Q01
Long Text
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Distributed Tracing Sampling Strategies Benchmark
A developer-focused research instrument for benchmarking distributed tracing sampling adoption, practices, and trade-offs across OpenTelemetry and related observability tooling. Designed for engineering teams seeking to understand how peers approach head-based, tail-based, and adaptive sampling decisions.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
23 questions · ~4 min
Q02
Multiple Choice
Which of the following tracing or observability tools have you used in the last 6 months? Select all that apply.
Q03
Multiple Choice
Which sampling approaches have you implemented or configured in the last 6 months? Select all that apply.
Q04
Long Text
At peak hours, approximately how many spans per minute does your system generate?
Q05
Long Text
To what extent do you agree: Our current sampling rate provides sufficient trace coverage for debugging production issues.
Q06
Multiple Choice
Scenario: A consumer-facing API averages 10,000 requests per second with periodic traffic spikes and a limited observability budget. Which baseline sampling strategy would you start with?
Q07
Long Text
Based on your responses in this survey, please share any additional thoughts or context about your tracing and sampling strategy.
Q08
Long Text
What is your primary role?
Q09
Long Text
Thank you for completing this survey — your responses will help improve tracing and sampling practices across the community. Your data will be reported in aggregate only.
Q10
Long Text
How familiar are you with tracing sampling concepts (e.g., head-based, tail-based, rate-limited sampling)?
Q11
Multiple Choice
When using tail-based sampling, what most commonly triggers retaining a trace in your environment? Select the primary trigger.
Q12
Long Text
Rank the following tracing objectives from most important (1) to least important in your environment.
Q13
Long Text
To what extent do you agree: The cost of storing and processing traces significantly influences our sampling decisions.
Q14
Long Text
Briefly explain your reasoning for the sampling strategy you selected in the scenario above.
Q15
Long Text
How many years have you worked with distributed systems?
Q16
Multiple Choice
What are the main reasons you have not adopted tail-based sampling? Select all that apply.
Q17
Long Text
To what extent do you agree: Configuring and maintaining sampling rules is straightforward in our current tooling.
Q18
AI Interview
We'd like to explore your sampling decisions in a bit more depth. An AI moderator will ask you a couple of follow-up questions based on your responses so far.
Q19
Long Text
Approximately how many employees are in your organization?
Q20
Long Text
Where are sampling decisions primarily enforced in your current environment?
Q21
Long Text
Rank the signals you most want your sampling strategy to capture reliably (1 = highest priority).
Q22
Long Text
Which region do you primarily work in?
Q23
Long Text
How likely are you to adjust your sampling strategy in the next 3 months?
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.