The OpenTelemetry project is merging a profiling data model into its specification and working towards a stable implementation this year.
Austin Parker, Director of Open Source at Honeycomb, said: "Profiling is a method to dynamically inspect the behavior and performance of application code at run-time. Continuous profiling gives insights into resource utilization at a code-level and allows for this profiling data to be stored, queried, and analyzed over time and across different attributes. It’s an important technique for developers and performance engineers to understand exactly what’s happening in their code. OpenTelemetry’s profiling signal expands upon the work that has been done in this space and, as a first for the industry, connects profiles with other telemetry signals from applications and infrastructure. This allows developers and operators to correlate resource exhaustion or poor user experience across their services with not just the specific service or pod being impacted, but the function or line of code most responsible for it."
OpenTelemetry also announced the following two donations to accelerate the delivery and implementation of OpenTelemetry profiling:
- Elastic has pledged to donate their proprietary eBPF-based profiling agent
- Splunk has begun the process of donating their .NET based profiler
Profiles will support bi-directional links between themselves and other signals, such as logs, metrics, and traces. You’ll be able to easily jump from resource telemetry to a corresponding profile. For example:
- Metrics to profiles: You will be able to go from a spike in CPU usage or memory usage to the specific pieces of the code which are consuming that resource
- Traces to profiles: You will be able to understand not just the location of latency across your services, but when that latency is caused by pieces of the code it will be reflected in a profile attached to a trace or span
- Logs to profiles: Logs often give the context that something is wrong, but profiling will allow you to go from just tracking something (i.e. Out Of Memory errors) to seeing exactly which parts of the code are using up memory resources
More generally profiling helps deliver on the promise of observability by making it easier for users to query and understand an entire new dimension about their applications with minimal additional code/effort.
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