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airflow-mcd-0.0.7
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Monte Carlo's Apache Airflow Provider
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | airflow-mcd-0.0.7 |
| نام | airflow-mcd |
| نسخه کتابخانه | 0.0.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Monte Carlo Data, Inc |
| ایمیل نویسنده | info@montecarlodata.com |
| آدرس صفحه اصلی | https://www.montecarlodata.com/ |
| آدرس اینترنتی | https://pypi.org/project/airflow-mcd/ |
| مجوز | Apache Software License (Apache 2.0) |
# airflow-mcd
Monte Carlo's alpha 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`).
## Basic usage
### Hooks:
- **SessionHook**
Creates a [pycarlo](https://pypi.org/project/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](https://docs.getmontecarlo.com/docs/creating-an-api-token) for details on how to generate a token.
### Operators:
- **BaseMcdOperator**
This operator can be extended to build your own operator using our [SDK](https://pypi.org/project/pycarlo/) 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](https://airflow.apache.org/docs/apache-airflow/stable/concepts/dags.html#trigger-rules)
param for any downstream tasks to `none_failed` in this case.
## Tests and releases
Locally make test will run all tests. See [README-dev.md](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](http://www.apache.org/licenses/LICENSE-2.0) for more information.
نیازمندی
| مقدار | نام |
|---|---|
| >=0.2.4 | pycarlo |
| >=1.10.14 | apache-airflow |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl airflow-mcd-0.0.7:
pip install airflow-mcd-0.0.7.whl
نصب پکیج tar.gz airflow-mcd-0.0.7:
pip install airflow-mcd-0.0.7.tar.gz