DBT_PROJECT_EVALUATOR', 'true') | lower == 'true' | as_bool }}"

2. Run this package for each pull request

Now, you can run this package as a step of your CI job/pipeline. In dbt Cloud, for example, you could update the commands of your CI job to:

dbt build --select state:modified+ --exclude package:dbt_project_evaluator
dbt build --select package:dbt_project_evaluator

Or, if you've configured any exceptions, to:

dbt build --select state:modified+ --exclude package:dbt_project_evaluator
dbt build --select package:dbt_project_evaluator dbt_project_evaluator_exceptions

Note: ensure you have properly set up your dbt Cloud CI job using deferral and a webhook trigger by following this documentation.

---

Querying the DAG with SQL

The model int_all_dag_relationships (source), created with the package, lists all the dbt nodes (models, exposures, sources, metrics, seeds, snapshots) along with all their dependencies (including indirect ones) and the path between them.

Building additional models and snapshots on top of this model could allow:

• creating a dashboard that provides
  • a list of all the sources used by a given exposure
  • a list of all the exposures or metrics using a given source
  • the dependencies between different models
• building metrics/KPIs on top of a dbt project
  • evolution of the number of models over time
  • evolution of the number of metrics and exposures over time
• getting insights on potential refactoring work
  • looking at the longest "chains" of models in a project
  • reviewing models with many/few direct dependents
  • identifying potential bottlenecks

---

Limitations

BigQuery and Databricks

BigQuery current support for recursive CTEs is limited and Databricks SQL doesn't support recursive CTEs.

For those Data Warehouses, the model int_all_dag_relationships needs to be created by looping CTEs instead. The number of loops is configured with max_depth_dag and defaulted to 9. This means that dependencies between models of more than 9 levels of separation won't show in the model int_all_dag_relationships but tests on the DAG will still be correct. With a number of loops higher than 9 BigQuery sometimes raises an error saying the query is too complex.

---

Contributing

If you'd like to add models to flag new areas, please update this README and add an integration test (more details here).