un-inception/AGGREGATED-PERFORMANCE.md

UN Inception: Aggregated Performance Analysis

Analysis Date: 1769203039.9327624 Reports Analyzed: 4.2.20, 4.2.21

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Executive Summary

Analysis of 2 performance reports reveals significant variance in execution metrics across releases. Different languages rank as slowest/fastest in different runs, indicating non-deterministic execution patterns likely caused by:

1. Orchestrator placement on CPU-bound pool (not an SRE best practice)
2. Resource contention between the orchestrator & test jobs
3. Undefined or exceeded concurrency limits
4. Non-deterministic scheduling of the matrix jobs

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Key Findings

1. Extreme Metric Variance

Release	Avg Duration	Slowest	Fastest	Change from Previous
4.2.20	114s	java (272s)	crystal (49s)	baseline
4.2.21	102s	nim (215s)	erlang (22s)	-12s (-10.5%)

Observation: Average duration increased 0.0% from 0s to 0s.

This 2-3x variance is NOT normal for identical workloads. Indicates:

• Orchestrator fighting for CPU with test jobs
• Tests running in different order each time
• No consistent resource allocation

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2. Unstable Language Rankings

The same language changes dramatically in rank between runs:

COBOL:

• 4.2.20: 198s
• 4.2.21: 62s
• Range: 62s → 198s (219.4% variance)

AWK:

• 4.2.20: 145s
• 4.2.21: 33s
• Range: 33s → 145s (339.4% variance)

NIM:

• 4.2.20: 103s
• 4.2.21: 215s
• Range: 103s → 215s (108.7% variance)

TYPESCRIPT:

• 4.2.20: 138s
• 4.2.21: 39s
• Range: 39s → 138s (253.8% variance)

CPP:

• 4.2.20: 107s
• 4.2.21: 204s
• Range: 107s → 204s (90.7% variance)

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3. Execution Order Non-Determinism

Fastest Languages by Run:

4.2.20: crystal, v, deno, r, csharp 4.2.21: erlang, r, ruby, awk, typescript

Slowest Languages by Run:

4.2.20: java, zig, cobol, perl, haskell 4.2.21: nim, dart, java, cpp, rust

Conclusion: No consistent "fast" or "slow" languages across runs. This proves:

• Execution order is random or system-dependent
• Resource availability varies dramatically
• Each run experiences different contention patterns

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The Orchestrator Problem: DevOps 101

Why This Matters

Running the orchestrator on a CPU-bound pool node violates fundamental SRE principles:

❌ BAD:  [ORCHESTRATOR] + [TEST JOB 1] + [TEST JOB 2] ... on same CPU pool
✅ GOOD: [ORCHESTRATOR] on dedicated node, [TESTS] on separate pool

What happens:

1. Orchestrator needs CPU to schedule/coordinate jobs
2. Test jobs need CPU to run
3. Both compete for limited CPU cycles
4. Context switching & cache thrashing = unpredictable timing
5. Matrix generation order becomes random as scheduler equilibrates

Why It's Fun for Chaos Engineering

From a chaos testing perspective, this setup is perfect:

• Reproduces real-world resource contention
• Tests system behavior under adversarial conditions
• Reveals race conditions & timing bugs
• No two runs are identical (true chaos)

But for production CI/CD? It's a nightmare for:

• Performance benchmarking
• SLA guarantees
• Debug reproducibility
• Billing/cost predictability

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Concurrency Hypothesis

Theory: Matrix Hydra Execution Limits

Given 42 languages with 15 tests each, if there were a concurrency limit, we'd expect:

Observed avg duration: 33-70s If truly serialized (1 job at a time): ~500s minimum If unlimited parallel: ~50-70s

This suggests jobs run in parallel batches, but the batch size varies:

Possible Concurrency Models:

1. Kubernetes Executor (default 32-64 parallel): Each release has different load
2. GitLab runner queue saturation: Some runs hit limits, others don't
3. Node CPU throttling: Kubernetes QoS class limits being applied
4. No explicit limit, but OS scheduler bottleneck: ~64 thread context limit

Evidence from Timing Patterns

If concurrency was fixed at N parallel jobs:

• Total time = ceiling(42 / N) * (average job time)
• For 4.2.0 (33s avg): ~42 concurrent or very efficient scheduling
• For 4.2.3 (63s avg): ~20 concurrent (slower overall, more contention)
• For 4.2.4 (70s avg): ~18 concurrent (even more contention)

Implication: Concurrency limit is either:

• Dynamic (based on available resources)
• Not enforced (unlimited, but OS scheduler creates natural limit)
• Degrading (orchestrator consuming more CPU over versions)

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Detailed Language Analysis

Most Variable Languages

COBOL: 62s → 198s (+219.4%)

AWK: 33s → 145s (+339.4%)

NIM: 103s → 215s (+108.7%)

TYPESCRIPT: 39s → 138s (+253.8%)

CPP: 107s → 204s (+90.7%)

PERL: 104s → 191s (+83.7%)

BASH: 104s → 186s (+78.8%)

DART: 127s → 208s (+63.8%)

RAKU: 83s → 154s (+85.5%)

FORTH: 73s → 142s (+94.5%)

These languages are most affected by resource contention. Likely reasons:

• Dynamic languages (Python, Ruby, JavaScript): Startup time varies with GC/JIT
• Compiled languages with heavy linking (C++, Rust): Linker contention
• Language VMs (Java, Elixir): VM startup sensitive to system load

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Recommendations

For Production CI/CD

1. Separate orchestrator from compute pool

   • Dedicated small node for GitLab runner/orchestrator
   • Dedicated larger pool for test jobs
   • Isolate using Kubernetes node affinity or taints

2. Set explicit concurrency limits

   # GitLab .gitlab-ci.yml
   trigger-test-matrix:
     parallel: 32  # Fixed concurrency
     max_parallel_builds: 32
   

3. Monitor resource usage

   • CPU utilization on runner nodes
   • Memory pressure & swap activity
   • Context switch rates

4. Implement backpressure

   • Queue jobs when pool is full
   • Implement exponential backoff for retries
   • Monitor orchestrator health separately

For Chaos Engineering

This setup is excellent for:

• Testing flaky test detection systems
• Validating retry logic
• Measuring performance under contention
• Finding race conditions in test infrastructure

Keep it as-is for stress testing, but in separate test environment.

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Raw Data: Language Variance Table

Language	Min (s)	Max (s)	Avg (s)	Range (s)	Variance %
AWK	33	145	89.0	112	339.4%
ERLANG	22	79	50.5	57	259.1%
TYPESCRIPT	39	138	88.5	99	253.8%
COBOL	62	198	130.0	136	219.4%
RUBY	30	89	59.5	59	196.7%
R	24	57	40.5	33	137.5%
V	56	121	88.5	65	116.1%
CSHARP	58	123	90.5	65	112.1%
NIM	103	215	159.0	112	108.7%
JAVASCRIPT	42	87	64.5	45	107.1%
PYTHON	43	88	65.5	45	104.7%
FORTH	73	142	107.5	69	94.5%
CPP	107	204	155.5	97	90.7%
C	76	144	110.0	68	89.5%
RAKU	83	154	118.5	71	85.5%
PERL	104	191	147.5	87	83.7%
BASH	104	186	145.0	82	78.8%
FSHARP	73	125	99.0	52	71.2%
DART	127	208	167.5	81	63.8%
CRYSTAL	49	76	62.5	27	55.1%

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Visualizations

Duration Degradation Trend

Shows: Average test duration increasing 2.1x from 4.2.0 → 4.2.4

Language Variance Heatmap

Shows: Top 15 most unstable languages, with Elixir, TCL, and C showing >300% variance

Ranking Instability

Shows: The same languages moving dramatically in performance rankings across releases

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Conclusion

The variance in performance metrics across these three releases is not random noise—it's a symptom of architectural misplacement.

The orchestrator running on the CPU-bound pool creates cascading effects:

1. Reduced CPU available for jobs → slower execution
2. Random scheduling order → different languages hit different contention levels
3. Each run has unique timing → metrics become meaningless for benchmarking

For SRE/DevOps: This is textbook example of why infrastructure placement matters. For Chaos Engineering: This is gold—true adversarial execution.

The solution is simple: separate the orchestrator from the compute pool.

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Reproducibility & Methodology

Pipeline Overview

This aggregated report is generated from individual performance reports collected during CI/CD runs. The pipeline combines data analysis, statistical variance calculation, and visualization rendering.

Architecture:

Individual Reports → Aggregation Script → Chart Generation (via UN) → Final Report
     (perf.json)         (Python)              (matplotlib)          (Markdown)

Data Sources

Input Files:

• reports/4.2.20/perf.json - 685 tests, generated 2026-01-23T20:41:23Z
• reports/4.2.21/perf.json - 661 tests, generated 2026-01-23T21:16:07Z

Each perf.json contains:

• Pipeline metadata (tag, timestamp, pipeline IDs)
• Summary statistics (avg, min, max durations)
• Per-language results (42 languages × ~15 tests each)
• Queue times & execution durations

Data Collection:

1. GitLab CI triggers test matrix (42 languages in parallel)
2. Each language job reports timing via GitLab API
3. scripts/generate-perf-report.sh queries API & generates perf.json
4. Report committed to reports/{TAG}/ directory

Analysis Pipeline

Step 1: Variance Analysis (scripts/aggregate-performance-reports.py)

# Load all reports
for version_dir in Path('reports').iterdir():
    reports[version] = json.loads((version_dir / 'perf.json').read_text())

# Extract language timings
for version, perf_data in reports.items():
    for lang_entry in perf_data['languages']:
        language_timings[version][lang_entry['language']] = lang_entry['duration_seconds']

# Calculate variance per language
for lang in all_languages:
    durations = [language_timings[v][lang] for v in versions if lang in language_timings[v]]
    percent_variance = ((max(durations) - min(durations)) / min(durations) * 100)

Step 2: Chart Generation (scripts/generate-aggregated-charts.py)

Charts are generated using matplotlib inside UN sandbox (not local environment):

# Copy reports with version-tagged names
cp reports/4.2.0/perf.json perf-4.2.0.json
cp reports/4.2.3/perf.json perf-4.2.3.json
cp reports/4.2.4/perf.json perf-4.2.4.json

# Execute chart generation via UN (includes matplotlib)
build/un -a \
  -f perf-4.2.0.json \
  -f perf-4.2.3.json \
  -f perf-4.2.4.json \
  scripts/generate-aggregated-charts.py

# Artifacts returned: *.png files

Why UN for Charts?

• Matplotlib not installed locally (by design)
• UN sandbox provides pre-configured Python environment with matplotlib
• Ensures reproducibility across different machines
• Same approach used in GitLab CI/CD pipeline

Step 3: Report Generation

# Generate markdown report (no matplotlib needed locally)
python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md

Reproducing This Report

Prerequisites:

• Git repository checked out
• build/un binary (UN Inception CLI client)
• Python 3.x (for report generation, not charts)
• Access to reports/ directory with historical data

Command:

make perf-aggregate-report

Or manually:

# Step 1: Generate charts
cp reports/4.2.0/perf.json perf-4.2.0.json
cp reports/4.2.3/perf.json perf-4.2.3.json
cp reports/4.2.4/perf.json perf-4.2.4.json
build/un -a -f perf-4.2.0.json -f perf-4.2.3.json -f perf-4.2.4.json scripts/generate-aggregated-charts.py
rm -f perf-*.json
mv *.png reports/

# Step 2: Generate markdown report
python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md

Stepping Back in Time

To regenerate this report with historical data:

1. Checkout the specific commit:

   git checkout <commit-sha>
   

2. Verify reports exist:

   ls -la reports/4.2.0/perf.json
   ls -la reports/4.2.3/perf.json
   ls -la reports/4.2.4/perf.json
   

3. Run analysis:

   make perf-aggregate-report
   

CI/CD Integration

This report auto-generates on release tags via GitLab CI:

perf-aggregate-report:
  stage: report
  needs: [perf-report]
  script:
    - echo "Generating aggregated analysis..."
    - cp reports/4.2.0/perf.json perf-4.2.0.json
    - cp reports/4.2.3/perf.json perf-4.2.3.json
    - cp reports/4.2.4/perf.json perf-4.2.4.json
    - build/un -a -f perf-4.2.0.json -f perf-4.2.3.json -f perf-4.2.4.json scripts/generate-aggregated-charts.py
    - python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
    - git add reports/ AGGREGATED-PERFORMANCE.md
    - git commit -m "perf: Update aggregated performance analysis [ci skip]"
    - git push origin main
  rules:
    - if: '$CI_COMMIT_TAG =~ /^\d+\.\d+\.\d+$/'

When new release tagged: Pipeline automatically updates aggregated report with new data point.

Statistical Methods

Variance Calculation:

• Per-language min/max/avg across all releases
• Percent variance: ((max - min) / min) * 100
• Languages with <2 data points excluded

Ranking Analysis:

• Languages sorted by duration per release
• Top 10 slowest tracked across releases
• Ranking position changes indicate non-determinism

Concurrency Estimation:

• Average duration vs theoretical serialized time
• Estimated parallel capacity: ceiling(42 langs / avg_duration) * per_job_time
• Variance suggests dynamic (not fixed) concurrency

Tools & Dependencies

Local Environment:

• Python 3.x (standard library only)
• build/un - UN Inception CLI
• Git (for version control)
• Bash (for scripting)

UN Sandbox Environment:

• Python 3.x with matplotlib, numpy
• Pre-configured visualization environment
• Isolated execution (no local dependencies)

GitLab CI:

• GitLab Runner with build tag
• Environment variables: UNSANDBOX_PUBLIC_KEY, UNSANDBOX_SECRET_KEY
• Deploy key for auto-commit

Data Integrity

Validation:

• JSON schema validation on input files
• Version tag format validation (X.Y.Z)
• Minimum 2 releases required for variance analysis

Timestamps:

• All reports include generation timestamp
• Commit history provides audit trail
• CI pipeline IDs link back to source runs

Contact & Questions

For questions about this methodology or to report issues:

• Repository: git.unturf.com/engineering/unturf/un-inception
• Methodology issues: Open issue with [methodology] tag
• Data integrity concerns: Check commit history & CI pipeline logs

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Generated by UN Inception Performance Analysis Pipeline Analysis Date: 2026-01-23T16:21:28.533747 Report Version: 1.0.0