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UN Inception: Aggregated Performance Analysis

Analysis Date: 1769287519.037188 Reports Analyzed: 4.2.0, 4.2.10, 4.2.11, 4.2.12, 4.2.13, 4.2.14, 4.2.15, 4.2.16, 4.2.17, 4.2.18, 4.2.19, 4.2.20, 4.2.21, 4.2.22, 4.2.23, 4.2.24, 4.2.3, 4.2.4, 4.2.5, 4.2.6, 4.2.7, 4.2.8, 4.2.9

Executive Summary

Analysis of 23 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

Key Findings

1. Extreme Metric Variance

Release Avg Duration Slowest Fastest Change from Previous
4.2.0 33s raku (93s) ocaml (19s) baseline
4.2.10 153s scheme (320s) bash (29s) +120s (+363.6%)
4.2.11 103s deno (289s) cpp (43s) -50s (-32.7%)
4.2.12 142s go (406s) erlang (21s) +39s (+37.9%)
4.2.13 126s deno (290s) haskell (23s) -16s (-11.3%)
4.2.14 97s javascript (172s) go (38s) -29s (-23.0%)
4.2.15 103s d (203s) cobol (49s) +6s (+6.2%)
4.2.16 98s javascript (361s) c (40s) -5s (-4.9%)
4.2.17 104s ruby (270s) objc (17s) +6s (+6.1%)
4.2.18 100s r (298s) scheme (48s) -4s (-3.8%)
4.2.19 151s lua (472s) dart (40s) +51s (+51.0%)
4.2.20 114s java (272s) crystal (49s) -37s (-24.5%)
4.2.21 102s nim (215s) erlang (22s) -12s (-10.5%)
4.2.22 300s javascript (2173s) clojure (14s) +198s (+194.1%)
4.2.23 373s zig (1058s) perl (8s) +73s (+24.3%)
4.2.24 129s python (494s) clojure (8s) -244s (-65.4%)
4.2.3 63s rust (142s) v (40s) -66s (-51.2%)
4.2.4 70s python (110s) c (23s) +7s (+11.1%)
4.2.5 67s v (114s) erlang (44s) -3s (-4.3%)
4.2.6 54s haskell (128s) awk (23s) -13s (-19.4%)
4.2.7 117s typescript (319s) dotnet (5s) +63s (+116.7%)
4.2.8 111s kotlin (313s) fortran (28s) -6s (-5.1%)
4.2.9 107s ruby (279s) d (19s) -4s (-3.6%)

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

2. Unstable Language Rankings

The same language changes dramatically in rank between runs:

JAVASCRIPT:

  • 4.2.0: 90s
  • 4.2.10: 107s
  • 4.2.11: 60s
  • 4.2.12: 32s
  • 4.2.13: 173s
  • 4.2.14: 172s
  • 4.2.15: 130s
  • 4.2.16: 361s
  • 4.2.17: 127s
  • 4.2.18: 79s
  • 4.2.19: 68s
  • 4.2.20: 87s
  • 4.2.21: 42s
  • 4.2.22: 2173s
  • 4.2.23: 425s
  • 4.2.24: 242s
  • 4.2.3: 75s
  • 4.2.4: 109s
  • 4.2.5: 60s
  • 4.2.6: 50s
  • 4.2.7: 155s
  • 4.2.8: 253s
  • 4.2.9: 166s
  • Range: 32s → 2173s (6690.6% variance)

R:

  • 4.2.0: 25s
  • 4.2.10: 181s
  • 4.2.11: 95s
  • 4.2.12: 169s
  • 4.2.13: 107s
  • 4.2.14: 144s
  • 4.2.15: 164s
  • 4.2.16: 64s
  • 4.2.17: 94s
  • 4.2.18: 298s
  • 4.2.19: 106s
  • 4.2.20: 57s
  • 4.2.21: 24s
  • 4.2.22: 1834s
  • 4.2.23: 9s
  • 4.2.24: 434s
  • 4.2.3: 64s
  • 4.2.4: 74s
  • 4.2.5: 52s
  • 4.2.6: 47s
  • 4.2.7: 313s
  • 4.2.8: 126s
  • 4.2.9: 54s
  • Range: 9s → 1834s (20277.8% variance)

NIM:

  • 4.2.0: 31s
  • 4.2.10: 78s
  • 4.2.11: 68s
  • 4.2.12: 36s
  • 4.2.13: 41s
  • 4.2.14: 97s
  • 4.2.15: 99s
  • 4.2.16: 80s
  • 4.2.17: 98s
  • 4.2.18: 71s
  • 4.2.19: 102s
  • 4.2.20: 103s
  • 4.2.21: 215s
  • 4.2.22: 1217s
  • 4.2.23: 286s
  • 4.2.24: 8s
  • 4.2.3: 52s
  • 4.2.4: 76s
  • 4.2.5: 58s
  • 4.2.6: 58s
  • 4.2.7: 77s
  • 4.2.8: 93s
  • 4.2.9: 42s
  • Range: 8s → 1217s (15112.5% variance)

ZIG:

  • 4.2.0: 32s
  • 4.2.10: 185s
  • 4.2.11: 68s
  • 4.2.12: 199s
  • 4.2.13: 145s
  • 4.2.14: 61s
  • 4.2.15: 151s
  • 4.2.16: 131s
  • 4.2.17: 175s
  • 4.2.18: 72s
  • 4.2.19: 90s
  • 4.2.20: 224s
  • 4.2.21: 192s
  • 4.2.22: 1014s
  • 4.2.23: 1058s
  • 4.2.24: 8s
  • 4.2.3: 61s
  • 4.2.4: 60s
  • 4.2.5: 59s
  • 4.2.6: 59s
  • 4.2.7: 79s
  • 4.2.8: 187s
  • 4.2.9: 153s
  • Range: 8s → 1058s (13125.0% variance)

V:

  • 4.2.0: 22s
  • 4.2.10: 78s
  • 4.2.11: 67s
  • 4.2.12: 100s
  • 4.2.13: 31s
  • 4.2.14: 97s
  • 4.2.15: 63s
  • 4.2.16: 78s
  • 4.2.17: 167s
  • 4.2.18: 115s
  • 4.2.19: 86s
  • 4.2.20: 56s
  • 4.2.21: 121s
  • 4.2.22: 21s
  • 4.2.23: 1040s
  • 4.2.24: 101s
  • 4.2.3: 40s
  • 4.2.4: 49s
  • 4.2.5: 114s
  • 4.2.6: 58s
  • 4.2.7: 76s
  • 4.2.8: 109s
  • 4.2.9: 42s
  • Range: 21s → 1040s (4852.4% variance)

3. Execution Order Non-Determinism

Fastest Languages by Run:

4.2.0: ocaml, tcl, elixir, csharp, cobol 4.2.10: bash, powershell, erlang, ruby, typescript 4.2.11: cpp, forth, lua, typescript, ruby 4.2.12: erlang, php, python, javascript, haskell 4.2.13: haskell, v, groovy, nim, kotlin 4.2.14: go, cpp, powershell, erlang, typescript 4.2.15: cobol, csharp, ocaml, objc, kotlin 4.2.16: cpp, c, raku, awk, groovy 4.2.17: objc, python, erlang, csharp, perl 4.2.18: scheme, tcl, fortran, c, raku 4.2.19: dart, python, typescript, javascript, dotnet 4.2.20: crystal, v, deno, r, csharp 4.2.21: erlang, r, ruby, awk, typescript 4.2.22: powershell, clojure, scheme, objc, v 4.2.23: perl, r, d, groovy, powershell 4.2.24: zig, nim, kotlin, fortran, forth 4.2.3: v, d, kotlin, awk, raku 4.2.4: c, d, cobol, raku, v 4.2.5: erlang, awk, bash, deno, tcl 4.2.6: awk, powershell, crystal, raku, erlang 4.2.7: dotnet, deno, awk, fortran, commonlisp 4.2.8: fortran, groovy, crystal, java, powershell 4.2.9: d, julia, csharp, v, objc

Slowest Languages by Run:

4.2.0: raku, javascript, cpp, rust, go 4.2.10: scheme, clojure, deno, c, julia 4.2.11: deno, awk, erlang, elixir, clojure 4.2.12: go, crystal, groovy, deno, awk 4.2.13: deno, raku, awk, cpp, java 4.2.14: javascript, python, php, bash, elixir 4.2.15: d, cpp, ruby, bash, lua 4.2.16: javascript, clojure, crystal, lua, fsharp 4.2.17: ruby, typescript, php, cobol, commonlisp 4.2.18: r, go, elixir, rust, forth 4.2.19: lua, perl, java, ruby, powershell 4.2.20: java, zig, cobol, perl, haskell 4.2.21: nim, dart, java, cpp, rust 4.2.22: javascript, r, nim, zig, lua 4.2.23: zig, v, commonlisp, deno, elixir 4.2.24: python, php, r, elixir, deno 4.2.3: rust, c, python, typescript, javascript 4.2.4: python, javascript, elixir, scheme, bash 4.2.5: v, haskell, scheme, ocaml, powershell 4.2.6: haskell, go, cpp, rust, forth 4.2.7: typescript, ruby, r, elixir, crystal 4.2.8: kotlin, python, javascript, tcl, raku 4.2.9: ruby, deno, rust, crystal, java

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

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

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)

Detailed Language Analysis

Most Variable Languages

JAVASCRIPT: 32s → 2173s (+6690.6%)

R: 9s → 1834s (+20277.8%)

NIM: 8s → 1217s (+15112.5%)

ZIG: 8s → 1058s (+13125.0%)

V: 21s → 1040s (+4852.4%)

LUA: 9s → 975s (+10733.3%)

COMMONLISP: 27s → 956s (+3440.7%)

DENO: 24s → 926s (+3758.3%)

ELIXIR: 20s → 917s (+4485.0%)

PHP: 23s → 824s (+3482.6%)

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

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.

Raw Data: Language Variance Table

Language Min (s) Max (s) Avg (s) Range (s) Variance %
R 9 1834 197.2 1825 20277.8%
NIM 8 1217 134.2 1209 15112.5%
ZIG 8 1058 194.0 1050 13125.0%
LUA 9 975 159.1 966 10733.3%
FORTH 8 628 117.2 620 7750.0%
DOTNET 5 360 96.6 355 7100.0%
JAVASCRIPT 32 2173 227.7 2141 6690.6%
PERL 8 468 109.0 460 5750.0%
V 21 1040 118.7 1019 4852.4%
CSHARP 8 385 101.9 377 4712.5%
ELIXIR 20 917 164.1 897 4485.0%
OBJC 17 693 111.2 676 3976.5%
KOTLIN 8 313 95.3 305 3812.5%
CLOJURE 8 310 109.1 302 3775.0%
DENO 24 926 163.6 902 3758.3%
COBOL 20 759 141.4 739 3695.0%
HASKELL 21 754 132.3 733 3490.5%
PHP 23 824 153.5 801 3482.6%
TCL 20 712 117.4 692 3460.0%
COMMONLISP 27 956 124.0 929 3440.7%

Visualizations

Duration Degradation Trend

Duration Trend

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

Language Variance Heatmap

Language Variance

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

Ranking Instability

Ranking Changes

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

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.

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/4.2.0/perf.json - 642 tests, generated 2026-01-18T23:20:51Z
  • reports/4.2.10/perf.json - 673 tests, generated 2026-01-23T11:46:18Z
  • reports/4.2.11/perf.json - 669 tests, generated 2026-01-23T12:14:08Z
  • reports/4.2.12/perf.json - 665 tests, generated 2026-01-23T13:30:32Z
  • reports/4.2.13/perf.json - 673 tests, generated 2026-01-23T14:19:49Z
  • reports/4.2.14/perf.json - 661 tests, generated 2026-01-23T14:48:26Z
  • reports/4.2.15/perf.json - 657 tests, generated 2026-01-23T15:14:36Z
  • reports/4.2.16/perf.json - 665 tests, generated 2026-01-23T15:25:53Z
  • reports/4.2.17/perf.json - 665 tests, generated 2026-01-23T15:34:55Z
  • reports/4.2.18/perf.json - 665 tests, generated 2026-01-23T16:05:03Z
  • reports/4.2.19/perf.json - 701 tests, generated 2026-01-23T20:20:06Z
  • 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
  • reports/4.2.22/perf.json - 697 tests, generated 2026-01-24T17:57:56Z
  • reports/4.2.23/perf.json - 713 tests, generated 2026-01-24T19:14:09Z
  • reports/4.2.24/perf.json - 665 tests, generated 2026-01-24T19:13:51Z
  • reports/4.2.3/perf.json - 642 tests, generated 2026-01-19T11:58:45Z
  • reports/4.2.4/perf.json - 682 tests, generated 2026-01-19T12:02:14Z
  • reports/4.2.5/perf.json - 658 tests, generated 2026-01-19T19:10:23Z
  • reports/4.2.6/perf.json - 642 tests, generated 2026-01-19T20:22:16Z
  • reports/4.2.7/perf.json - 631 tests, generated 2026-01-23T09:36:18Z
  • reports/4.2.8/perf.json - 645 tests, generated 2026-01-23T10:01:33Z
  • reports/4.2.9/perf.json - 645 tests, generated 2026-01-23T10:05:34Z

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

Generated by UN Inception Performance Analysis Pipeline Analysis Date: 2026-01-24T15:45:42.561682 Report Version: 1.0.0