# How to Setup and Run Apache Airflow Locally?

> tl;dr [get the bash script](https://gist.github.com/bhavaniravi/bca59c263fff1e9e2924326c0139c421#file-airflow_local_setup-bash)

1. Have Python installed in your system, 3.8+
    
2. Create a folder
    

```bash
mkdir -p "/Users/$(whoami)/projects/airflow-local"
export AIRFLOW_HOME="/Users/$(whoami)/projects/airflow-local"
```

1. `cd airflow-local`
    
2. Set airflow version `AIRFLOW_VERSION=2.4.3`
    
3. Set airflow version `PYTHON_VERSION="$(python --version | cut -d " " -f 2 | cut -d "." -f 1-2)"`
    
4. Set constraints version `CONSTRAINT_URL="`[`https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt`](https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt)`"`
    
5. Create virtualenv and install airflow
    

```bash
cd "/Users/$(whoami)/projects/airflow-local"
python -m venv venv
source venv/bin/activate
pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"
```

1. `mkdir -p "${AIRFLOW_HOME}/dags"`
    
2. Run airflow with `airflow standalone`
    

This will run, creating an SQLite DB. This is not really production friendly. You can use them to run basic pipelines and playgrounds. We can also make our local environment production friendly. We will see that in a bit.

## The Airflow Project

1. `standalone_admin_password.txt` has the password for your local airflow password is `admin`
    
2. `dags` the folder is where you add your dags, a.k.a pipelines
    
3. `logs` the folder will have your logs
    

## Using LocalExecutor

Airflow, by default, used `SequentialExecutor` this is not great for production-level systems. When exploring Airflow, it is a good idea to use `LocalExecutor` along with Postgres or MySQL right away.

Stop the Airflow instance. Update the `airflow.cfg` with the following configs. Replace `sql_alchemy_conn` with respective DB credentials

```apache
[core]
load_examples = False
executor=LocalExecutor

[database]
sql_alchemy_conn = postgresql://<pg-user>:<pg-password>@<host>:<port:5423>/<db-name>
```

If you use Postgres, You will need `psycopg` the library, which you can download using the following command

`pip install "apache-airflow[postgres]"`

## Sample Dag

Copy the example dag from the [Airflow repo](https://github.com/apache/airflow/blob/2.4.3/airflow/example_dags/example_bash_operator.py) and place it under the dags folder

## Restart Airflow

`airflow standalone`

---

Airflow has a steep learning curve. I can help you adopt Airflow for your Data engineering pipeline and your team's ecosystem. [Schedule a free call today.](https://zcal.co/bhavaniravi/airflow-starterpack)