Refactored MongoDB Data Query and Debugging

📅 2025-01-27 — Session: Refactored MongoDB Data Query and Debugging

🕒 16:10–16:50
🏷️ Labels: Mongodb, Debugging, Data Processing, Python, Flask
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to refine and debug MongoDB queries, ensuring accurate data retrieval and processing.

Key Activities

• Queried the last inserted tasks in MongoDB using Python and pymongo, focusing on sorting and limiting results.
• Checked document existence in MongoDB collections with updated methods to avoid deprecated functions.
• Diagnosed missing fields in MongoDB collections, identifying reasons for None values and proposing diagnostic steps.
• Debugged schema mismatches in task_processed_emails collection, addressing data ingestion and query logic issues.
• Refined query logic for processed_at timestamps to ensure correct retrieval of recent entries.
• Resolved sorting issues in MongoDB queries, focusing on the processed_at field.
• Updated Flask API endpoint to sort documents by processed_at, ensuring the most recent records are fetched.
• Reflected on job execution and data processing insights, integrating Google Sheets with MongoDB and highlighting warnings and improvements.

Achievements

• Improved data retrieval accuracy by addressing sorting and schema issues in MongoDB.
• Enhanced the Flask API endpoint for better document sorting.
• Identified and proposed solutions for missing field issues in data processing workflows.

Pending Tasks

• Further testing of the updated Flask API endpoint in production.
• Continuous monitoring of MongoDB queries to ensure ongoing accuracy and performance.