"Certainly! The interviewer asked how I ensured the accuracy and efficiency of data collection in stock data crawling project using technologies like MongoDB and Redis. To break it down, here’s my understanding of the question and the approach I took:
**Background**: The primary goal of the project was to collect daily stock data efficiently while ensuring its accuracy for analysis. We focused on various data sources like East Money and stock exchanges.
2. **Challenge**: The challenge was to handle large volumes of data consistently while avoiding any data loss or duplication, which could mislead our analysis.
3. **Solution**: To address this, we employed a structured solution. We utilized MongoDB for data storage due to its flexibility and ease in handling large datasets. We implemented Redis for caching frequently queried data. This combination allowed us to reduce response times and improve data retrieval efficiency. Additionally, I scheduled crawls using a crontab to ensure updates without overloading the source servers.
4. **Results**: As a result, we achieved high accuracy in the collected data, improved our data processing speed, and optimized storage, allowing our researchers to access reliable data quickly for their analyses.
This structured approach ensured we maintained accuracy while enhancing efficiency in data collection."
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