russell@unturf.com 5c14ccc3af perf: regenerate aggregate with just 4.2.11 and 4.2.12 baseline

2026-01-23 08:52:34 -05:00

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UN Inception: Aggregated Performance Analysis

Analysis Date: 1769176325.3071244 Reports Analyzed: 4.2.11, 4.2.12

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:

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

Key Findings

1. Extreme Metric Variance

Release	Avg Duration	Slowest	Fastest	Change from Previous
4.2.11	103s	deno (289s)	cpp (43s)	baseline
4.2.12	142s	go (406s)	erlang (21s)	+39s (+37.9%)

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:

CRYSTAL:

4.2.11: 90s
4.2.12: 397s
Range: 90s → 397s (341.1% variance)

GROOVY:

4.2.11: 90s
4.2.12: 393s
Range: 90s → 393s (336.7% variance)

GO:

4.2.11: 177s
4.2.12: 406s
Range: 177s → 406s (129.4% variance)

COBOL:

4.2.11: 72s
4.2.12: 290s
Range: 72s → 290s (302.8% variance)

ERLANG:

4.2.11: 235s
4.2.12: 21s
Range: 21s → 235s (1019.0% variance)

3. Execution Order Non-Determinism

Fastest Languages by Run:

4.2.11: cpp, forth, lua, typescript, ruby 4.2.12: erlang, php, python, javascript, haskell

Slowest Languages by Run:

4.2.11: deno, awk, erlang, elixir, clojure 4.2.12: go, crystal, groovy, deno, awk

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:

Orchestrator needs CPU to schedule/coordinate jobs
Test jobs need CPU to run
Both compete for limited CPU cycles
Context switching & cache thrashing = unpredictable timing
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:

Kubernetes Executor (default 32-64 parallel): Each release has different load
GitLab runner queue saturation: Some runs hit limits, others don't
Node CPU throttling: Kubernetes QoS class limits being applied
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

CRYSTAL: 90s → 397s (+341.1%)

GROOVY: 90s → 393s (+336.7%)

GO: 177s → 406s (+129.4%)

COBOL: 72s → 290s (+302.8%)

ERLANG: 21s → 235s (+1019.0%)

ZIG: 68s → 199s (+192.6%)

FORTH: 53s → 178s (+235.8%)

POWERSHELL: 58s → 175s (+201.7%)

SCHEME: 92s → 205s (+122.8%)

RUST: 115s → 227s (+97.4%)

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

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

Set explicit concurrency limits

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

Monitor resource usage
- CPU utilization on runner nodes
- Memory pressure & swap activity
- Context switch rates
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 %
ERLANG	21	235	128.0	214	1019.0%
CRYSTAL	90	397	243.5	307	341.1%
GROOVY	90	393	241.5	303	336.7%
PHP	26	108	67.0	82	315.4%
COBOL	72	290	181.0	218	302.8%
FORTH	53	178	115.5	125	235.8%
POWERSHELL	58	175	116.5	117	201.7%
ZIG	68	199	133.5	131	192.6%
BASH	58	169	113.5	111	191.4%
CSHARP	59	157	108.0	98	166.1%
FSHARP	59	156	107.5	97	164.4%
CPP	43	106	74.5	63	146.5%
RUBY	58	136	97.0	78	134.5%
COMMONLISP	39	91	65.0	52	133.3%
PYTHON	29	67	48.0	38	131.0%
GO	177	406	291.5	229	129.4%
TYPESCRIPT	58	132	95.0	74	127.6%
SCHEME	92	205	148.5	113	122.8%
DOTNET	59	125	92.0	66	111.9%
PROLOG	61	124	92.5	63	103.3%

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

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:

Reduced CPU available for jobs → slower execution
Random scheduling order → different languages hit different contention levels
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/4.2.11/perf.json - 669 tests, generated 2026-01-23T12:14:08Z
reports/4.2.12/perf.json - 665 tests, generated 2026-01-23T13:30:32Z

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:

GitLab CI triggers test matrix (42 languages in parallel)
Each language job reports timing via GitLab API
scripts/generate-perf-report.sh queries API & generates perf.json
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:

Checkout the specific commit:
```
git checkout <commit-sha>
```

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

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: 2026-01-23T08:52:20.299366 Report Version: 1.0.0

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