airflow-mcd

Monte Carlo's Airflow provider.

Installation

Requires Python 3.7 or greater and is compatible with Airflow 1.10.14 or greater.

You can install and update using pip. For instance:

pip install -U airflow-mcd

This package can be added like any other python dependency to Airflow (e.g. via requirements.txt).

apache-airflow>=1.10.14 is required for this package to work so it also need to be installed.

pip install -U apache-airflow>=1.10.14
pip install -U airflow-mcd

Note: There are critical security vulnerabilities in apache-airflow versions under 2.6.0b1 so we recommend installing that version or greater.

Basic usage

Callbacks

Sends a webhook back to Monte Carlo upon an event in Airflow. [Detailed examples and documentation here] (https://docs.getmontecarlo.com/docs/airflow-incidents-dags-and-tasks). Callbacks are at the DAG or Task level.

To import: from airflow_mcd.callbacks import mcd_callbacks

Broad Callbacks

if you don't have existing callbacks, these provide all-in-one callbacks:

dag_callbacks

task_callbacks

examples:

dag = DAG(
    'dag_name',~~~~
    **mcd_callbacks.dag_callbacks,
)

task = BashOperator(
    task_id='task_name',
    bash_command='command',
    dag=dag,
    **mcd_callbacks.task_callbacks,
)

Explicit Callbacks

examples:

dag = DAG(
    'dag_name',
    on_success_callback=mcd_callbacks.mcd_dag_success_callback,
    on_failure_callback=mcd_callbacks.mcd_dag_failure_callback,
    sla_miss_callback=mcd_callbacks.mcd_sla_miss_callback,
)

task = BashOperator(
    task_id='task_name',
    bash_command='command',
    dag=dag,
    on_success_callback=mcd_callbacks.mcd_task_success_callback,
    on_failure_callback=mcd_callbacks.mcd_task_failure_callback,
    on_execute_callback=mcd_callbacks.mcd_task_execute_callback,
    task_retry_callback=mcd_callbacks.mcd_task_retry_callback,
)

Hooks:

• SessionHook

  Creates a pycarlo compatible session. This is useful for creating your own operator built on top of our Python SDK.

  This hook expects an Airflow HTTP connection with the Monte Carlo API id as the "login" and the API token as the "password".

  Alternatively, you could define both the Monte Carlo API id and token in "extra" with the following format:

  {
      "mcd_id": "<ID>",
      "mcd_token": "<TOKEN>"
  }
  

  See here for details on how to generate a token.

Operators:

• BaseMcdOperator

  This operator can be extended to build your own operator using our SDK or any other dependencies. This is useful if you want implement your own custom logic (e.g. creating custom lineage after a task completes).

• SimpleCircuitBreakerOperator

  This operator can be used to execute a circuit breaker compatible rule (custom SQL monitor) to run integrity tests before allowing any downstream tasks to execute. Raises an AirflowFailException if the rule condition is in breach when using an Airflow version newer than 1.10.11, as that is preferred for tasks that can be failed without retrying. Older Airflow versions raise an AirflowException. For instance:

  from datetime import datetime, timedelta
  
  from airflow import DAG
  
  try:
    from airflow.operators.bash import BashOperator
  except ImportError:
    # For airflow versions <= 2.0.0. This module was deprecated in 2.0.0.
    from airflow.operators.bash_operator import BashOperator
  
  from airflow_mcd.operators import SimpleCircuitBreakerOperator
  
  mcd_connection_id = 'mcd_default_session'
  
  with DAG('sample-dag', start_date=datetime(2022, 2, 8), catchup=False, schedule_interval=timedelta(1)) as dag:
      task1 = BashOperator(
          task_id='example_elt_job_1',
          bash_command='echo I am transforming a very important table!',
      )
      breaker = SimpleCircuitBreakerOperator(
          task_id='example_circuit_breaker',
          mcd_session_conn_id=mcd_connection_id,
          rule_uuid='<RULE_UUID>'
      )
      task2 = BashOperator(
          task_id='example_elt_job_2',
          bash_command='echo I am building a very important dashboard from the table created in task1!',
          trigger_rule='none_failed'
      )
  
      task1 >> breaker >> task2
  

  This operator expects the following parameters:

  • mcd_session_conn_id: A SessionHook compatible connection.
  • rule_uuid: UUID of the rule (custom SQL monitor) to execute.

  The following parameters can also be passed:

  • timeout_in_minutes [default=5]: Polling timeout in minutes. Note that The Data Collector Lambda has a max timeout of 15 minutes when executing a query. Queries that take longer to execute are not supported, so we recommend filtering down the query output to improve performance (e.g limit WHERE clause). If you expect a query to take the full 15 minutes we recommend padding the timeout to 20 minutes.
  • fail_open [default=True]: Prevent any errors or timeouts when executing a rule from stopping your pipeline. Raises AirflowSkipException if set to True and any issues are encountered. Recommended to set the trigger_rule param for any downstream tasks to none_failed in this case.

• dbt Operators

  The following suite of Airflow operators can be used to execute dbt commands. They include our dbt Core integration (via our Python SDK), to automatically send dbt artifacts to Monte Carlo.

  • DbtBuildOperator
  • DbtRunOperator
  • DbtSeedOperator
  • DbtSnapshotOperator
  • DbtTestOperator

  Example of usage:

  from airflow_mcd.operators.dbt import DbtRunOperator
  
  dbt_run = DbtRunOperator(
      task_id='run-model',          # Airflow task id
      project_name='some_project',  # name of project to associate dbt results
      job_name='some_job',          # name of job to associate dbt results
      models='some_model',          # dbt model selector
      mc_conn_id='monte_carlo',     # id of Monte Carlo API connection configured in Airflow
  )
  

  Many more operator options are available. See the base DbtOperator for a comprehensive list.

  Advanced Configuration

  To reduce repetitive configuration of the dbt operators, you can define a DefaultConfigProvider that would apply configuration to every Monte Carlo dbt operator.

  Example of usage:

  from airflow_mcd.operators.dbt import DefaultConfig, DefaultConfigProvider
  
  class DefaultConfig(DefaultConfigProvider):
    """
    This default configuration will be applied to all Monte Carlo dbt operators.
    Any property defined here can be overridden with arguments provided to an operator.
    """
    def config(self) -> DbtConfig:
        return DbtConfig(
            mc_conn_id='monte_carlo',
            env={
                'foo': 'bar',
            }
        )
  

  The location of this class should be provided in an environment variable:

  AIRFLOW_MCD_DBT_CONFIG_PROVIDER=configs.dbt.DefaultConfig
  

  If you are using AWS Managed Apache Airflow (MWAA), the location of this class should be defined in a configuration option in your Airflow environment:

  mc.airflow_mcd_dbt_config_provider=configs.dbt.DefaultConfig
  

Tests and releases

Locally make test will run all tests. See README-dev.md for additional details on development. When ready for a review, create a PR against main.

When ready to release, create a new Github release with a tag using semantic versioning (e.g. v0.42.0) and CircleCI will test and publish to PyPI. Note that an existing version will not be deployed.

License

Apache 2.0 - See the LICENSE for more information.