Package: airflow-mcd
airflow-mcdMonte Carlo's Airflow provider.InstallationRequires 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-mcdThis package can be added like any other python dependency to Airflow (e.g. viarequirements.txt).Basic usageCallbacksSends 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_callbacksBroad Callbacksif you don't have existing callbacks, these provide all-in-one callbacks:dag_callbackstask_callbacksexamples:dag = DAG(
    'dag_name',~~~~
    **mcd_callbacks.dag_callbacks,
)

task = BashOperator(
    task_id='task_name',
    bash_command='command',
    dag=dag,
    **mcd_callbacks.task_callbacks,
)Explicit CallbacksCallback TypeDescriptionDAGTaskon_success_callbackInvoked when the DAG/task succeedsmcd_dag_success_callbackmcd_task_success_callbackon_failure_callbackInvoked when the DAG/task failsmcd_dag_failure_callbackmcd_task_failure_callbacksla_miss_callbackInvoked when task(s) in a DAG misses its defined SLAmcd_sla_miss_callbackN/Aon_retry_callbackInvoked when the task is up for retryN/Amcd_task_retry_callbackon_execute_callbackInvoked right before the task begins executing.N/Amcd_task_execute_callbackexamples: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:SessionHookCreates apycarlocompatible 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>"
}Seeherefor details on how to generate a token.Operators:BaseMcdOperatorThis operator can be extended to build your own operator using ourSDKor any other
dependencies. This is useful if you want implement your own custom logic (e.g. creating custom lineage after a task completes).SimpleCircuitBreakerOperatorThis 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 anAirflowFailExceptionif 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 anAirflowException. 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 >> task2This 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.
RaisesAirflowSkipExceptionif set to True and any issues are encountered. Recommended to set thetrigger_ruleparam for any downstream tasks tonone_failedin this case.dbt OperatorsThe following suite of Airflow operators can be used to execute dbt commands. They include ourdbt Coreintegration (via ourPython SDK), to automatically send dbt artifacts to Monte Carlo.DbtBuildOperatorDbtRunOperatorDbtSeedOperatorDbtSnapshotOperatorDbtTestOperatorExample 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 baseDbtOperatorfor a comprehensive list.Advanced ConfigurationTo reduce repetitive configuration of the dbt operators, you can define aDefaultConfigProviderthat 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.DefaultConfigIf 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.DefaultConfigTests and releasesLocally make test will run all tests. SeeREADME-dev.mdfor 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.LicenseApache 2.0 - See theLICENSEfor more information.