Kris
36d0add930
本次提交包含多维度功能增强： 1. 新增TaskRecycleCleanupHandler，实现过期软删除任务物理清理能力 2. 扩展任务统计模型，新增死信任务计数并完善统计逻辑 3. 新增异常任务聚合查询接口，支持按死信/连续失败/长期未执行分类返回 4. 实现任务批量暂停/恢复/软删除操作 5. 扩展运行日志与任务查询过滤条件，新增handler_name维度 6. 优化健康检查逻辑，支持动态健康窗口并返回配置快照 7. 完善数据映射与DTO定义，补充缺失字段与类型支持
236 lines
8.5 KiB
Python
236 lines
8.5 KiB
Python
"""领域模型(dataclass) ↔ Output DTO(Pydantic) 转换。
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规范 §6.1/§7.3（对齐 ``external_systems/use_cases/mappers.py``）：
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- persistence 返回领域模型 dataclass，service 返回 Output DTO
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- scheduler 域无敏感字段，mapper 仅做字段映射与 DateTime → ISO 字符串格式化
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- use_cases 层不调用 ORM 的 ``to_dict`` 方法
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DateTime 字段（``run_at`` / ``last_run_at`` / ``next_run_at`` / ``started_at`` /
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``finished_at`` / ``created_at`` / ``updated_at`` / ``deleted_at``）在 dataclass
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中以 naive UTC ``datetime`` 表达，mapper 通过 ``format_utc_datetime`` 统一格式化
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为 ISO 8601 字符串写入 Output DTO。
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"""
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from __future__ import annotations
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from typing import Any
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from yuxi.scheduler.core.models import (
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DailyStat,
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HandlerSummary,
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ScheduledTask,
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ScheduledTaskRunLog,
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)
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from yuxi.scheduler.use_cases.dto.scheduler import (
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CountByStatusOutput,
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DailyStatOutput,
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GetHealthOutput,
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HandlerSummaryOutput,
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ListAnomaliesOutput,
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ListDailyStatsOutput,
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ListHandlerSummaryOutput,
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ListUpcomingOutput,
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RunLogOutput,
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SchedulerConfigOutput,
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TaskOutput,
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TriggerTaskOutput,
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)
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from yuxi.utils.datetime_utils import format_utc_datetime
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__all__ = [
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"daily_stat_to_dict",
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"to_count_by_status_output",
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"to_daily_stat_output",
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"to_get_health_output",
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"to_handler_summary_output",
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"to_list_anomalies_output",
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"to_list_daily_stats_output",
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"to_run_log_output",
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"to_task_output",
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"to_trigger_task_output",
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]
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def to_task_output(task: ScheduledTask) -> TaskOutput:
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"""``ScheduledTask`` dataclass → ``TaskOutput`` DTO。
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DateTime 字段通过 ``format_utc_datetime`` 格式化为 ISO 8601 字符串。
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"""
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return TaskOutput(
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task_id=task.task_id,
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handler_name=task.handler_name,
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owner_scope=task.owner_scope,
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owner_id=task.owner_id,
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schedule_kind=task.schedule_kind,
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cron_expression=task.cron_expression,
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run_at=format_utc_datetime(task.run_at),
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tz=task.tz,
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payload=task.payload or {},
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enabled=task.enabled,
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delete_after_run=task.delete_after_run,
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block_strategy=task.block_strategy,
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stagger_seconds=task.stagger_seconds,
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consecutive_errors=task.consecutive_errors,
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status=task.status,
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last_run_at=format_utc_datetime(task.last_run_at),
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next_run_at=format_utc_datetime(task.next_run_at),
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last_error=task.last_error,
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created_by=task.created_by,
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updated_by=task.updated_by,
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id=task.id,
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created_at=format_utc_datetime(task.created_at),
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updated_at=format_utc_datetime(task.updated_at),
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is_deleted=task.is_deleted,
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deleted_at=format_utc_datetime(task.deleted_at),
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)
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def to_trigger_task_output(task: ScheduledTask, run_id: str) -> TriggerTaskOutput:
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"""``ScheduledTask`` dataclass + run_id → ``TriggerTaskOutput`` DTO。"""
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return TriggerTaskOutput(run_id=run_id, task=to_task_output(task))
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def to_run_log_output(log: ScheduledTaskRunLog) -> RunLogOutput:
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"""``ScheduledTaskRunLog`` dataclass → ``RunLogOutput`` DTO。
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计算派生字段 ``duration_seconds``：当 ``started_at`` 与 ``finished_at``
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均非 None 时返回秒级执行时长，否则返回 None（running 状态）。
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"""
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duration_seconds: float | None = None
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if log.started_at is not None and log.finished_at is not None:
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delta = log.finished_at - log.started_at
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duration_seconds = round(delta.total_seconds(), 3)
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return RunLogOutput(
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task_id=log.task_id,
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handler_name=log.handler_name,
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run_id=log.run_id,
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triggered_by=log.triggered_by,
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status=log.status,
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error_message=log.error_message,
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output=log.output,
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started_at=format_utc_datetime(log.started_at),
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finished_at=format_utc_datetime(log.finished_at),
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duration_seconds=duration_seconds,
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created_by=log.created_by,
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updated_by=log.updated_by,
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id=log.id,
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created_at=format_utc_datetime(log.created_at),
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updated_at=format_utc_datetime(log.updated_at),
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is_deleted=log.is_deleted,
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deleted_at=format_utc_datetime(log.deleted_at),
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)
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def to_handler_summary_output(summary: HandlerSummary) -> HandlerSummaryOutput:
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"""``HandlerSummary`` dataclass → ``HandlerSummaryOutput`` DTO。
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``active_task_count`` / ``dead_letter_count`` 已由仓储层聚合，此处直接透传。
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"""
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return HandlerSummaryOutput(
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handler_name=summary.name,
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task_count=summary.task_count,
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active_task_count=summary.active_task_count,
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dead_letter_count=summary.dead_letter_count,
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last_active_at=format_utc_datetime(summary.last_active_at),
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)
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def to_count_by_status_output(dist: dict[str, int]) -> CountByStatusOutput:
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"""状态分布字典 → ``CountByStatusOutput`` DTO。
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仓储层 ``count_by_status`` 返回 ``{status: count}``，未出现的状态不在字典中，
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这里统一补 0。
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"""
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return CountByStatusOutput(
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active=dist.get("active", 0),
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paused=dist.get("paused", 0),
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dead_letter=dist.get("dead_letter", 0),
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)
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def to_get_health_output(
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*,
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last_tick_at: Any,
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active_task_count: int,
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dead_letter_count: int,
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running_count: int,
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healthy: bool,
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config: SchedulerConfigOutput,
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) -> GetHealthOutput:
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"""构造 ``GetHealthOutput`` DTO。
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``healthy`` 由调用方按 worker 活跃度判断后传入；``config`` 为从
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``app_config`` 读取的当前生效配置快照。
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"""
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return GetHealthOutput(
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status="healthy" if healthy else "unhealthy",
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last_tick_at=format_utc_datetime(last_tick_at),
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active_task_count=active_task_count,
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dead_letter_count=dead_letter_count,
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running_count=running_count,
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config=config,
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)
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def daily_stat_to_dict(stat: DailyStat) -> dict[str, Any]:
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"""``DailyStat`` dataclass → dict（供指标导出使用）。
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指标服务（``SchedulerMetricService``）从 ``DailyStat`` 提取字段构造
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Prometheus 文本格式，此处提供统一的 dict 转换避免重复字段读取。
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"""
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return {
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"stat_date": stat.stat_date,
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"handler_name": stat.handler_name,
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"success_count": stat.success_count,
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"failure_count": stat.failure_count,
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"timeout_count": stat.timeout_count,
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"dead_letter_count": stat.dead_letter_count,
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}
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def to_daily_stat_output(stat: DailyStat) -> DailyStatOutput:
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"""``DailyStat`` dataclass → ``DailyStatOutput`` DTO。
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计算派生字段 ``total_count`` / ``success_rate``，``stat_date`` 格式化为
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ISO 日期字符串（``YYYY-MM-DD``）。``dead_letter_count`` 视为失败终态，
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纳入 ``total_count`` 分母以反映真实成功率。
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"""
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total_count = stat.success_count + stat.failure_count + stat.timeout_count + stat.dead_letter_count
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success_rate = stat.success_count / total_count if total_count > 0 else 0.0
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return DailyStatOutput(
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stat_date=stat.stat_date.isoformat(),
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handler_name=stat.handler_name,
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success_count=stat.success_count,
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failure_count=stat.failure_count,
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timeout_count=stat.timeout_count,
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dead_letter_count=stat.dead_letter_count,
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total_count=total_count,
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success_rate=success_rate,
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)
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def to_list_daily_stats_output(stats: list[DailyStat]) -> ListDailyStatsOutput:
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"""``DailyStat`` 列表 → ``ListDailyStatsOutput`` DTO。"""
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return ListDailyStatsOutput(items=[to_daily_stat_output(s) for s in stats])
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def to_list_upcoming_output(tasks: list[ScheduledTask]) -> ListUpcomingOutput:
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"""``ScheduledTask`` 列表 → ``ListUpcomingOutput`` DTO。"""
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return ListUpcomingOutput(items=[to_task_output(t) for t in tasks])
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def to_list_anomalies_output(groups: dict[str, list[ScheduledTask]]) -> ListAnomaliesOutput:
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"""异常任务分组映射 → ``ListAnomaliesOutput`` DTO。"""
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return ListAnomaliesOutput(
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dead_letter=[to_task_output(t) for t in groups.get("dead_letter", [])],
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consecutive_failure=[to_task_output(t) for t in groups.get("consecutive_failure", [])],
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stale=[to_task_output(t) for t in groups.get("stale", [])],
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)
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def to_list_handler_summary_output(
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summaries: list[HandlerSummary],
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) -> ListHandlerSummaryOutput:
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"""``HandlerSummary`` 列表 → ``ListHandlerSummaryOutput`` DTO。"""
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return ListHandlerSummaryOutput(items=[to_handler_summary_output(s) for s in summaries])
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