Context
DataCorp, a leading data analytics firm, has deployed a Python microservice using a LangChain agent to automate data extraction from various APIs. The microservice needs robust monitoring to ensure data quality, performance, and operational reliability in production. Current monitoring is minimal, leading to undetected failures and degraded performance.
Scale Requirements
- Throughput: Handle up to 1,000 API calls per minute
- Latency: Response time should be < 200 ms per request
- Data Volume: Process approximately 2TB of data per month
- Uptime Requirement: 99.9% availability
Requirements
- Implement logging for all incoming requests and outgoing responses, including timestamps and response times.
- Establish data quality checks to validate incoming data against predefined schemas.
- Introduce performance monitoring to track latency and throughput metrics in real-time.
- Set up alerting mechanisms for anomalies, such as high error rates or latency spikes.
- Ensure compliance with data privacy regulations by anonymizing sensitive data before logging.
Constraints
- Infrastructure: Limited to AWS Lambda for deployment, which imposes cold start latency.
- Budget: Monthly monitoring costs must not exceed $500.
- Compliance: Must adhere to GDPR and HIPAA for data handling.
