feat(日志): 任务步骤明细表 + 「日志 → 步骤明细」面板(按设备/任务/时间过滤、按运行归组、导出 CSV)

文本日志只能 grep,"这台设备这次运行为什么失败"翻起来很费劲。新增一张
**结构化**的步骤明细表,把"哪一步、什么类型、哪个选择器、结果、耗时"落库。

- core/models.py:新增 task_step_log(run_id/job/设备/step_path/selector/
  result/detail/duration_ms),索引 run_id、created_at、(serial,created_at)、
  (job_id,created_at);
- core/step_log.py(新):**专用写线程 + 有界队列**批量落库——步骤执行是热路径,
  任务线程只 put_nowait(实测 9000 次入队 31ms),队列满丢弃并计数,绝不阻塞;
  另有保留期清理(默认 14 天,每日 04:13 + 每次启动);
- tasks/generic/task.py:_exec_one 记一条(异常=error / handler 返回 False=miss /
  未知类型=unknown / 概率未触发=skip);_exec_steps 维护路径栈得到 "2.1.3"
  这样的嵌套位置;**单次运行封顶 2000 条**——forever 循环任务否则会写爆表;
- 运行上下文 ctx(run_id/job_id/job_name/device_name)由 TaskManager 生成,
  经 create_worker(serial, params, ctx=None) 传入 worker(扩展点向后兼容);
- 接口:/api/step_logs(过滤+分页)、/runs(按运行归组)、/filters(下拉选项)、
  /download(CSV,带 BOM);
- 前端:日志页拆成「文件日志 / 步骤明细」子分栏 + static/admin/steplog.js。

红线:新表自动进备份覆盖清单(SUMMARY_TABLES 由元数据派生),已补
TABLE_LABELS 中文标签,导出实测 14 张表、coverage_missing 为空。

文档:DATA_MODEL §2.8/§1、API §10.2、ARCHITECTURE §1.1/§2.2/§3.1、
DEVELOPMENT §5.2 与红线表、DEPLOY §5.2、TASK_DEV §8.3、README。
This commit is contained in:
2026-09-16 08:58:43 +08:00
parent 2c32c397c8
commit 621d48c800
20 changed files with 838 additions and 38 deletions
+114 -3
View File
@@ -1,7 +1,8 @@
"""管理域 API:用户管理/日志查看。"""
import time
from io import BytesIO
from flask import Blueprint, jsonify, request
from flask import Blueprint, jsonify, request, send_file
from flask_login import current_user
from flask import session
@@ -125,8 +126,6 @@ def api_logs_download():
用 BytesIO 发送而不是 send_file(路径):Windows 上流式发送时文件句柄可能
到 close 仍未释放,而日志文件正被日志线程持续写入,按路径发容易踩锁。
"""
from io import BytesIO
from flask import send_file
name = request.args.get("file", "")
text, err = logger.read_log_text(name, **_log_filters())
if err:
@@ -139,5 +138,117 @@ def api_logs_download():
mimetype="text/plain; charset=utf-8")
# ================== API:任务步骤明细 ==================
#
# 数据源是 task_step_log 表(结构化),不是 logs/task.log 文本——所以能按设备/
# 任务/时间/结果过滤。写入侧见 core/step_log.py(异步写线程 + 保留期清理)。
def _step_filters():
"""从 query string 取步骤明细的过滤条件(列表与下载共用)。"""
return dict(serial=request.args.get("serial", ""),
job_id=request.args.get("job_id", ""),
result=request.args.get("result", ""),
run_id=request.args.get("run_id", ""),
keyword=request.args.get("q", ""),
since=logger.norm_ts(request.args.get("since", "")),
until=logger.norm_ts(request.args.get("until", ""), end=True))
def _step_to_dict(r):
return {"id": r.id, "run_id": r.run_id, "job_id": r.job_id,
"job_name": r.job_name, "serial": r.serial,
"device_name": r.device_name, "step_path": r.step_path,
"step_label": r.step_label, "step_type": r.step_type,
"selector": r.selector, "result": r.result, "detail": r.detail,
"duration_ms": r.duration_ms, "created_at": r.created_at}
@bp.route("/api/step_logs")
@perm_required(PERM_LOGS)
def api_step_logs():
"""任务步骤明细(分页,最近的在前)。"""
from core import step_log
try:
limit = int(request.args.get("limit", 200))
offset = int(request.args.get("offset", 0))
except (TypeError, ValueError):
limit, offset = 200, 0
rows, total = step_log.query(limit=limit, offset=offset, **_step_filters())
return jsonify({"ok": True, "rows": [_step_to_dict(r) for r in rows],
"total": total, "limit": limit, "offset": offset,
"stats": step_log.stats(),
"keep_days": step_log.KEEP_DAYS,
"max_rows_per_run": step_log.MAX_ROWS_PER_RUN})
@bp.route("/api/step_logs/runs")
@perm_required(PERM_LOGS)
def api_step_logs_runs():
"""按 run_id 归组的一次运行概览(哪次运行、几步、失败几步)。"""
from core import step_log
f = _step_filters()
rows = step_log.runs(serial=f["serial"], job_id=f["job_id"],
since=f["since"], until=f["until"],
limit=request.args.get("limit", 100))
return jsonify({"ok": True, "runs": [
{"run_id": r.run_id, "job_id": r.job_id, "job_name": r.job_name,
"serial": r.serial, "device_name": r.device_name,
"started_at": r.started_at, "ended_at": r.ended_at,
"steps": int(r.steps or 0), "failures": int(r.failures or 0)}
for r in rows]})
@bp.route("/api/step_logs/filters")
@perm_required(PERM_LOGS)
def api_step_logs_filters():
"""筛选下拉的可选项:**只在明细里出现过的**设备与任务(按数据自洽,不依赖
设备池/任务表,也不要求调用方另有 task/device 权限)。"""
from core import step_log
from core.models import TaskStepLog, db
devices = (db.session.query(TaskStepLog.serial, TaskStepLog.device_name)
.filter(TaskStepLog.serial != "").distinct().limit(500).all())
jobs = (db.session.query(TaskStepLog.job_id, TaskStepLog.job_name)
.filter(TaskStepLog.job_id != "").distinct().limit(500).all())
return jsonify({"ok": True,
"devices": sorted(({"serial": s, "device_name": n}
for s, n in devices),
key=lambda x: x["device_name"] or x["serial"]),
"jobs": sorted(({"job_id": i, "job_name": n} for i, n in jobs),
key=lambda x: x["job_name"] or x["job_id"]),
"results": ["ok", "miss", "error", "unknown", "skip", "cap"],
"keep_days": step_log.KEEP_DAYS,
"max_rows_per_run": step_log.MAX_ROWS_PER_RUN})
@bp.route("/api/step_logs/download")
@perm_required(PERM_LOGS)
def api_step_logs_download():
"""导出步骤明细为 CSV(带同样的过滤条件)。
加 UTF-8 BOM:不加的话 Excel 打开中文是乱码(这是给运维看的表,
大概率会被 Excel 打开)。
"""
import csv
from io import StringIO
from core import step_log
f = _step_filters()
# 导出上限:和列表页共用同一次查询,最多 2 万行(再多请缩小时间范围)
rows, total = step_log.query(limit=20000, offset=0, **f)
buf = StringIO()
w = csv.writer(buf)
w.writerow(["时间", "设备", "设备名", "任务", "运行ID", "步骤路径", "步骤",
"类型", "选择器", "结果", "耗时(ms)", "详情"])
for r in reversed(rows): # 导出的时间序与页面相反:文件里按正序更好读
w.writerow([r.created_at, r.serial, r.device_name, r.job_name,
r.run_id, r.step_path, r.step_label, r.step_type,
r.selector, r.result, r.duration_ms, r.detail])
data = buf.getvalue().encode("utf-8-sig") # utf-8-sig = UTF-8 带 BOM(Excel 中文不乱码)
fname = f"step_log_{time.strftime('%Y%m%d_%H%M%S')}.csv"
_log.info("导出步骤明细: %d 行(命中 %d)by %s", len(rows), total,
getattr(current_user, "username", ""))
return send_file(BytesIO(data), as_attachment=True, download_name=fname,
mimetype="text/csv; charset=utf-8")
# ================== API:运行控制 ==================