Now we need to edit airflow.cfg, please find sql_alchemy_conn and set it to Also please set load_examples = False, this option is responsible for loading unnecessary example DAGs, we do not need them.īecause we are using PostgreSQL, we need to install additional python package called psycopg2. Postgres=# grant all privileges on database airflow_metadata to airflow ![]() Postgres=# CREATE USER airflow WITH password 'airflow' You have to install PostgreSQL on your local machine or use postgres docker image.Ĭreate fresh database: postgres=# create database airflow_metadata Now you should initialize a database: $ airflow initdbīy default Airflow uses SQLite as a database, for production I would recommend to use PostgreSQL or MySQL. You can change the location by redefining environment variable. By default it is located in ~/airflow/airflow.cfg. You can run it in order to check if everything is OK.Ĭonfiguration file for Airflow is called airflow.cfg. venvĪpache Airflow has lots of dependencies in comparison to Luigi: alembic=1.4.0Īfter the installation venv will have airflow binary which can be executed. I am not going to use Airflow docker container.įirst we need to create a virtual environment and install Airflow package into it: $ python3 -m venv. Scheduler, which is responsible for scheduling jobs/dagsĪirflow needs a database, the best practice is to use PostgreSQL or MySQL in production.WebApp with the dashboard, written using Flask.It is time to practice □ Apache Airflow consists of few parts: If you have PostgresOperator in your pipeline it uses PostgresHook under the hood. They are building blocks for Operators in terms of communication with these services. Hooks are interfaces to external services like databases, HTTP APIs, Cloud services etc. If you want to build your own Sensor you have to inherit from BaseSensorOperator and redefine poke method. RedisKeySensor - checks if a key exists in Redis. ![]()
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