Summary
Between May 15 and May 28, 2026, a processing delay was identified affecting dependency update workflows for several large accounts. During this period, customers may have experienced significant lag in the time required to process and reflect dependency updates within the system. The issue was caused by an inefficiency in how the database retrieved specific project records, leading to temporary performance degradation. The issue has been fully resolved, and processing speeds have returned to expected levels.
Key Timeline (IDT)
May 17, 2026, 09:00 IDT: Initial reports of increased processing times for dependency updates were observed.
May 24, 2026, 14:30 IDT: System resources were increased to provide temporary relief while the investigation continued.
May 28, 2026, 11:15 IDT: Technical analysis identified a specific database query that was not utilizing the most efficient lookup path.
May 30, 2026, 16:45 IDT: A corrective update to the database indexing structure was deployed.
May 31, 2026, 10:00 IDT: Monitoring confirmed that processing latency returned to sub-millisecond levels and the backlog was cleared.
Root Cause
The root cause was a performance regression in the update workflow’s data retrieval process. The database’s automated query planner began selecting a less efficient path to locate project references. Because the system was searching through a very large volume of records, this inefficient path resulted in the system scanning thousands of unnecessary rows for each update. This led to high disk activity and significant delays, particularly for accounts with a high density of shared dependencies across many projects.
Actions Taken
Resource Scaling: Temporarily increased database memory and processing power to mitigate the immediate impact on customers.
Database Optimization: Performed a deep analysis of query execution plans to identify the specific bottleneck.
Index Refinement: Replaced an existing database index with an optimized version that ensures the query planner always selects the most direct path for data retrieval.
Validation: Verified the fix across all environments to ensure the new indexing structure supports both data integrity and high-speed lookups.
Action Items
Enhanced Monitoring: Implement more granular alerts for database query performance to detect similar planning inefficiencies before they impact customers.
Optimization Standards: Update internal database design guidelines to prevent overlapping index patterns that can confuse automated query planners.
Performance Testing: Incorporate “cold cache” scenarios into performance testing for large-scale data operations to better simulate real-world disk I/O constraints.
Resolved
Summary
Between May 15 and May 28, 2026, a processing delay was identified affecting dependency update workflows for several large accounts. During this period, customers may have experienced significant lag in the time required to process and reflect dependency updates within the system. The issue was caused by an inefficiency in how the database retrieved specific project records, leading to temporary performance degradation. The issue has been fully resolved, and processing speeds have returned to expected levels.
Key Timeline (IDT)
May 17, 2026, 09:00 IDT: Initial reports of increased processing times for dependency updates were observed.
May 24, 2026, 14:30 IDT: System resources were increased to provide temporary relief while the investigation continued.
May 28, 2026, 11:15 IDT: Technical analysis identified a specific database query that was not utilizing the most efficient lookup path.
May 30, 2026, 16:45 IDT: A corrective update to the database indexing structure was deployed.
May 31, 2026, 10:00 IDT: Monitoring confirmed that processing latency returned to sub-millisecond levels and the backlog was cleared.
Root Cause
The root cause was a performance regression in the update workflow’s data retrieval process. The database’s automated query planner began selecting a less efficient path to locate project references. Because the system was searching through a very large volume of records, this inefficient path resulted in the system scanning thousands of unnecessary rows for each update. This led to high disk activity and significant delays, particularly for accounts with a high density of shared dependencies across many projects.
Actions Taken
Resource Scaling: Temporarily increased database memory and processing power to mitigate the immediate impact on customers.
Database Optimization: Performed a deep analysis of query execution plans to identify the specific bottleneck.
Index Refinement: Replaced an existing database index with an optimized version that ensures the query planner always selects the most direct path for data retrieval.
Validation: Verified the fix across all environments to ensure the new indexing structure supports both data integrity and high-speed lookups.
Action Items
Enhanced Monitoring: Implement more granular alerts for database query performance to detect similar planning inefficiencies before they impact customers.
Optimization Standards: Update internal database design guidelines to prevent overlapping index patterns that can confuse automated query planners.
Performance Testing: Incorporate “cold cache” scenarios into performance testing for large-scale data operations to better simulate real-world disk I/O constraints.
Identified
We are seeing latency in SCA scans. On demand scans may take longer time to complete. Pull request scans are not affected.