PostgreSQL 社区周报：从 MemSQL 迁移到 HorizonDB 的实战启示与本地 Meetup 动态

#!/usr/bin/env python3
"""
migration_assess.py — 从 MySQL/MemSQL 兼容源扫描专有语法，评估 PG 迁移差距。
用法: python migration_assess.py
输出: JSON 格式的兼容性报告，列出需手动处理的 SQL 模式。
"""

import re
import json
import sys
from pathlib import Path

# ---------- 配置区 ----------
SOURCE_CONFIG = {
    "host": "localhost",
    "port": 3306,
    "user": "root",
    "password": "secret",
    "database": "my_app",
}

# MemSQL / MySQL 专有语法模式 → PG 替代建议
PATTERNS = [
    {
        "name": "REFERENCE table (MemSQL)",
        "regex": r"CREATE\s+REFERENCE\s+TABLE",
        "pg_alternative": "用普通表 + 外键替代，或考虑 Citus 分布式引用表",
        "severity": "high",
    },
    {
        "name": "聚合索引 (MemSQL)",
        "regex": r"CREATE\s+INDEX\s+.*\s+ON\s+.*\s*\(\s*.*\s*\)\s*AGGREGATE",
        "pg_alternative": "用 MATERIALIZED VIEW 替代聚合索引，定期 REFRESH",
        "severity": "high",
    },
    {
        "name": "UNSIGNED 整数 (MySQL)",
        "regex": r"\bUNSIGNED\b",
        "pg_alternative": "PostgreSQL 无 UNSIGNED，用 CHECK 约束: CHECK (col >= 0)",
        "severity": "medium",
    },
    {
        "name": "AUTO_INCREMENT (MySQL)",
        "regex": r"\bAUTO_INCREMENT\b",
        "pg_alternative": "用 SERIAL 或 GENERATED ALWAYS AS IDENTITY",
        "severity": "low",
    },
    {
        "name": "GROUP_CONCAT (MySQL)",
        "regex": r"\bGROUP_CONCAT\b",
        "pg_alternative": "用 string_agg(col, delimiter)",
        "severity": "medium",
    },
    {
        "name": "IF() 函数 (MySQL)",
        "regex": r"\bIF\s*\(",
        "pg_alternative": "用 CASE WHEN ... THEN ... ELSE ... END",
        "severity": "low",
    },
]

# ---------- 扫描逻辑 ----------

def fetch_schema_sql(config: dict) -> list[str]:
    """从源库导出 CREATE 语句。这里用文件模拟；生产环境可用 SHOW CREATE TABLE。"""
    # 实际生产中，用 mysql-connector-python 执行:
    #   cursor.execute("SHOW TABLES")
    #   for table in cursor.fetchall():
    #       cursor.execute(f"SHOW CREATE TABLE {table[0]}")
    #       sql_list.append(cursor.fetchone()[1])
    #
    # 此处为可运行示例，从本地 SQL 文件读取：
    sql_file = Path("schema_dump.sql")
    if sql_file.exists():
        return sql_file.read_text().split(";")
    # 无文件时返回模拟数据
    return [
        "CREATE REFERENCE TABLE ref_orders (id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY)",
        "CREATE TABLE events (name VARCHAR(255), tags GROUP_CONCAT)",
        "SELECT IF(status='active', 1, 0) FROM users",
    ]


def assess_compatibility(sql_list: list[str]) -> dict:
    """逐条扫描 SQL，匹配专有模式，生成报告。"""
    findings = []
    for sql in sql_list:
        sql_stripped = sql.strip()
        if not sql_stripped:
            continue
        for pattern in PATTERNS:
            if re.search(pattern["regex"], sql_stripped, re.IGNORECASE):
                findings.append({
                    "pattern": pattern["name"],
                    "severity": pattern["severity"],
                    "pg_alternative": pattern["pg_alternative"],
                    "sample_sql": sql_stripped[:120],
                })
    # 汇总
    high = sum(1 for f in findings if f["severity"] == "high")
    medium = sum(1 for f in findings if f["severity"] == "medium")
    low = sum(1 for f in findings if f["severity"] == "low")
    return {
        "total_issues": len(findings),
        "high": high,
        "medium": medium,
        "low": low,
        "recommendation": (
            "高风险项 > 0：建议先做专项 PoC，验证替代方案性能"
            if high > 0 else
            "中风险项为主：可分批迁移，逐模块替换语法"
            if medium > 0 else
            "仅低风险项：可直接用 pg_dump 迁移后微调"
        ),
        "findings": findings,
    }


def main():
    sql_list = fetch_schema_sql(SOURCE_CONFIG)
    report = assess_compatibility(sql_list)
    print(json.dumps(report, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()