sim-analyze

Testing & Quality

Analyze a cache trace file to understand its characteristics and recommend policies

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Analyze the given cache trace to understand its access pattern characteristics and recommend which cache policies would perform best.

Input

Trace file: $ARGUMENTS

Workflow

Identify the trace format and read the trace:
- Check file extension, try to parse first lines
- Determine: key-only or weighted? Timestamps included?
- Count total accesses and distinct keys
Compute trace statistics. Write a small analysis script or use the simulator's synthetic tools to characterize:
- Working set size: number of distinct keys
- Temporal working set: distinct keys in sliding windows
- Frequency distribution: how many keys account for 80% of accesses? (Zipfian? Uniform? Bimodal?)
- Recency patterns: what fraction of accesses are repeats within the last N accesses?
- Scan detection: are there bursts of sequential unique keys?
- Temporal shifts: does the popular set change over time? (Compare first half vs second half frequency rankings)
Characterize the workload:
- Frequency-biased: few hot keys dominate → LFU/TinyLFU excel
- Recency-biased: recent items are reaccessed → LRU/LIRS excel
- Mixed: both signals matter → W-TinyLFU, ARC
- Scan-heavy: sequential scans pollute → scan-resistant policies (S3-FIFO, 2Q)
- Shifting: popular set changes over time → adaptive policies (hill climber)
Recommend policies for comparison:
- Match trace characteristics to policy strengths
- Suggest cache sizes based on working set (10%, 25%, 50% of distinct keys)
- Predict which policies will likely win and why
Run a quick validation. Execute a single-size simulation using simulator:run (not simulator:simulate which does multi-size):
```
./gradlew simulator:run -q \
  -Dcaffeine.simulator.files.paths.0="format:path" \
  -Dcaffeine.simulator.maximum-size=SIZE \
  -Dcaffeine.simulator.policies.0=product.Caffeine \
  -Dcaffeine.simulator.policies.1=opt.Clairvoyant \
  -Dcaffeine.simulator.policies.2=linked.Lru
```
Note: each policy creates instances per admission filter (default: Always, TinyLfu, Clairvoyant). If a trace has no weight data, weighted-only policies are silently skipped.
Report:
- Trace summary (accesses, distinct keys, frequency distribution shape)
- Workload characterization (frequency vs recency bias)
- Recommended policies with reasoning
- Quick validation results
- Suggested /sim-compare invocation for full analysis