training

Module
Platform Services / Logging Framework / Performance Optimization

Background

Several application services generate a large volume of logs, resulting in increased database storage consumption, archive costs, and operational overhead. A comprehensive review is required to identify unnecessary, duplicate, verbose, and low-value logs across all services.

Objective

Review all microservices and applications to ensure only meaningful, actionable, and audit-required logs are retained while eliminating excessive logging that does not provide operational or business value.

Scope
Service Review
Review logging implementation across all services.
Identify:
Duplicate log entries
Debug logs enabled in production
Repeated heartbeat/status logs
Excessive request/response payload logging
Unused informational logs
High-frequency logs with low business value
Redundant exception logging
Log Classification

Categorize logs into:

Critical
Error
Warning
Audit
Information
Debug

Review whether each log category is required in production.

Optimization Activities
Remove unnecessary logs.
Convert verbose logs to debug level where applicable.
Reduce repeated logs generated in loops, schedulers, and polling operations.
Ensure sensitive information is not logged.
Standardize logging format across services.
Impact Assessment
Estimate reduction in:
Log volume
MongoDB storage usage
Archive storage usage
Log processing overhead
Deliverables
Service-wise logging review report.
List of logs removed or downgraded.
Recommended logging standards document.
Estimated storage savings after implementation.
Acceptance Criteria
All services reviewed for logging efficiency.
Unnecessary and duplicate logs identified and removed.
Production logging levels validated.
Critical audit and error logs remain unaffected.
Logging standards documented and shared with the team.
Storage and performance impact assessment completed.
No impact on troubleshooting, monitoring, or audit requirements.
Expected Outcome

Reduced log storage growth, lower archive costs, improved application performance, and a standardized logging strategy across all services.