
During May 2025, Lizzi worked on the Shopify/opentelemetry-ebpf-profiler repository, focusing on stabilizing frame handling under concurrent access. She addressed a race condition in the reporter’s FrameKnown logic by upgrading the lock from read to write, ensuring exclusive access during frame map operations. This targeted fix, implemented in Go, prevented data corruption and inconsistent states, especially under high telemetry load. Lizzi’s work leveraged her skills in concurrency, debugging, and system programming to enhance the reliability of telemetry data collection. Although the period involved a single bug fix, the solution demonstrated careful attention to data integrity and robust concurrent system design.

May 2025 monthly summary for Shopify/opentelemetry-ebpf-profiler: Stabilized frame handling under concurrent access by fixing a race condition in the reporter's FrameKnown logic. The fix upgrades the FrameKnown lock from read to write to ensure exclusive access during frame map operations, preventing data corruption and inconsistent states, particularly under high telemetry load. This change reduces risk of data loss and production incidents related to frame data and improves overall tracer reliability.
May 2025 monthly summary for Shopify/opentelemetry-ebpf-profiler: Stabilized frame handling under concurrent access by fixing a race condition in the reporter's FrameKnown logic. The fix upgrades the FrameKnown lock from read to write to ensure exclusive access during frame map operations, preventing data corruption and inconsistent states, particularly under high telemetry load. This change reduces risk of data loss and production incidents related to frame data and improves overall tracer reliability.
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