Senior LevelSystem Design & Architecture
How do Head-Based vs Tail-Based Sampling strategies balance tracing overhead in OpenTelemetry?
❓ Interview Prompt: Explain Distributed Tracing Sampling, Head-Based Sampling (probabilistic at trace start) vs Tail-Based Sampling (inspecting full trace outcome before saving errors).
Collecting 100% of distributed trace spans in high-throughput systems creates massive network bandwidth and storage overhead:
2 Sampling Strategies:
1
Head-Based Sampling: Sampling decision is made at the start of the trace (e.g. sample $1\%$ of requests probabilistically). Downside: May drop rare error traces!
2
Tail-Based Sampling: Collects all spans in memory at the OpenTelemetry Collector. The sampling decision is made at the end of the trace: automatically saves $100\%$ of traces that contain HTTP 5xx errors or high latency while dropping normal $200\text{ OK}$ traces!
Code Implementation Example
yaml
# OpenTelemetry Collector Tail-Based Sampling Config
processors:
tail_sampling:
decision_wait: 10s
expected_trajectories: 100000
policies:
- name: drop_errors_policy
type: status_code
status_code: { status_codes: [ ERROR ] } # Keep 100% of error traces!💡Configuring Tail-Based Sampling rules in OpenTelemetry Collector.
🎯 Interview Key Takeaway
Use Tail-Based Sampling in OpenTelemetry Collectors to retain 100% of error and high-latency traces while trimming routine success traces.
#System Design#Distributed Tracing#OpenTelemetry#Sampling#Senior
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