Testing dbt project: jaffle_shop

jaffle_shop is a fictional ecommerce store. This dbt project transforms raw data from an app database into a customers and orders model ready for analytics.

What is this repo?

What this repo is:

• A self-contained dbt project, useful for demonstrating how to make vscode-dbt-power-user extension work in a devcontainer

What's in this repo?

This repo contains seeds that includes some (fake) raw data from a fictional app along with some basic dbt models, tests, and docs for this data.

The raw data consists of customers, orders, and payments, with the following entity-relationship diagram:

Why should I care about this repo?

If you're just starting your cloud data warehouse journey and are hungry to get started with dbt before your organization officially gets a data warehouse, you should check out this repo.

If you want to run 28 SQL operations with dbt in less than 1 second, for free, and all on your local machine, you should check out this repo.

If you want an adrenaline rush from a process that used to take dbt newcomers 1 hour and is now less than 1 minute, you should check out this repo.

Verified GitHub Action on dbt Performance

Running this project

Prerequisities: Python >= 3.5

Mach Speed: No explanation needed

Run dbt as fast as possible in a single copy and paste motion!

POSIX bash/zsh

git clone https://github.com/dbt-labs/jaffle_shop_duckdb.git
cd jaffle_shop_duckdb
python3 -m venv venv
source venv/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
source venv/bin/activate
dbt build
dbt docs generate
dbt docs serve

POSIX fish

git clone https://github.com/dbt-labs/jaffle_shop_duckdb.git
cd jaffle_shop_duckdb
python3 -m venv venv
source venv/bin/activate.fish
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
source venv/bin/activate.fish
dbt build
dbt docs generate
dbt docs serve

POSIX csh/tcsh

git clone https://github.com/dbt-labs/jaffle_shop_duckdb.git
cd jaffle_shop_duckdb
python3 -m venv venv
source venv/bin/activate.csh
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
source venv/bin/activate.csh
dbt build
dbt docs generate
dbt docs serve

POSIX PowerShell Core

git clone https://github.com/dbt-labs/jaffle_shop_duckdb.git
cd jaffle_shop_duckdb
python3 -m venv venv
venv/bin/Activate.ps1
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
venv/bin/Activate.ps1
dbt build
dbt docs generate
dbt docs serve

Windows cmd.exe

git clone https://github.com/dbt-labs/jaffle_shop_duckdb.git
cd jaffle_shop_duckdb
python -m venv venv
venv\Scripts\activate.bat
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
venv\Scripts\activate.bat
dbt build
dbt docs generate
dbt docs serve

Windows PowerShell

git clone https://github.com/dbt-labs/jaffle_shop_duckdb.git
cd jaffle_shop_duckdb
python -m venv venv
venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
venv\Scripts\Activate.ps1
dbt build
dbt docs generate
dbt docs serve

GitHub Codespaces / Dev Containers

Steps

1. Ensure you have Codespaces enabled for your GitHub organization or turned on as a beta feature if you're an individual user
2. Click the green Code button on near the top right of the page of this repo's homepage (you may already be on it)
3. Instead of cloning the repo like you normally would, instead select the Codespaces tab of the pop out, then "Create codespace on duckdb"
4. Wait for codespace to boot (~1 min?)
5. Decide whether you'd like to use the Web IDE or open the codespace in your local environment
6. When the codespace opens, a Task pane will show up and call dbt build just to show you how it's done
7. Decide whether or not you'd like the recommended extensions installed (like dbt Power User extension)
8. Open up a new terminal and type:

   dbt build
   

9. Explore some of the bells and whistles (see below)

If you don't have Codespaces or would like to just run the environment in a local Docker container, you can by:

1. Install Docker Desktop
2. Install the VSCode Dev Containers extension (formerly known as the "Remote - Containers" extension). Video tutorial here.
3. Clone this repo and open it in VSCode
4. First time: View > Command Palette > Remote-Containers: Open Folder in Container
   • Wait for container to build -- expected to take several minutes
   • Open a new terminal
5. Subsequent times: Click Reopen in Container and wait for container to spin up
6. Continue on step 7 above

bells and whistles

There's some bells and whistles defined in the .devcontainer.json.devcontainer.json) that are worth calling out. Also a great reference is the Setting up VSCode for dbt guide.

1. there is syntax highlighting provided by the vdcode-dbt extension. However, it is configured such that files in your target/run and target/compiled folder are not syntax highlighted, as a reminder that these files are not where you should be making changes!
2. basic sqlfluff linting is enabled as you type. Syntax errors will be underlined in red at the error, and will also be surfaced in the Problems tab of the Terminal pane. It's configured to lint as you type.
3. Autocompletion is enabled for generic dbt macros via the vdcode-dbt extension. For example, if you type macro you'll notice a pop up that you can select with the arrow keys then click tab to get a macro snippet.
4. the find-related extension allows an easy shortcut to navigating using CMD+Rto jump from
   • a model file to it's corresponding compiled version,
   • from a compiled file to either the original model file or the version in target/run
5. The vscode-yaml YAML, combined with the JSON schema defined in dbt-labs/dbt-jsonschema, autocomplete options while working with dbt's YAML files: i.e. :
   • Project definition files (dbt_project.yml)
   • Package files (packages.yml)
   • Selectors files (selectors.yml)
   • Property files (models/whatever.yml)

Step-by-step explanation

To get up and running with this project:

1. Clone this repository.

2. Change into the jaffle_shop_duck directory from the command line:

   cd jaffle_shop_duckdb

3. Install dbt and DuckDB in a virtual environment.

   Expand your shell below:

   POSIX bash/zsh

   python3 -m venv venv
   source venv/bin/activate
   python3 -m pip install --upgrade pip
   python3 -m pip install -r requirements.txt
   source venv/bin/activate

   POSIX fish

   python3 -m venv venv
   source venv/bin/activate.fish
   python3 -m pip install --upgrade pip
   python3 -m pip install -r requirements.txt
   source venv/bin/activate.fish

   POSIX csh/tcsh

   python3 -m venv venv
   source venv/bin/activate.csh
   python3 -m pip install --upgrade pip
   python3 -m pip install -r requirements.txt
   source venv/bin/activate.csh

   POSIX PowerShell Core

   python3 -m venv venv
   venv/bin/Activate.ps1
   python3 -m pip install --upgrade pip
   python3 -m pip install -r requirements.txt
   venv/bin/Activate.ps1

   Windows cmd.exe

   python -m venv venv
   venv\Scripts\activate.bat
   python -m pip install --upgrade pip
   python -m pip install -r requirements.txt
   venv\Scripts\activate.bat

   Windows PowerShell

   python -m venv venv
   venv\Scripts\Activate.ps1
   python -m pip install --upgrade pip
   python -m pip install -r requirements.txt
   venv\Scripts\Activate.ps1

   Why a 2nd activation of the virtual environment?

   This may not be necessary for many users, but might be for some. Read on for a first-person report from @dbeatty10.

   I use zsh as my shell on my MacBook Pro, and I use pyenv to manage my Python environments. I already had an alpha version of dbt Core 1.2 installed (and yet another via pipx):

   $ which dbt
   /Users/dbeatty/.pyenv/shims/dbt

   $ dbt --version
   Core:
     - installed: 1.2.0-a1
     - latest:    1.1.1    - Ahead of latest version!
   
   Plugins:
     - bigquery:  1.2.0a1 - Ahead of latest version!
     - snowflake: 1.2.0a1 - Ahead of latest version!
     - redshift:  1.2.0a1 - Ahead of latest version!
     - postgres:  1.2.0a1 - Ahead of latest version!

   Then I ran all the steps to create a virtual environment and install the requirements of our DuckDB-based Jaffle Shop repo:

   $ python3 -m venv venv
   $ source venv/bin/activate
   (venv) $ python3 -m pip install --upgrade pip
   (venv) $ python3 -m pip install -r requirements.txt

   Let's examine where dbt is installed and which version it is reporting:

   (venv) $ which dbt
   /Users/dbeatty/projects/jaffle_duck/venv/bin/dbt

   (venv) $ dbt --version
   Core:
     - installed: 1.2.0-a1
     - latest:    1.1.1    - Ahead of latest version!
   
   Plugins:
     - bigquery:  1.2.0a1 - Ahead of latest version!
     - snowflake: 1.2.0a1 - Ahead of latest version!
     - redshift:  1.2.0a1 - Ahead of latest version!
     - postgres:  1.2.0a1 - Ahead of latest version!

   ❌ That isn't what we expected -- something isn't right. 😢

   So let's reactivate the virtual environment and try again...

   (venv) $ source venv/bin/activate

   (venv) $ dbt --version
   Core:
     - installed: 1.1.1
     - latest:    1.1.1 - Up to date!
   
   Plugins:
     - postgres: 1.1.1 - Up to date!
     - duckdb:   1.1.3 - Up to date!

   ✅ This is what we want -- the 2nd reactivation worked. 😎

4. Ensure your profile is setup correctly from the command line:

   dbt --version
   dbt debug

5. Load the CSVs with the demo data set, run the models, and test the output of the models using the dbt build command:

   dbt build

6. Query the data:

   Launch a DuckDB command-line interface (CLI):

   duckcli jaffle_shop.duckdb

   Run a query at the prompt and exit:

   select * from customers where customer_id = 42;
   exit;
   

   Alternatively, use a single-liner to perform the query:

   duckcli jaffle_shop.duckdb -e "select * from customers where customer_id = 42"

   or:

   echo 'select * from customers where customer_id = 42' | duckcli jaffle_shop.duckdb

7. Generate and view the documentation for the project:

   dbt docs generate
   dbt docs serve

Running build steps independently

1. Load the CSVs with the demo data set. This materializes the CSVs as tables in your target schema. Note that a typical dbt project does not require this step since dbt assumes your raw data is already in your warehouse.

   dbt seed

2. Run the models:

   dbt run

   NOTE: If you decide to run this project in your own data warehouse (outside of this DuckDB demo) and steps fail, it might mean that you need to make small changes to the SQL in the models folder to adjust for the flavor of SQL of your target database. Definitely consider this if you are using a community-contributed adapter.

3. Test the output of the models using the test command:

   dbt test

Browsing the data

Some options:

• duckcli
• DuckDB CLI
• How to set up DBeaver SQL IDE for DuckDB

Troubleshooting

You may get an error like this, in which case you will need to disconnect from any sessions that are locking the database:

IO Error: Could not set lock on file "jaffle_shop.duckdb": Resource temporarily unavailable

This is a known issue in DuckDB. If you are using DBeaver, this means shutting down DBeaver (merely disconnecting didn't work for me).

Very worst-case, deleting the database file will get you back in action (BUT you will lose all your data).

GitHub Codespaces and VSCode Remote Container

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