209 lines
6.5 KiB
Python
209 lines
6.5 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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慢 SQL 指标（轻量内存版）
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- 记录超过阈值的 SQL 执行样本
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- 维护近窗口期（默认24小时）聚合统计
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- 输出 TOP SQL 与最近慢 SQL 列表
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"""
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from __future__ import annotations
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import os
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import threading
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import time
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from collections import deque
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_SLOW_SQL_THRESHOLD_MS = max(0.0, float(os.environ.get("DB_SLOW_QUERY_MS", "120") or 120))
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_WINDOW_SECONDS = max(600, int(os.environ.get("DB_SLOW_SQL_WINDOW_SECONDS", "86400") or 86400))
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_TOP_LIMIT = max(5, int(os.environ.get("DB_SLOW_SQL_TOP_LIMIT", "12") or 12))
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_RECENT_LIMIT = max(10, int(os.environ.get("DB_SLOW_SQL_RECENT_LIMIT", "50") or 50))
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_MAX_EVENTS = max(_RECENT_LIMIT, int(os.environ.get("DB_SLOW_SQL_MAX_EVENTS", "20000") or 20000))
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_SQL_MAX_LEN = max(80, int(os.environ.get("DB_SLOW_QUERY_SQL_MAX_LEN", "240") or 240))
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_runtime_lock = threading.Lock()
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_runtime_threshold_ms = _SLOW_SQL_THRESHOLD_MS
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_runtime_sql_max_len = _SQL_MAX_LEN
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_lock = threading.Lock()
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_state = {
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"start_ts": time.time(),
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"last_slow_ts": 0.0,
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"events": deque(),
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"recent": deque(maxlen=_RECENT_LIMIT),
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}
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def _compact_text(value: str, max_len: int) -> str:
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text = " ".join(str(value or "").split())
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if len(text) <= max_len:
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return text
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return f"{text[: max_len - 3]}..."
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def _get_runtime_values() -> tuple[float, int]:
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with _runtime_lock:
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return float(_runtime_threshold_ms), int(_runtime_sql_max_len)
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def configure_slow_sql_runtime(*, threshold_ms=None, sql_max_len=None) -> dict:
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global _runtime_threshold_ms, _runtime_sql_max_len
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with _runtime_lock:
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if threshold_ms is not None:
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_runtime_threshold_ms = max(0.0, float(threshold_ms))
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if sql_max_len is not None:
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_runtime_sql_max_len = max(80, int(sql_max_len))
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return {
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"threshold_ms": float(_runtime_threshold_ms),
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"sql_max_len": int(_runtime_sql_max_len),
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}
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def _compact_sql(sql: str) -> str:
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_, sql_max_len = _get_runtime_values()
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return _compact_text(str(sql or ""), sql_max_len)
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def _compact_params(params_info: str) -> str:
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return _compact_text(str(params_info or "none"), 64)
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def _prune_events_locked(now_ts: float) -> None:
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cutoff_ts = now_ts - float(_WINDOW_SECONDS)
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events = _state["events"]
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while events and float(events[0].get("time", 0.0) or 0.0) < cutoff_ts:
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events.popleft()
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overflow = len(events) - int(_MAX_EVENTS)
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while overflow > 0 and events:
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events.popleft()
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overflow -= 1
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def record_slow_sql(*, sql: str, duration_ms: float, params_info: str = "none") -> None:
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duration = max(0.0, float(duration_ms or 0.0))
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now = time.time()
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sql_text = _compact_sql(sql)
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params_text = _compact_params(params_info)
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event = {
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"time": now,
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"sql": sql_text,
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"duration_ms": round(duration, 2),
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"params": params_text,
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}
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with _lock:
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_prune_events_locked(now)
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_state["events"].append(event)
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_state["recent"].append(event)
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_state["last_slow_ts"] = now
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def get_slow_sql_metrics_snapshot() -> dict:
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now = time.time()
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with _lock:
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_prune_events_locked(now)
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events = list(_state["events"])
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recent_rows = list(_state["recent"])
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last_slow_ts = float(_state.get("last_slow_ts") or 0.0)
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grouped: dict[str, dict] = {}
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total_duration_ms = 0.0
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max_duration_ms = 0.0
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for item in events:
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sql_text = str(item.get("sql") or "-")
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duration = float(item.get("duration_ms") or 0.0)
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ts = float(item.get("time") or 0.0)
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params_text = str(item.get("params") or "none")
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total_duration_ms += duration
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if duration > max_duration_ms:
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max_duration_ms = duration
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bucket = grouped.get(sql_text)
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if bucket is None:
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bucket = {
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"sql": sql_text,
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"count": 0,
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"total_ms": 0.0,
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"max_ms": 0.0,
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"last_ts": 0.0,
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"params": params_text,
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}
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grouped[sql_text] = bucket
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bucket["count"] = int(bucket["count"] or 0) + 1
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bucket["total_ms"] = float(bucket["total_ms"] or 0.0) + duration
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if duration > float(bucket["max_ms"] or 0.0):
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bucket["max_ms"] = duration
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bucket["params"] = params_text
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if ts >= float(bucket["last_ts"] or 0.0):
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bucket["last_ts"] = ts
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top_sql_rows = sorted(
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grouped.values(),
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key=lambda row: (
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int(row.get("count", 0) or 0),
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float(row.get("max_ms", 0.0) or 0.0),
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float(row.get("total_ms", 0.0) or 0.0),
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),
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reverse=True,
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)[:_TOP_LIMIT]
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top_sql = []
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for idx, row in enumerate(top_sql_rows, start=1):
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count = int(row.get("count", 0) or 0)
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total_ms = float(row.get("total_ms", 0.0) or 0.0)
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avg_ms = (total_ms / count) if count > 0 else 0.0
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top_sql.append(
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{
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"rank": idx,
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"sql": row.get("sql") or "-",
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"count": count,
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"avg_ms": round(avg_ms, 2),
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"max_ms": round(float(row.get("max_ms", 0.0) or 0.0), 2),
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"last_ts": int(float(row.get("last_ts", 0.0) or 0.0)),
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"sample_params": row.get("params") or "none",
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}
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)
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cutoff_ts = now - float(_WINDOW_SECONDS)
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recent = [
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{
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"time": int(float(item.get("time") or 0.0)),
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"sql": str(item.get("sql") or "-"),
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"duration_ms": round(float(item.get("duration_ms") or 0.0), 2),
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"params": str(item.get("params") or "none"),
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}
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for item in recent_rows
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if float(item.get("time") or 0.0) >= cutoff_ts
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]
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total_events = len(events)
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avg_duration_ms = round((total_duration_ms / total_events), 2) if total_events > 0 else 0.0
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runtime_threshold_ms, _ = _get_runtime_values()
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return {
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"since_ts": int(float(events[0].get("time") or 0.0)) if events else 0,
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"window_seconds": _WINDOW_SECONDS,
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"top_limit": _TOP_LIMIT,
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"recent_limit": _RECENT_LIMIT,
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"slow_threshold_ms": runtime_threshold_ms,
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"total_slow_queries": total_events,
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"unique_sql": len(grouped),
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"avg_duration_ms": avg_duration_ms,
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"max_duration_ms": round(max_duration_ms, 2),
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"last_slow_ts": int(last_slow_ts) if last_slow_ts > 0 else 0,
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"top_sql": top_sql,
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"recent_slow_sql": recent,
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}
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