Python API Reference¶

Operators¶

Operators allow for generation of certain types of tasks that become nodes in the DAG when instantiated. All operators derive from BaseOperator and inherit many attributes and methods that way.

There are 3 main types of operators:

Operators that performs an action, or tell another system to perform an action
Transfer operators move data from one system to another
Sensors are a certain type of operator that will keep running until a certain criterion is met. Examples include a specific file landing in HDFS or S3, a partition appearing in Hive, or a specific time of the day. Sensors are derived from BaseSensorOperator and run a poke method at a specified poke_interval until it returns True.

BaseOperator¶

All operators are derived from BaseOperator and acquire much functionality through inheritance. Since this is the core of the engine, it's worth taking the time to understand the parameters of BaseOperator to understand the primitive features that can be leveraged in your DAGs.

BaseSensorOperator¶

All sensors are derived from BaseSensorOperator. All sensors inherit the timeout and poke_interval on top of the BaseOperator attributes.

Operators packages¶

All operators are in the following packages:

Hooks¶

Hooks are interfaces to external platforms and databases, implementing a common interface when possible and acting as building blocks for operators. All hooks are derived from BaseHook.

Hooks packages¶

All hooks are in the following packages:

airflow.hooks

Executors¶

Executors are the mechanism by which task instances get run. All executors are derived from BaseExecutor.

Executors packages¶

All executors are in the following packages:

airflow.executors

Models¶

Models are built on top of the SQLAlchemy ORM Base class, and instances are persisted in the database.

airflow.models

Exceptions¶

airflow.exceptions

Secrets Backends¶

Airflow relies on secrets backends to retrieve Connection objects. All secrets backends derive from BaseSecretsBackend.

airflow.secrets

Timetables¶

Custom timetable implementations provide Airflow's scheduler additional logic to schedule DAG runs in ways not possible with built-in schedule expressions.

airflow.timetables