"Certainly! You asked about how I structured the program for exception handling and logging in Stock Data Crawling using requests and Mongo. I appreciate the question and it down into a key points.
First, I established a robust exception handling mechanism to capture different types of errors such as network failures and data parsing issues. involved using `try-except` blocks to exceptions gracefully and ensure the program wouldn't crash unexpectedly.
Second, I implemented logging throughout the crawling process. I used Python's `logging` module to record important events, such as successful data retrievals and errors. This not only helped in debugging but also allowed for a comprehensive view of the crawler's performance over time.
Third, I structured the program in an organized manner, separating the crawling logic, error handling, and logging functions into distinct modules. This modular approach made the code more readable and maintainable.
Finally, this structured approach led to enhanced reliability in the crawling process and real-time monitoring of the system, enabling proactive issue resolution and overall better data quality.
In summary:
1. Robust exception handling was established.
2. Logging was systematically implemented.
3. Modular code organization enhanced maintainability.
4. Improved reliability and data quality in the crawling process. "
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