view-selection-python 0.1.5

View Selection Tool for dbt (python part)

ViewSelectionAdvisor

Welcome to ViewSelectionAdvisor, a tool designed to help dbt users address the problem of model materialization. This tool consists of two separate packages, each hosted in its own GitHub repository:

• A dbt package. This package is dependent on another dbt package: Elementary
• A python package

What does it do?

Most dbt projects are structured with a DAG that includes staging, intermediate, and marts models. Typically, staging and intermediate models are stored as views, while marts models are stored as tables. However, this default configuration may not always be the most efficient from a performance perspective. Determining which models should be materialized and which should not can be challenging. This is where ViewSelectionAdvisor comes in to help. By using this tool, you are advised on the best materialization strategy for you models in dbt.

How does it work?

ViewSelectionAdvisor determines the optimal configuration of materialized models by evaluating all possible configurations. For each configuration, it estimates the total cost of building your entire DAG using PostgreSQL's EXPLAIN command.

Note: ViewSelectionAdvisor assumes that all destination nodes are already materialized as tables. Consequently, these nodes will not appear in the provided advice.

Note 2: By default, ViewSelectionAdvisor only looks at materialization configurations of at most 2 models. This can be changed using the max_materializations variable (see overview of variables).

A note on Elementary's defaults materializations warning

Please note that when running any of the dbt run commands in the coming steps, it is possible that you observe a warning from dbt on elementary trying to override default materializations. This is not a problem, as the developers of Elementary are aware of this and working on a solution. Furthermore, the parts of the elementary package that are affected by this are not relevant for ViewSelectionAdvisor.

Installation Instructions

dbt part

1. Include ViewSelectionAdvisor in your packages.yml file:

     - git: "https://github.com/bramreinders97/view_selection_tool_dbt.git"
       revision: 4b7990736f651ae08f8d4a7a260f2c10ad1d862b
   

2. Update your dbt_project.yml file:

   • Schema Configuration: Specify the schema appendix where dbt should store the relevant tables:

       models:
         elementary:
           +schema: elementary
         view_selection_tool:
           +schema: view_selection_tool
     

     These settings ensure that if your project's tables are stored in schema x, then the tables from elementary will be stored in x_elementary, and those from ViewSelectionAdvisor will be stored in x_view_selection_tool.

   • Variable Configuration: Set the following variables:

       vars:
         view_selection_tool:
           # Database where the elementary tables are located
           # (same as in your target profile from profiles.yml)
           elementary_src_db:  
     
           # Schema where the elementary tables are stored (e.g., `x_elementary`)
           src_schema:  
     
           # Name of your project as specified in this dbt_project.yml
           relevant_package:  
     

     This information allows ViewSelectionAdvisor to identify the data sources (elementary_src_db and src_schema) and the models to focus on (relevant_package).

3. Import the packages and build Elementary models

   dbt deps
   dbt run --select elementary
   

   This will install both the view_selection_tool and elementary packages, and create empty tables for Elementary to fill (at schema x_elementary).

Python part

4. Install the package using your preferred method:

   pip install view-selection-python
   

   or

   poetry add view-selection-python
   

Usage Instructions

Because ViewSelectionAdvisor relies entirely on the tables created by Elementary, it is crucial to ensure these tables are populated with the necessary information before running ViewSelectionAdvisor. Whenever you want to receive advice on the materialization of a DAG in dbt, follow these steps:

1. Populate Elementary tables with the latest information:

   dbt run --select <your_project_name>
   

   Running your project populates the Elementary tables with the data required by ViewSelectionAdvisor.

   Note: This command only runs the models in your project, not the individual models from Elementary. However, the on-run-end hook of Elementary will execute automatically and provide all the necessary data.

2. Run ViewSelectionAdvisor:

   • Transform Info From Elementary:

   dbt run --select view_selection_tool
   

   This transforms the information provided in the Elementary tables and fills the database schema x_view_selection_tool with all information the python part of the ViewSelectionAdvisor requires in order to give a proper advice.

   • Transform Info From Elementary:

   vst-advise
   

   This command compares all possible materialization configurations, and advises on the configuration with the lowest estimated cost.

Possible Variables for vst-advise

The following variables can be used to change the behavior of vst-advise:

Option	Description
-h, --help	Show this help message and exit
-mm <MAX_MATERIALIZATIONS>, --max_materializations <MAX_MATERIALIZATIONS>	Set the maximum number of models to consider for materialization. Higher values provide more options but may increase runtime. Default is 2.
-p <PROFILE>, --profile <PROFILE>	Select the profile to use
-t <TARGET>, --target <TARGET>	Select the target profile to use