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96 lines
4.2 KiB
Markdown
96 lines
4.2 KiB
Markdown
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## Airflow
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[Apache Airflow](https://github.com/apache/airflow) was a tool [developed by Airbnb in 2014 and later open-sourced](https://medium.com/airbnb-engineering/airflow-a-workflow-management-platform-46318b977fd8). It is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
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You can use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
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Here is [a very simple toy example of an Airflow job](https://gist.github.com/robert8138/c6e492d00cd7b7e7626670ba2ed32e6a) that simply prints the date in bash every day after waiting for one second to pass, after the execution date is reached:
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```python
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from datetime import datetime, timedelta
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from airflow.models import DAG # Import the DAG class
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from airflow.operators.bash_operator import BashOperator
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from airflow.operators.sensors import TimeDeltaSensor
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default_args = {
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'owner': 'you',
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'depends_on_past': False,
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'start_date': datetime(2018, 1, 8),
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}
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dag = DAG(
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dag_id='anatomy_of_a_dag',
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description="This describes my DAG",
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default_args=default_args,
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schedule_interval=timedelta(days=1)) # This is a daily DAG.
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# t0 and t1 are examples of tasks created by instantiating operators
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t0 = TimeDeltaSensor(
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task_id='wait_a_second',
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delta=timedelta(seconds=1),
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dag=dag)
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t1 = BashOperator(
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task_id='print_date_in_bash',
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bash_command='date',
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dag=dag)
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t1.set_upstream(t0)
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```
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### Luigi
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[Luigi data pipelining](https://github.com/spotify/luigi) is Spotify's Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, etc. It also comes with Hadoop support built in.
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The basic units of Luigi are task classes that model an atomic ETL operation, in three parts: a requirements part that includes pointers to other tasks that need to run before this task, the data transformation step, and the output. All tasks can be feed into a final table (e.g. on Redshift) into one file.
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Here is [an example of a simple workflow in Luigi](https://towardsdatascience.com/data-pipelines-luigi-airflow-everything-you-need-to-know-18dc741449b7):
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```python
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import luigi
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class WritePipelineTask(luigi.Task):
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def output(self):
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return luigi.LocalTarget("data/output_one.txt")
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def run(self):
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with self.output().open("w") as output_file:
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output_file.write("pipeline")
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class AddMyTask(luigi.Task):
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def output(self):
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return luigi.LocalTarget("data/output_two.txt")
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def requires(self):
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return WritePipelineTask()
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def run(self):
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with self.input().open("r") as input_file:
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line = input_file.read()
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with self.output().open("w") as output_file:
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decorated_line = "My "+line
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output_file.write(decorated_line)
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```
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### Airflow vs. Luigi
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| | Airflow | Luigi |
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|---------------------------------------|-----------------------|------------------------|
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| web dashboard | very nice | minimal |
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| Built-in scheduler | yes | no |
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| Separates output data and task state | yes | no |
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| calendar scheduling | yes | no, use cron |
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| parallelism | yes, workers | threads per workers |
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| finds new deployed tasks | yes | no |
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| persists state | yes, to db | sort of |
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| sync tasks to workers | yes | no |
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| scheduling | yes | no |
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