前十零变动：2026 年 5 月中国数据库排行榜释放的存量信号

"""
database_eval.py — 数据库选型加权评估脚本
使用方法：
  1. 修改 CANDIDATES 中的数据库列表和各项指标得分（1-10）
  2. 运行：python database_eval.py
  3. 输出按总分排序的评估结果

指标说明：
  - stability: 生产稳定性（故障率、长期运行表现）
  - ecosystem: 生态完善度（工具链、文档、社区活跃度）
  - industry_fit: 行业适配度（本行业生产案例数量与深度）
  - ops_support: 运维支持（监控、备份、迁移工具、厂商响应）
  - talent_pool: 人才供给（招聘市场、培训认证、社区人力）
"""

import json
from dataclasses import dataclass, field

# ---- 权重配置：根据你的团队优先级调整 ----
WEIGHTS = {
    "stability": 0.25,      # 存量时代最看重
    "ecosystem": 0.20,
    "industry_fit": 0.25,   # 行业护城河指标
    "ops_support": 0.20,
    "talent_pool": 0.10,
}

# ---- 候选数据库与指标打分（1-10，请按实际情况修改） ----
CANDIDATES = {
    "OceanBase": {
        "stability": 9,
        "ecosystem": 7,
        "industry_fit": 9,   # 金融行业深度落地
        "ops_support": 8,
        "talent_pool": 6,
    },
    "TiDB": {
        "stability": 8,
        "ecosystem": 9,     # 开源社区活跃
        "industry_fit": 7,
        "ops_support": 8,
        "talent_pool": 7,
    },
    "PolarDB": {
        "stability": 9,
        "ecosystem": 8,
        "industry_fit": 8,  # 云原生场景优势
        "ops_support": 9,   # 阿里云体系
        "talent_pool": 7,
    },
    "openGauss": {
        "stability": 8,
        "ecosystem": 6,
        "industry_fit": 8,  # 政务、电信
        "ops_support": 7,
        "talent_pool": 5,
    },
}

@dataclass
class EvalResult:
    name: str
    scores: dict = field(default_factory=dict)
    total: float = 0.0
    details: dict = field(default_factory=dict)

def evaluate(candidates: dict, weights: dict) -> list[EvalResult]:
    results = []
    for name, scores in candidates.items():
        details = {}
        total = 0.0
        for metric, score in scores.items():
            w = weights.get(metric, 0)
            weighted = score * w
            details[metric] = {"raw": score, "weight": w, "weighted": round(weighted, 2)}
            total += weighted
        results.append(EvalResult(
            name=name,
            scores=scores,
            total=round(total, 2),
            details=details,
        ))
    results.sort(key=lambda r: r.total, reverse=True)
    return results

def print_report(results: list[EvalResult]):
    print("=" * 60)
    print("数据库选型评估报告（存量深耕模式）")
    print("=" * 60)
    for i, r in enumerate(results, 1):
        print(f"\n#{i} {r.name}  总分: {r.total}")
        print("-" * 40)
        for metric, d in r.details.items():
            print(f"  {metric:14s}  原始分: {d['raw']}  "
                  f"权重: {d['weight']}  加权分: {d['weighted']}")
    print("\n" + "=" * 60)
    print("建议：优先考察 #1 和 #2 在你所在行业的生产案例数量")
    print("      与运维工具链完整度，再做最终决策。")

if __name__ == "__main__":
    results = evaluate(CANDIDATES, WEIGHTS)
    print_report(results)

    # 也可导出 JSON 供后续分析
    with open("eval_results.json", "w", encoding="utf-8") as f:
        json.dump([{"name": r.name, "total": r.total, "details": r.details}
                   for r in results], f, ensure_ascii=False, indent=2)
    print("\n详细结果已写入 eval_results.json")