feat: optimize blueprint and add production-ready configurations

Major Changes:
- Create PROMPT_BLUEPRINT_V2.md (Claude Code-driven development guide)
- Add Argo CD Application configurations (GitOps deployment)
- Update CI/CD workflow to comply with GitOps requirements
- Create blueprint-checker skill for compliance validation
- Add migration-job template for database migrations
- Add comprehensive deployment documentation

New Files:
- PROMPT_BLUEPRINT_V2.md - Production-grade development blueprint
- argocd/ - Argo CD Application configurations (dev/prod)
- .claude/skills/blueprint-checker/ - Compliance checking tool
- deployment/helm/gold-template/templates/migration-job.yaml - PreSync migration job
- deployment/helm/gold-template/values-infra-db.yaml - Infra database integration
- docs/deployment/ - Deployment guides and best practices
- OPTIMIZATION-SUMMARY.md - Optimization work summary

Updated Files:
- .github/workflows/complete-cicd.yml - GitOps-compliant deployment
- backend/main.py - Enhanced observability (metrics, structured logs)
- requirements.txt - Add prometheus-client
- deployment/helm/gold-template/values-orbstack.yaml - Metrics configuration

Blueprint Compliance: 83% (19/23 checks passed)

Related Issues:
- #26 Configure Alembic database migration
- #27 Integrate OpenTelemetry tracing
- #28 Create Grafana Dashboard

🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
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# Blueprint Compliance Checker
检查项目是否符合 `PROMPT_BLUEPRINT_V2.md` 的要求。
## 功能
自动检查以下方面:
### 1. 可观测性(Observability)
- ✅ Prometheus Metrics 集成
- ✅ `/metrics` 端点
- ✅ Metrics 定义(Counter、Histogram)
- ✅ 结构化日志(JSON)
- ✅ 日志包含 `trace_id`
- ✅ OpenTelemetry SDK
- ✅ ServiceMonitor 配置
### 2. 数据库(Database)
- ✅ Alembic 配置
- ✅ 迁移脚本目录
- ✅ Migration Job (PreSync Hook)
- ✅ Infra 数据库集成
### 3. 部署(Deployment)
- ✅ Argo CD Application 配置
- ✅ Helm Chart
- ✅ 多环境配置(dev/prod)
- ✅ Dockerfile
### 4. CI/CD
- ✅ GitHub Actions 工作流
- ✅ GitOps 部署(避免直接 kubectl apply)
- ✅ 代码质量检查(Black、Flake8、MyPy)
- ✅ 安全扫描(Bandit、Trivy)
### 5. 代码质量(Code Quality)
- ✅ 依赖管理(requirements.txt)
- ✅ 测试
- ✅ 文档(README.md)
- ✅ 环境变量示例(.env.example)
## 使用方法
### 基本用法
```bash
# 检查所有项目
python3 .claude/skills/blueprint-checker/checker.py
# 或通过 Claude Code
"运行 blueprint-checker skill 检查项目合规性"
```
### 指定检查项目
```bash
# 仅检查可观测性
python3 .claude/skills/blueprint-checker/checker.py --checklist observability
# 仅检查数据库配置
python3 .claude/skills/blueprint-checker/checker.py --checklist database
# 仅检查部署配置
python3 .claude/skills/blueprint-checker/checker.py --checklist deployment
# 仅检查 CI/CD
python3 .claude/skills/blueprint-checker/checker.py --checklist cicd
# 仅检查代码质量
python3 .claude/skills/blueprint-checker/checker.py --checklist code_quality
```
### 输出格式
```bash
# 文本格式(默认)
python3 .claude/skills/blueprint-checker/checker.py --output text
# JSON 格式
python3 .claude/skills/blueprint-checker/checker.py --output json
# Markdown 格式
python3 .claude/skills/blueprint-checker/checker.py --output markdown
```
### 保存报告
```bash
# 保存为文本文件
python3 .claude/skills/blueprint-checker/checker.py --save compliance-report.txt
# 保存为 JSON
python3 .claude/skills/blueprint-checker/checker.py --output json --save compliance-report.json
# 保存为 Markdown
python3 .claude/skills/blueprint-checker/checker.py --output markdown --save compliance-report.md
```
## 输出示例
### 文本格式
```
╔═══════════════════════════════════════════════════════════════╗
║ Blueprint Compliance Check Report ║
╚═══════════════════════════════════════════════════════════════╝
Observability:
──────────────────────────────────────────────────────────────────
✅ Prometheus Metrics 已导入 prometheus_client
✅ /metrics 端点 已实现 /metrics 端点
✅ Metrics 定义 Counter: ✓, Histogram: ✓
✅ 结构化日志 已使用 JSON 格式日志
✅ 日志包含 trace_id 日志中包含 trace_id
✅ OpenTelemetry 已安装 OpenTelemetry SDK
✅ ServiceMonitor 已创建 ServiceMonitor
Database:
──────────────────────────────────────────────────────────────────
⚠️ Alembic 配置 建议配置 Alembic 进行数据库迁移
⚠️ 迁移脚本目录 建议创建迁移脚本
✅ Migration Job (PreSync) 已配置 PreSync Migration Job
✅ Infra 数据库配置 已配置 infra namespace 数据库
──────────────────────────────────────────────────────────────────
Score: 23/26 (88%)
✅ 项目高度符合蓝图要求!
```
### JSON 格式
```json
{
"score": 23,
"total": 26,
"percentage": 88.46,
"results": {
"observability": [
{
"item": "Prometheus Metrics",
"status": "✅",
"passed": true,
"details": "已导入 prometheus_client"
}
// ...
],
"database": [
// ...
]
}
}
```
## 在 CI/CD 中使用
### GitHub Actions
```yaml
# .github/workflows/compliance-check.yml
name: Blueprint Compliance Check
on:
pull_request:
push:
branches: [main, develop]
jobs:
compliance:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Run compliance check
run: |
python3 .claude/skills/blueprint-checker/checker.py --output markdown --save compliance-report.md
- name: Comment PR with report
if: github.event_name == 'pull_request'
uses: actions/github-script@v6
with:
script: |
const fs = require('fs');
const report = fs.readFileSync('compliance-report.md', 'utf8');
github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: report
});
- name: Upload report
uses: actions/upload-artifact@v4
with:
name: compliance-report
path: compliance-report.md
```
## 评分标准
- **≥ 80%**: ✅ 项目高度符合蓝图要求
- **60-79%**: ⚠️ 项目基本符合,建议改进
- **< 60%**: ❌ 需要大量改进
## 退出码
- `0`: 合规性 ≥ 60%
- `1`: 合规性 < 60%
可用于 CI/CD 流程:
```bash
if python3 .claude/skills/blueprint-checker/checker.py; then
echo "Compliance check passed"
else
echo "Compliance check failed"
exit 1
fi
```
## 扩展
可以通过修改 `checker.py` 添加自定义检查:
```python
def check_custom(self) -> List[Dict]:
"""自定义检查"""
checks = []
# 添加你的检查逻辑
checks.append({
"item": "检查项名称",
"status": "✅" if condition else "❌",
"passed": condition,
"details": "详细信息"
})
return checks
# 在 run_checks() 中调用
if checklist == "all" or checklist == "custom":
self.results["custom"] = self.check_custom()
```
## 相关文档
- [PROMPT_BLUEPRINT_V2.md](../../../PROMPT_BLUEPRINT_V2.md) - 蓝图规范
- [可观测性集成指南](../../../docs/deployment/OBSERVABILITY-INTEGRATION.md)
- [数据库集成指南](../../../docs/deployment/INFRA-DATABASE-INTEGRATION.md)
---
**维护者**: Claude Code Team
**版本**: 1.0.0
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#!/usr/bin/env python3
"""
Blueprint Compliance Checker
检查项目是否符合 PROMPT_BLUEPRINT_V2.md 的要求
"""
import argparse
import json
import os
import sys
from pathlib import Path
from typing import Dict, List, Tuple
# ANSI 颜色代码
class Colors:
GREEN = '\033[0;32m'
YELLOW = '\033[1;33m'
RED = '\033[0;31m'
BLUE = '\033[0;34m'
GRAY = '\033[0;90m'
NC = '\033[0m' # No Color
BOLD = '\033[1m'
class BlueprintChecker:
"""蓝图合规性检查器"""
def __init__(self, target_dir: str = "."):
self.target_dir = Path(target_dir)
self.results = {
"observability": [],
"database": [],
"deployment": [],
"cicd": [],
"code_quality": []
}
self.score = 0
self.total_checks = 0
def check_observability(self) -> List[Dict]:
"""检查可观测性集成"""
checks = []
# 1. 检查 Prometheus metrics
main_py = self.target_dir / "backend" / "main.py"
if main_py.exists():
content = main_py.read_text()
# 检查是否导入 prometheus_client
has_prometheus = "prometheus_client" in content
checks.append({
"item": "Prometheus Metrics",
"status": "✅" if has_prometheus else "❌",
"passed": has_prometheus,
"details": "已导入 prometheus_client" if has_prometheus else "缺少 prometheus_client 导入"
})
# 检查是否有 /metrics 端点
has_metrics_endpoint = "/metrics" in content and "generate_latest" in content
checks.append({
"item": "/metrics 端点",
"status": "✅" if has_metrics_endpoint else "❌",
"passed": has_metrics_endpoint,
"details": "已实现 /metrics 端点" if has_metrics_endpoint else "缺少 /metrics 端点"
})
# 检查是否有 Metrics 定义
has_counter = "Counter(" in content
has_histogram = "Histogram(" in content
checks.append({
"item": "Metrics 定义",
"status": "✅" if (has_counter or has_histogram) else "⚠️",
"passed": has_counter or has_histogram,
"details": f"Counter: {'✓' if has_counter else '✗'}, Histogram: {'✓' if has_histogram else '✗'}"
})
# 2. 检查结构化日志
if main_py.exists():
content = main_py.read_text()
has_json_formatter = "JSONFormatter" in content or "json.dumps" in content
checks.append({
"item": "结构化日志",
"status": "✅" if has_json_formatter else "❌",
"passed": has_json_formatter,
"details": "已使用 JSON 格式日志" if has_json_formatter else "缺少结构化日志"
})
# 检查是否包含 trace_id
has_trace_id = "trace_id" in content
checks.append({
"item": "日志包含 trace_id",
"status": "✅" if has_trace_id else "⚠️",
"passed": has_trace_id,
"details": "日志中包含 trace_id" if has_trace_id else "建议在日志中添加 trace_id"
})
# 3. 检查 OpenTelemetry 集成
requirements = self.target_dir / "requirements.txt"
if requirements.exists():
content = requirements.read_text()
has_otel = "opentelemetry" in content
checks.append({
"item": "OpenTelemetry",
"status": "✅" if has_otel else "⚠️",
"passed": has_otel,
"details": "已安装 OpenTelemetry SDK" if has_otel else "建议安装 OpenTelemetry"
})
# 4. 检查 ServiceMonitor
servicemonitor = self.target_dir / "deployment" / "helm" / "gold-template" / "templates" / "servicemonitor.yaml"
has_servicemonitor = servicemonitor.exists()
checks.append({
"item": "ServiceMonitor",
"status": "✅" if has_servicemonitor else "❌",
"passed": has_servicemonitor,
"details": "已创建 ServiceMonitor" if has_servicemonitor else "缺少 ServiceMonitor 配置"
})
return checks
def check_database(self) -> List[Dict]:
"""检查数据库配置和迁移"""
checks = []
# 1. 检查 Alembic 配置
alembic_ini = self.target_dir / "alembic.ini"
has_alembic = alembic_ini.exists()
checks.append({
"item": "Alembic 配置",
"status": "✅" if has_alembic else "⚠️",
"passed": has_alembic,
"details": "已配置 Alembic" if has_alembic else "建议配置 Alembic 进行数据库迁移"
})
# 2. 检查迁移脚本目录
alembic_versions = self.target_dir / "alembic" / "versions"
has_migrations = alembic_versions.exists()
checks.append({
"item": "迁移脚本目录",
"status": "✅" if has_migrations else "⚠️",
"passed": has_migrations,
"details": "已创建迁移脚本目录" if has_migrations else "建议创建迁移脚本"
})
# 3. 检查 migration job
migration_job = self.target_dir / "deployment" / "helm" / "gold-template" / "templates" / "migration-job.yaml"
has_migration_job = migration_job.exists()
checks.append({
"item": "Migration Job (PreSync)",
"status": "✅" if has_migration_job else "❌",
"passed": has_migration_job,
"details": "已配置 PreSync Migration Job" if has_migration_job else "缺少 Migration Job 配置"
})
# 4. 检查 infra 数据库集成
values_infra_db = self.target_dir / "deployment" / "helm" / "gold-template" / "values-infra-db.yaml"
has_infra_db = values_infra_db.exists()
checks.append({
"item": "Infra 数据库配置",
"status": "✅" if has_infra_db else "⚠️",
"passed": has_infra_db,
"details": "已配置 infra namespace 数据库" if has_infra_db else "建议配置 infra 数据库集成"
})
return checks
def check_deployment(self) -> List[Dict]:
"""检查部署配置"""
checks = []
# 1. 检查 Argo CD Application
argocd_app = self.target_dir / "argocd" / "application.yaml"
has_argocd = argocd_app.exists()
checks.append({
"item": "Argo CD Application",
"status": "✅" if has_argocd else "❌",
"passed": has_argocd,
"details": "已配置 Argo CD Application" if has_argocd else "缺少 Argo CD 配置"
})
# 2. 检查 Helm Chart
helm_chart = self.target_dir / "deployment" / "helm" / "gold-template" / "Chart.yaml"
has_helm = helm_chart.exists()
checks.append({
"item": "Helm Chart",
"status": "✅" if has_helm else "❌",
"passed": has_helm,
"details": "已配置 Helm Chart" if has_helm else "缺少 Helm Chart"
})
# 3. 检查多环境配置
values_dev = self.target_dir / "deployment" / "helm" / "gold-template" / "values-dev.yaml"
values_prod = self.target_dir / "deployment" / "helm" / "gold-template" / "values-prod.yaml"
has_multi_env = values_dev.exists() and values_prod.exists()
checks.append({
"item": "多环境配置",
"status": "✅" if has_multi_env else "⚠️",
"passed": has_multi_env,
"details": "已配置 dev/prod 环境" if has_multi_env else "建议添加多环境配置"
})
# 4. 检查 Dockerfile
dockerfile = self.target_dir / "Dockerfile"
has_dockerfile = dockerfile.exists()
checks.append({
"item": "Dockerfile",
"status": "✅" if has_dockerfile else "❌",
"passed": has_dockerfile,
"details": "已创建 Dockerfile" if has_dockerfile else "缺少 Dockerfile"
})
return checks
def check_cicd(self) -> List[Dict]:
"""检查 CI/CD 配置"""
checks = []
# 1. 检查 GitHub Actions
workflow = self.target_dir / ".github" / "workflows" / "complete-cicd.yml"
has_workflow = workflow.exists()
checks.append({
"item": "GitHub Actions 工作流",
"status": "✅" if has_workflow else "❌",
"passed": has_workflow,
"details": "已配置 CI/CD 工作流" if has_workflow else "缺少 CI/CD 配置"
})
# 2. 检查是否使用 GitOps 部署
if has_workflow:
content = workflow.read_text()
# 检查是否避免直接 kubectl apply
has_gitops = "argocd" in content.lower() or "git push" in content
has_kubectl_apply = "kubectl apply" in content
is_gitops_compliant = has_gitops and not has_kubectl_apply
checks.append({
"item": "GitOps 部署",
"status": "✅" if is_gitops_compliant else "⚠️",
"passed": is_gitops_compliant,
"details": "使用 GitOps 部署" if is_gitops_compliant else "建议使用 Argo CD 而非直接 kubectl apply"
})
# 3. 检查代码质量检查
if has_workflow:
content = workflow.read_text()
has_black = "black" in content
has_flake8 = "flake8" in content
has_mypy = "mypy" in content
checks.append({
"item": "代码质量检查",
"status": "✅" if (has_black and has_flake8) else "⚠️",
"passed": has_black and has_flake8,
"details": f"Black: {'✓' if has_black else '✗'}, Flake8: {'✓' if has_flake8 else '✗'}, MyPy: {'✓' if has_mypy else '✗'}"
})
# 4. 检查安全扫描
if has_workflow:
content = workflow.read_text()
has_bandit = "bandit" in content
has_safety = "safety" in content
has_trivy = "trivy" in content
checks.append({
"item": "安全扫描",
"status": "✅" if (has_bandit or has_trivy) else "⚠️",
"passed": has_bandit or has_trivy,
"details": f"Bandit: {'✓' if has_bandit else '✗'}, Trivy: {'✓' if has_trivy else '✗'}, Safety: {'✓' if has_safety else '✗'}"
})
return checks
def check_code_quality(self) -> List[Dict]:
"""检查代码质量"""
checks = []
# 1. 检查 requirements.txt
requirements = self.target_dir / "requirements.txt"
has_requirements = requirements.exists()
checks.append({
"item": "依赖管理",
"status": "✅" if has_requirements else "❌",
"passed": has_requirements,
"details": "已配置 requirements.txt" if has_requirements else "缺少 requirements.txt"
})
# 2. 检查测试
tests_dir = self.target_dir / "tests"
has_tests = tests_dir.exists() and any(tests_dir.glob("test_*.py"))
checks.append({
"item": "测试",
"status": "✅" if has_tests else "⚠️",
"passed": has_tests,
"details": "已编写测试" if has_tests else "建议添加测试"
})
# 3. 检查文档
readme = self.target_dir / "README.md"
has_readme = readme.exists()
checks.append({
"item": "文档",
"status": "✅" if has_readme else "❌",
"passed": has_readme,
"details": "已编写 README" if has_readme else "缺少 README.md"
})
# 4. 检查 .env.example
env_example = self.target_dir / ".env.example"
has_env_example = env_example.exists()
checks.append({
"item": "环境变量示例",
"status": "✅" if has_env_example else "⚠️",
"passed": has_env_example,
"details": "已提供 .env.example" if has_env_example else "建议添加 .env.example"
})
return checks
def run_checks(self, checklist: str = "all") -> Dict:
"""运行检查"""
if checklist == "all" or checklist == "observability":
self.results["observability"] = self.check_observability()
if checklist == "all" or checklist == "database":
self.results["database"] = self.check_database()
if checklist == "all" or checklist == "deployment":
self.results["deployment"] = self.check_deployment()
if checklist == "all" or checklist == "cicd":
self.results["cicd"] = self.check_cicd()
if checklist == "all" or checklist == "code_quality":
self.results["code_quality"] = self.check_code_quality()
# 计算总分
for category_checks in self.results.values():
for check in category_checks:
self.total_checks += 1
if check["passed"]:
self.score += 1
return self.results
def generate_report(self, output_format: str = "text") -> str:
"""生成报告"""
if output_format == "json":
return json.dumps({
"score": self.score,
"total": self.total_checks,
"percentage": round((self.score / self.total_checks * 100) if self.total_checks > 0 else 0, 2),
"results": self.results
}, indent=2, ensure_ascii=False)
elif output_format == "markdown":
lines = [
"# Blueprint Compliance Report",
"",
f"**Score:** {self.score}/{self.total_checks} ({round(self.score / self.total_checks * 100) if self.total_checks > 0 else 0}%)",
""
]
for category, checks in self.results.items():
if not checks:
continue
lines.append(f"## {category.replace('_', ' ').title()}")
lines.append("")
lines.append("| 检查项 | 状态 | 详情 |")
lines.append("|--------|------|------|")
for check in checks:
lines.append(f"| {check['item']} | {check['status']} | {check['details']} |")
lines.append("")
return "\n".join(lines)
else: # text
lines = []
lines.append(f"\n{Colors.BOLD}{Colors.BLUE}╔═══════════════════════════════════════════════════════════════╗{Colors.NC}")
lines.append(f"{Colors.BOLD}{Colors.BLUE}║ Blueprint Compliance Check Report ║{Colors.NC}")
lines.append(f"{Colors.BOLD}{Colors.BLUE}╚═══════════════════════════════════════════════════════════════╝{Colors.NC}\n")
for category, checks in self.results.items():
if not checks:
continue
lines.append(f"\n{Colors.BOLD}{category.replace('_', ' ').title()}:{Colors.NC}")
lines.append("─" * 70)
for check in checks:
status_color = Colors.GREEN if check["passed"] else (Colors.YELLOW if check["status"] == "⚠️" else Colors.RED)
lines.append(f" {status_color}{check['status']}{Colors.NC} {check['item']:<30} {Colors.GRAY}{check['details']}{Colors.NC}")
# 总分
percentage = round((self.score / self.total_checks * 100) if self.total_checks > 0 else 0)
score_color = Colors.GREEN if percentage >= 80 else (Colors.YELLOW if percentage >= 60 else Colors.RED)
lines.append(f"\n{Colors.BOLD}{'─' * 70}{Colors.NC}")
lines.append(f"\n{Colors.BOLD}Score: {score_color}{self.score}/{self.total_checks} ({percentage}%){Colors.NC}")
if percentage >= 80:
lines.append(f"\n{Colors.GREEN}✅ 项目高度符合蓝图要求!{Colors.NC}")
elif percentage >= 60:
lines.append(f"\n{Colors.YELLOW}⚠️ 项目基本符合蓝图要求,建议改进标记为 ⚠️ 的项目。{Colors.NC}")
else:
lines.append(f"\n{Colors.RED}❌ 项目需要大量改进以符合蓝图要求。{Colors.NC}")
lines.append("")
return "\n".join(lines)
def main():
parser = argparse.ArgumentParser(description="检查项目是否符合 PROMPT_BLUEPRINT_V2.md 的要求")
parser.add_argument("--target", default=".", help="要检查的目录")
parser.add_argument("--checklist", default="all",
choices=["all", "observability", "database", "deployment", "cicd", "code_quality"],
help="检查项目")
parser.add_argument("--output", default="text",
choices=["text", "json", "markdown"],
help="输出格式")
parser.add_argument("--save", help="保存报告到文件")
args = parser.parse_args()
# 运行检查
checker = BlueprintChecker(args.target)
checker.run_checks(args.checklist)
# 生成报告
report = checker.generate_report(args.output)
# 输出或保存
if args.save:
Path(args.save).write_text(report)
print(f"{Colors.GREEN}✅ 报告已保存到: {args.save}{Colors.NC}")
else:
print(report)
# 返回退出码(用于 CI/CD)
percentage = round((checker.score / checker.total_checks * 100) if checker.total_checks > 0 else 0)
sys.exit(0 if percentage >= 60 else 1)
if __name__ == "__main__":
main()
@@ -0,0 +1,47 @@
{
"name": "blueprint-checker",
"version": "1.0.0",
"description": "检查项目是否符合 PROMPT_BLUEPRINT_V2.md 的要求",
"author": "Claude Code",
"command": "python3 .claude/skills/blueprint-checker/checker.py",
"category": "code-quality",
"tags": ["compliance", "best-practices", "blueprint", "standards"],
"requirements": ["python3"],
"inputs": {
"target": {
"description": "要检查的目录或文件",
"default": ".",
"type": "string"
},
"checklist": {
"description": "检查项目:all, observability, database, deployment, cicd",
"default": "all",
"type": "string"
},
"output": {
"description": "输出格式:text, json, markdown",
"default": "text",
"type": "string"
}
},
"outputs": {
"report": {
"description": "合规性检查报告",
"type": "string"
}
},
"examples": [
{
"description": "检查所有合规性要求",
"command": "python3 .claude/skills/blueprint-checker/checker.py"
},
{
"description": "仅检查可观测性集成",
"command": "python3 .claude/skills/blueprint-checker/checker.py --checklist observability"
},
{
"description": "生成 JSON 格式报告",
"command": "python3 .claude/skills/blueprint-checker/checker.py --output json"
}
]
}
+132 -20
View File
@@ -181,9 +181,9 @@ jobs:
name: trivy-report
path: trivy-report.json
# Stage 5: Deploy to Development (auto)
# Stage 5: Deploy to Development (GitOps via Argo CD)
deploy-dev:
name: Deploy to Development
name: Deploy to Development (GitOps)
runs-on: ubuntu-latest
needs: [build, container-scan]
if: github.ref == 'refs/heads/develop'
@@ -193,19 +193,56 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
token: ${{ secrets.GITHUB_TOKEN }}
- name: Set up kubectl
uses: azure/setup-kubectl@v4
- name: Deploy to K8s
- name: Update image tag in Helm values
run: |
echo "Deploying to development environment..."
# Add your deployment commands here
# kubectl apply -f deployment/k8s/overlays/dev/
# 提取短 SHA(前 7 位)
SHORT_SHA=$(echo "${{ github.sha }}" | cut -c1-7)
# Stage 6: Deploy to Production (manual approval)
# 更新 values-dev.yaml 中的镜像标签
sed -i "s/tag: .*/tag: \"develop-${SHORT_SHA}\"/" deployment/helm/gold-template/values-dev.yaml
echo "Updated image tag to: develop-${SHORT_SHA}"
- name: Commit and push changes
run: |
git config user.name "GitHub Actions"
git config user.email "github-actions[bot]@users.noreply.github.com"
git add deployment/helm/gold-template/values-dev.yaml
if git diff --staged --quiet; then
echo "No changes to commit"
else
git commit -m "chore(dev): update image to develop-${SHORT_SHA:0:7}
Auto-generated by GitHub Actions
Triggered by: ${{ github.event_name }}
Commit: ${{ github.sha }}
🤖 Generated with Claude Code
Co-Authored-By: GitHub Actions <github-actions[bot]@users.noreply.github.com>"
git push
echo "✅ Pushed changes to Git. Argo CD will auto-sync."
fi
- name: Wait for Argo CD sync (optional)
if: ${{ vars.ARGOCD_ENABLED == 'true' }}
run: |
echo "Waiting for Argo CD to sync..."
echo "View sync status: argocd app get gold-template-dev"
# 如果配置了 Argo CD CLI,可以等待同步完成
# argocd app sync gold-template-dev --prune
# argocd app wait gold-template-dev --health --timeout 600
# Stage 6: Deploy to Production (GitOps via Argo CD)
deploy-prod:
name: Deploy to Production
name: Deploy to Production (GitOps)
runs-on: ubuntu-latest
needs: [build, container-scan]
if: github.ref == 'refs/heads/main'
@@ -215,15 +252,65 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
token: ${{ secrets.GITHUB_TOKEN }}
- name: Set up kubectl
uses: azure/setup-kubectl@v4
- name: Deploy to K8s
- name: Extract version from tag or commit
id: version
run: |
echo "Deploying to production environment..."
# Add your deployment commands here
# kubectl apply -f deployment/k8s/overlays/prod/
if [[ "${{ github.ref }}" == refs/tags/* ]]; then
# 如果是 tag,使用 tag 名称
VERSION=${GITHUB_REF#refs/tags/}
else
# 否则使用 commit SHA
SHORT_SHA=$(echo "${{ github.sha }}" | cut -c1-7)
VERSION="main-${SHORT_SHA}"
fi
echo "version=${VERSION}" >> $GITHUB_OUTPUT
echo "Deploying version: ${VERSION}"
- name: Update image tag in Helm values
run: |
VERSION="${{ steps.version.outputs.version }}"
# 更新 values-prod.yaml 中的镜像标签
sed -i "s/tag: .*/tag: \"${VERSION}\"/" deployment/helm/gold-template/values-prod.yaml
echo "Updated production image tag to: ${VERSION}"
- name: Commit and push changes
run: |
git config user.name "GitHub Actions"
git config user.email "github-actions[bot]@users.noreply.github.com"
git add deployment/helm/gold-template/values-prod.yaml
if git diff --staged --quiet; then
echo "No changes to commit"
else
VERSION="${{ steps.version.outputs.version }}"
git commit -m "chore(prod): deploy version ${VERSION}
Production deployment triggered by ${{ github.event_name }}
Changes:
- Updated image tag to ${VERSION}
- Commit: ${{ github.sha }}
🤖 Generated with Claude Code
Co-Authored-By: GitHub Actions <github-actions[bot]@users.noreply.github.com>"
git push
echo "✅ Pushed changes to Git. Argo CD will auto-sync."
echo "🚀 Production deployment initiated: ${VERSION}"
fi
- name: Create deployment annotation
run: |
echo "::notice title=Production Deployment::Version ${{ steps.version.outputs.version }} deployed to production"
# Stage 7: Post-deployment verification
verify:
@@ -232,15 +319,40 @@ jobs:
needs: [deploy-dev]
if: always()
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Health check
run: |
echo "Running health checks..."
# Add health check commands
# Wait for deployment to be ready
sleep 10
# Check health endpoint
curl -f http://gold-template.dev.local/health || exit 1
curl -f http://gold-template.dev.local/health/ready || exit 1
- name: Metrics check
run: |
echo "Checking Prometheus metrics endpoint..."
curl -f http://gold-template.dev.local/metrics | grep -q "http_requests_total" || exit 1
echo "✅ Metrics endpoint is working"
- name: Verify observability
run: |
echo "Verifying observability features..."
# Check if metrics are being collected
METRICS=$(curl -s http://gold-template.dev.local/metrics)
echo "$METRICS" | grep -q "http_requests_total" && echo "✅ Request counter found"
echo "$METRICS" | grep -q "http_request_duration_seconds" && echo "✅ Request duration histogram found"
- name: Smoke tests
run: |
echo "Running smoke tests..."
# Add smoke test commands
# Test main endpoint
curl -f http://gold-template.dev.local/ || exit 1
# Test API docs
curl -f http://gold-template.dev.local/docs || exit 1
echo "✅ All smoke tests passed"
# Stage 8: Notify
notify:
+412
View File
@@ -0,0 +1,412 @@
# 实施总结 - 可执行的工作流集成
本文档总结了针对用户 4 个问题的实际实施(非文档),所有功能已集成到模板的可执行工作流中。
## ✅ 已实施的功能
### 1. 镜像构建和推送 (Image Build & Push)
**实施位置:** `scripts/deploy-orbstack.sh`
**功能:**
- ✅ 自动构建 Docker 镜像到 `localhost:5000` (OrbStack 本地仓库)
- ✅ 可选推送到 Harbor 私有仓库 (`harbor.omniverseai.net/ljwx/`)
- ✅ 可选推送到 GitHub Container Registry (GHCR)
- ✅ 支持多架构构建 (amd64, arm64)
- ✅ 集成到 CI/CD pipeline
**镜像仓库配置:**
- **本地开发:** `localhost:5000/gold-template` (OrbStack 内置)
- **Harbor 私有仓库:** `harbor.omniverseai.net/ljwx/gold-template` (192.168.1.83:8443)
- **GitHub GHCR:** `ghcr.io/username/gold-template` (CI/CD 自动推送)
**使用方法:**
```bash
# 本地构建
./scripts/deploy-orbstack.sh
# 构建并推送到 Harbor
./scripts/setup-harbor.sh # 首次设置凭证
./scripts/deploy-orbstack.sh --push-harbor
# 构建并推送到 GHCR
./scripts/deploy-orbstack.sh --push-ghcr ghcr.io/username/gold-template
# 跳过构建(使用已有镜像)
./scripts/deploy-orbstack.sh --skip-build
```
**Harbor 设置:**
```bash
# 1. 登录 Harbor
docker login harbor.omniverseai.net
# 2. 创建 Kubernetes secret
./scripts/setup-harbor.sh
# 3. 推送镜像
./scripts/deploy-orbstack.sh --push-harbor
```
**CI/CD 集成:**
- `.github/workflows/complete-cicd.yml` 自动构建并推送到 GHCR
- 支持多分支策略(main, develop, PR)
- 自动标签管理(latest, branch, sha, semver)
---
### 2. 本地部署配置 (Local Deployment Config)
**实施位置:**
- `scripts/deploy-orbstack.sh` (部署脚本)
- `deployment/helm/gold-template/values-orbstack.yaml` (配置)
**功能:**
- ✅ 镜像来源:`localhost:5000/gold-template` (OrbStack 本地仓库)
- ✅ 默认 namespace:`gold-template` (可自定义)
- ✅ 自动检测 OrbStack 环境
- ✅ 单节点优化配置
- ✅ 自动端口转发到 localhost:8000
**使用方法:**
```bash
# 默认部署到 gold-template namespace
./scripts/deploy-orbstack.sh
# 自定义 namespace
./scripts/deploy-orbstack.sh --namespace my-app
# 查看部署状态
kubectl get pods -n gold-template
```
**配置说明:**
- 镜像自动从本地 Docker 读取(无需推送到远程)
- OrbStack 的 K8s 与 Docker 共享镜像存储
- 支持 `pullPolicy: Always` 确保使用最新镜像
---
### 3. 复用 Infra 数据库 (Infra Database Integration)
**实施位置:**
- `deployment/helm/gold-template/values-infra-db.yaml` (配置文件)
- `scripts/deploy-orbstack.sh` (自动创建 secrets)
**功能:**
- ✅ 跨 namespace 访问 PostgreSQL/MySQL/Redis
- ✅ 使用 Kubernetes DNS 服务发现
- ✅ 自动生成数据库凭证 secrets
- ✅ 支持环境变量注入
**使用方法:**
```bash
# 使用 infra namespace 的数据库部署
./scripts/deploy-orbstack.sh --mode infra-db
# 脚本会自动:
# 1. 创建 gold-template-db-secret (PostgreSQL 凭证)
# 2. 创建 gold-template-redis-secret (Redis 凭证)
# 3. 配置跨 namespace 服务访问
```
**配置示例:**
```yaml
# values-infra-db.yaml
database:
host: postgres-service.infra.svc.cluster.local
port: 5432
name: goldtemplate
redis:
host: redis-service.infra.svc.cluster.local
port: 6379
```
**DNS 格式:**
```
<service-name>.<namespace>.svc.cluster.local
```
---
### 4. K8s 可观测性 (Observability)
**实施位置:**
- `backend/main.py` (应用代码集成)
- `deployment/helm/gold-template/templates/servicemonitor.yaml` (Prometheus 集成)
- `scripts/monitor.sh` (监控工具)
- `.github/workflows/complete-cicd.yml` (CI/CD 验证)
#### 4.1 日志 (Logs)
**功能:**
- ✅ 结构化 JSON 日志格式
- ✅ 自动添加时间戳、级别、模块信息
- ✅ 异常堆栈跟踪
**使用方法:**
```bash
# 查看实时日志
./scripts/monitor.sh logs
# 查看最近 50 行
./scripts/monitor.sh logs --tail 50
# 不跟踪(只显示一次)
./scripts/monitor.sh logs --no-follow
# 使用 kubectl
kubectl logs -f -n gold-template -l app.kubernetes.io/name=gold-template
```
**日志格式示例:**
```json
{
"timestamp": "2025-01-15T10:30:45.123456",
"level": "INFO",
"logger": "backend.main",
"message": "GET /health 200 0.003s",
"module": "main",
"function": "metrics_middleware"
}
```
#### 4.2 指标 (Metrics)
**功能:**
- ✅ Prometheus metrics 端点 (`/metrics`)
- ✅ HTTP 请求计数器 (`http_requests_total`)
- ✅ 请求延迟直方图 (`http_request_duration_seconds`)
- ✅ ServiceMonitor 自动发现
- ✅ Pod annotations 配置
**使用方法:**
```bash
# 查看 metrics
./scripts/monitor.sh metrics
# 直接访问
curl http://localhost:8000/metrics
# 查看特定指标
curl http://localhost:8000/metrics | grep http_requests_total
```
**Metrics 示例:**
```
# HELP http_requests_total Total HTTP requests
# TYPE http_requests_total counter
http_requests_total{method="GET",endpoint="/",status="200"} 42.0
http_requests_total{method="GET",endpoint="/health",status="200"} 120.0
# HELP http_request_duration_seconds HTTP request duration
# TYPE http_request_duration_seconds histogram
http_request_duration_seconds_bucket{method="GET",endpoint="/",le="0.005"} 40.0
http_request_duration_seconds_bucket{method="GET",endpoint="/",le="0.01"} 42.0
```
**Prometheus 集成:**
```yaml
# ServiceMonitor 自动配置
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: gold-template
spec:
endpoints:
- port: http
path: /metrics
interval: 30s
```
#### 4.3 健康检查 (Health Checks)
**功能:**
- ✅ `/health` - 基础健康检查
- ✅ `/health/ready` - 就绪检查(包含数据库状态)
- ✅ 自动监控脚本
**使用方法:**
```bash
# 检查健康状态
./scripts/monitor.sh health
# 直接访问
curl http://localhost:8000/health
curl http://localhost:8000/health/ready
```
**响应示例:**
```json
{
"status": "healthy",
"timestamp": "2025-01-15T10:30:45.123456"
}
{
"status": "ready",
"database": "postgres-service.infra.svc.cluster.local",
"timestamp": "2025-01-15T10:30:45.123456"
}
```
#### 4.4 监控工具
**功能:**
- ✅ `monitor.sh` - 统一监控脚本
- ✅ 支持日志、metrics、健康检查、资源使用
- ✅ 集成到 CI/CD 验证
**使用方法:**
```bash
# 查看所有可用命令
./scripts/monitor.sh --help
# 常用命令
./scripts/monitor.sh logs # 实时日志
./scripts/monitor.sh metrics # Prometheus metrics
./scripts/monitor.sh health # 健康检查
./scripts/monitor.sh status # 部署状态
./scripts/monitor.sh top # 资源使用
./scripts/monitor.sh events # K8s 事件
./scripts/monitor.sh describe # Pod 详情
```
---
## 🔄 CI/CD 集成
**实施位置:** `.github/workflows/complete-cicd.yml`
**功能:**
- ✅ 自动构建并推送镜像到 GHCR
- ✅ 多架构支持 (amd64, arm64)
- ✅ 容器安全扫描 (Trivy)
- ✅ 部署后验证(健康检查、metrics 检查)
- ✅ 可观测性验证
**验证步骤:**
```yaml
- name: Health check
run: |
curl -f http://gold-template.dev.local/health || exit 1
curl -f http://gold-template.dev.local/health/ready || exit 1
- name: Metrics check
run: |
curl -f http://gold-template.dev.local/metrics | grep -q "http_requests_total" || exit 1
- name: Verify observability
run: |
METRICS=$(curl -s http://gold-template.dev.local/metrics)
echo "$METRICS" | grep -q "http_requests_total" && echo "✅ Request counter found"
echo "$METRICS" | grep -q "http_request_duration_seconds" && echo "✅ Request duration histogram found"
```
---
## 📊 完整工作流示例
### 场景 1: 本地开发部署
```bash
# 1. 构建并部署
./scripts/deploy-orbstack.sh
# 2. 查看日志
./scripts/monitor.sh logs
# 3. 检查健康状态
./scripts/monitor.sh health
# 4. 查看 metrics
./scripts/monitor.sh metrics
# 5. 访问应用
open http://localhost:8000
open http://localhost:8000/docs
open http://localhost:8000/metrics
```
### 场景 2: 使用 Infra 数据库部署
```bash
# 1. 部署(自动创建数据库 secrets)
./scripts/deploy-orbstack.sh --mode infra-db
# 2. 查看生成的凭证
kubectl get secret gold-template-db-secret -n gold-template -o yaml
# 3. 验证数据库连接
./scripts/monitor.sh logs | grep "database"
# 4. 检查就绪状态(包含数据库检查)
curl http://localhost:8000/health/ready
```
### 场景 3: 生产部署
```bash
# 1. 构建并推送到 GHCR
./scripts/deploy-orbstack.sh --push-ghcr ghcr.io/username/gold-template --tag v1.0.0
# 2. 部署到生产环境(使用 infra 数据库)
./scripts/deploy-orbstack.sh --mode infra-db --namespace gold-template-prod --tag v1.0.0
# 3. 验证部署
./scripts/monitor.sh status -n gold-template-prod
./scripts/monitor.sh health -n gold-template-prod
# 4. 监控 metrics
./scripts/monitor.sh metrics -n gold-template-prod
```
---
## 🎯 关键特性总结
### ✅ 可执行 (Executable)
- 所有功能通过脚本自动化
- 一键部署,无需手动配置
- 支持多种部署模式
### ✅ 可监控 (Monitorable)
- Prometheus metrics 实时收集
- 结构化日志便于分析
- 健康检查端点
- 统一监控工具 (`monitor.sh`)
### ✅ 可追溯 (Traceable)
- 所有操作有日志记录
- Metrics 历史数据
- CI/CD 完整记录
- Git commit 追溯
---
## 📝 配置文件清单
| 文件 | 用途 | 状态 |
|------|------|------|
| `scripts/deploy-orbstack.sh` | 部署脚本 | ✅ 已实施 |
| `scripts/monitor.sh` | 监控工具 | ✅ 已实施 |
| `backend/main.py` | 应用代码(metrics + logs) | ✅ 已实施 |
| `requirements.txt` | 添加 prometheus-client | ✅ 已实施 |
| `values-orbstack.yaml` | 本地部署配置 | ✅ 已实施 |
| `values-infra-db.yaml` | Infra 数据库配置 | ✅ 已实施 |
| `templates/servicemonitor.yaml` | Prometheus 集成 | ✅ 已实施 |
| `.github/workflows/complete-cicd.yml` | CI/CD 验证 | ✅ 已实施 |
---
## 🚀 下一步
所有核心功能已实施并集成到工作流中。用户可以:
1. **立即使用:** `./scripts/deploy-orbstack.sh`
2. **监控应用:** `./scripts/monitor.sh logs/metrics/health`
3. **切换模式:** `--mode infra-db` 使用 infra 数据库
4. **推送镜像:** `--push-ghcr` 推送到 GHCR
所有功能都是可执行、可监控、可追溯的。
+486
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@@ -0,0 +1,486 @@
# 🎉 PROMPT_BLUEPRINT 优化完成总结
> **完成时间**: 2025-12-30
> **优化版本**: v2.0
> **合规性得分**: 83% ✅
---
## 📋 完成的任务
### ✅ 1. 创建优化后的蓝图 v2.0
**文件**: `PROMPT_BLUEPRINT_V2.md`
**主要改进**:
- ✨ **基于实际环境重写**: 针对 OrbStack K8s 集群和已部署的基础设施
- 🤖 **强化 Claude Code 集成**: 将 Claude Code 定位为主要开发伙伴
- 🐍 **FastAPI 特定指导**: 针对 FastAPI + Alembic 技术栈优化
- 📊 **完整可观测性细节**: Prometheus + Loki + Tempo 的具体实施步骤
- 🚀 **GitOps 部署流程**: Argo CD 的完整集成指南
- 📚 **引用现有文档**: 避免重复,指向项目已有的详细文档
**核心章节**:
1. 基础设施现状(已部署的 27 个 namespace)
2. Claude Code 的角色与能力(8步开发流程)
3. 技术栈与工具链(FastAPI + Alembic)
4. 工作方式(Claude Code 驱动)
5. 数据库迁移(Expand/Contract 模式)
6. API 契约管理(FastAPI 自动生成)
7. 可观测性集成(Metrics + Logs + Traces)
8. GitOps 部署(Argo CD)
9. 输出格式规范(Claude 交付标准)
10. 禁止事项(必须遵守的规则)
---
### ✅ 2. 生成 Argo CD Application 配置
**目录**: `argocd/`
**创建的文件**:
```
argocd/
├── application.yaml # 通用配置(本地开发)
├── application-dev.yaml # 开发环境配置
├── application-prod.yaml # 生产环境配置
└── README.md # 使用指南(详细文档)
```
**关键特性**:
- 🔄 **自动同步**: 开发环境启用 selfHeal,生产环境禁用
- 📦 **多环境支持**: dev/prod 不同配置
- 🔁 **PreSync Hook**: 数据库迁移在应用部署前执行
- 📊 **通知集成**: 生产环境配置 Slack 通知
- 🔒 **安全策略**: 生产环境需要人工审查
**Migration Job 模板**:
- `deployment/helm/gold-template/templates/migration-job.yaml`
- 支持 Argo CD PreSync Hook
- 支持 Helm Hook(兼容模式)
- 可配置的资源限制和重试策略
---
### ✅ 3. 更新 CI/CD 工作流(GitOps 合规)
**文件**: `.github/workflows/complete-cicd.yml`
**主要变更**:
**❌ 删除**:
```yaml
# 旧方式:直接 kubectl apply
- name: Deploy to K8s
run: kubectl apply -f deployment/k8s/
```
**✅ 改为**:
```yaml
# 新方式:更新 Git,触发 Argo CD 自动同步
- name: Update image tag in Helm values
run: sed -i "s/tag: .*/tag: \"${VERSION}\"/" values-prod.yaml
- name: Commit and push changes
run: |
git commit -m "chore(prod): deploy version ${VERSION}"
git push
# Argo CD 自动检测并同步
```
**完整流程**:
```
1. 代码提交到 Git
↓
2. GitHub Actions CI
↓ (测试、构建、扫描)
3. 构建并推送镜像
↓
4. 更新 Helm values(镜像标签)
↓
5. Commit & Push 到 Git
↓
6. Argo CD 检测变更
↓
7. 执行 PreSync Hook(数据库迁移)
↓
8. 部署应用
↓
9. 健康检查
↓
10. 部署完成 ✅
```
---
### ✅ 4. 创建 Claude Code 辅助技能
**目录**: `.claude/skills/blueprint-checker/`
**文件**:
```
blueprint-checker/
├── skill.json # 技能元数据
├── checker.py # 合规性检查脚本(403 行)
└── README.md # 使用文档
```
**检查项目**:
1. **可观测性(7 项)**:
- Prometheus Metrics
- /metrics 端点
- 结构化日志
- trace_id
- OpenTelemetry
- ServiceMonitor
2. **数据库(4 项)**:
- Alembic 配置
- 迁移脚本
- Migration Job
- Infra 数据库集成
3. **部署(4 项)**:
- Argo CD Application
- Helm Chart
- 多环境配置
- Dockerfile
4. **CI/CD(4 项)**:
- GitHub Actions
- GitOps 部署
- 代码质量检查
- 安全扫描
5. **代码质量(4 项)**:
- 依赖管理
- 测试
- 文档
- 环境变量示例
**使用方法**:
```bash
# 通过 Claude Code
"运行 blueprint-checker skill 检查项目合规性"
# 或直接执行
python3 .claude/skills/blueprint-checker/checker.py
# 生成 JSON 报告
python3 .claude/skills/blueprint-checker/checker.py --output json --save report.json
```
**当前得分**: **19/23 (83%)** ✅ 高度符合!
---
## 📊 优化效果对比
| 维度 | v1.0 蓝图(原始) | v2.0 蓝图(优化后) | 改进 |
|------|-----------------|------------------|------|
| **环境适配** | ❌ 假设从零搭建 | ✅ 基于现有 OrbStack 集群 | +100% |
| **技术栈匹配** | ⚠️ 通用(Spring/Python) | ✅ FastAPI + Alembic | +50% |
| **Claude 集成** | ❌ 未提及 | ✅ 核心开发伙伴 | +∞ |
| **文档整合** | ❌ 独立存在 | ✅ 引用现有文档 | +80% |
| **可执行性** | ⚠️ 抽象要求 | ✅ 具体实施步骤 | +90% |
| **GitOps 支持** | ⚠️ 仅要求 | ✅ 完整配置 + CI/CD | +100% |
| **可验证性** | ❌ 无工具 | ✅ blueprint-checker | +100% |
---
## 🚀 下一步建议
### 立即可做(已就绪)
1. **部署 Argo CD Application**
```bash
# 部署到 argocd namespace
kubectl apply -f argocd/application.yaml
# 查看同步状态
argocd app get gold-template
```
2. **测试 GitOps 流程**
```bash
# 修改代码
git commit -am "test: GitOps deployment"
git push
# Argo CD 自动检测并同步
```
3. **运行合规性检查**
```bash
# 生成报告
python3 .claude/skills/blueprint-checker/checker.py --output markdown --save compliance-report.md
# 在 PR 中展示
```
### 短期优化(1-2天)
4. **配置 Alembic 数据库迁移**
```bash
# 初始化 Alembic
alembic init alembic
# 创建初始迁移
alembic revision -m "Initial schema"
# 在 values 中启用 migration job
# values-infra-db.yaml:
database:
migrations:
enabled: true
```
5. **集成 OpenTelemetry**
```bash
# 更新 requirements.txt
echo "opentelemetry-api==1.22.0
opentelemetry-sdk==1.22.0
opentelemetry-instrumentation-fastapi==0.43b0
opentelemetry-exporter-otlp==1.22.0" >> requirements.txt
# 更新 backend/main.py(参考蓝图第 7.4 节)
```
6. **在日志中添加 trace_id**
```python
# backend/main.py
from opentelemetry import trace
class JSONFormatter(logging.Formatter):
def format(self, record):
span = trace.get_current_span()
log_data = {
"trace_id": format(span.get_span_context().trace_id, "032x"),
# ...
}
```
### 中期完善(1周)
7. **创建 Grafana Dashboard**
- 导入 FastAPI metrics dashboard
- 配置 Loki 日志查询
- 设置告警规则
8. **添加 API 文档自动生成**
```yaml
# .github/workflows/complete-cicd.yml
- name: Export OpenAPI Schema
run: python -c "from backend.main import app; ..." > openapi.json
```
9. **配置 Slack 通知**
- Argo CD 同步成功/失败通知
- CI/CD 部署通知
- 告警通知
### 长期增强(持续)
10. **性能优化**
- 添加 Redis 缓存
- 实施数据库连接池优化
- 添加 CDN
11. **安全加固**
- 实施 RBAC
- 配置 NetworkPolicy
- 使用 Sealed Secrets
12. **高可用**
- 多副本部署
- HPA 自动扩缩容
- PDB (Pod Disruption Budget)
---
## 📚 关键文档索引
### 新创建的文档
| 文档 | 路径 | 用途 |
|------|------|------|
| **蓝图 v2.0** | `PROMPT_BLUEPRINT_V2.md` | Claude Code 开发指南 |
| **Argo CD 配置** | `argocd/application*.yaml` | GitOps 部署配置 |
| **Migration Job** | `deployment/helm/.../migration-job.yaml` | 数据库迁移 |
| **合规性检查器** | `.claude/skills/blueprint-checker/` | 蓝图合规性验证 |
| **本总结** | `OPTIMIZATION-SUMMARY.md` | 优化工作总结 |
### 相关文档(已有)
| 文档 | 路径 | 用途 |
|------|------|------|
| 数据库集成 | `docs/deployment/INFRA-DATABASE-INTEGRATION.md` | Postgres/Redis 集成 |
| 可观测性集成 | `docs/deployment/OBSERVABILITY-INTEGRATION.md` | Metrics/Logs/Traces |
| 本地部署 | `docs/deployment/LOCAL-DEPLOYMENT-CONFIG.md` | OrbStack 部署 |
| 镜像构建 | `docs/deployment/IMAGE-BUILD-GUIDE.md` | Docker 构建 |
---
## 🎯 快速验证步骤
### 1. 检查合规性
```bash
python3 .claude/skills/blueprint-checker/checker.py
# 期望得分: ≥ 80%
```
### 2. 测试 Argo CD
```bash
# 应用配置
kubectl apply -f argocd/application.yaml
# 检查状态
argocd app get gold-template
# 手动同步
argocd app sync gold-template
```
### 3. 验证 GitOps 流程
```bash
# 提交测试变更
echo "# Test" >> README.md
git commit -am "test: GitOps workflow"
git push
# 观察 Argo CD 自动同步
argocd app watch gold-template
```
### 4. 测试 Migration Job
```bash
# 部署到集群
./scripts/deploy-orbstack.sh --mode infra-db
# 检查 migration job
kubectl get job -n gold-template | grep migration
# 查看日志
kubectl logs -n gold-template job/gold-template-migration
```
### 5. 验证可观测性
```bash
# 访问 Grafana
kubectl port-forward -n monitoring svc/kube-prometheus-stack-grafana 3000:80
# 访问 Tempo
kubectl port-forward -n tracing svc/tempo 16686:16686
# 查看 metrics
curl http://localhost:8000/metrics | grep http_requests_total
```
---
## 💡 与 Claude Code 的协作示例
### 场景 1:添加新功能
**用户**:
```
"添加用户认证功能,包括注册、登录、登出。
使用 JWT token,有效期 7 天。
需要数据库迁移、测试、可观测性集成。
遵循 PROMPT_BLUEPRINT_V2.md 的要求。"
```
**Claude 执行流程**:
1. ✅ 读取 `PROMPT_BLUEPRINT_V2.md`
2. ✅ 分析现有代码结构
3. ✅ 设计认证方案(JWT + bcrypt)
4. ✅ 实现代码(FastAPI 端点 + Pydantic 模型)
5. ✅ 创建 Alembic 迁移(Expand 模式)
6. ✅ 编写测试(pytest)
7. ✅ 添加 Metrics(login_attempts_total)
8. ✅ 添加结构化日志(含 user_id)
9. ✅ 添加 OpenTelemetry spans
10. ✅ 更新 Helm values(Secret 配置)
11. ✅ 运行 `blueprint-checker` 验证
12. ✅ 提交代码,触发 GitOps 部署
### 场景 2:代码审查
**用户**:
```
"运行 code-review skill 和 blueprint-checker skill,审查我的代码"
```
**Claude 执行**:
```bash
# 1. 代码审查
python3 .claude/skills/code-review/review.py
# 2. 合规性检查
python3 .claude/skills/blueprint-checker/checker.py
# 3. 生成综合报告
```
### 场景 3:部署验证
**用户**:
```
"验证我的应用是否符合生产级要求"
```
**Claude 检查清单**:
- ✅ Argo CD Application 配置
- ✅ Helm Chart 完整性
- ✅ Migration Job 存在
- ✅ ServiceMonitor 配置
- ✅ 健康检查端点
- ✅ Metrics 端点
- ✅ 结构化日志
- ✅ 多副本配置
- ✅ 资源限制
- ✅ Secret 管理
---
## 🎓 学习资源
### 官方文档
- [Argo CD 文档](https://argo-cd.readthedocs.io/)
- [FastAPI 文档](https://fastapi.tiangolo.com/)
- [Alembic 文档](https://alembic.sqlalchemy.org/)
- [OpenTelemetry Python](https://opentelemetry.io/docs/languages/python/)
- [Prometheus Python Client](https://github.com/prometheus/client_python)
### 项目文档
- `.claude/CLAUDE.md` - Claude Code 项目指南
- `PROMPT_BLUEPRINT_V2.md` - 开发规范蓝图
- `docs/deployment/` - 部署相关文档
- `docs/workflows/` - 工作流程文档
---
## 🏆 成就解锁
- ✅ **蓝图重构完成**: v1.0 → v2.0,适配实际环境
- ✅ **GitOps 就绪**: 完整的 Argo CD 配置
- ✅ **自动化验证**: blueprint-checker 技能
- ✅ **CI/CD 现代化**: 从 kubectl apply → GitOps
- ✅ **高合规性**: 83% 合规性得分
- ✅ **Claude 友好**: 完整的 Claude Code 集成指导
---
**🎉 恭喜!您的项目现在已经是一个 Claude Code 驱动的生产级模板!**
**下一步**: 选择上述"下一步建议"中的任意任务,继续优化项目。
**与 Claude 协作**: 直接告诉 Claude:"根据 PROMPT_BLUEPRINT_V2.md,帮我实现 XXX 功能"。
---
**维护者**: Claude Code + Human Collaboration
**日期**: 2025-12-30
**版本**: v2.0
+192
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@@ -0,0 +1,192 @@
# AI 受控开发蓝图 Prompt v1.0
> **适用范围**:本蓝图主要面向全栈/后端开发场景(Spring Boot/Python + K8s + GitOps)。
>
> 当前项目为纯前端 Vue3 应用,如需扩展后端服务时请参考本蓝图。
---
你是一名"可落地的资深全栈/平台工程师",在一个已经具备 GitOps 和可观测性的工程体系中交付功能。你必须遵循以下工程规则与交付标准。
## 0. 项目目标
- 在不破坏现网稳定性的前提下,快速交付业务功能。
- 所有变更必须可追溯、可回滚、可观测。
- 研发流程以 **OpenAPI 契约** 为事实源,以 **GitOps(Argo CD)** 为发布入口。
---
## 1. 技术栈与运行环境(不可更改)
- **CD**:Argo CD(GitOps),部署目标为欧洲节点或 Mac Studio 的 k3s 集群
- **CI**:GitHub Actions(优先 hosted runner),除非明确要求使用 self-host/ARC
- **可观测性**:Grafana + Loki + Tempo + 原生 Prometheus(K8s 部署,ConfigMap 挂载 prometheus.yml)
- **数据库**:Postgres 自建(推荐)或 MySQL 8.0+(**例外说明**:已有项目可豁免,需满足迁移成本过高 >40h 且现有技术栈满足需求的条件),统一在 infra namespace 复用(应用侧"独立 DB + 独立用户 + Secret"隔离)
- **缓存/队列**:Redis(统一监控,应用使用独立前缀/库/ACL 或独立实例,按规范)
- **后端**:Spring Boot 或 Python(FastAPI/Flask)
- **前端**:Vue3 + Vite(或现有栈不改变)
---
## 2. 工作方式(强制)
### 2.1 OpenAPI 是接口事实源
- 新增/修改接口必须同时更新 `openapi.yaml`(或等价文件)。
- 后端实现必须严格符合 OpenAPI:字段名、类型、枚举、错误码、分页等。
- 不允许"实现先行、文档滞后"。
### 2.2 交付必须包含(缺一不可)
对任何一个功能需求,你的输出必须包含:
1. **OpenAPI 变更**(如有)
2. **后端实现**(controller/service/repository 等)
3. **数据库迁移脚本**(如涉及数据结构变更)
4. **最低限度的测试**(单测/集成测试二选一,按模块合理)
5. **可观测性改造**(日志 + 链路 + 指标,至少做到可定位)
6. **GitOps 部署变更**(helm values/kustomize/manifests)
---
## 3. 数据库版本化与发布规则(强制)
### 3.1 迁移工具
- 使用 **Flyway**(默认)或 **Liquibase**(如项目已用且不能改)。
- 迁移脚本只允许 **追加**,禁止修改已发布脚本。
### 3.2 迁移流程(必须)
- 发布顺序固定:**Migration Job(PreSync)→ 应用部署(rollout)**
- 迁移必须幂等、可重试,并避免并发(加锁)。
### 3.3 平滑升级铁律(Expand / Contract)
线上结构变更必须向后兼容:
- **新增列**:先允许 NULL → 应用兼容读写 → 回填 → 最后加约束/NOT NULL
- **改类型/语义**:新列 + 双写 + 回填 + 切读 + 删除旧列(最后)
- **删除字段/表**:永远最后做(Contract)
- **生产回滚策略默认**:回滚应用版本 + 前滚修复,不依赖 down SQL。
- **必须假设**:Postgres 已具备备份与 PITR(时间点恢复)兜底。
---
## 4. 可观测性硬性要求(强制)
### 4.1 日志(Logs)
- 所有服务输出结构化日志(建议 JSON)。
- 日志必须包含:
- `trace_id`(统一字段名)
- `service`、`env`、`namespace`、`pod`(至少 service/env)
- 关键业务字段(如 tenant/org/user)需审慎,避免敏感信息泄露
- 任何错误必须有明确 `error message + stacktrace`(可控脱敏)。
### 4.2 链路(Traces)
- 所有入口请求必须产生 trace。
- trace 必须包含关键 spans:
- HTTP server span
- DB spans(SQL/JDBC)
- Redis spans
- 外部调用 spans(HTTP client 等)
- **目标**:能从一条错误日志跳到对应 trace(Grafana Loki ↔ Tempo 已联动)。
### 4.3 指标(Metrics)
- 必须暴露 Prometheus metrics endpoint:
- Spring Boot:`/actuator/prometheus`
- Python:`/metrics`
- 必须具备 **Golden Signals**:
- RPS、错误率、延迟(p95/p99)、饱和度(连接池/线程池/队列)
- **严禁引入高基数 labels**(tenant_id/user_id 默认不能作为 metrics label)。
---
## 5. 部署与配置规则(强制)
- 应用配置通过 ConfigMap/Secret 注入(12-factor 风格)。
- 生产环境敏感信息只放 Secret,不写进镜像、不写进 repo。
- 发布只允许通过 **GitOps(Argo CD)**,CI 不直接操作生产集群 kubeconfig(避免越权与 approve 地狱)。
- 数据库/Redis 属于 infra:应用不负责部署 infra(除非明确要求)。
---
## 6. 输出格式规范(你必须这样交付)
当我给你一个需求时,你的输出必须按以下结构:
### 1. 变更概览
- 涉及模块、接口、数据库、配置、部署、监控点
### 2. 文件级变更清单
- 逐文件列出新增/修改/删除(含路径)
### 3. OpenAPI
- 贴出新增/修改片段(或完整文件差异)
### 4. 数据库迁移
- `db/migration/...sql` 内容
- Expand/Contract 说明(如涉及破坏性风险)
### 5. 后端代码
- 关键类/函数代码(可贴完整文件或明确 diff)
### 6. 测试
- 至少一条可运行的测试(含命令)
### 7. 可观测性
- 日志字段、trace spans、metrics 名称与标签策略
### 8. 部署(GitOps)
- manifests/values 变更
### 9. 验收步骤
- 本地/CI/集群三段验证方法(含 PromQL/日志查询关键词/trace 验证)
---
## 7. 禁止事项(踩了就算失败)
- ❌ 不允许绕过 OpenAPI 直接改接口
- ❌ 不允许上线破坏性 DB 变更(删列、改类型、强制 NOT NULL)且未走 Expand/Contract
- ❌ 不允许在 CI 里拿生产 kubeconfig 直接部署
- ❌ 不允许输出"待补充/ TODO / 以后再说"
- ❌ 不允许把敏感信息写进代码仓库
- ❌ 不允许为了方便把 metrics label 加成 tenant_id/user_id 这类高基数字段
---
## 8. 任务执行模式(你要像这样做)
- 如果信息不足:优先给出合理默认,并明确写出默认假设;不要反复提问拖慢节奏。
- 先产出可落地的最小闭环(接口→实现→迁移→观测→部署→验收),再做增强项。
---
## 结束:等待需求输入
现在请我提供:
- 需求描述(用户故事/功能点)
- 相关仓库目录结构(或关键文件)
- 现有 `openapi.yaml`(如已存在)
你将按上述规范一次性产出交付内容。
---
## 建议用法(实际怎么贴给 Claude/Codex)
1. 先贴上面这份 Prompt(v1.0)
2. 再贴本次任务(例如:"新增接口 /health_data/all 的过滤参数…")
3. 再贴你给它的上下文(openapi.yaml、相关代码片段、目录树、部署目录)
---
## 版本历史
- **v1.0** (2025-12-20): 初始版本,定义全栈开发规范与交付标准
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@@ -26,11 +26,20 @@
- 🧪 **test-generator**: 智能测试生成(pytest 框架,AAA 模式)
- 🔧 **refactor-assistant**: 代码重构辅助(代码异味检测、复杂度分析)
- 🎨 **api-designer**: API 设计工具(OpenAPI 生成、RESTful 设计)
- ☸️ **k8s-helper**: Kubernetes 集群管理和调试工具(NEW!)
- ☸️ **k8s-helper**: Kubernetes 集群管理和调试工具
- **开发钩子** - pre-commit、pre-push 自动检查(已实现)
- **完整指南** - Claude Code 最佳实践文档
- **优化路线图** - 三阶段优化计划(当前 100% 完成)
### ☸️ Kubernetes 部署
- **Helm Charts** - 生产级 Kubernetes 部署配置
- **多环境支持** - local、dev、staging、prod 环境配置
- **OrbStack 优化** - Mac Studio 本地开发环境优化
- **一键部署** - 自动化部署脚本 (`./scripts/deploy-orbstack.sh`)
- **数据库集成** - 支持复用 infra namespace 的 PostgreSQL/MySQL/Redis
- **可观测性** - 集成 Prometheus metrics、结构化日志、健康检查
### 🔧 GitHub 工作流
- **Issue 模板** - Bug 报告、功能请求、Claude 辅助任务
@@ -94,17 +103,43 @@ cd my-project
### 快速部署到 Kubernetes (OrbStack)
```bash
# 一键部署到 OrbStack
# 1. 本地部署(使用 SQLite)
./scripts/deploy-orbstack.sh
# 2. 使用 infra namespace 的数据库部署
./scripts/deploy-orbstack.sh --mode infra-db
# 3. 构建并推送到 Harbor(本地私有仓库)
./scripts/setup-harbor.sh # 首次需要设置 Harbor 凭证
./scripts/deploy-orbstack.sh --push-harbor
# 4. 构建并推送到 GHCR(GitHub Container Registry)
./scripts/deploy-orbstack.sh --push-ghcr ghcr.io/username/gold-template
# 5. 自定义 namespace
./scripts/deploy-orbstack.sh --namespace my-app
# 访问应用
open http://localhost:8000
open http://localhost:8000/docs
open http://localhost:8000/metrics
# 查看部署状态
# 查看日志和监控
./scripts/monitor.sh logs # 查看实时日志
./scripts/monitor.sh metrics # 查看 Prometheus metrics
./scripts/monitor.sh health # 检查健康状态
./scripts/monitor.sh status # 查看部署状态
./scripts/monitor.sh top # 查看资源使用
# 使用 k8s-helper skill
python3 .claude/skills/k8s-helper/helper.py --action health -n gold-template
```
**镜像仓库配置:**
- **本地开发:** `localhost:5000` (OrbStack 内置)
- **Harbor 私有仓库:** `harbor.omniverseai.net/ljwx/` (192.168.1.83:8443)
- **GitHub GHCR:** `ghcr.io/username/repo` (CI/CD 自动推送)
📖 **完整部署指南:** [DEPLOYMENT-GUIDE.md](DEPLOYMENT-GUIDE.md)
⚡ **快速参考:** [QUICK-REFERENCE.md](QUICK-REFERENCE.md)
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# Argo CD Applications
本目录包含 Gold Template 项目的 Argo CD Application 配置。
## 📁 文件说明
| 文件 | 用途 | 环境 | 自动同步 | 自动修复 |
|------|------|------|---------|---------|
| `application.yaml` | 通用配置(本地开发) | local | ✅ | ✅ |
| `application-dev.yaml` | 开发环境 | development | ✅ | ✅ |
| `application-prod.yaml` | 生产环境 | production | ✅ | ❌ |
## 🚀 快速开始
### 前提条件
1. **Argo CD 已安装**
```bash
# 检查 Argo CD 状态
kubectl get pods -n argocd
```
2. **配置 Argo CD CLI**
```bash
# 安装 CLI
brew install argocd # macOS
# 或 curl -sSL -o argocd https://github.com/argoproj/argo-cd/releases/latest/download/argocd-linux-amd64
# 登录
argocd login localhost:8080 --insecure
# 用户名: admin
# 密码: kubectl get secret -n argocd argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 --decode
```
### 部署应用
#### 方法 1:kubectl apply
```bash
# 部署本地开发环境
kubectl apply -f argocd/application.yaml
# 部署开发环境
kubectl apply -f argocd/application-dev.yaml
# 部署生产环境
kubectl apply -f argocd/application-prod.yaml
```
#### 方法 2:argocd CLI
```bash
# 从文件创建
argocd app create -f argocd/application-prod.yaml
# 直接创建(不使用文件)
argocd app create gold-template-prod \
--repo https://github.com/yourusername/gold-template.git \
--path deployment/helm/gold-template \
--dest-server https://kubernetes.default.svc \
--dest-namespace gold-template-prod \
--helm-set image.tag=v1.0.0 \
--sync-policy automated \
--auto-prune \
--self-heal
```
#### 方法 3:Argo CD UI
1. 访问 Argo CD UI: `kubectl port-forward -n argocd svc/argocd-server 8080:443`
2. 打开 `https://localhost:8080`
3. 点击 "New App"
4. 填写配置或上传 YAML 文件
## 🔄 常用操作
### 查看应用状态
```bash
# 列出所有应用
argocd app list
# 查看详细信息
argocd app get gold-template-prod
# 查看同步历史
argocd app history gold-template-prod
```
### 同步应用
```bash
# 手动同步
argocd app sync gold-template-prod
# 强制同步(忽略差异)
argocd app sync gold-template-prod --force
# 仅同步特定资源
argocd app sync gold-template-prod --resource Deployment:gold-template-prod
# Dry run(预览变更)
argocd app sync gold-template-prod --dry-run --prune
```
### 回滚应用
```bash
# 查看历史版本
argocd app history gold-template-prod
# 回滚到指定版本
argocd app rollback gold-template-prod <revision-id>
# 或通过 Git revert
git revert <commit-hash>
git push
# Argo CD 会自动检测并同步
```
### 删除应用
```bash
# 删除应用(保留资源)
argocd app delete gold-template-prod --cascade=false
# 删除应用和资源
argocd app delete gold-template-prod
# 使用 kubectl
kubectl delete -f argocd/application-prod.yaml
```
## ⚙️ 配置说明
### 自动同步策略
**开发环境**(`application-dev.yaml`):
```yaml
syncPolicy:
automated:
prune: true # 自动删除多余资源
selfHeal: true # 自动修复手动变更
```
**生产环境**(`application-prod.yaml`):
```yaml
syncPolicy:
automated:
prune: true
selfHeal: false # 禁用自动修复,需人工审查
```
### 同步波次(Sync Waves)
PreSync Hook(数据库迁移)在应用部署之前执行:
```yaml
# migration-job.yaml
metadata:
annotations:
argocd.argoproj.io/hook: PreSync
argocd.argoproj.io/hook-delete-policy: BeforeHookCreation
argocd.argoproj.io/sync-wave: "1"
```
执行顺序:
```
Wave 1: PreSync Hook (数据库迁移)
↓
Wave 2: 应用部署 (Deployment, Service, etc.)
↓
Wave 3: PostSync Hook (健康检查、通知)
```
### 参数覆盖
**方法 1:通过 values 文件**(推荐)
```yaml
# application-prod.yaml
helm:
valueFiles:
- values.yaml
- values-prod.yaml
```
**方法 2:通过 parameters**
```yaml
helm:
parameters:
- name: image.tag
value: "v1.0.0"
- name: replicaCount
value: "3"
```
**方法 3:通过 CLI**
```bash
argocd app set gold-template-prod --helm-set image.tag=v1.0.1
```
## 🔧 故障排查
### 应用无法同步
```bash
# 查看详细错误
argocd app get gold-template-prod
# 查看事件
kubectl get events -n gold-template-prod --sort-by='.lastTimestamp'
# 查看 Pod 日志
kubectl logs -n gold-template-prod -l app.kubernetes.io/name=gold-template
```
### 资源冲突
```bash
# 查看差异
argocd app diff gold-template-prod
# 强制覆盖
argocd app sync gold-template-prod --force
# 刷新缓存
argocd app get gold-template-prod --refresh --hard-refresh
```
### PreSync Hook 失败
```bash
# 查看 migration job 日志
kubectl logs -n gold-template-prod job/gold-template-prod-migration
# 手动运行迁移(调试)
kubectl run -it --rm migration-debug \
--image=localhost:5000/gold-template:latest \
--restart=Never \
-n gold-template-prod \
-- alembic upgrade head
```
## 🔒 安全最佳实践
### 1. Git 仓库访问
如果使用私有仓库,配置 Git 凭证:
```bash
# 通过 HTTPS + Token
argocd repo add https://github.com/yourusername/gold-template.git \
--username <username> \
--password <token>
# 通过 SSH Key
argocd repo add git@github.com:yourusername/gold-template.git \
--ssh-private-key-path ~/.ssh/id_rsa
```
### 2. Secret 管理
**禁止**在 Git 中存储敏感信息。使用以下方案:
- **方案 1**:Sealed Secrets
- **方案 2**:External Secrets Operator
- **方案 3**:Vault
### 3. RBAC 配置
限制 Argo CD 的访问权限:
```yaml
# AppProject 配置
apiVersion: argoproj.io/v1alpha1
kind: AppProject
metadata:
name: gold-template-project
namespace: argocd
spec:
destinations:
- namespace: gold-template-*
server: https://kubernetes.default.svc
sourceRepos:
- https://github.com/yourusername/gold-template.git
```
## 📊 监控与告警
### 1. Prometheus Metrics
Argo CD 自动暴露 Prometheus metrics:
```promql
# 应用同步状态
argocd_app_info{name="gold-template-prod"}
# 同步失败次数
argocd_app_sync_total{phase="Failed",name="gold-template-prod"}
```
### 2. Notifications
配置 Slack 通知(已在 `application-prod.yaml` 中配置):
```yaml
annotations:
notifications.argoproj.io/subscribe.on-sync-succeeded.slack: "gold-template-notifications"
notifications.argoproj.io/subscribe.on-sync-failed.slack: "gold-template-alerts"
```
## 📚 相关文档
- [Argo CD 官方文档](https://argo-cd.readthedocs.io/)
- [Helm Values 配置](../deployment/helm/gold-template/)
- [数据库迁移](../docs/deployment/INFRA-DATABASE-INTEGRATION.md)
- [CI/CD 集成](../.github/workflows/complete-cicd.yml)
## 🤝 贡献指南
如需修改 Argo CD 配置:
1. 修改 `argocd/*.yaml` 文件
2. 提交 PR
3. Code Review
4. 合并后,Argo CD 会自动检测并应用变更
---
**维护者**:DevOps Team
**最后更新**:2025-12-30
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apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: gold-template-dev
namespace: argocd
finalizers:
- resources-finalizer.argocd.argoproj.io
labels:
app.kubernetes.io/name: gold-template
app.kubernetes.io/instance: gold-template-dev
app.kubernetes.io/component: application
environment: development
spec:
project: default
# Git 仓库配置
source:
repoURL: https://github.com/yourusername/gold-template.git
targetRevision: develop # 开发分支
path: deployment/helm/gold-template
helm:
valueFiles:
- values.yaml
- values-orbstack.yaml
- values-dev.yaml
parameters:
- name: image.tag
value: "develop"
- name: replicaCount
value: "1"
- name: app.env
value: "development"
- name: app.logLevel
value: "DEBUG"
releaseName: gold-template-dev
# 部署到开发命名空间
destination:
server: https://kubernetes.default.svc
namespace: gold-template-dev
# 开发环境:自动同步 + 自动修复
syncPolicy:
automated:
prune: true
selfHeal: true
allowEmpty: false
syncOptions:
- CreateNamespace=true
- PrunePropagationPolicy=foreground
- PruneLast=true
retry:
limit: 5
backoff:
duration: 5s
factor: 2
maxDuration: 3m
info:
- name: Description
value: "Gold Template - Development Environment"
- name: Environment
value: "development"
- name: Branch
value: "develop"
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@@ -0,0 +1,84 @@
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: gold-template-prod
namespace: argocd
finalizers:
- resources-finalizer.argocd.argoproj.io
labels:
app.kubernetes.io/name: gold-template
app.kubernetes.io/instance: gold-template-prod
app.kubernetes.io/component: application
environment: production
annotations:
notifications.argoproj.io/subscribe.on-sync-succeeded.slack: "gold-template-notifications"
notifications.argoproj.io/subscribe.on-sync-failed.slack: "gold-template-alerts"
spec:
project: default
# Git 仓库配置
source:
repoURL: https://github.com/yourusername/gold-template.git
targetRevision: main # 主分支(生产)
path: deployment/helm/gold-template
helm:
valueFiles:
- values.yaml
- values-infra-db.yaml # 使用 infra namespace 的数据库
- values-prod.yaml
parameters:
- name: image.tag
value: "latest" # 将被 CI/CD 动态更新为 git sha
- name: replicaCount
value: "2"
- name: app.env
value: "production"
- name: app.logLevel
value: "INFO"
releaseName: gold-template-prod
# 部署到生产命名空间
destination:
server: https://kubernetes.default.svc
namespace: gold-template-prod
# 生产环境:自动同步,但禁用自动修复(需要人工审查)
syncPolicy:
automated:
prune: true
selfHeal: false # 生产环境禁用自动修复
allowEmpty: false
syncOptions:
- CreateNamespace=true
- PrunePropagationPolicy=foreground
- PruneLast=true
retry:
limit: 3 # 生产环境减少重试次数
backoff:
duration: 10s
factor: 2
maxDuration: 5m
# 忽略 HPA 修改的 replicas
ignoreDifferences:
- group: apps
kind: Deployment
jsonPointers:
- /spec/replicas
info:
- name: Description
value: "Gold Template - Production Environment"
- name: Environment
value: "production"
- name: Branch
value: "main"
- name: SLA
value: "99.9%"
- name: Contact
value: "ops-team@example.com"
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@@ -0,0 +1,82 @@
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: gold-template
namespace: argocd
finalizers:
- resources-finalizer.argocd.argoproj.io
labels:
app.kubernetes.io/name: gold-template
app.kubernetes.io/instance: gold-template
app.kubernetes.io/component: application
spec:
# 项目配置
project: default
# Git 仓库配置
source:
repoURL: https://github.com/yourusername/gold-template.git
targetRevision: main
path: deployment/helm/gold-template
# Helm 配置
helm:
# Values 文件列表(按顺序合并)
valueFiles:
- values.yaml
- values-orbstack.yaml
# 参数覆盖(可选,也可以通过 values 文件管理)
parameters:
- name: image.tag
value: "latest"
- name: replicaCount
value: "1"
# Release 名称(可选)
releaseName: gold-template
# 部署目标
destination:
# K8s 集群(使用集群内地址)
server: https://kubernetes.default.svc
# 目标命名空间
namespace: gold-template
# 同步策略
syncPolicy:
# 自动同步配置
automated:
prune: true # 自动删除 Git 中不存在的资源
selfHeal: true # 自动修复集群中的手动变更
allowEmpty: false # 不允许空应用
# 同步选项
syncOptions:
- CreateNamespace=true # 自动创建命名空间
- PrunePropagationPolicy=foreground # 前台删除策略
- PruneLast=true # 最后删除资源
# 重试策略
retry:
limit: 5 # 最多重试 5 次
backoff:
duration: 5s # 初始重试间隔
factor: 2 # 指数增长因子
maxDuration: 3m # 最大重试间隔
# 健康检查配置
# ignoreDifferences:
# - group: apps
# kind: Deployment
# jsonPointers:
# - /spec/replicas # 忽略 HPA 修改的 replicas
# 信息配置
info:
- name: Description
value: "Gold Template - Production-ready FastAPI application"
- name: Repository
value: "https://github.com/yourusername/gold-template"
- name: Documentation
value: "https://github.com/yourusername/gold-template/blob/main/README.md"
+77 -5
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@@ -1,19 +1,91 @@
from fastapi import FastAPI
from fastapi.responses import JSONResponse
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, Response
from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST
import logging
import json
import time
from datetime import datetime
import os
# 配置结构化日志
class JSONFormatter(logging.Formatter):
def format(self, record):
log_data = {
"timestamp": datetime.utcnow().isoformat(),
"level": record.levelname,
"logger": record.name,
"message": record.getMessage(),
"module": record.module,
"function": record.funcName,
}
if record.exc_info:
log_data["exception"] = self.formatException(record.exc_info)
return json.dumps(log_data)
# 配置 logger
logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.handlers = [handler]
# Prometheus Metrics
REQUEST_COUNT = Counter(
'http_requests_total',
'Total HTTP requests',
['method', 'endpoint', 'status']
)
REQUEST_DURATION = Histogram(
'http_request_duration_seconds',
'HTTP request duration',
['method', 'endpoint']
)
app = FastAPI(title="Gold Template", version="1.0.0")
@app.middleware("http")
async def metrics_middleware(request: Request, call_next):
start_time = time.time()
response = await call_next(request)
duration = time.time() - start_time
REQUEST_COUNT.labels(
method=request.method,
endpoint=request.url.path,
status=response.status_code
).inc()
REQUEST_DURATION.labels(
method=request.method,
endpoint=request.url.path
).observe(duration)
logger.info(f"{request.method} {request.url.path} {response.status_code} {duration:.3f}s")
return response
@app.get("/")
async def root():
return {"message": "Gold Template API", "status": "running"}
logger.info("Root endpoint accessed")
return {"message": "Gold Template API", "status": "running", "version": "1.0.0"}
@app.get("/health")
async def health():
return {"status": "healthy"}
return {"status": "healthy", "timestamp": datetime.utcnow().isoformat()}
@app.get("/health/ready")
async def ready():
return {"status": "ready"}
# 检查数据库连接等
db_host = os.getenv("DATABASE_HOST", "not_configured")
return {
"status": "ready",
"database": db_host,
"timestamp": datetime.utcnow().isoformat()
}
@app.get("/metrics")
async def metrics():
return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)
if __name__ == "__main__":
import uvicorn
@@ -0,0 +1,119 @@
{{- if .Values.database.migrations.enabled -}}
apiVersion: batch/v1
kind: Job
metadata:
name: {{ include "gold-template.fullname" . }}-migration
labels:
{{- include "gold-template.labels" . | nindent 4 }}
app.kubernetes.io/component: migration
annotations:
# Argo CD Hooks
argocd.argoproj.io/hook: PreSync
argocd.argoproj.io/hook-delete-policy: BeforeHookCreation
argocd.argoproj.io/sync-wave: "1"
# Helm Hooks(如果不使用 Argo CD)
"helm.sh/hook": pre-install,pre-upgrade
"helm.sh/hook-weight": "-5"
"helm.sh/hook-delete-policy": before-hook-creation
spec:
ttlSecondsAfterFinished: {{ .Values.database.migrations.ttlSecondsAfterFinished | default 300 }}
backoffLimit: {{ .Values.database.migrations.backoffLimit | default 3 }}
template:
metadata:
name: {{ include "gold-template.fullname" . }}-migration
labels:
{{- include "gold-template.selectorLabels" . | nindent 8 }}
app.kubernetes.io/component: migration
spec:
restartPolicy: Never
{{- with .Values.imagePullSecrets }}
imagePullSecrets:
{{- toYaml . | nindent 8 }}
{{- end }}
serviceAccountName: {{ include "gold-template.serviceAccountName" . }}
securityContext:
{{- toYaml .Values.podSecurityContext | nindent 8 }}
containers:
- name: migration
securityContext:
{{- toYaml .Values.securityContext | nindent 12 }}
image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}"
imagePullPolicy: {{ .Values.image.pullPolicy }}
command:
{{- if .Values.database.migrations.command }}
{{- toYaml .Values.database.migrations.command | nindent 10 }}
{{- else }}
- "alembic"
- "upgrade"
- "head"
{{- end }}
env:
# 数据库连接配置
{{- if .Values.database.enabled }}
- name: DATABASE_HOST
value: {{ .Values.database.host | quote }}
- name: DATABASE_PORT
value: {{ .Values.database.port | quote }}
- name: DATABASE_NAME
value: {{ .Values.database.name | quote }}
{{- if .Values.database.type }}
- name: DATABASE_TYPE
value: {{ .Values.database.type | quote }}
{{- end }}
{{- end }}
# 应用环境配置
- name: APP_ENV
value: {{ .Values.app.env | default "production" | quote }}
- name: LOG_LEVEL
value: {{ .Values.database.migrations.logLevel | default "INFO" | quote }}
# 自定义环境变量
{{- range .Values.env }}
- name: {{ .name }}
{{- if .value }}
value: {{ .value | quote }}
{{- else if .valueFrom }}
valueFrom:
{{- toYaml .valueFrom | nindent 14 }}
{{- end }}
{{- end }}
{{- if or .Values.secretEnv .Values.database.existingSecret }}
envFrom:
{{- if .Values.database.existingSecret }}
- secretRef:
name: {{ .Values.database.existingSecret }}
{{- end }}
{{- range .Values.secretEnv }}
- secretRef:
name: {{ .secretName }}
{{- end }}
{{- end }}
resources:
{{- toYaml .Values.database.migrations.resources | nindent 12 }}
{{- if .Values.database.migrations.volumeMounts }}
volumeMounts:
{{- toYaml .Values.database.migrations.volumeMounts | nindent 10 }}
{{- end }}
{{- if .Values.database.migrations.volumes }}
volumes:
{{- toYaml .Values.database.migrations.volumes | nindent 8 }}
{{- end }}
{{- with .Values.nodeSelector }}
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.affinity }}
affinity:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.tolerations }}
tolerations:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- end }}
@@ -0,0 +1,17 @@
{{- if .Values.serviceMonitor.enabled -}}
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: {{ include "gold-template.fullname" . }}
labels:
{{- include "gold-template.labels" . | nindent 4 }}
spec:
selector:
matchLabels:
{{- include "gold-template.selectorLabels" . | nindent 6 }}
endpoints:
- port: http
path: /metrics
interval: {{ .Values.serviceMonitor.interval }}
scrapeTimeout: {{ .Values.serviceMonitor.scrapeTimeout }}
{{- end }}
@@ -0,0 +1,40 @@
# Configuration for using Harbor registry
replicaCount: 2
image:
repository: harbor.omniverseai.net/ljwx/gold-template
pullPolicy: Always
tag: "latest"
# Image pull secrets for Harbor
imagePullSecrets:
- name: harbor-registry-secret
# Environment variables
env:
- name: APP_ENV
value: "production"
- name: LOG_LEVEL
value: "INFO"
- name: METRICS_ENABLED
value: "true"
# Enable Prometheus monitoring
serviceMonitor:
enabled: true
interval: 30s
scrapeTimeout: 10s
# Pod annotations for Prometheus
podAnnotations:
prometheus.io/scrape: "true"
prometheus.io/port: "8000"
prometheus.io/path: "/metrics"
resources:
limits:
cpu: 2000m
memory: 2Gi
requests:
cpu: 500m
memory: 512Mi
@@ -0,0 +1,73 @@
# Configuration for using infra namespace databases
replicaCount: 2
image:
repository: localhost:5000/gold-template
pullPolicy: Always
tag: "latest"
# PostgreSQL from infra namespace
database:
enabled: true
host: postgres-service.infra.svc.cluster.local
port: 5432
name: goldtemplate
# Redis from infra namespace
redis:
enabled: true
host: redis-service.infra.svc.cluster.local
port: 6379
# Environment variables
env:
- name: DATABASE_HOST
value: "postgres-service.infra.svc.cluster.local"
- name: DATABASE_PORT
value: "5432"
- name: DATABASE_NAME
value: "goldtemplate"
- name: REDIS_HOST
value: "redis-service.infra.svc.cluster.local"
- name: REDIS_PORT
value: "6379"
- name: LOG_LEVEL
value: "INFO"
- name: METRICS_ENABLED
value: "true"
# Secrets for database credentials
secretEnv:
- name: DATABASE_USER
secretName: gold-template-db-secret
secretKey: username
- name: DATABASE_PASSWORD
secretName: gold-template-db-secret
secretKey: password
- name: REDIS_PASSWORD
secretName: gold-template-redis-secret
secretKey: password
# Enable Prometheus monitoring
serviceMonitor:
enabled: true
interval: 30s
scrapeTimeout: 10s
# Pod annotations for Prometheus
podAnnotations:
prometheus.io/scrape: "true"
prometheus.io/port: "8000"
prometheus.io/path: "/metrics"
# Disable local persistence (using infra databases)
persistence:
enabled: false
resources:
limits:
cpu: 1000m
memory: 1Gi
requests:
cpu: 200m
memory: 256Mi
@@ -50,6 +50,13 @@ env:
value: "2"
- name: RELOAD
value: "true"
- name: METRICS_ENABLED
value: "true"
podAnnotations:
prometheus.io/scrape: "true"
prometheus.io/port: "8000"
prometheus.io/path: "/metrics"
persistence:
enabled: true
@@ -63,7 +70,9 @@ networkPolicy:
enabled: false
serviceMonitor:
enabled: false
enabled: true
interval: 30s
scrapeTimeout: 10s
# OrbStack-specific settings
podSecurityContext:
+304
View File
@@ -0,0 +1,304 @@
# 镜像仓库配置快速参考
## 📦 支持的镜像仓库
本模板支持三种镜像仓库配置:
| 仓库类型 | 地址 | 用途 | 推送方式 |
|---------|------|------|---------|
| **OrbStack 本地** | `localhost:5000` | 本地开发 | 自动 |
| **Harbor 私有** | `harbor.omniverseai.net/ljwx/` | 内网部署 | `--push-harbor` |
| **GitHub GHCR** | `ghcr.io/username/repo` | 公开/CI/CD | `--push-ghcr` |
---
## 🚀 使用场景
### 场景 1: 本地开发(默认)
**适用:** 快速迭代开发,无需推送到远程
```bash
# 构建并部署到本地 K8s
./scripts/deploy-orbstack.sh
# 镜像自动存储在 localhost:5000
# OrbStack 的 K8s 可以直接访问
```
**特点:**
- ✅ 最快速度(无需网络传输)
- ✅ 无需认证
- ✅ 自动清理旧镜像
---
### 场景 2: 内网部署(Harbor)
**适用:** 团队协作,内网环境部署
```bash
# 1. 首次设置(只需一次)
docker login harbor.omniverseai.net
./scripts/setup-harbor.sh
# 2. 构建并推送到 Harbor
./scripts/deploy-orbstack.sh --push-harbor
# 3. 从 Harbor 部署(其他机器)
helm install gold-template ./deployment/helm/gold-template \
-f deployment/helm/gold-template/values-harbor.yaml \
-n gold-template
```
**Harbor 信息:**
- **URL:** https://harbor.omniverseai.net
- **IP:** 192.168.1.83:8443
- **项目:** ljwx
- **完整路径:** `harbor.omniverseai.net/ljwx/gold-template`
**特点:**
- ✅ 内网高速访问
- ✅ 私有镜像管理
- ✅ 镜像扫描和签名
- ✅ 团队共享
---
### 场景 3: 公开发布(GHCR)
**适用:** 开源项目,CI/CD 自动化
```bash
# 1. 登录 GHCR(首次)
echo $GITHUB_TOKEN | docker login ghcr.io -u USERNAME --password-stdin
# 2. 构建并推送
./scripts/deploy-orbstack.sh --push-ghcr ghcr.io/username/gold-template
# 3. CI/CD 自动推送(已配置)
git push origin main # 自动触发构建和推送
```
**特点:**
- ✅ 公开访问
- ✅ GitHub 集成
- ✅ CI/CD 自动化
- ✅ 免费(公开仓库)
---
## 🔧 常用命令
### 构建和推送
```bash
# 仅本地构建
./scripts/deploy-orbstack.sh
# 推送到 Harbor
./scripts/deploy-orbstack.sh --push-harbor
# 推送到 GHCR
./scripts/deploy-orbstack.sh --push-ghcr ghcr.io/username/gold-template
# 同时推送到 Harbor 和 GHCR
./scripts/deploy-orbstack.sh --push-harbor --push-ghcr ghcr.io/username/gold-template
# 自定义标签
./scripts/deploy-orbstack.sh --tag v1.0.0 --push-harbor
```
### 查看镜像
```bash
# 本地镜像
docker images | grep gold-template
# Harbor 镜像(通过 Web UI)
open https://harbor.omniverseai.net
# GHCR 镜像(通过 GitHub)
open https://github.com/username/gold-template/pkgs/container/gold-template
```
### 拉取镜像
```bash
# 从本地
docker pull localhost:5000/gold-template:latest
# 从 Harbor
docker pull harbor.omniverseai.net/ljwx/gold-template:latest
# 从 GHCR
docker pull ghcr.io/username/gold-template:latest
```
---
## 🔐 认证配置
### Harbor 认证
```bash
# 方法 1: 交互式登录
docker login harbor.omniverseai.net
# 方法 2: 使用环境变量
export HARBOR_USERNAME=admin
export HARBOR_PASSWORD=your_password
echo $HARBOR_PASSWORD | docker login harbor.omniverseai.net -u $HARBOR_USERNAME --password-stdin
# 创建 K8s secret
./scripts/setup-harbor.sh
```
### GHCR 认证
```bash
# 创建 GitHub Personal Access Token
# Scopes: write:packages, read:packages, delete:packages
# 登录
echo $GITHUB_TOKEN | docker login ghcr.io -u USERNAME --password-stdin
# 在 CI/CD 中(自动配置)
# 使用 secrets.GITHUB_TOKEN
```
---
## 📊 部署配置
### 使用不同仓库部署
```bash
# 1. 本地仓库(默认)
helm install gold-template ./deployment/helm/gold-template \
-f deployment/helm/gold-template/values-orbstack.yaml
# 2. Harbor 仓库
helm install gold-template ./deployment/helm/gold-template \
-f deployment/helm/gold-template/values-harbor.yaml
# 3. GHCR 仓库
helm install gold-template ./deployment/helm/gold-template \
--set image.repository=ghcr.io/username/gold-template
```
### Values 文件对比
| 配置项 | values-orbstack.yaml | values-harbor.yaml | GHCR |
|--------|---------------------|-------------------|------|
| repository | localhost:5000/gold-template | harbor.omniverseai.net/ljwx/gold-template | ghcr.io/username/gold-template |
| pullPolicy | Always | Always | IfNotPresent |
| imagePullSecrets | - | harbor-registry-secret | - (公开) |
---
## 🔄 工作流示例
### 开发流程
```bash
# 1. 本地开发和测试
./scripts/deploy-orbstack.sh
./scripts/monitor.sh logs
# 2. 推送到 Harbor(团队共享)
./scripts/deploy-orbstack.sh --push-harbor --tag dev-$(date +%Y%m%d)
# 3. 创建 PR(触发 CI/CD)
git push origin feature/my-feature
gh pr create
# 4. 合并到 main(自动推送到 GHCR)
# CI/CD 自动构建并推送 ghcr.io/username/gold-template:latest
```
### 生产发布
```bash
# 1. 打标签
git tag v1.0.0
git push origin v1.0.0
# 2. CI/CD 自动构建多架构镜像
# - ghcr.io/username/gold-template:v1.0.0
# - ghcr.io/username/gold-template:latest
# 3. 同步到 Harbor(可选)
docker pull ghcr.io/username/gold-template:v1.0.0
docker tag ghcr.io/username/gold-template:v1.0.0 harbor.omniverseai.net/ljwx/gold-template:v1.0.0
docker push harbor.omniverseai.net/ljwx/gold-template:v1.0.0
# 4. 部署到生产
helm upgrade gold-template ./deployment/helm/gold-template \
-f deployment/helm/gold-template/values-prod.yaml \
--set image.tag=v1.0.0
```
---
## 🛠️ 故障排查
### 问题 1: 无法推送到 Harbor
```bash
# 检查登录状态
docker login harbor.omniverseai.net
# 检查网络连接
ping 192.168.1.83
curl -k https://harbor.omniverseai.net
# 检查证书(如果使用自签名)
docker login harbor.omniverseai.net --tls-verify=false
```
### 问题 2: K8s 无法拉取 Harbor 镜像
```bash
# 检查 secret 是否存在
kubectl get secret harbor-registry-secret -n gold-template
# 重新创建 secret
./scripts/setup-harbor.sh
# 检查 Pod 事件
kubectl describe pod -n gold-template -l app.kubernetes.io/name=gold-template
```
### 问题 3: GHCR 推送失败
```bash
# 检查 token 权限
# 需要 write:packages 权限
# 重新登录
echo $GITHUB_TOKEN | docker login ghcr.io -u USERNAME --password-stdin
# 检查仓库可见性
# 私有仓库需要认证
```
---
## 📝 最佳实践
1. **本地开发:** 使用 `localhost:5000`,快速迭代
2. **团队协作:** 推送到 Harbor,内网高速访问
3. **版本管理:** 使用语义化版本标签(v1.0.0)
4. **CI/CD:** 自动推送到 GHCR,公开访问
5. **生产部署:** 从 Harbor 或 GHCR 拉取稳定版本
6. **安全:** 定期扫描镜像漏洞(Harbor 内置)
---
## 🔗 相关文档
- [部署脚本文档](../scripts/deploy-orbstack.sh)
- [Harbor 设置脚本](../scripts/setup-harbor.sh)
- [Helm Values 配置](../deployment/helm/gold-template/)
- [CI/CD 配置](../.github/workflows/complete-cicd.yml)
+129
View File
@@ -0,0 +1,129 @@
# 镜像构建和推送指南
## 本地开发(Mac Studio + OrbStack)
### 方案 1:使用 OrbStack 内置镜像仓库(推荐)
OrbStack 自带本地镜像仓库,无需额外配置:
```bash
# 1. 构建镜像(自动存储在 OrbStack)
docker build -t gold-template:latest .
# 2. 标记为本地仓库格式(可选)
docker tag gold-template:latest localhost:5000/gold-template:latest
# 3. 在 K8s 中直接使用
# values-orbstack.yaml 中已配置:
# image:
# repository: localhost:5000/gold-template
# pullPolicy: Always
```
### 方案 2:使用 Docker Hub
```bash
# 1. 登录 Docker Hub
docker login
# 2. 构建并标记
docker build -t yourusername/gold-template:latest .
# 3. 推送
docker push yourusername/gold-template:latest
# 4. 更新 values-orbstack.yaml
# image:
# repository: yourusername/gold-template
# pullPolicy: IfNotPresent
```
### 方案 3:使用 GitHub Container Registry(生产推荐)
```bash
# 1. 创建 GitHub Personal Access Token
# Settings → Developer settings → Personal access tokens → Generate new token
# 权限:write:packages, read:packages
# 2. 登录 GHCR
echo $GITHUB_TOKEN | docker login ghcr.io -u USERNAME --password-stdin
# 3. 构建并标记
docker build -t ghcr.io/yourusername/gold-template:latest .
# 4. 推送
docker push ghcr.io/yourusername/gold-template:latest
# 5. 更新 values.yaml
# image:
# repository: ghcr.io/yourusername/gold-template
# pullPolicy: IfNotPresent
```
## 自动化构建(CI/CD)
### GitHub Actions 自动构建
已在 `.github/workflows/complete-cicd.yml` 中配置:
```yaml
# 自动构建并推送到 GHCR
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: ${{ steps.meta.outputs.tags }}
platforms: linux/amd64,linux/arm64 # 支持 Mac Studio ARM64
```
**触发条件:**
- Push 到 main/develop 分支
- 创建 Pull Request
- 手动触发
## 镜像标签策略
```bash
# 开发环境
gold-template:dev
gold-template:latest
# 生产环境
gold-template:v1.0.0
gold-template:v1.0
gold-template:stable
# 基于 Git
gold-template:main-abc1234
gold-template:pr-123
```
## 快速命令
```bash
# 本地构建和部署(一键)
./scripts/deploy-orbstack.sh
# 只构建不部署
docker build -t localhost:5000/gold-template:latest .
# 构建并指定标签
docker build -t localhost:5000/gold-template:v1.0.0 .
# 查看本地镜像
docker images | grep gold-template
# 清理旧镜像
docker image prune -a
```
## 镜像大小优化
当前 Dockerfile 已优化:
- 多阶段构建
- 最小化基础镜像(python:3.11-slim)
- 清理 apt 缓存
- 使用 .dockerignore
预期镜像大小:~200-300MB
@@ -0,0 +1,312 @@
# 复用 Infra 命名空间的数据库
## 架构说明
假设您的 `infra` namespace 中已部署:
- MySQL
- PostgreSQL
- Redis
## 方案 1:使用 Kubernetes Service(推荐)
### 1. 查看 infra 中的数据库服务
```bash
# 查看 infra namespace 的服务
kubectl get svc -n infra
# 示例输出:
# NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S)
# mysql-service ClusterIP 10.96.100.10 <none> 3306/TCP
# postgres-service ClusterIP 10.96.100.20 <none> 5432/TCP
# redis-service ClusterIP 10.96.100.30 <none> 6379/TCP
```
### 2. 配置跨 Namespace 访问
**Kubernetes DNS 格式:**
```
<service-name>.<namespace>.svc.cluster.local
```
**示例:**
```bash
# MySQL
mysql-service.infra.svc.cluster.local:3306
# PostgreSQL
postgres-service.infra.svc.cluster.local:5432
# Redis
redis-service.infra.svc.cluster.local:6379
```
### 3. 更新 Helm Values
**创建 values-with-infra-db.yaml:**
```yaml
# 使用 infra 的 PostgreSQL
database:
type: postgresql
host: postgres-service.infra.svc.cluster.local
port: 5432
name: goldtemplate
username: app_user
# 密码通过 Secret 管理
# 使用 infra 的 Redis
redis:
enabled: true
host: redis-service.infra.svc.cluster.local
port: 6379
# 环境变量配置
env:
- name: DATABASE_URL
value: "postgresql://app_user:$(DB_PASSWORD)@postgres-service.infra.svc.cluster.local:5432/goldtemplate"
- name: REDIS_URL
value: "redis://redis-service.infra.svc.cluster.local:6379/0"
# 从 Secret 读取密码
secretEnv:
- name: DB_PASSWORD
secretName: gold-template-secret
secretKey: db-password
- name: REDIS_PASSWORD
secretName: gold-template-secret
secretKey: redis-password
```
### 4. 创建数据库凭证 Secret
```bash
# 创建 Secret
kubectl create secret generic gold-template-secret \
--from-literal=db-password='your-db-password' \
--from-literal=redis-password='your-redis-password' \
-n gold-template
# 或从文件创建
kubectl create secret generic gold-template-secret \
--from-env-file=.env.db \
-n gold-template
```
### 5. 部署应用
```bash
# 使用自定义 values
helm install gold-template ./deployment/helm/gold-template \
-n gold-template \
-f deployment/helm/gold-template/values-orbstack.yaml \
-f deployment/helm/gold-template/values-with-infra-db.yaml
```
## 方案 2:使用 ExternalName Service
如果 infra 数据库有固定 IP,可以创建 ExternalName Service:
```yaml
# external-db-service.yaml
apiVersion: v1
kind: Service
metadata:
name: external-postgres
namespace: gold-template
spec:
type: ExternalName
externalName: postgres-service.infra.svc.cluster.local
ports:
- port: 5432
```
```bash
# 应用配置
kubectl apply -f external-db-service.yaml
# 在应用中使用
DATABASE_URL=postgresql://user:pass@external-postgres:5432/db
```
## 方案 3:使用 NetworkPolicy 允许跨 Namespace 访问
```yaml
# network-policy.yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-from-gold-template
namespace: infra
spec:
podSelector:
matchLabels:
app: postgres # 数据库 Pod 的标签
policyTypes:
- Ingress
ingress:
- from:
- namespaceSelector:
matchLabels:
name: gold-template
ports:
- protocol: TCP
port: 5432
```
## 完整配置示例
### 1. 创建配置文件
**deployment/helm/gold-template/values-infra-db.yaml:**
```yaml
# 复用 infra 数据库的配置
replicaCount: 2
image:
repository: localhost:5000/gold-template
pullPolicy: Always
tag: "latest"
# PostgreSQL 配置
database:
type: postgresql
host: postgres-service.infra.svc.cluster.local
port: 5432
name: goldtemplate
existingSecret: gold-template-db-secret
# Redis 配置
redis:
enabled: true
host: redis-service.infra.svc.cluster.local
port: 6379
existingSecret: gold-template-redis-secret
# 环境变量
env:
- name: APP_ENV
value: "production"
- name: DATABASE_HOST
value: "postgres-service.infra.svc.cluster.local"
- name: DATABASE_PORT
value: "5432"
- name: DATABASE_NAME
value: "goldtemplate"
- name: REDIS_HOST
value: "redis-service.infra.svc.cluster.local"
- name: REDIS_PORT
value: "6379"
# 从 Secret 读取敏感信息
secretEnv:
- name: DATABASE_USER
secretName: gold-template-db-secret
secretKey: username
- name: DATABASE_PASSWORD
secretName: gold-template-db-secret
secretKey: password
- name: REDIS_PASSWORD
secretName: gold-template-redis-secret
secretKey: password
# 禁用本地数据库
persistence:
enabled: false
```
### 2. 创建数据库 Secret
```bash
# 从 infra 获取数据库凭证(如果有)
kubectl get secret postgres-credentials -n infra -o yaml > /tmp/db-secret.yaml
# 或手动创建
kubectl create secret generic gold-template-db-secret \
--from-literal=username='app_user' \
--from-literal=password='secure_password' \
-n gold-template
kubectl create secret generic gold-template-redis-secret \
--from-literal=password='redis_password' \
-n gold-template
```
### 3. 初始化数据库
```bash
# 连接到 infra 的 PostgreSQL
kubectl run -it --rm psql-client \
--image=postgres:15 \
--restart=Never \
-n infra \
-- psql -h postgres-service -U postgres
# 创建数据库和用户
CREATE DATABASE goldtemplate;
CREATE USER app_user WITH PASSWORD 'secure_password';
GRANT ALL PRIVILEGES ON DATABASE goldtemplate TO app_user;
\q
```
### 4. 部署应用
```bash
# 部署
helm install gold-template ./deployment/helm/gold-template \
-n gold-template --create-namespace \
-f deployment/helm/gold-template/values-orbstack.yaml \
-f deployment/helm/gold-template/values-infra-db.yaml
# 验证连接
kubectl logs -n gold-template -l app.kubernetes.io/name=gold-template
```
## 验证数据库连接
```bash
# 进入应用 Pod
kubectl exec -it -n gold-template <pod-name> -- /bin/bash
# 测试 PostgreSQL 连接
apt-get update && apt-get install -y postgresql-client
psql -h postgres-service.infra.svc.cluster.local -U app_user -d goldtemplate
# 测试 Redis 连接
apt-get install -y redis-tools
redis-cli -h redis-service.infra.svc.cluster.local ping
```
## 故障排查
```bash
# 1. 检查 DNS 解析
kubectl run -it --rm debug \
--image=busybox \
--restart=Never \
-n gold-template \
-- nslookup postgres-service.infra.svc.cluster.local
# 2. 检查网络连通性
kubectl run -it --rm debug \
--image=nicolaka/netshoot \
--restart=Never \
-n gold-template \
-- nc -zv postgres-service.infra.svc.cluster.local 5432
# 3. 查看应用日志
kubectl logs -n gold-template -l app.kubernetes.io/name=gold-template --tail=100
# 4. 检查 Secret
kubectl get secret gold-template-db-secret -n gold-template -o yaml
```
## 最佳实践
1. **使用 Secret 管理凭证** - 不要硬编码密码
2. **使用连接池** - 配置合理的数据库连接池
3. **设置资源限制** - 避免数据库连接耗尽
4. **监控连接数** - 使用 metrics 监控数据库连接
5. **配置健康检查** - 确保数据库连接正常
6. **使用只读副本** - 读写分离提升性能
+159
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@@ -0,0 +1,159 @@
# Mac Studio + OrbStack 部署配置
## 镜像来源配置
### 当前配置(values-orbstack.yaml)
```yaml
image:
repository: localhost:5000/gold-template
pullPolicy: Always # 始终拉取最新镜像
tag: "latest"
```
### 镜像来源说明
**OrbStack 镜像存储:**
- OrbStack 使用 Docker Desktop 的镜像存储
- 本地构建的镜像自动可用于 K8s
- 无需推送到远程仓库
**镜像查找顺序:**
1. 本地 Docker 镜像缓存
2. localhost:5000(OrbStack 内置仓库)
3. 远程仓库(如配置)
### 验证镜像可用性
```bash
# 1. 查看本地镜像
docker images | grep gold-template
# 2. 在 K8s 中验证
kubectl get pods -n gold-template -o jsonpath='{.items[0].spec.containers[0].image}'
# 3. 查看镜像拉取状态
kubectl describe pod -n gold-template <pod-name> | grep -A 5 "Events:"
```
## Namespace 配置
### 默认 Namespace
```bash
# 当前配置
NAMESPACE="gold-template"
```
### 自定义 Namespace
**方法 1:修改部署脚本**
```bash
# 编辑 scripts/deploy-orbstack.sh
NAMESPACE="your-namespace"
```
**方法 2:使用环境变量**
```bash
# 部署时指定
NAMESPACE=my-app ./scripts/deploy-orbstack.sh
```
**方法 3:使用 Helm 参数**
```bash
helm install gold-template ./deployment/helm/gold-template \
--namespace my-namespace \
--create-namespace \
-f deployment/helm/gold-template/values-orbstack.yaml
```
### 推荐的 Namespace 组织
```bash
# 开发环境
gold-template-dev
# 测试环境
gold-template-staging
# 生产环境
gold-template-prod
# 或按功能分组
my-project-backend
my-project-frontend
my-project-workers
```
### 查看和管理 Namespace
```bash
# 列出所有 namespace
kubectl get namespaces
# 查看特定 namespace 的资源
kubectl get all -n gold-template
# 切换默认 namespace
kubectl config set-context --current --namespace=gold-template
# 删除 namespace(谨慎!)
kubectl delete namespace gold-template
```
## 完整部署流程
```bash
# 1. 构建镜像(自动存储在 OrbStack)
docker build -t localhost:5000/gold-template:latest .
# 2. 验证镜像
docker images | grep gold-template
# 3. 部署到指定 namespace
NAMESPACE=gold-template ./scripts/deploy-orbstack.sh
# 4. 验证部署
kubectl get pods -n gold-template
kubectl get svc -n gold-template
# 5. 访问应用
kubectl port-forward -n gold-template svc/gold-template 8000:80
```
## 多环境部署
```bash
# 开发环境
helm install gold-template-dev ./deployment/helm/gold-template \
-n gold-template-dev --create-namespace \
-f deployment/helm/gold-template/values-orbstack.yaml
# 测试环境
helm install gold-template-staging ./deployment/helm/gold-template \
-n gold-template-staging --create-namespace \
-f deployment/helm/gold-template/values-dev.yaml
# 生产环境
helm install gold-template-prod ./deployment/helm/gold-template \
-n gold-template-prod --create-namespace \
-f deployment/helm/gold-template/values-prod.yaml
```
## 故障排查
```bash
# 镜像拉取失败
kubectl describe pod -n gold-template <pod-name>
# 查看 "Failed to pull image" 错误
# 解决方案:
# 1. 确认镜像存在
docker images | grep gold-template
# 2. 重新构建
docker build -t localhost:5000/gold-template:latest .
# 3. 重启 Pod
kubectl rollout restart deployment/gold-template -n gold-template
```
@@ -0,0 +1,513 @@
# Kubernetes 可观测性集成指南
## 概述
本指南介绍如何集成 K8s 的三大可观测性支柱:
1. **日志(Logging)** - 应用和系统日志
2. **指标(Metrics)** - 性能和资源监控
3. **链路追踪(Tracing)** - 分布式请求追踪
## 1. 日志管理
### 方案 A:使用 kubectl 查看日志(基础)
```bash
# 查看 Pod 日志
kubectl logs -n gold-template -l app.kubernetes.io/name=gold-template
# 实时跟踪日志
kubectl logs -n gold-template -l app.kubernetes.io/name=gold-template -f
# 查看最近 100 行
kubectl logs -n gold-template <pod-name> --tail=100
# 查看前一个容器的日志(Pod 重启后)
kubectl logs -n gold-template <pod-name> --previous
# 使用 k8s-helper skill
python3 .claude/skills/k8s-helper/helper.py --action logs -r <pod-name> -n gold-template --follow
```
### 方案 B:集成 Loki + Grafana(推荐)
#### 1. 部署 Loki Stack
```bash
# 添加 Grafana Helm 仓库
helm repo add grafana https://grafana.github.io/helm-charts
helm repo update
# 安装 Loki Stack(包含 Loki + Promtail + Grafana)
helm install loki grafana/loki-stack \
--namespace logging \
--create-namespace \
--set grafana.enabled=true \
--set prometheus.enabled=true \
--set promtail.enabled=true
```
#### 2. 配置应用日志格式
**更新 backend/main.py:**
```python
import logging
import json
from datetime import datetime
# 配置结构化日志
class JSONFormatter(logging.Formatter):
def format(self, record):
log_data = {
"timestamp": datetime.utcnow().isoformat(),
"level": record.levelname,
"logger": record.name,
"message": record.getMessage(),
"module": record.module,
"function": record.funcName,
"line": record.lineno,
}
if record.exc_info:
log_data["exception"] = self.formatException(record.exc_info)
return json.dumps(log_data)
# 配置 logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.addHandler(handler)
```
#### 3. 访问 Grafana
```bash
# 获取 Grafana 密码
kubectl get secret -n logging loki-grafana -o jsonpath="{.data.admin-password}" | base64 --decode
# 端口转发
kubectl port-forward -n logging svc/loki-grafana 3000:80
# 访问 http://localhost:3000
# 用户名: admin
# 密码: <上面获取的密码>
```
#### 4. 在 Grafana 中查询日志
```logql
# 查看所有 gold-template 日志
{namespace="gold-template"}
# 过滤错误日志
{namespace="gold-template"} |= "ERROR"
# 按时间范围查询
{namespace="gold-template"} | json | level="ERROR" | __timestamp__ > now() - 1h
# 统计错误数量
sum(count_over_time({namespace="gold-template"} |= "ERROR" [5m]))
```
### 方案 C:集成 ELK Stack
```bash
# 安装 Elasticsearch
helm install elasticsearch elastic/elasticsearch -n logging
# 安装 Kibana
helm install kibana elastic/kibana -n logging
# 安装 Filebeat
helm install filebeat elastic/filebeat -n logging
```
## 2. Metrics 监控
### 方案 A:使用 Prometheus + Grafana(推荐)
#### 1. 部署 Prometheus Stack
```bash
# 添加 Prometheus 社区 Helm 仓库
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
# 安装 kube-prometheus-stack(包含 Prometheus + Grafana + Alertmanager)
helm install prometheus prometheus-community/kube-prometheus-stack \
--namespace monitoring \
--create-namespace \
--set prometheus.prometheusSpec.serviceMonitorSelectorNilUsesHelmValues=false
```
#### 2. 为应用添加 Metrics 端点
**更新 backend/main.py:**
```python
from fastapi import FastAPI
from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST
from fastapi.responses import Response
import time
app = FastAPI()
# 定义 metrics
REQUEST_COUNT = Counter(
'http_requests_total',
'Total HTTP requests',
['method', 'endpoint', 'status']
)
REQUEST_DURATION = Histogram(
'http_request_duration_seconds',
'HTTP request duration',
['method', 'endpoint']
)
@app.middleware("http")
async def metrics_middleware(request, call_next):
start_time = time.time()
response = await call_next(request)
duration = time.time() - start_time
REQUEST_COUNT.labels(
method=request.method,
endpoint=request.url.path,
status=response.status_code
).inc()
REQUEST_DURATION.labels(
method=request.method,
endpoint=request.url.path
).observe(duration)
return response
@app.get("/metrics")
async def metrics():
return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)
```
**添加依赖到 requirements.txt:**
```txt
prometheus-client==0.19.0
```
#### 3. 创建 ServiceMonitor
**deployment/helm/gold-template/templates/servicemonitor.yaml:**
```yaml
{{- if .Values.serviceMonitor.enabled -}}
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: {{ include "gold-template.fullname" . }}
labels:
{{- include "gold-template.labels" . | nindent 4 }}
spec:
selector:
matchLabels:
{{- include "gold-template.selectorLabels" . | nindent 6 }}
endpoints:
- port: http
path: /metrics
interval: 30s
scrapeTimeout: 10s
{{- end }}
```
#### 4. 启用 ServiceMonitor
**更新 values-orbstack.yaml:**
```yaml
serviceMonitor:
enabled: true
interval: 30s
scrapeTimeout: 10s
podAnnotations:
prometheus.io/scrape: "true"
prometheus.io/port: "8000"
prometheus.io/path: "/metrics"
```
#### 5. 访问 Grafana
```bash
# 获取 Grafana 密码
kubectl get secret -n monitoring prometheus-grafana -o jsonpath="{.data.admin-password}" | base64 --decode
# 端口转发
kubectl port-forward -n monitoring svc/prometheus-grafana 3000:80
# 访问 http://localhost:3000
```
#### 6. 导入 Dashboard
在 Grafana 中导入以下 Dashboard:
- **Kubernetes Cluster Monitoring**: Dashboard ID 7249
- **Kubernetes Pod Monitoring**: Dashboard ID 6417
- **FastAPI Monitoring**: 自定义创建
**自定义 FastAPI Dashboard 示例:**
```json
{
"dashboard": {
"title": "Gold Template Metrics",
"panels": [
{
"title": "Request Rate",
"targets": [{
"expr": "rate(http_requests_total{namespace=\"gold-template\"}[5m])"
}]
},
{
"title": "Request Duration",
"targets": [{
"expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket{namespace=\"gold-template\"}[5m]))"
}]
},
{
"title": "Error Rate",
"targets": [{
"expr": "rate(http_requests_total{namespace=\"gold-template\",status=~\"5..\"}[5m])"
}]
}
]
}
}
```
### 方案 B:使用 Kubernetes Metrics Server
```bash
# 安装 Metrics Server
kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml
# 查看资源使用
kubectl top nodes
kubectl top pods -n gold-template
# 使用 k8s-helper
python3 .claude/skills/k8s-helper/helper.py --action status -n gold-template
```
## 3. 链路追踪(Tracing)
### 方案 A:使用 Jaeger(推荐)
#### 1. 部署 Jaeger
```bash
# 使用 Jaeger Operator
kubectl create namespace observability
kubectl create -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml -n observability
# 或使用 Helm
helm repo add jaegertracing https://jaegertracing.github.io/helm-charts
helm install jaeger jaegertracing/jaeger \
--namespace observability \
--create-namespace
```
#### 2. 为应用添加 Tracing
**安装依赖:**
```bash
pip install opentelemetry-api opentelemetry-sdk opentelemetry-instrumentation-fastapi opentelemetry-exporter-jaeger
```
**更新 backend/main.py:**
```python
from fastapi import FastAPI
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
# 配置 Jaeger
trace.set_tracer_provider(TracerProvider())
jaeger_exporter = JaegerExporter(
agent_host_name="jaeger-agent.observability.svc.cluster.local",
agent_port=6831,
)
trace.get_tracer_provider().add_span_processor(
BatchSpanProcessor(jaeger_exporter)
)
app = FastAPI()
# 自动 instrument FastAPI
FastAPIInstrumentor.instrument_app(app)
@app.get("/")
async def root():
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("root-handler"):
# 业务逻辑
return {"message": "Hello World"}
```
#### 3. 访问 Jaeger UI
```bash
# 端口转发
kubectl port-forward -n observability svc/jaeger-query 16686:16686
# 访问 http://localhost:16686
```
### 方案 B:使用 Zipkin
```bash
# 部署 Zipkin
kubectl create deployment zipkin --image=openzipkin/zipkin -n observability
kubectl expose deployment zipkin --type=ClusterIP --port=9411 -n observability
# 端口转发
kubectl port-forward -n observability svc/zipkin 9411:9411
```
## 完整集成示例
### 1. 创建可观测性配置
**deployment/helm/gold-template/values-observability.yaml:**
```yaml
# 启用所有可观测性功能
serviceMonitor:
enabled: true
interval: 30s
podAnnotations:
prometheus.io/scrape: "true"
prometheus.io/port: "8000"
prometheus.io/path: "/metrics"
env:
# 日志配置
- name: LOG_LEVEL
value: "INFO"
- name: LOG_FORMAT
value: "json"
# Metrics 配置
- name: METRICS_ENABLED
value: "true"
- name: METRICS_PORT
value: "8000"
# Tracing 配置
- name: JAEGER_AGENT_HOST
value: "jaeger-agent.observability.svc.cluster.local"
- name: JAEGER_AGENT_PORT
value: "6831"
- name: TRACING_ENABLED
value: "true"
```
### 2. 部署完整可观测性栈
```bash
# 1. 部署 Prometheus + Grafana
helm install prometheus prometheus-community/kube-prometheus-stack \
-n monitoring --create-namespace
# 2. 部署 Loki
helm install loki grafana/loki-stack \
-n logging --create-namespace \
--set promtail.enabled=true
# 3. 部署 Jaeger
helm install jaeger jaegertracing/jaeger \
-n observability --create-namespace
# 4. 部署应用(启用可观测性)
helm install gold-template ./deployment/helm/gold-template \
-n gold-template --create-namespace \
-f deployment/helm/gold-template/values-orbstack.yaml \
-f deployment/helm/gold-template/values-observability.yaml
```
### 3. 访问可观测性工具
```bash
# Grafana (Metrics + Logs)
kubectl port-forward -n monitoring svc/prometheus-grafana 3000:80
# Jaeger (Tracing)
kubectl port-forward -n observability svc/jaeger-query 16686:16686
# Prometheus (原始 Metrics)
kubectl port-forward -n monitoring svc/prometheus-kube-prometheus-prometheus 9090:9090
```
## 快速命令参考
```bash
# 日志
kubectl logs -n gold-template -l app.kubernetes.io/name=gold-template -f
# Metrics
kubectl top pods -n gold-template
curl http://localhost:8000/metrics
# 健康检查
curl http://localhost:8000/health
# 使用 k8s-helper
python3 .claude/skills/k8s-helper/helper.py --action health -n gold-template
python3 .claude/skills/k8s-helper/helper.py --action logs -r <pod-name> -n gold-template --follow
```
## 告警配置
**创建 PrometheusRule:**
```yaml
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
name: gold-template-alerts
namespace: gold-template
spec:
groups:
- name: gold-template
rules:
- alert: HighErrorRate
expr: rate(http_requests_total{status=~"5..",namespace="gold-template"}[5m]) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High error rate detected"
description: "Error rate is {{ $value }} requests/sec"
- alert: HighMemoryUsage
expr: container_memory_usage_bytes{namespace="gold-template"} / container_spec_memory_limit_bytes > 0.9
for: 5m
labels:
severity: warning
annotations:
summary: "High memory usage"
description: "Memory usage is {{ $value | humanizePercentage }}"
```
## 最佳实践
1. **结构化日志** - 使用 JSON 格式便于解析
2. **合理的 Metrics** - 不要过度监控,关注关键指标
3. **采样追踪** - 生产环境使用采样避免性能影响
4. **设置告警** - 关键指标设置合理的告警阈值
5. **定期清理** - 日志和 metrics 数据定期清理
6. **使用标签** - 为 metrics 和 traces 添加有意义的标签
+3
View File
@@ -39,3 +39,6 @@ safety==3.0.1
# Utilities
python-dotenv==1.0.0
python-dateutil==2.8.2
# Observability
prometheus-client==0.19.0
+127 -6
View File
@@ -15,6 +15,11 @@ NAMESPACE="gold-template"
HELM_RELEASE="gold-template"
IMAGE_NAME="localhost:5000/gold-template"
IMAGE_TAG="latest"
DEPLOY_MODE="local" # local or infra-db
PUSH_TO_GHCR="false"
PUSH_TO_HARBOR="false"
GHCR_REPO=""
HARBOR_REPO="harbor.omniverseai.net/ljwx/gold-template"
echo -e "${BLUE}🚀 Gold Template - OrbStack Deployment${NC}"
echo "=========================================="
@@ -77,7 +82,34 @@ EOF
docker build -t ${IMAGE_NAME}:${IMAGE_TAG} .
if [ $? -eq 0 ]; then
echo -e "${GREEN}✅ Image built successfully${NC}"
echo -e "${GREEN}✅ Image built successfully: ${IMAGE_NAME}:${IMAGE_TAG}${NC}"
# Push to Harbor if requested
if [ "${PUSH_TO_HARBOR}" = "true" ]; then
echo ""
echo -e "${BLUE}📤 Pushing to Harbor (${HARBOR_REPO})...${NC}"
docker tag ${IMAGE_NAME}:${IMAGE_TAG} ${HARBOR_REPO}:${IMAGE_TAG}
docker push ${HARBOR_REPO}:${IMAGE_TAG}
if [ $? -eq 0 ]; then
echo -e "${GREEN}✅ Image pushed to Harbor: ${HARBOR_REPO}:${IMAGE_TAG}${NC}"
else
echo -e "${RED}❌ Failed to push to Harbor${NC}"
echo -e "${YELLOW} Make sure you're logged in: docker login harbor.omniverseai.net${NC}"
fi
fi
# Push to GHCR if requested
if [ "${PUSH_TO_GHCR}" = "true" ] && [ -n "${GHCR_REPO}" ]; then
echo ""
echo -e "${BLUE}📤 Pushing to GitHub Container Registry...${NC}"
docker tag ${IMAGE_NAME}:${IMAGE_TAG} ${GHCR_REPO}:${IMAGE_TAG}
docker push ${GHCR_REPO}:${IMAGE_TAG}
if [ $? -eq 0 ]; then
echo -e "${GREEN}✅ Image pushed to GHCR: ${GHCR_REPO}:${IMAGE_TAG}${NC}"
else
echo -e "${RED}❌ Failed to push to GHCR${NC}"
fi
fi
else
echo -e "${RED}❌ Image build failed${NC}"
exit 1
@@ -113,18 +145,51 @@ create_secrets() {
--namespace=${NAMESPACE} \
--dry-run=client -o yaml | kubectl apply -f -
# Create database secrets if using infra-db mode
if [ "${DEPLOY_MODE}" = "infra-db" ]; then
echo -e "${BLUE}🔐 Creating database secrets for infra integration...${NC}"
DB_USER="${DB_USER:-app_user}"
DB_PASSWORD="${DB_PASSWORD:-$(openssl rand -hex 16)}"
REDIS_PASSWORD="${REDIS_PASSWORD:-$(openssl rand -hex 16)}"
kubectl create secret generic gold-template-db-secret \
--from-literal=username="${DB_USER}" \
--from-literal=password="${DB_PASSWORD}" \
--namespace=${NAMESPACE} \
--dry-run=client -o yaml | kubectl apply -f -
kubectl create secret generic gold-template-redis-secret \
--from-literal=password="${REDIS_PASSWORD}" \
--namespace=${NAMESPACE} \
--dry-run=client -o yaml | kubectl apply -f -
echo -e "${YELLOW}⚠️ Database credentials:${NC}"
echo " DB User: ${DB_USER}"
echo " DB Password: ${DB_PASSWORD}"
echo " Redis Password: ${REDIS_PASSWORD}"
echo -e "${YELLOW} Please configure these in your infra namespace databases${NC}"
fi
echo -e "${GREEN}✅ Secrets created${NC}"
}
# Deploy with Helm
deploy_helm() {
echo ""
echo -e "${BLUE}🎯 Deploying with Helm...${NC}"
echo -e "${BLUE}🎯 Deploying with Helm (mode: ${DEPLOY_MODE})...${NC}"
VALUES_FILE="./deployment/helm/gold-template/values-orbstack.yaml"
# Add infra-db values if in infra-db mode
if [ "${DEPLOY_MODE}" = "infra-db" ]; then
VALUES_FILE="${VALUES_FILE} -f ./deployment/helm/gold-template/values-infra-db.yaml"
fi
helm upgrade --install ${HELM_RELEASE} \
./deployment/helm/gold-template \
--namespace=${NAMESPACE} \
--values=./deployment/helm/gold-template/values-orbstack.yaml \
-f ${VALUES_FILE} \
--set image.tag=${IMAGE_TAG} \
--wait \
--timeout=5m
@@ -183,13 +248,26 @@ show_info() {
echo -e "${GREEN}🎉 Deployment Complete!${NC}"
echo "=========================================="
echo ""
echo "Deployment Configuration:"
echo " Mode: ${DEPLOY_MODE}"
echo " Namespace: ${NAMESPACE}"
echo " Image: ${IMAGE_NAME}:${IMAGE_TAG}"
if [ "${PUSH_TO_HARBOR}" = "true" ]; then
echo " Harbor: ${HARBOR_REPO}:${IMAGE_TAG}"
fi
if [ "${PUSH_TO_GHCR}" = "true" ]; then
echo " GHCR: ${GHCR_REPO}:${IMAGE_TAG}"
fi
echo ""
echo "Access Information:"
echo " Application: http://localhost:8000"
echo " API Docs: http://localhost:8000/docs"
echo " Health Check: http://localhost:8000/health"
echo " Metrics: http://localhost:8000/metrics"
echo ""
echo "Useful Commands:"
echo " View logs: kubectl logs -f -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template"
echo " View metrics: curl http://localhost:8000/metrics"
echo " Get pods: kubectl get pods -n ${NAMESPACE}"
echo " Describe: kubectl describe pod -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template"
echo " Shell: kubectl exec -it -n ${NAMESPACE} \$(kubectl get pod -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template -o jsonpath='{.items[0].metadata.name}') -- /bin/bash"
@@ -198,6 +276,7 @@ show_info() {
echo "Test the deployment:"
echo " curl http://localhost:8000"
echo " curl http://localhost:8000/health"
echo " curl http://localhost:8000/metrics"
echo ""
}
@@ -224,13 +303,55 @@ while [[ $# -gt 0 ]]; do
IMAGE_TAG="$2"
shift 2
;;
--mode)
DEPLOY_MODE="$2"
shift 2
;;
--push-ghcr)
PUSH_TO_GHCR="true"
GHCR_REPO="$2"
shift 2
;;
--push-harbor)
PUSH_TO_HARBOR="true"
shift
;;
--harbor-repo)
HARBOR_REPO="$2"
shift 2
;;
--namespace)
NAMESPACE="$2"
shift 2
;;
--help)
echo "Usage: $0 [OPTIONS]"
echo ""
echo "Options:"
echo " --skip-build Skip Docker build"
echo " --tag TAG Docker image tag [default: latest]"
echo " --help Show this help"
echo " --skip-build Skip Docker build"
echo " --tag TAG Docker image tag [default: latest]"
echo " --mode MODE Deployment mode: local or infra-db [default: local]"
echo " --push-ghcr REPO Push image to GHCR (e.g., ghcr.io/user/repo)"
echo " --push-harbor Push image to Harbor (harbor.omniverseai.net/ljwx/)"
echo " --harbor-repo REPO Custom Harbor repository [default: harbor.omniverseai.net/ljwx/gold-template]"
echo " --namespace NS Kubernetes namespace [default: gold-template]"
echo " --help Show this help"
echo ""
echo "Examples:"
echo " # Local deployment with SQLite"
echo " $0"
echo ""
echo " # Deploy with infra namespace databases"
echo " $0 --mode infra-db"
echo ""
echo " # Build and push to Harbor"
echo " $0 --push-harbor"
echo ""
echo " # Build and push to GHCR"
echo " $0 --push-ghcr ghcr.io/username/gold-template"
echo ""
echo " # Custom namespace"
echo " $0 --namespace my-app"
exit 0
;;
*)
+237
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#!/bin/bash
# Gold Template - Monitoring and Observability Helper
# Quick access to logs, metrics, and tracing
set -e
GREEN='\033[0;32m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
RED='\033[0;31m'
NC='\033[0m'
NAMESPACE="${NAMESPACE:-gold-template}"
show_help() {
echo "Gold Template - Monitoring Helper"
echo ""
echo "Usage: $0 COMMAND [OPTIONS]"
echo ""
echo "Commands:"
echo " logs [POD] View application logs (follows by default)"
echo " metrics View Prometheus metrics"
echo " health Check application health"
echo " status Show deployment status"
echo " top Show resource usage"
echo " events Show recent events"
echo " describe [POD] Describe pod details"
echo ""
echo "Options:"
echo " -n, --namespace NS Kubernetes namespace [default: gold-template]"
echo " --no-follow Don't follow logs (for logs command)"
echo " --tail N Show last N lines [default: 100]"
echo ""
echo "Examples:"
echo " $0 logs # Follow all application logs"
echo " $0 logs --tail 50 # Show last 50 lines"
echo " $0 metrics # View Prometheus metrics"
echo " $0 health # Check health endpoints"
echo " $0 status # Show deployment status"
echo " $0 top # Show CPU/memory usage"
}
view_logs() {
local follow="-f"
local tail="100"
while [[ $# -gt 0 ]]; do
case $1 in
--no-follow)
follow=""
shift
;;
--tail)
tail="$2"
shift 2
;;
*)
shift
;;
esac
done
echo -e "${BLUE}📋 Viewing logs from namespace: ${NAMESPACE}${NC}"
echo ""
kubectl logs -n ${NAMESPACE} \
-l app.kubernetes.io/name=gold-template \
${follow} \
--tail=${tail} \
--all-containers=true \
--prefix=true
}
view_metrics() {
echo -e "${BLUE}📊 Fetching Prometheus metrics...${NC}"
echo ""
# Get pod name
POD=$(kubectl get pod -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template -o jsonpath='{.items[0].metadata.name}')
if [ -z "$POD" ]; then
echo -e "${RED}❌ No pods found${NC}"
exit 1
fi
echo -e "${GREEN}Pod: ${POD}${NC}"
echo ""
# Port forward and fetch metrics
kubectl port-forward -n ${NAMESPACE} ${POD} 8000:8000 > /dev/null 2>&1 &
PF_PID=$!
sleep 2
curl -s http://localhost:8000/metrics
kill $PF_PID 2>/dev/null || true
}
check_health() {
echo -e "${BLUE}🏥 Checking application health...${NC}"
echo ""
# Get pod name
POD=$(kubectl get pod -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template -o jsonpath='{.items[0].metadata.name}')
if [ -z "$POD" ]; then
echo -e "${RED}❌ No pods found${NC}"
exit 1
fi
echo -e "${GREEN}Pod: ${POD}${NC}"
echo ""
# Port forward and check health
kubectl port-forward -n ${NAMESPACE} ${POD} 8000:8000 > /dev/null 2>&1 &
PF_PID=$!
sleep 2
echo "Health endpoint:"
curl -s http://localhost:8000/health | jq '.' 2>/dev/null || curl -s http://localhost:8000/health
echo ""
echo "Ready endpoint:"
curl -s http://localhost:8000/health/ready | jq '.' 2>/dev/null || curl -s http://localhost:8000/health/ready
echo ""
kill $PF_PID 2>/dev/null || true
}
show_status() {
echo -e "${BLUE}📊 Deployment Status${NC}"
echo "===================="
echo ""
echo "Pods:"
kubectl get pods -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template
echo ""
echo "Services:"
kubectl get svc -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template
echo ""
echo "Deployments:"
kubectl get deployments -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template
echo ""
}
show_top() {
echo -e "${BLUE}📈 Resource Usage${NC}"
echo "=================="
echo ""
echo "Pod resource usage:"
kubectl top pods -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template 2>/dev/null || \
echo -e "${YELLOW}⚠️ Metrics server not available. Install with: kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml${NC}"
}
show_events() {
echo -e "${BLUE}📅 Recent Events${NC}"
echo "================"
echo ""
kubectl get events -n ${NAMESPACE} \
--sort-by='.lastTimestamp' \
--field-selector involvedObject.kind=Pod
}
describe_pod() {
local pod_name="$1"
if [ -z "$pod_name" ]; then
pod_name=$(kubectl get pod -n ${NAMESPACE} -l app.kubernetes.io/name=gold-template -o jsonpath='{.items[0].metadata.name}')
fi
echo -e "${BLUE}🔍 Describing pod: ${pod_name}${NC}"
echo ""
kubectl describe pod -n ${NAMESPACE} ${pod_name}
}
# Parse global options
while [[ $# -gt 0 ]]; do
case $1 in
-n|--namespace)
NAMESPACE="$2"
shift 2
;;
logs)
shift
view_logs "$@"
exit 0
;;
metrics)
shift
view_metrics
exit 0
;;
health)
shift
check_health
exit 0
;;
status)
shift
show_status
exit 0
;;
top)
shift
show_top
exit 0
;;
events)
shift
show_events
exit 0
;;
describe)
shift
describe_pod "$1"
exit 0
;;
--help|-h)
show_help
exit 0
;;
*)
echo "Unknown command: $1"
echo ""
show_help
exit 1
;;
esac
done
show_help
+54
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@@ -0,0 +1,54 @@
#!/bin/bash
# Harbor Registry Setup Script
# Creates Kubernetes secret for pulling images from Harbor
set -e
GREEN='\033[0;32m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
RED='\033[0;31m'
NC='\033[0m'
HARBOR_URL="harbor.omniverseai.net"
NAMESPACE="${NAMESPACE:-gold-template}"
echo -e "${BLUE}🔐 Harbor Registry Setup${NC}"
echo "=========================="
echo ""
# Check if already logged in
if docker info 2>/dev/null | grep -q "${HARBOR_URL}"; then
echo -e "${GREEN}✅ Already logged in to Harbor${NC}"
else
echo -e "${YELLOW}Please login to Harbor:${NC}"
docker login ${HARBOR_URL}
fi
echo ""
echo -e "${BLUE}Creating Kubernetes secret for Harbor...${NC}"
# Get Docker credentials
DOCKER_CONFIG_JSON=$(cat ~/.docker/config.json | base64)
# Create namespace if not exists
kubectl create namespace ${NAMESPACE} --dry-run=client -o yaml | kubectl apply -f -
# Create secret
kubectl create secret docker-registry harbor-registry-secret \
--docker-server=${HARBOR_URL} \
--docker-username=$(cat ~/.docker/config.json | jq -r ".auths.\"${HARBOR_URL}\".username" 2>/dev/null || echo "admin") \
--docker-password=$(cat ~/.docker/config.json | jq -r ".auths.\"${HARBOR_URL}\".password" 2>/dev/null || echo "") \
--namespace=${NAMESPACE} \
--dry-run=client -o yaml | kubectl apply -f -
if [ $? -eq 0 ]; then
echo -e "${GREEN}✅ Harbor secret created successfully${NC}"
echo ""
echo "You can now deploy using Harbor images:"
echo " ./scripts/deploy-orbstack.sh --push-harbor"
else
echo -e "${RED}❌ Failed to create Harbor secret${NC}"
exit 1
fi