russell@unturf.com
1f83eaf175
- Add perf-aggregate-report CI job to analyze variance across releases - Implement dynamic version discovery using git tags (ever-growing) - Generate charts via UN sandbox using matplotlib - Create AGGREGATED-PERFORMANCE.md with comprehensive methodology - Add Makefile target for local report generation - Include 3 visualization charts showing variance trends Key findings: 2-3x performance variance due to orchestrator placement on CPU-bound pool causing non-deterministic scheduling.
770 lines
25 KiB
Python
770 lines
25 KiB
Python
#!/usr/bin/env python3
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"""
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Aggregate performance reports across releases.
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Analyzes variance, patterns, and draws conclusions about orchestrator placement & concurrency.
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Generates charts using matplotlib.
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"""
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import json
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import sys
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from pathlib import Path
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from statistics import mean, median, stdev
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from collections import defaultdict
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from datetime import datetime
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try:
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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import numpy as np
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CHARTS_ENABLED = True
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except ImportError:
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CHARTS_ENABLED = False
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def load_perf_json(report_dir):
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"""Load performance data from perf.json"""
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perf_file = Path(report_dir) / "perf.json"
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if not perf_file.exists():
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return None
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try:
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return json.loads(perf_file.read_text())
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except:
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return None
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def analyze_reports(reports_dir):
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"""Analyze all performance reports"""
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reports_path = Path(reports_dir)
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# Load all reports
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reports = {}
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for version_dir in sorted(reports_path.iterdir()):
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if not version_dir.is_dir():
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continue
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version = version_dir.name
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perf_data = load_perf_json(version_dir)
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if perf_data:
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reports[version] = perf_data
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if not reports:
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print("No performance reports found")
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return None
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return reports
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def extract_language_timings(perf_data):
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"""Extract language: duration mapping from perf report"""
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langs = {}
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if "languages" in perf_data:
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for lang_entry in perf_data["languages"]:
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lang_name = lang_entry.get("language", "unknown")
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duration = lang_entry.get("duration_seconds", 0)
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langs[lang_name] = duration
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return langs
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def analyze_variance(reports):
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"""Analyze performance variance across releases"""
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# Extract metrics
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metrics = {}
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language_timings = {}
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for version, perf_data in reports.items():
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summary = perf_data.get("summary", {})
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metrics[version] = {
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"avg_duration": summary.get("avg_duration_seconds", 0),
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"slowest": summary.get("slowest_language", "unknown"),
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"slowest_duration": summary.get("max_duration_seconds", 0),
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"fastest": summary.get("fastest_language", "unknown"),
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"fastest_duration": summary.get("min_duration_seconds", 0),
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}
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langs = extract_language_timings(perf_data)
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language_timings[version] = langs
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# Analyze per-language consistency
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all_languages = set()
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for langs in language_timings.values():
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all_languages.update(langs.keys())
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lang_variance = {}
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for lang in sorted(all_languages):
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durations = []
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for version, langs in language_timings.items():
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if lang in langs:
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durations.append(langs[lang])
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if len(durations) >= 2:
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lang_variance[lang] = {
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"min": min(durations),
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"max": max(durations),
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"avg": mean(durations),
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"range": max(durations) - min(durations),
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"percent_change": ((max(durations) - min(durations)) / min(durations) * 100) if min(durations) > 0 else 0,
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"samples": durations,
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}
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# Find most unstable languages
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most_unstable = sorted(lang_variance.items(), key=lambda x: x[1]["range"], reverse=True)[:10]
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# Find slowest/fastest rankings across runs
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slowest_langs = {}
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for version, langs in language_timings.items():
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sorted_langs = sorted(langs.items(), key=lambda x: x[1], reverse=True)
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slowest_langs[version] = [l[0] for l in sorted_langs[:10]]
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fastest_langs = {}
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for version, langs in language_timings.items():
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sorted_langs = sorted(langs.items(), key=lambda x: x[1])
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fastest_langs[version] = [l[0] for l in sorted_langs[:10]]
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return {
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"metrics": metrics,
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"language_timings": language_timings,
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"lang_variance": lang_variance,
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"most_unstable": most_unstable,
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"slowest_rankings": slowest_langs,
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"fastest_rankings": fastest_langs,
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}
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def detect_concurrency_pattern(reports):
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"""
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Detect if there's a concurrency limit affecting execution.
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If processes are serialized or limited, we'd see:
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- Linear relationship between language count and total time
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- Consistent execution order
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- Predictable timing patterns
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"""
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# Get all language timings
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all_durations = []
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versions = []
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for version in sorted(reports.keys()):
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perf_data = reports[version]
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avg_dur = perf_data.get("avg_duration", 0)
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all_durations.append(avg_dur)
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versions.append(version)
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# Analyze patterns
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findings = {
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"versions": versions,
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"avg_durations": all_durations,
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"trend": "increasing" if all_durations[-1] > all_durations[0] else "decreasing",
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"variance": max(all_durations) - min(all_durations),
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"percent_variance": ((max(all_durations) - min(all_durations)) / min(all_durations) * 100) if min(all_durations) > 0 else 0,
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}
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# Check for concurrency limits
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# If avg duration ~= 33s and we have 42 languages with ~15 tests each
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# If concurrent (no limit): should be 33-50s total
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# If serialized: should be 33s * 42 languages = ~1386s
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# If limited to N parallel: should scale linearly
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# Theory: orchestrator on CPU-bound node causes resource contention
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# Result: random execution order, unpredictable timing
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return findings
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def generate_charts(analysis, reports, output_dir="reports"):
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"""Generate visualization charts for aggregated report"""
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if not CHARTS_ENABLED:
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return
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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# Set dark theme
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plt.style.use('dark_background')
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plt.rcParams['figure.facecolor'] = '#1a1a2e'
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plt.rcParams['axes.facecolor'] = '#16213e'
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versions = sorted(analysis["language_timings"].keys())
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# Chart 1: Variance Trend - Average Duration Over Time
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fig, ax = plt.subplots(figsize=(10, 6))
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avg_durations = [analysis["metrics"][v]["avg_duration"] for v in versions]
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ax.plot(versions, avg_durations, marker='o', linewidth=2, markersize=10, color='#e94560')
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ax.fill_between(range(len(versions)), avg_durations, alpha=0.3, color='#e94560')
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ax.set_xlabel('Release', fontsize=12)
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ax.set_ylabel('Average Duration (seconds)', fontsize=12)
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ax.set_title('Average Test Duration Degradation Over Releases', fontsize=14, fontweight='bold')
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ax.grid(True, alpha=0.3)
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for i, (v, d) in enumerate(zip(versions, avg_durations)):
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ax.text(i, d + 1, f'{d}s', ha='center', fontsize=10)
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plt.tight_layout()
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plt.savefig(output_path / 'aggregated-duration-trend.png', dpi=150, facecolor='#1a1a2e')
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print(f'✓ aggregated-duration-trend.png')
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plt.close()
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# Chart 2: Language Variance Scatter
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fig, ax = plt.subplots(figsize=(12, 8))
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langs = sorted(analysis["lang_variance"].items(), key=lambda x: x[1]["percent_change"], reverse=True)[:15]
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lang_names = [l[0].upper() for l, _ in langs]
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variances = [l[1]["percent_change"] for l, _ in langs]
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colors = ['#e94560' if v > 200 else '#f39c12' if v > 100 else '#27ae60' for v in variances]
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bars = ax.barh(lang_names, variances, color=colors, edgecolor='#fff', linewidth=1)
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ax.set_xlabel('Variance %', fontsize=12)
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ax.set_title('Top 15 Most Unstable Languages (% Variance)', fontsize=14, fontweight='bold')
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for i, (bar, var) in enumerate(zip(bars, variances)):
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ax.text(var + 5, bar.get_y() + bar.get_height()/2, f'{var:.0f}%', va='center', fontsize=9)
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plt.tight_layout()
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plt.savefig(output_path / 'aggregated-language-variance.png', dpi=150, facecolor='#1a1a2e')
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print(f'✓ aggregated-language-variance.png')
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plt.close()
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# Chart 3: Performance Ranking Changes
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fig, ax = plt.subplots(figsize=(14, 8))
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# Show how languages moved in rankings
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all_langs = sorted(analysis["lang_variance"].keys())
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for version in versions:
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timings = analysis["language_timings"][version]
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sorted_langs = sorted(timings.items(), key=lambda x: x[1], reverse=True)
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ranks = {lang: i for i, (lang, _) in enumerate(sorted_langs)}
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# Plot slowest 10
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slowest_10 = sorted_langs[:10]
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for i, (lang, duration) in enumerate(slowest_10):
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ax.scatter(versions.index(version), i, s=300, alpha=0.6, label=lang.upper() if version == versions[0] else "")
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ax.set_xlabel('Release', fontsize=12)
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ax.set_ylabel('Rank (0=Slowest)', fontsize=12)
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ax.set_title('Ranking Instability - Top 10 Slowest Languages Per Run', fontsize=14, fontweight='bold')
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ax.set_xticks(range(len(versions)))
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ax.set_xticklabels(versions)
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plt.tight_layout()
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plt.savefig(output_path / 'aggregated-ranking-changes.png', dpi=150, facecolor='#1a1a2e')
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print(f'✓ aggregated-ranking-changes.png')
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plt.close()
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def generate_report(reports, output_file, analysis=None):
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"""Generate aggregated analysis report"""
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if analysis is None:
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analysis = analyze_variance(reports)
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concurrency = detect_concurrency_pattern(reports)
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versions = sorted(reports.keys())
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version_dates = {v: reports[v].get("timestamp", "unknown") for v in versions}
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report = f"""# UN Inception: Aggregated Performance Analysis
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**Analysis Date:** {Path('.').absolute().stat().st_mtime}
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**Reports Analyzed:** {', '.join(versions)}
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---
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## Executive Summary
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Analysis of {len(versions)} 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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"""
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for v in versions:
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metrics = analysis["metrics"][v]
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report += f"| {v} | {metrics['avg_duration']}s | {metrics['slowest']} ({metrics['slowest_duration']}s) | {metrics['fastest']} ({metrics['fastest_duration']}s) | "
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if v != versions[0]:
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prev_metrics = analysis["metrics"][versions[versions.index(v)-1]]
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change = metrics['avg_duration'] - prev_metrics['avg_duration']
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pct = (change / prev_metrics['avg_duration'] * 100) if prev_metrics['avg_duration'] > 0 else 0
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report += f"+{change}s (+{pct:.1f}%)" if change > 0 else f"{change}s ({pct:.1f}%)"
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else:
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report += "baseline"
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report += " |\n"
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report += f"""
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**Observation:** Average duration increased **{concurrency['percent_variance']:.1f}%** from {concurrency['avg_durations'][0]}s to {concurrency['avg_durations'][-1]}s.
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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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"""
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for lang, variance in analysis["most_unstable"][:5]:
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report += f"\n**{lang.upper()}:**\n"
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for version in versions:
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if lang in analysis["language_timings"].get(version, {}):
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duration = analysis["language_timings"][version][lang]
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report += f" - {version}: {duration}s\n"
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report += f" - **Range:** {variance['min']}s → {variance['max']}s ({variance['percent_change']:.1f}% variance)\n"
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report += f"""
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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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"""
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for version in versions:
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fastest = analysis["fastest_rankings"][version][:5]
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report += f"\n{version}: {', '.join(fastest)}"
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report += f"""
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**Slowest Languages by Run:**
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"""
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for version in versions:
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slowest = analysis["slowest_rankings"][version][:5]
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report += f"\n{version}: {', '.join(slowest)}"
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report += f"""
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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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"""
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for lang, variance in analysis["most_unstable"][:10]:
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report += f"\n{lang.upper()}: {variance['min']}s → {variance['max']}s (+{variance['percent_change']:.1f}%)\n"
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report += f"""
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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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"""
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for lang, variance in sorted(analysis["lang_variance"].items(), key=lambda x: x[1]["percent_change"], reverse=True)[:20]:
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report += f"| {lang.upper()} | {variance['min']} | {variance['max']} | {variance['avg']:.1f} | {variance['range']} | {variance['percent_change']:.1f}% |\n"
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report += f"""
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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
|
||
|
||
### 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**:
|
||
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:**
|
||
"""
|
||
|
||
for version in versions:
|
||
perf_data = reports[version]
|
||
report += f"- `reports/{version}/perf.json` - {perf_data.get('summary', {}).get('total_tests', 'N/A')} tests, generated {perf_data.get('generated_at', 'unknown')}\n"
|
||
|
||
report += f"""
|
||
|
||
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`)
|
||
|
||
```python
|
||
# 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):
|
||
|
||
```bash
|
||
# 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**
|
||
|
||
```bash
|
||
# 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:**
|
||
```bash
|
||
make perf-aggregate-report
|
||
```
|
||
|
||
**Or manually:**
|
||
```bash
|
||
# 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:**
|
||
```bash
|
||
git checkout <commit-sha>
|
||
```
|
||
|
||
2. **Verify reports exist:**
|
||
```bash
|
||
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:** {datetime.now().isoformat()}
|
||
**Report Version:** 1.0.0
|
||
"""
|
||
|
||
return report
|
||
|
||
|
||
if __name__ == "__main__":
|
||
if len(sys.argv) < 2:
|
||
print("Usage: aggregate-performance-reports.py <reports_directory> [output_file]")
|
||
sys.exit(1)
|
||
|
||
reports_dir = sys.argv[1]
|
||
output_file = sys.argv[2] if len(sys.argv) > 2 else "AGGREGATED-PERFORMANCE.md"
|
||
|
||
reports = analyze_reports(reports_dir)
|
||
if not reports:
|
||
print("Failed to load reports")
|
||
sys.exit(1)
|
||
|
||
# Generate analysis
|
||
analysis = analyze_variance(reports)
|
||
|
||
# Generate charts (if matplotlib available)
|
||
generate_charts(analysis, reports, output_dir=reports_dir)
|
||
|
||
report = generate_report(reports, output_file, analysis=analysis)
|
||
|
||
Path(output_file).write_text(report)
|
||
print(f"Generated {output_file}")
|
||
print(f"\n{report}")
|