Observability and tracing · Lesson 7 of 15 · 5 min

Find a slow span

Compare span duration with the total trace and distinguish sequential from overlapping work. A long span is a starting point for investigation, not automatic proof of CPU saturation.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-observability-tracing-007.json. Separate the recorded facts from your assumptions.
Hint & worked example
Compare span duration with the total trace and distinguish sequential from overlapping work. A long span is a starting point for investigation, not automatic proof of CPU saturation.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-observability-tracing-007.json
```
Common mistake to avoid: Inference must dominate because the application uses a model.
2
Isolate the deciding signal
Select the retrieval_ms field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Investigate retrieval, which accounts for 700 of the 900 measured milliseconds.
A successful JSON query isolates retrieval_ms.
```
jq .retrieval_ms /opt/ai-lab/cases/aiops-observability-tracing-007.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: investigate-retrieval, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-observability-tracing-007-decision.txt.
Hint & worked example
Investigate retrieval, which accounts for 700 of the 900 measured milliseconds.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'investigate-retrieval' > /home/learner/lab/aiops-observability-tracing-007-decision.txt
```
Common mistake to avoid: Inference must dominate because the application uses a model.

Understand the pattern

Compare span duration with the total trace and distinguish sequential from overlapping work. A long span is a starting point for investigation, not automatic proof of CPU saturation.

Interpret the evidence

Investigate retrieval, which accounts for 700 of the 900 measured milliseconds.

A tempting mistake

Inference must dominate because the application uses a model.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-observability-tracing-007.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "total_ms": 900,
  "retrieval_ms": 700,
  "inference_ms": 150,
  "validation_ms": 50
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .retrieval_ms /opt/ai-lab/cases/aiops-observability-tracing-007.json

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'investigate-retrieval' > /home/learner/lab/aiops-observability-tracing-007-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “investigate-retrieval” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Find a slow span” justify?Inference must dominate because the application uses a model.Make the change now and gather the missing evidence afterward.Investigate retrieval, which accounts for 700 of the 900 measured milliseconds.

Apply it elsewhere

A colleague concludes: “Inference must dominate because the application uses a model.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Connect logs and traces Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision