给 trigger_rule_codes 加 JSON 多值索引;登记 P3 处理结果(docs/25 P3 #21)

实测确认 #21 描述准确:fin_risk_alert 只有 11 个普通 BTREE 索引 + 主键 + alert_no
唯一键,规则命中的 JSON_CONTAINS 查询 EXPLAIN 为 type=ALL、possible_keys=NULL,
即全表扫。MySQL 8.0.27 支持多值索引,故新增迁移 20260911_risk_rule_index:

    ADD INDEX idx_fin_risk_alert_trigger_rule_codes
      ((CAST(`trigger_rule_codes` AS CHAR(16) ARRAY)))

迁移幂等(先查 information_schema.STATISTICS),upgrade/downgrade 往返已验证。
生效后 EXPLAIN 变为 access_type=range 且 key 命中该索引,原始证据留档在
docs/evidence/risk-index-probe.json(由 tools/probe_risk_index.py 生成,只读探查)。

只解决一半,另一半如实记为限制:若干 like(f"%{keyword}%") 全表扫无法用 B-tree 索引,
根治需全文索引 + 中文分词组件(部署依赖),本轮不做。

revision 名刻意压到 32 字符以内 —— alembic_version.version_num 是 VARCHAR(32),
超长会在写版本号时报 1406,而 DDL 是非事务的,那时索引已经建好了。

docs/25 追加"P3 处理结果"表,逐条登记 17-25 的状态:#19 是协议级重做(keyset 分页)
不单方面改,#21 部分修复,#25 前半段不成立,其余已修。
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2026-09-11 14:19:05 +08:00
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"""只读探查:`fin_risk_alert.trigger_rule_codes` 能否用 JSON 多值索引(docs/25 P3 #21)。
只做 SELECT / EXPLAIN,不做任何写入。结果写成 JSON 便于在 GBK 控制台下查看:
python tools/probe_risk_index.py
排查点:
1. MySQL 版本是否 ≥ 8.0.17(多值索引的下限);
2. 列是否确实是 JSON、是否已存在同类索引;
3. 是否存在**非数组**取值 —— 有的话 `CAST(... AS CHAR ARRAY)` 建索引会直接失败;
4. 当前 `JSON_CONTAINS` 查询走的是什么访问路径(有没有可用的 key)。
"""
from __future__ import annotations
import asyncio
import json
from pathlib import Path
from typing import Any
from sqlalchemy import text
from app.infrastructure.db import SessionFactory
OUTPUT = Path("docs/evidence/risk-index-probe.json")
async def collect() -> dict[str, Any]:
report: dict[str, Any] = {}
async with SessionFactory() as session:
report["mysql_version"] = (
await session.execute(text("SELECT VERSION()"))
).scalar_one()
report["column"] = [
dict(row)
for row in (
(
await session.execute(
text(
"""
SELECT COLUMN_NAME, COLUMN_TYPE, DATA_TYPE, IS_NULLABLE
FROM information_schema.COLUMNS
WHERE TABLE_SCHEMA = DATABASE()
AND TABLE_NAME = 'fin_risk_alert'
AND COLUMN_NAME = 'trigger_rule_codes'
"""
)
)
)
.mappings()
.all()
)
]
report["indexes"] = [
dict(row)
for row in (
(
await session.execute(
text(
"""
SELECT INDEX_NAME, COLUMN_NAME, INDEX_TYPE, EXPRESSION
FROM information_schema.STATISTICS
WHERE TABLE_SCHEMA = DATABASE()
AND TABLE_NAME = 'fin_risk_alert'
ORDER BY INDEX_NAME, SEQ_IN_INDEX
"""
)
)
)
.mappings()
.all()
)
]
report["row_count"] = (
await session.execute(text("SELECT COUNT(*) FROM fin_risk_alert"))
).scalar_one()
report["non_array_rows"] = (
await session.execute(
text(
"SELECT COUNT(*) FROM fin_risk_alert "
"WHERE trigger_rule_codes IS NULL "
" OR JSON_TYPE(trigger_rule_codes) <> 'ARRAY'"
)
)
).scalar_one()
report["distinct_rule_codes"] = [
dict(row)
for row in (
(
await session.execute(
text(
"""
SELECT CAST(trigger_rule_codes AS CHAR) AS raw_codes,
COUNT(*) AS rows_count
FROM fin_risk_alert
GROUP BY 1
"""
)
)
)
.mappings()
.all()
)
]
# 用 FORMAT=JSON:传统 EXPLAIN 会把"该查询无法被缓存"写成 warning 打到 stderr,
# 让脚本在 CI 里看起来是失败的,而它其实成功了。
raw_explain = (
await session.execute(
text(
"""
EXPLAIN FORMAT=JSON
SELECT id FROM fin_risk_alert
WHERE JSON_CONTAINS(trigger_rule_codes, '"RW-018"')
"""
)
)
).scalar_one()
report["explain_json_contains"] = json.loads(raw_explain)
return report
async def main() -> None:
report = await collect()
OUTPUT.parent.mkdir(parents=True, exist_ok=True)
OUTPUT.write_text(
json.dumps(report, ensure_ascii=False, indent=2, default=str),
encoding="utf-8",
)
print(f"wrote {OUTPUT}")
if __name__ == "__main__":
asyncio.run(main())