Handling large-scale contact lists requires a reliable, asynchronous approach to data processing. When you need to verify which phone numbers are registered with WhatsApp, building a pipeline that manages file uploads and status polling ensures your application remains responsive while the heavy lifting happens in the background.
This guide covers how to implement a robust integration for bulk WhatsApp checking using a standard asynchronous batch workflow.
1. Preparing Your Data
Before interacting with the API, ensure your input data is clean. The system expects a text file with one phone number per line. Normalization is critical here; always convert your phone numbers to the E.164 format (e.g., +14155552671) before saving them to your input file. This prevents processing errors and ensures higher match accuracy.
2. Initiating the Task
To start a verification job, submit your file to the /v1/tasks endpoint. The API uses a multipart form-data request. You will need to provide your API key in the X-API-Key header.
# Conceptual: Creating a task
curl --location 'https://api.checknumber.ai/v1/tasks' \
--header 'X-API-Key: YOUR_API_KEY' \
--form 'file=@"./numbers.txt"' \
--form 'task_type="whatsapp_check"'
Upon success, the API returns a task_id. Store this identifier in your database—you will need it to track the progress of your request.
3. Asynchronous Polling Strategy
Because processing 5,000 numbers takes time, the system does not return results immediately. Instead, you must poll the /v1/gettasks endpoint using your task_id.
- Pending/Processing: The task is still in the queue or currently running.
- Exported: The task is complete, and your results are ready for download.
Avoid aggressive polling. Implement a configurable wait interval between requests to keep your integration lightweight.
4. Result Parsing and Integration
Once the status reaches exported, the response will contain a result_url. This URL points to a compressed file containing your processed data.
Best Practices for Result Handling:
- Downstream Mapping: When parsing the exported file, map the returned fields to your internal schema. Ensure your code is resilient to changes in column ordering.
-
Error Handling: Check the
failurecounter in the task status response. If specific rows failed, they will be documented in the final result file. -
Cleanup: Once you have successfully downloaded and processed the file, you can archive the
task_idlocally for your audit logs.
Conclusion
By following this asynchronous pattern, you can manage large-scale verification tasks without blocking your main application threads. For further details on specific integration constraints or to review your current API status, please refer to the official documentation.
This article was drafted with AI assistance and reviewed before publishing.
