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How to Handle API Response Errors in Airflow

Discover how to effectively manage API responses in Apache Airflow by separating successful tasks from error handling. Learn best practices for organizing your tasks based on API response outcomes. --- This video is based on the question https://stackoverflow.com/q/68166533/ asked by the user 'KristiLuna' ( https://stackoverflow.com/u/14444816/ ) and on the answer https://stackoverflow.com/a/68166792/ provided by the user 'Jarek Potiuk' ( https://stackoverflow.com/u/516701/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: using Airflow, from the same loop an API response with success continue to next task but a response w/ error has separate task that shows as failed Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Handling API Response Errors in Airflow In the world of data workflows, managing API responses efficiently can significantly impact your process's reliability and functionality. If you're using Apache Airflow to orchestrate your tasks, you may encounter situations where some API calls succeed while others result in errors. This raises a crucial question: How can you continue with the successful tasks while handling the failed ones appropriately? Let’s break down the problem and explore practical solutions for managing such scenarios effectively. Understanding the Problem Imagine you have a loop that iterates through a list of Facebook pages, fetching insights using an endpoint. However, not all API calls return successful responses. Here's the essence of the challenge: Pages that return success should proceed to the next task without hindrance. Pages that throw an error need to be captured for review without affecting the success of other tasks. The code snippet in question might look something like this: [[See Video to Reveal this Text or Code Snippet]] In this setup, how can you maintain flow for successful API calls while handling errors effectively? Solution: Organizing Tasks in Airflow Here’s how you can structure your Airflow tasks to efficiently handle the different outcomes of your API responses. For Small Numbers of Pages If the number of Facebook pages is relatively small, you can utilize XComs (cross-communications) in Airflow to manage the outcomes of your API calls. Here's how to set it up: Return Two Separate Dictionaries: Store successful page results and errors in two distinct dictionaries. Use XCom to Push Results: Use xcom.push() to send these dictionaries to subsequent tasks. Process Results in Following Tasks: Fetch the results using xcom.pull() in tasks that follow and handle them accordingly. For Larger Scale Pages When dealing with thousands of pages, the approach changes slightly due to scalability considerations. Here’s how to manage larger datasets: Save Results Externally: Instead of pushing everything through XComs, consider saving the successful and failed page results to external storage solutions such as Google Cloud Storage (GCS) or Amazon S3. Create two distinct objects or files, one for successes and another for errors. Reference with XComs: Use XComs to reference the storage paths or filenames where results are saved. Fetch and Handle in Separate Tasks: Create dedicated tasks to handle successful results and error messages by reading from those storage files. Example Workflow The possible structure of your workflow in Airflow would look something like this: Task A: Fetch data from the API. Task B: Process successful responses (pull from XCom). Task C: Handle error responses (read from external storage). This creates a clean separation between success and failure, allowing you to analyze errors without disrupting the main workflow. Conclusion Effectively managing API responses is crucial for robust data workflows in Airflow. By utilizing mechanisms such as XComs for smaller projects and external storage for larger tasks, you ensure your workflow remains efficient and adaptable. With these strategies in place, you can focus on processing data effectively while minimizing errors and failures in your data orchestration processes. By following these guidelines, you can build a resilient and scalable data pipeline that elegantly handles API response outcomes.