Airflow is a scheduler libraries for Python used to configure multi-system workflows executed in parallel across any number of users. A single Airflow pipeline comprises of SQL, bash, and Python operations. The tool works by specifying on dependencies between tasks, a critical element that helps determine the tasks to be run in parallel and which ones to be executed after the other functions are complete.
Airflow tool is written in Python, giving you the advantage to add your operators to the already set custom functionality. This tool allows you to scrape data through transformations from a website to a well-structured datasheet. Airflow uses Directed Acyclic Graphs (DAG) to represent a specific workflow. In this case, a workflow refers to a collection of tasks that comprises of directional dependencies.
Airflow is a Warehouse Management System that works to define tasks as their ultimate dependencies as the code executes the functions on a schedule and distributes the task execution across all the worker processes. This tool offers a user interface that displays the state of both running and past tasks.
Airflow displays diagnostic information to users regarding the task execution process and allows the end-user to manage execution of tasks manually. Note that a directed acyclic graph is only used to set the execution context and to organize tasks. In Airflow, tasks are the crucial elements that run a scraping script. In scraping, tasks comprise of two flavors that include:
In some cases, tasks work as operators where they execute operations as specified by the end users. Operators are designed to run scraping script and other functions that can be performed in Python programming language.
Tasks are also developed to work as sensors. In such a case, execution of tasks that depend on each other can be paused until a criterion where a workflow runs smoothly has been met.
Airflow is used in different fields to run a scraping script. Below is a guide on how to use Airflow.
- Open your browser and check your user interface
- Check the workflow that failed and clicks on it to see the tasks that went wrong
- Click on "View log" to check the cause of failure. In many cases, password authentication failure causes the workflow failure
- Go to the admin section and click on "Connections." Edit the Postgres connection to retrieve the new password and click "Save."
- Re-visit your browser and click on the task that had failed. Click on the task and tap "Clear" so that the task runs successfully next time.
Other Python schedulers to consider
Cron
Cron is a Unix-based OS used to run scraping scripts periodically at fixed intervals, dates, and times. This library is mostly used to maintain and set up software environments.
Luigi
Luigi is a Python module that will allow you to handle visualization and dependency resolution. Luigi is used for creating complex pipelines of jobs collection.
Airflow is a scheduler library for Python used to handle dependency management projects. In Airflow, running tasks depends on each other. To obtain consistent results, you can set your Airflow script to run automatically after every an hour or two.
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