perf: add aggregate report for 4.2.25 and 4.2.26 [ci skip]
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# UN Inception: Aggregated Performance Analysis
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**Analysis Date:** 1769296457.6742005
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**Reports Analyzed:** 4.2.25, 4.2.26
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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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| 4.2.25 | 97s | fortran (151s) | bash (58s) | baseline |
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| 4.2.26 | 78s | cpp (130s) | python (25s) | -19s (-19.6%) |
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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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**FORTRAN:**
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- 4.2.25: 151s
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- 4.2.26: 59s
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- **Range:** 59s → 151s (155.9% variance)
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**FSHARP:**
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- 4.2.25: 102s
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- 4.2.26: 34s
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- **Range:** 34s → 102s (200.0% variance)
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**GO:**
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- 4.2.25: 130s
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- 4.2.26: 62s
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- **Range:** 62s → 130s (109.7% variance)
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**AWK:**
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- 4.2.25: 140s
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- 4.2.26: 78s
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- **Range:** 78s → 140s (79.5% variance)
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**JULIA:**
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- 4.2.25: 113s
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- 4.2.26: 52s
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- **Range:** 52s → 113s (117.3% 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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4.2.25: bash, powershell, forth, r, prolog
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4.2.26: python, fsharp, ocaml, haskell, julia
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**Slowest Languages by Run:**
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4.2.25: fortran, crystal, perl, awk, cpp
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4.2.26: cpp, raku, cobol, javascript, ruby
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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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FORTRAN: 59s → 151s (+155.9%)
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FSHARP: 34s → 102s (+200.0%)
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GO: 62s → 130s (+109.7%)
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AWK: 78s → 140s (+79.5%)
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JULIA: 52s → 113s (+117.3%)
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DART: 52s → 111s (+113.5%)
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CRYSTAL: 89s → 145s (+62.9%)
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ZIG: 55s → 108s (+96.4%)
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HASKELL: 51s → 103s (+102.0%)
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KOTLIN: 59s → 110s (+86.4%)
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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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| PYTHON | 25 | 76 | 50.5 | 51 | 204.0% |
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| FSHARP | 34 | 102 | 68.0 | 68 | 200.0% |
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| FORTRAN | 59 | 151 | 105.0 | 92 | 155.9% |
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| JULIA | 52 | 113 | 82.5 | 61 | 117.3% |
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| DART | 52 | 111 | 81.5 | 59 | 113.5% |
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| GO | 62 | 130 | 96.0 | 68 | 109.7% |
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| OCAML | 49 | 99 | 74.0 | 50 | 102.0% |
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| HASKELL | 51 | 103 | 77.0 | 52 | 102.0% |
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| ZIG | 55 | 108 | 81.5 | 53 | 96.4% |
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| NIM | 53 | 100 | 76.5 | 47 | 88.7% |
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| KOTLIN | 59 | 110 | 84.5 | 51 | 86.4% |
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| D | 58 | 107 | 82.5 | 49 | 84.5% |
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| V | 54 | 97 | 75.5 | 43 | 79.6% |
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| AWK | 78 | 140 | 109.0 | 62 | 79.5% |
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| BASH | 58 | 101 | 79.5 | 43 | 74.1% |
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| OBJC | 54 | 93 | 73.5 | 39 | 72.2% |
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| CRYSTAL | 89 | 145 | 117.0 | 56 | 62.9% |
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| RUST | 63 | 99 | 81.0 | 36 | 57.1% |
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| JAVASCRIPT | 74 | 114 | 94.0 | 40 | 54.1% |
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| PHP | 73 | 107 | 90.0 | 34 | 46.6% |
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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/4.2.25/perf.json` - 681 tests, generated 2026-01-24T21:04:06Z
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- `reports/4.2.26/perf.json` - 661 tests, generated 2026-01-24T21:08:03Z
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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
|
||||||
|
rm -f perf-*.json
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||||||
|
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:**
|
||||||
|
```bash
|
||||||
|
git checkout <commit-sha>
|
||||||
|
```
|
||||||
|
|
||||||
|
2. **Verify reports exist:**
|
||||||
|
```bash
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||||||
|
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:**
|
||||||
|
```bash
|
||||||
|
make perf-aggregate-report
|
||||||
|
```
|
||||||
|
|
||||||
|
### CI/CD Integration
|
||||||
|
|
||||||
|
This report auto-generates on release tags via GitLab CI:
|
||||||
|
|
||||||
|
```yaml
|
||||||
|
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:** 2026-01-24T18:14:22.062723
|
||||||
|
**Report Version:** 1.0.0
|
||||||
Loading…
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Reference in a new issue