dbt-airflow-docker-compose

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dbt-airflow-docker-compose

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Execution of DBT models using Apache Airflow through Docker Compose

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최종 버전 다운로드 (.zip)

- infrastructure_model.png
- - dag.py
  - initialise_data.py
- airflow.cfg
- - schema.yml
- .gitignore
- dbt_project.yml
- profiles.yml
- aisles.csv.zip
- departments.csv.zip
- order_products__train.csv.zip
- orders.csv.zip
- products.csv.zip
- init.sh
- init-user-db.sh
.dockerignore
.gitignore
docker-compose.yml
dockerfile
LICENSE
README.md

// repository documentation

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# Apache Airflow and DBT using Docker Compose
Stand-alone project that utilises public eCommerce data from Instacart to demonstrate how to schedule dbt models through Airflow.

For more Data & Analytics related reading, check https://analyticsmayhem.com

## Requirements 
* Install [Docker](https://www.docker.com/products/docker-desktop)
* Install [Docker Compose](https://docs.docker.com/compose/install/)
* Download the [Kaggle Instacart eCommerce dataset](https://www.kaggle.com/c/instacart-market-basket-analysis/data)

## Setup 
* Clone the repository
* Extract the CSV files within ./sample_data directory (files are needed as seed data)

Change directory within the repository and run `docker-compose up`. This will perform the following:
* Based on the definition of [`docker-compose.yml`](https://github.com/konosp/dbt-airflow-docker-compose/blob/master/docker-compose.yml) will download the necessary images to run the project. This includes the following services:
  * postgres-airflow: DB for Airflow to connect and store task execution information
  * postgres-dbt: DB for the DBT seed data and SQL models to be stored
  * airflow: Python-based image to execute Airflow scheduler and webserver
  * adminer: a lightweight DB client

## Connections
* Adminer UI: [http://localhost:8080](http://localhost:8080/?pgsql=postgres-dbt&username=dbtuser&db=dbtdb&ns=dbt) Credentials as defined at [`docker-compose.yml`](https://github.com/konosp/dbt-airflow-docker-compose/blob/master/docker-compose.yml)
* Airflow UI: http://localhost:8000

## How to ran the DAGs
Once everything is up and running, navigate to the Airflow UI (see connections above). You will be presented with the list of DAGs, all Off by default.

You will need to run to execute them in correct order. 
- 1_load_initial_data: Load the raw Kaggle dataset
- 2_init_once_dbt_models: Perform some basic transformations (i.e. build an artificial date for the orders)
- 3_snapshot_dbt_models: Build the snapshot tables
- 4_daily_dbt_models: Schedule the daily models. The starting date is set on Jan 6th, 2019. This will force Ariflow to backfill all date for those dates. So leave that for last.

If everything goes well, you should have the daily model execute successfully and see similar task durations as per below.

Finally, within Adminer you can view the final models.
<img src="https://storage.googleapis.com/analyticsmayhem-blog-files/dbt-airflow-docker/dbt-adminer-view.png" width="70%"></img>

## Docker Compose Commands
* Enable the services: `docker-compose up` or `docker-compose up -d` (detatches the terminal from the services' log)
* Disable the services: `docker-compose down` Non-destructive operation.
* Delete the services: `docker-compose rm` Ddeletes all associated data. The database will be empty on next run.
* Re-build the services: `docker-compose build` Re-builds the containers based on the docker-compose.yml definition. Since only the Airflow service is based on local files, this is the only image that is re-build (useful if you apply changes on the `./scripts_airflow/init.sh` file.

If you need to connect to the running containers, use `docker-compose ps` to view the running services.

For example, to connect to the Airflow service, you can execute `docker exec -it dbt-airflow-docker_airflow_1 /bin/bash`. This will attach your terminal to the selected container and activate a bash terminal.

## Project Notes and Docker Volumes
Because the project directories (`./scripts_postgres`, `./sample_data`, `./dbt` and `./airflow`) are defined as volumes in `docker-compose.yml`, they are directly accessible from within the containers. This means:
* On Airflow startup the existing models are compiled as part of the initialisation script. If you make changes to the models, you need to re-compile them. Two options:
  * From the host machine navigate to `./dbt` and then `dbt compile`
  * Attach to the container by `docker exec -it dbt-airflow-docker_airflow_1 /bin/bash`. This will open a session directly in the container running Airflow. Then CD into `/dbt` and  `dbt compile`. In general attaching to the container, helps a lot in debugging.
* You can make changes to the dbt models from the host machine, `dbt compile` them and on the next DAG update they will be available (beware of changes that are major and require `--full-refresh`). It is suggested to connect to the container (`docker exec ...`) to run a full refresh of the models. Alternatively you can `docker-compose down && docker-compose rm && docker-compose up`. 
* The folder `./airflow/dags` stores the DAG files. Changes on them appear after a few seconds in the Airflow admin.
  * The `initialise_data.py` file contains the upfront data loading operation of the seed data.
  * The `dag.py` file contains all the handling of the DBT models. Keep aspect is the parsing of `manifest.json` which holdes the models' tree structure and tag details

Credit to the very helpful repository: https://github.com/puckel/docker-airflow

### Infrastructure model

![Infrastructure model](https://raw.githubusercontent.com/konosp/dbt-airflow-docker-compose/master/.infragenie/infrastructure_model.png)

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