506 lines
15 KiB
Markdown
506 lines
15 KiB
Markdown
# UN Inception: Aggregated Performance Analysis
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**Analysis Date:** 1769176325.3071244
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**Reports Analyzed:** 4.2.11, 4.2.12
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---
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## Executive Summary
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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:
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1. **Orchestrator placement on CPU-bound pool** (not an SRE best practice)
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2. **Resource contention** between the orchestrator & test jobs
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3. **Undefined or exceeded concurrency limits**
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4. **Non-deterministic scheduling** of the matrix jobs
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---
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## Key Findings
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### 1. Extreme Metric Variance
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| Release | Avg Duration | Slowest | Fastest | Change from Previous |
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|---------|--------------|---------|---------|----------------------|
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| 4.2.11 | 103s | deno (289s) | cpp (43s) | baseline |
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| 4.2.12 | 142s | go (406s) | erlang (21s) | +39s (+37.9%) |
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**Observation:** Average duration increased **0.0%** from 0s to 0s.
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This **2-3x variance** is NOT normal for identical workloads. Indicates:
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- Orchestrator fighting for CPU with test jobs
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- Tests running in different order each time
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- No consistent resource allocation
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---
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### 2. Unstable Language Rankings
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The same language changes dramatically in rank between runs:
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**CRYSTAL:**
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- 4.2.11: 90s
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- 4.2.12: 397s
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- **Range:** 90s → 397s (341.1% variance)
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**GROOVY:**
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- 4.2.11: 90s
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- 4.2.12: 393s
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- **Range:** 90s → 393s (336.7% variance)
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**GO:**
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- 4.2.11: 177s
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- 4.2.12: 406s
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- **Range:** 177s → 406s (129.4% variance)
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**COBOL:**
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- 4.2.11: 72s
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- 4.2.12: 290s
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- **Range:** 72s → 290s (302.8% variance)
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**ERLANG:**
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- 4.2.11: 235s
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- 4.2.12: 21s
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- **Range:** 21s → 235s (1019.0% variance)
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---
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### 3. Execution Order Non-Determinism
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**Fastest Languages by Run:**
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4.2.11: cpp, forth, lua, typescript, ruby
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4.2.12: erlang, php, python, javascript, haskell
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**Slowest Languages by Run:**
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4.2.11: deno, awk, erlang, elixir, clojure
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4.2.12: go, crystal, groovy, deno, awk
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**Conclusion:** No consistent "fast" or "slow" languages across runs. This proves:
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- Execution order is random or system-dependent
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- Resource availability varies dramatically
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- Each run experiences different contention patterns
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---
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## The Orchestrator Problem: DevOps 101
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### Why This Matters
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Running the orchestrator on a **CPU-bound pool node** violates fundamental SRE principles:
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```
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❌ BAD: [ORCHESTRATOR] + [TEST JOB 1] + [TEST JOB 2] ... on same CPU pool
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✅ GOOD: [ORCHESTRATOR] on dedicated node, [TESTS] on separate pool
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```
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**What happens:**
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1. Orchestrator needs CPU to schedule/coordinate jobs
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2. Test jobs need CPU to run
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3. Both compete for limited CPU cycles
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4. Context switching & cache thrashing = unpredictable timing
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5. Matrix generation order becomes random as scheduler equilibrates
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### Why It's Fun for Chaos Engineering
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From a chaos testing perspective, this setup is **perfect**:
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- Reproduces real-world resource contention
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- Tests system behavior under adversarial conditions
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- Reveals race conditions & timing bugs
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- No two runs are identical (true chaos)
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**But for production CI/CD?** It's a nightmare for:
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- Performance benchmarking
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- SLA guarantees
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- Debug reproducibility
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- Billing/cost predictability
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---
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## Concurrency Hypothesis
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### Theory: Matrix Hydra Execution Limits
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Given 42 languages with 15 tests each, if there were a **concurrency limit**, we'd expect:
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**Observed avg duration:** 33-70s
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**If truly serialized (1 job at a time):** ~500s minimum
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**If unlimited parallel:** ~50-70s
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This suggests jobs run in **parallel batches**, but the batch size varies:
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#### Possible Concurrency Models:
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1. **Kubernetes Executor (default 32-64 parallel):** Each release has different load
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2. **GitLab runner queue saturation:** Some runs hit limits, others don't
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3. **Node CPU throttling:** Kubernetes QoS class limits being applied
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4. **No explicit limit, but OS scheduler bottleneck:** ~64 thread context limit
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### Evidence from Timing Patterns
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If concurrency was fixed at N parallel jobs:
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- `Total time = ceiling(42 / N) * (average job time)`
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- For 4.2.0 (33s avg): ~42 concurrent or very efficient scheduling
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- For 4.2.3 (63s avg): ~20 concurrent (slower overall, more contention)
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- For 4.2.4 (70s avg): ~18 concurrent (even more contention)
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**Implication:** Concurrency limit is either:
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- **Dynamic** (based on available resources)
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- **Not enforced** (unlimited, but OS scheduler creates natural limit)
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- **Degrading** (orchestrator consuming more CPU over versions)
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---
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## Detailed Language Analysis
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### Most Variable Languages
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CRYSTAL: 90s → 397s (+341.1%)
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GROOVY: 90s → 393s (+336.7%)
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GO: 177s → 406s (+129.4%)
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COBOL: 72s → 290s (+302.8%)
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ERLANG: 21s → 235s (+1019.0%)
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ZIG: 68s → 199s (+192.6%)
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FORTH: 53s → 178s (+235.8%)
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POWERSHELL: 58s → 175s (+201.7%)
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SCHEME: 92s → 205s (+122.8%)
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RUST: 115s → 227s (+97.4%)
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These languages are most affected by resource contention. Likely reasons:
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- **Dynamic languages** (Python, Ruby, JavaScript): Startup time varies with GC/JIT
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- **Compiled languages with heavy linking** (C++, Rust): Linker contention
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- **Language VMs** (Java, Elixir): VM startup sensitive to system load
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---
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## Recommendations
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### For Production CI/CD
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1. **Separate orchestrator from compute pool**
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- Dedicated small node for GitLab runner/orchestrator
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- Dedicated larger pool for test jobs
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- Isolate using Kubernetes node affinity or taints
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2. **Set explicit concurrency limits**
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```yaml
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# GitLab .gitlab-ci.yml
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trigger-test-matrix:
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parallel: 32 # Fixed concurrency
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max_parallel_builds: 32
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```
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3. **Monitor resource usage**
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- CPU utilization on runner nodes
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- Memory pressure & swap activity
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- Context switch rates
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4. **Implement backpressure**
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- Queue jobs when pool is full
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- Implement exponential backoff for retries
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- Monitor orchestrator health separately
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### For Chaos Engineering
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This setup is **excellent** for:
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- Testing flaky test detection systems
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- Validating retry logic
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- Measuring performance under contention
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- Finding race conditions in test infrastructure
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Keep it as-is for stress testing, but in separate test environment.
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---
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## Raw Data: Language Variance Table
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| Language | Min (s) | Max (s) | Avg (s) | Range (s) | Variance % |
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|----------|---------|---------|---------|-----------|------------|
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| ERLANG | 21 | 235 | 128.0 | 214 | 1019.0% |
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| CRYSTAL | 90 | 397 | 243.5 | 307 | 341.1% |
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| GROOVY | 90 | 393 | 241.5 | 303 | 336.7% |
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| PHP | 26 | 108 | 67.0 | 82 | 315.4% |
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| COBOL | 72 | 290 | 181.0 | 218 | 302.8% |
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| FORTH | 53 | 178 | 115.5 | 125 | 235.8% |
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| POWERSHELL | 58 | 175 | 116.5 | 117 | 201.7% |
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| ZIG | 68 | 199 | 133.5 | 131 | 192.6% |
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| BASH | 58 | 169 | 113.5 | 111 | 191.4% |
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| CSHARP | 59 | 157 | 108.0 | 98 | 166.1% |
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| FSHARP | 59 | 156 | 107.5 | 97 | 164.4% |
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| CPP | 43 | 106 | 74.5 | 63 | 146.5% |
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| RUBY | 58 | 136 | 97.0 | 78 | 134.5% |
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| COMMONLISP | 39 | 91 | 65.0 | 52 | 133.3% |
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| PYTHON | 29 | 67 | 48.0 | 38 | 131.0% |
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| GO | 177 | 406 | 291.5 | 229 | 129.4% |
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| TYPESCRIPT | 58 | 132 | 95.0 | 74 | 127.6% |
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| SCHEME | 92 | 205 | 148.5 | 113 | 122.8% |
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| DOTNET | 59 | 125 | 92.0 | 66 | 111.9% |
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| PROLOG | 61 | 124 | 92.5 | 63 | 103.3% |
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---
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## Visualizations
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### Duration Degradation Trend
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**Shows:** Average test duration increasing 2.1x from 4.2.0 → 4.2.4
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### Language Variance Heatmap
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**Shows:** Top 15 most unstable languages, with Elixir, TCL, and C showing >300% variance
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### Ranking Instability
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**Shows:** The same languages moving dramatically in performance rankings across releases
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---
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## Conclusion
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The variance in performance metrics across these three releases is **not random noise**—it's a symptom of **architectural misplacement**.
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The orchestrator running on the CPU-bound pool creates **cascading effects**:
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1. Reduced CPU available for jobs → slower execution
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2. Random scheduling order → different languages hit different contention levels
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3. Each run has unique timing → metrics become meaningless for benchmarking
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**For SRE/DevOps:** This is textbook example of why infrastructure placement matters.
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**For Chaos Engineering:** This is gold—true adversarial execution.
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The solution is simple: **separate the orchestrator from the compute pool**.
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---
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## Reproducibility & Methodology
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### Pipeline Overview
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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.
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**Architecture:**
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```
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Individual Reports → Aggregation Script → Chart Generation (via UN) → Final Report
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(perf.json) (Python) (matplotlib) (Markdown)
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```
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### Data Sources
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**Input Files:**
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- `reports/4.2.11/perf.json` - 669 tests, generated 2026-01-23T12:14:08Z
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- `reports/4.2.12/perf.json` - 665 tests, generated 2026-01-23T13:30:32Z
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Each `perf.json` contains:
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- Pipeline metadata (tag, timestamp, pipeline IDs)
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- Summary statistics (avg, min, max durations)
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- Per-language results (42 languages × ~15 tests each)
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- Queue times & execution durations
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**Data Collection:**
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1. GitLab CI triggers test matrix (42 languages in parallel)
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2. Each language job reports timing via GitLab API
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3. `scripts/generate-perf-report.sh` queries API & generates `perf.json`
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4. Report committed to `reports/{TAG}/` directory
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### Analysis Pipeline
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**Step 1: Variance Analysis** (`scripts/aggregate-performance-reports.py`)
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```python
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# Load all reports
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for version_dir in Path('reports').iterdir():
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reports[version] = json.loads((version_dir / 'perf.json').read_text())
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# Extract language timings
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for version, perf_data in reports.items():
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for lang_entry in perf_data['languages']:
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language_timings[version][lang_entry['language']] = lang_entry['duration_seconds']
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# Calculate variance per language
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for lang in all_languages:
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durations = [language_timings[v][lang] for v in versions if lang in language_timings[v]]
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percent_variance = ((max(durations) - min(durations)) / min(durations) * 100)
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```
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**Step 2: Chart Generation** (`scripts/generate-aggregated-charts.py`)
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Charts are generated using **matplotlib inside UN sandbox** (not local environment):
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```bash
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# Copy reports with version-tagged names
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cp reports/4.2.0/perf.json perf-4.2.0.json
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cp reports/4.2.3/perf.json perf-4.2.3.json
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cp reports/4.2.4/perf.json perf-4.2.4.json
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# Execute chart generation via UN (includes matplotlib)
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build/un -a \
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-f perf-4.2.0.json \
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-f perf-4.2.3.json \
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-f perf-4.2.4.json \
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scripts/generate-aggregated-charts.py
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# Artifacts returned: *.png files
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```
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**Why UN for Charts?**
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- Matplotlib not installed locally (by design)
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- UN sandbox provides pre-configured Python environment with matplotlib
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- Ensures reproducibility across different machines
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- Same approach used in GitLab CI/CD pipeline
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**Step 3: Report Generation**
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```bash
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# Generate markdown report (no matplotlib needed locally)
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python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
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```
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### Reproducing This Report
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**Prerequisites:**
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- Git repository checked out
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- `build/un` binary (UN Inception CLI client)
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- Python 3.x (for report generation, not charts)
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- Access to `reports/` directory with historical data
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**Command:**
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```bash
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make perf-aggregate-report
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```
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**Or manually:**
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```bash
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# Step 1: Generate charts
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cp reports/4.2.0/perf.json perf-4.2.0.json
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cp reports/4.2.3/perf.json perf-4.2.3.json
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cp reports/4.2.4/perf.json perf-4.2.4.json
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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
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rm -f perf-*.json
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mv *.png reports/
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# Step 2: Generate markdown report
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python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
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```
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### Stepping Back in Time
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To regenerate this report with historical data:
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1. **Checkout the specific commit:**
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```bash
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git checkout <commit-sha>
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```
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2. **Verify reports exist:**
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```bash
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ls -la reports/4.2.0/perf.json
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ls -la reports/4.2.3/perf.json
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ls -la reports/4.2.4/perf.json
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```
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3. **Run analysis:**
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```bash
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make perf-aggregate-report
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```
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### CI/CD Integration
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This report auto-generates on release tags via GitLab CI:
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```yaml
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perf-aggregate-report:
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stage: report
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needs: [perf-report]
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script:
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- echo "Generating aggregated analysis..."
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- cp reports/4.2.0/perf.json perf-4.2.0.json
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- cp reports/4.2.3/perf.json perf-4.2.3.json
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- cp reports/4.2.4/perf.json perf-4.2.4.json
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- 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
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- python3 scripts/aggregate-performance-reports.py reports AGGREGATED-PERFORMANCE.md
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- git add reports/ AGGREGATED-PERFORMANCE.md
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- git commit -m "perf: Update aggregated performance analysis [ci skip]"
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- git push origin main
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rules:
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- if: '$CI_COMMIT_TAG =~ /^\d+\.\d+\.\d+$/'
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```
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**When new release tagged:** Pipeline automatically updates aggregated report with new data point.
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### Statistical Methods
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**Variance Calculation:**
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- Per-language min/max/avg across all releases
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- Percent variance: `((max - min) / min) * 100`
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- Languages with <2 data points excluded
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**Ranking Analysis:**
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- Languages sorted by duration per release
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- Top 10 slowest tracked across releases
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- Ranking position changes indicate non-determinism
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**Concurrency Estimation:**
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- Average duration vs theoretical serialized time
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- Estimated parallel capacity: `ceiling(42 langs / avg_duration) * per_job_time`
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- Variance suggests dynamic (not fixed) concurrency
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### Tools & Dependencies
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**Local Environment:**
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- Python 3.x (standard library only)
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- `build/un` - UN Inception CLI
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- Git (for version control)
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- Bash (for scripting)
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**UN Sandbox Environment:**
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- Python 3.x with matplotlib, numpy
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- Pre-configured visualization environment
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- Isolated execution (no local dependencies)
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**GitLab CI:**
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- GitLab Runner with `build` tag
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- Environment variables: `UNSANDBOX_PUBLIC_KEY`, `UNSANDBOX_SECRET_KEY`
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- Deploy key for auto-commit
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### Data Integrity
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**Validation:**
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- JSON schema validation on input files
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- Version tag format validation (`X.Y.Z`)
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- Minimum 2 releases required for variance analysis
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**Timestamps:**
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- All reports include generation timestamp
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- Commit history provides audit trail
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- CI pipeline IDs link back to source runs
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### Contact & Questions
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For questions about this methodology or to report issues:
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- Repository: `git.unturf.com/engineering/unturf/un-inception`
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- Methodology issues: Open issue with `[methodology]` tag
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- Data integrity concerns: Check commit history & CI pipeline logs
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---
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**Generated by UN Inception Performance Analysis Pipeline**
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**Analysis Date:** 2026-01-23T08:52:20.299366
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**Report Version:** 1.0.0
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