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feat: add daily model statistics aggregation with stats_daily_model table
This commit is contained in:
12
src/services/cache/aware_scheduler.py
vendored
12
src/services/cache/aware_scheduler.py
vendored
@@ -589,14 +589,14 @@ class CacheAwareScheduler:
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target_format = normalize_api_format(api_format)
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# 0. 解析 model_name 到 GlobalModel(支持直接匹配和别名匹配,使用 ModelCacheService)
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# 0. 解析 model_name 到 GlobalModel(支持直接匹配和映射名匹配,使用 ModelCacheService)
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global_model = await ModelCacheService.resolve_global_model_by_name_or_alias(db, model_name)
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if not global_model:
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logger.warning(f"GlobalModel not found: {model_name}")
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raise ModelNotSupportedException(model=model_name)
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# 使用 GlobalModel.id 作为缓存亲和性的模型标识,确保别名和规范名都能命中同一个缓存
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# 使用 GlobalModel.id 作为缓存亲和性的模型标识,确保映射名和规范名都能命中同一个缓存
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global_model_id: str = str(global_model.id)
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requested_model_name = model_name
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resolved_model_name = str(global_model.name)
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@@ -751,19 +751,19 @@ class CacheAwareScheduler:
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支持两种匹配方式:
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1. 直接匹配 GlobalModel.name
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2. 通过 ModelCacheService 匹配别名(全局查找)
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2. 通过 ModelCacheService 匹配映射名(全局查找)
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Args:
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db: 数据库会话
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provider: Provider 对象
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model_name: 模型名称(可以是 GlobalModel.name 或别名)
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model_name: 模型名称(可以是 GlobalModel.name 或映射名)
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is_stream: 是否是流式请求,如果为 True 则同时检查流式支持
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capability_requirements: 能力需求(可选),用于检查模型是否支持所需能力
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Returns:
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(is_supported, skip_reason, supported_capabilities) - 是否支持、跳过原因、模型支持的能力列表
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"""
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# 使用 ModelCacheService 解析模型名称(支持别名)
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# 使用 ModelCacheService 解析模型名称(支持映射名)
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global_model = await ModelCacheService.resolve_global_model_by_name_or_alias(db, model_name)
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if not global_model:
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@@ -914,7 +914,7 @@ class CacheAwareScheduler:
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db: 数据库会话
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providers: Provider 列表
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target_format: 目标 API 格式
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model_name: 模型名称(用户请求的名称,可能是别名)
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model_name: 模型名称(用户请求的名称,可能是映射名)
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affinity_key: 亲和性标识符(通常为API Key ID)
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resolved_model_name: 解析后的 GlobalModel.name(用于 Key.allowed_models 校验)
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max_candidates: 最大候选数
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32
src/services/cache/model_cache.py
vendored
32
src/services/cache/model_cache.py
vendored
@@ -198,7 +198,7 @@ class ModelCacheService:
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provider_id: Optional[str] = None,
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global_model_id: Optional[str] = None,
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provider_model_name: Optional[str] = None,
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provider_model_aliases: Optional[list] = None,
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provider_model_mappings: Optional[list] = None,
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) -> None:
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"""清除 Model 缓存
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@@ -207,7 +207,7 @@ class ModelCacheService:
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provider_id: Provider ID(用于清除 provider_global 缓存)
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global_model_id: GlobalModel ID(用于清除 provider_global 缓存)
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provider_model_name: provider_model_name(用于清除 resolve 缓存)
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provider_model_aliases: 映射名称列表(用于清除 resolve 缓存)
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provider_model_mappings: 映射名称列表(用于清除 resolve 缓存)
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"""
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# 清除 model:id 缓存
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await CacheService.delete(f"model:id:{model_id}")
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@@ -222,16 +222,16 @@ class ModelCacheService:
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else:
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logger.debug(f"Model 缓存已清除: {model_id}")
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# 清除 resolve 缓存(provider_model_name 和 aliases 可能都被用作解析 key)
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# 清除 resolve 缓存(provider_model_name 和 mappings 可能都被用作解析 key)
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resolve_keys_to_clear = []
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if provider_model_name:
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resolve_keys_to_clear.append(provider_model_name)
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if provider_model_aliases:
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for alias_entry in provider_model_aliases:
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if isinstance(alias_entry, dict):
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alias_name = alias_entry.get("name", "").strip()
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if alias_name:
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resolve_keys_to_clear.append(alias_name)
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if provider_model_mappings:
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for mapping_entry in provider_model_mappings:
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if isinstance(mapping_entry, dict):
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mapping_name = mapping_entry.get("name", "").strip()
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if mapping_name:
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resolve_keys_to_clear.append(mapping_name)
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for key in resolve_keys_to_clear:
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await CacheService.delete(f"global_model:resolve:{key}")
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@@ -261,8 +261,8 @@ class ModelCacheService:
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2. 通过 provider_model_name 匹配(查询 Model 表)
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3. 直接匹配 GlobalModel.name(兜底)
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注意:此方法不使用 provider_model_aliases 进行全局解析。
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provider_model_aliases 是 Provider 级别的别名配置,只在特定 Provider 上下文中生效,
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注意:此方法不使用 provider_model_mappings 进行全局解析。
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provider_model_mappings 是 Provider 级别的映射配置,只在特定 Provider 上下文中生效,
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由 resolve_provider_model() 处理。
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Args:
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@@ -301,9 +301,9 @@ class ModelCacheService:
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logger.debug(f"GlobalModel 缓存命中(映射解析): {normalized_name}")
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return ModelCacheService._dict_to_global_model(cached_data)
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# 2. 通过 provider_model_name 匹配(不考虑 provider_model_aliases)
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# 重要:provider_model_aliases 是 Provider 级别的别名配置,只在特定 Provider 上下文中生效
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# 全局解析不应该受到某个 Provider 别名配置的影响
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# 2. 通过 provider_model_name 匹配(不考虑 provider_model_mappings)
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# 重要:provider_model_mappings 是 Provider 级别的映射配置,只在特定 Provider 上下文中生效
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# 全局解析不应该受到某个 Provider 映射配置的影响
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# 例如:Provider A 把 "haiku" 映射到 "sonnet",不应该影响 Provider B 的 "haiku" 解析
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from src.models.database import Provider
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@@ -401,7 +401,7 @@ class ModelCacheService:
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"provider_id": model.provider_id,
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"global_model_id": model.global_model_id,
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"provider_model_name": model.provider_model_name,
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"provider_model_aliases": getattr(model, "provider_model_aliases", None),
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"provider_model_mappings": getattr(model, "provider_model_mappings", None),
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"is_active": model.is_active,
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"is_available": model.is_available if hasattr(model, "is_available") else True,
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"price_per_request": (
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@@ -424,7 +424,7 @@ class ModelCacheService:
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provider_id=model_dict["provider_id"],
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global_model_id=model_dict["global_model_id"],
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provider_model_name=model_dict["provider_model_name"],
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provider_model_aliases=model_dict.get("provider_model_aliases"),
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provider_model_mappings=model_dict.get("provider_model_mappings"),
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is_active=model_dict["is_active"],
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is_available=model_dict.get("is_available", True),
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price_per_request=model_dict.get("price_per_request"),
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@@ -443,7 +443,7 @@ class ModelCostService:
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Args:
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provider: Provider 对象或提供商名称
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model: 用户请求的模型名(可能是 GlobalModel.name 或别名)
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model: 用户请求的模型名(可能是 GlobalModel.name 或映射名)
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Returns:
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按次计费价格,如果没有配置则返回 None
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@@ -84,11 +84,11 @@ class ModelMapperMiddleware:
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获取模型映射
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简化后的逻辑:
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1. 通过 GlobalModel.name 或别名解析 GlobalModel
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1. 通过 GlobalModel.name 或映射名解析 GlobalModel
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2. 找到 GlobalModel 后,查找该 Provider 的 Model 实现
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Args:
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source_model: 用户请求的模型名(可以是 GlobalModel.name 或别名)
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source_model: 用户请求的模型名(可以是 GlobalModel.name 或映射名)
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provider_id: 提供商ID (UUID)
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Returns:
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@@ -101,7 +101,7 @@ class ModelMapperMiddleware:
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mapping = None
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# 步骤 1: 解析 GlobalModel(支持别名)
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# 步骤 1: 解析 GlobalModel(支持映射名)
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global_model = await ModelCacheService.resolve_global_model_by_name_or_alias(
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self.db, source_model
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)
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@@ -51,7 +51,7 @@ class ModelService:
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provider_id=provider_id,
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global_model_id=model_data.global_model_id,
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provider_model_name=model_data.provider_model_name,
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provider_model_aliases=model_data.provider_model_aliases,
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provider_model_mappings=model_data.provider_model_mappings,
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price_per_request=model_data.price_per_request,
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tiered_pricing=model_data.tiered_pricing,
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supports_vision=model_data.supports_vision,
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@@ -153,9 +153,9 @@ class ModelService:
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if not model:
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raise NotFoundException(f"模型 {model_id} 不存在")
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# 保存旧的别名,用于清除缓存
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# 保存旧的映射,用于清除缓存
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old_provider_model_name = model.provider_model_name
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old_provider_model_aliases = model.provider_model_aliases
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old_provider_model_mappings = model.provider_model_mappings
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# 更新字段
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update_data = model_data.model_dump(exclude_unset=True)
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@@ -174,26 +174,26 @@ class ModelService:
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db.refresh(model)
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# 清除 Redis 缓存(异步执行,不阻塞返回)
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# 先清除旧的别名缓存
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# 先清除旧的映射缓存
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asyncio.create_task(
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ModelCacheService.invalidate_model_cache(
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model_id=model.id,
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provider_id=model.provider_id,
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global_model_id=model.global_model_id,
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provider_model_name=old_provider_model_name,
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provider_model_aliases=old_provider_model_aliases,
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provider_model_mappings=old_provider_model_mappings,
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)
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)
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# 再清除新的别名缓存(如果有变化)
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# 再清除新的映射缓存(如果有变化)
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if (model.provider_model_name != old_provider_model_name or
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model.provider_model_aliases != old_provider_model_aliases):
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model.provider_model_mappings != old_provider_model_mappings):
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asyncio.create_task(
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ModelCacheService.invalidate_model_cache(
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model_id=model.id,
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provider_id=model.provider_id,
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global_model_id=model.global_model_id,
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provider_model_name=model.provider_model_name,
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provider_model_aliases=model.provider_model_aliases,
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provider_model_mappings=model.provider_model_mappings,
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)
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)
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@@ -246,7 +246,7 @@ class ModelService:
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"provider_id": model.provider_id,
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"global_model_id": model.global_model_id,
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"provider_model_name": model.provider_model_name,
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"provider_model_aliases": model.provider_model_aliases,
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"provider_model_mappings": model.provider_model_mappings,
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}
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try:
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@@ -260,7 +260,7 @@ class ModelService:
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provider_id=cache_info["provider_id"],
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global_model_id=cache_info["global_model_id"],
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provider_model_name=cache_info["provider_model_name"],
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provider_model_aliases=cache_info["provider_model_aliases"],
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provider_model_mappings=cache_info["provider_model_mappings"],
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)
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)
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@@ -297,7 +297,7 @@ class ModelService:
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provider_id=model.provider_id,
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global_model_id=model.global_model_id,
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provider_model_name=model.provider_model_name,
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provider_model_aliases=model.provider_model_aliases,
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provider_model_mappings=model.provider_model_mappings,
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)
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)
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@@ -390,7 +390,7 @@ class ModelService:
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provider_id=model.provider_id,
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global_model_id=model.global_model_id,
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provider_model_name=model.provider_model_name,
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provider_model_aliases=model.provider_model_aliases,
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provider_model_mappings=model.provider_model_mappings,
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# 原始配置值(可能为空)
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price_per_request=model.price_per_request,
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tiered_pricing=model.tiered_pricing,
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@@ -259,6 +259,9 @@ class CleanupScheduler:
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StatsAggregatorService.aggregate_daily_stats(
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db, current_date_local
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)
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StatsAggregatorService.aggregate_daily_model_stats(
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db, current_date_local
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)
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for (user_id,) in users:
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try:
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StatsAggregatorService.aggregate_user_daily_stats(
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@@ -291,6 +294,7 @@ class CleanupScheduler:
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yesterday_local = today_local - timedelta(days=1)
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StatsAggregatorService.aggregate_daily_stats(db, yesterday_local)
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StatsAggregatorService.aggregate_daily_model_stats(db, yesterday_local)
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users = db.query(DBUser.id).filter(DBUser.is_active.is_(True)).all()
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for (user_id,) in users:
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@@ -16,6 +16,7 @@ from src.models.database import (
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ApiKey,
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RequestCandidate,
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StatsDaily,
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StatsDailyModel,
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StatsSummary,
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StatsUserDaily,
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Usage,
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@@ -219,6 +220,120 @@ class StatsAggregatorService:
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logger.info(f"[StatsAggregator] 聚合日期 {date.date()} 完成: {computed['total_requests']} 请求")
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return stats
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@staticmethod
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def aggregate_daily_model_stats(db: Session, date: datetime) -> list[StatsDailyModel]:
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"""聚合指定日期的模型维度统计数据
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Args:
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db: 数据库会话
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date: 要聚合的业务日期
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Returns:
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StatsDailyModel 记录列表
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"""
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day_start, day_end = _get_business_day_range(date)
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# 按模型分组统计
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model_stats = (
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db.query(
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Usage.model,
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func.count(Usage.id).label("total_requests"),
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func.sum(Usage.input_tokens).label("input_tokens"),
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func.sum(Usage.output_tokens).label("output_tokens"),
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func.sum(Usage.cache_creation_input_tokens).label("cache_creation_tokens"),
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func.sum(Usage.cache_read_input_tokens).label("cache_read_tokens"),
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func.sum(Usage.total_cost_usd).label("total_cost"),
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func.avg(Usage.response_time_ms).label("avg_response_time"),
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)
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.filter(and_(Usage.created_at >= day_start, Usage.created_at < day_end))
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.group_by(Usage.model)
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.all()
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)
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results = []
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for stat in model_stats:
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if not stat.model:
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continue
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existing = (
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db.query(StatsDailyModel)
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.filter(and_(StatsDailyModel.date == day_start, StatsDailyModel.model == stat.model))
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.first()
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)
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if existing:
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record = existing
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else:
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record = StatsDailyModel(
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id=str(uuid.uuid4()), date=day_start, model=stat.model
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)
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record.total_requests = stat.total_requests or 0
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record.input_tokens = int(stat.input_tokens or 0)
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record.output_tokens = int(stat.output_tokens or 0)
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record.cache_creation_tokens = int(stat.cache_creation_tokens or 0)
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record.cache_read_tokens = int(stat.cache_read_tokens or 0)
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record.total_cost = float(stat.total_cost or 0)
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record.avg_response_time_ms = float(stat.avg_response_time or 0)
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if not existing:
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db.add(record)
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results.append(record)
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db.commit()
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logger.info(
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f"[StatsAggregator] 聚合日期 {date.date()} 模型统计完成: {len(results)} 个模型"
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)
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return results
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@staticmethod
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def get_daily_model_stats(db: Session, start_date: datetime, end_date: datetime) -> list[dict]:
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"""获取日期范围内的模型统计数据(优先使用预聚合)
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Args:
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db: 数据库会话
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start_date: 开始日期 (UTC)
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end_date: 结束日期 (UTC)
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Returns:
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模型统计数据列表
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"""
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from zoneinfo import ZoneInfo
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app_tz = ZoneInfo(APP_TIMEZONE)
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# 从预聚合表获取历史数据
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stats = (
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db.query(StatsDailyModel)
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.filter(and_(StatsDailyModel.date >= start_date, StatsDailyModel.date < end_date))
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.order_by(StatsDailyModel.date.asc(), StatsDailyModel.total_cost.desc())
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.all()
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)
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# 转换为字典格式,按日期分组
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result = []
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for stat in stats:
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# 转换日期为业务时区
|
||||
if stat.date.tzinfo is None:
|
||||
date_utc = stat.date.replace(tzinfo=timezone.utc)
|
||||
else:
|
||||
date_utc = stat.date.astimezone(timezone.utc)
|
||||
date_str = date_utc.astimezone(app_tz).date().isoformat()
|
||||
|
||||
result.append({
|
||||
"date": date_str,
|
||||
"model": stat.model,
|
||||
"requests": stat.total_requests,
|
||||
"tokens": (
|
||||
stat.input_tokens + stat.output_tokens +
|
||||
stat.cache_creation_tokens + stat.cache_read_tokens
|
||||
),
|
||||
"cost": stat.total_cost,
|
||||
"avg_response_time": stat.avg_response_time_ms / 1000.0 if stat.avg_response_time_ms else 0,
|
||||
})
|
||||
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def aggregate_user_daily_stats(
|
||||
db: Session, user_id: str, date: datetime
|
||||
@@ -497,6 +612,7 @@ class StatsAggregatorService:
|
||||
current_date = start_date
|
||||
while current_date < today_local:
|
||||
StatsAggregatorService.aggregate_daily_stats(db, current_date)
|
||||
StatsAggregatorService.aggregate_daily_model_stats(db, current_date)
|
||||
count += 1
|
||||
current_date += timedelta(days=1)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user