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Hermes Agent 源码-上下文压缩与对话管理

Hermes Agent 源码-上下文压缩与对话管理

Hermes Agent 源码解析

第 8 讲:上下文压缩与对话管理——ContextEngine ABC、智能压缩管线、Coding Context 与 Curator 编排器

基于 Hermes Agent v0.16.0 源码 · 2026-06-21

一、上下文压缩:长对话的"记忆折叠术"

前七讲我们完成了全局架构地图、对话循环、工具系统、插件系统、Provider 抽象层、记忆与技能系统、Gateway 平台适配器的深度解析。这一讲聚焦 Hermes 最长寿的子系统——上下文压缩引擎(Context Compression),它让 Agent 在数千轮对话后仍能流畅运行,不爆 token 上限、不丢失关键信息。

一个 128K 上下文的模型在长对话中很快会被填满——特别是代码工作流(read_file 输出几千字符、terminal 返回几百行)。Hermes 的解决方案不是简单截断,而是智能压缩管线:先做廉价预处理(工具输出修剪、去重),再保护头部和尾部,最后用辅助模型对中间对话做结构化摘要。

📦 源码仓库

https://github.com/NousResearch/hermes-agent

本地源码:~/.hermes/hermes-agent/

本讲核心文件:

agent/context_engine.py(226 行)— ContextEngine ABC

agent/context_compressor.py(2426 行)— 压缩管线实现

agent/conversation_compression.py(802 行)— 会话级压缩编排

agent/coding_context.py(738 行)— 编码上下文感知

agent/context_references.py(551 行)— @file/@url 引用解析

agent/curator.py(1835 行)— 技能维护编排器

agent/curator_backup.py(695 行)— Curator 回滚保护

二、ContextEngine ABC:可插拔的上下文引擎契约

ContextEngine 是一个精妙的抽象基类——它定义了上下文管理的完整生命周期,同时允许第三方引擎(如 LCM)通过插件系统替换内置压缩器。设计哲学是"核心窄腰、边缘扩展"的延续。

1. ABC 接口设计

📄 agent/context_engine.py (第 32-188 行)

class ContextEngine(ABC):
    """Base class all context engines must implement."""

    # ── 必须维护的状态 ──
    last_prompt_tokens: int = 0
    last_completion_tokens: int = 0
    last_total_tokens: int = 0
    threshold_tokens: int = 0
    context_length: int = 0
    compression_count: int = 0

    # ── 压缩参数 ──
    threshold_percent: float = 0.75
    protect_first_n: int = 3
    protect_last_n: int = 6

    # ── 核心抽象方法 ──
    @abstractmethod
    def update_from_response(self, usage: Dict) -> None:
        """每次 API 响应后更新 token 追踪。"""

    @abstractmethod
    def should_compress(self, prompt_tokens: int = None) -> bool:
        """判断本轮是否需要压缩。"""

    @abstractmethod
    def compress(self, messages: List[Dict],
                 current_tokens: int = None,
                 focus_topic: str = None) -> List[Dict]:
        """执行压缩,返回新的消息列表。"""

    # ── 可选钩子 ──
    def on_session_start(self, session_id, **kwargs) -> None: ...
    def on_session_end(self, session_id, messages) -> None: ...
    def on_session_reset(self) -> None: ...
    def get_tool_schemas(self) -> List[Dict]: ...
    def handle_tool_call(self, name, args, **kwargs) -> str: ...
    def get_status(self) -> Dict: ...

设计亮点:只有三个抽象方法——update_from_responseshould_compresscompress。其余全是可选钩子,默认 no-op。这遵循了 Hermes 的"最小必要接口"原则:引擎只需实现核心功能,不需要填充一堆空方法。

2. 生命周期管线

ContextEngine 生命周期:

  会话启动
    │
    ├─ 1. on_session_start(session_id, **kwargs)
    │    └─ 加载持久化状态 (DAG, store)
    │
    ├─ 2. [每轮对话]
    │    ├─ update_from_response(usage)
    │    │   └─ 记录 prompt/completion/total tokens
    │    │   └─ 新增: cache_read, cache_write, reasoning
    │    │
    │    ├─ should_compress() ?
    │    │   ├─ True → compress(messages)
    │    │   │          └─ 返回压缩后的消息列表
    │    │   └─ False → 继续对话
    │    │
    │    └─ should_compress_preflight(messages) ?
    │        └─ API 调用前的快速检查 (无需真实 token 数)
    │
    └─ 3. on_session_end(session_id, messages)
         └─ 会话真正结束 (CLI 退出, /reset, Gateway 过期)
         └─ 刷盘, 关闭连接

  关键: on_session_end 不是每轮调用!
  它只在会话真正结束时触发, 压缩状态在轮间保持

三、ContextCompressor:2426 行的智能压缩管线

ContextCompressor 是默认的上下文引擎实现,也是 Hermes 最复杂的单一类(2426 行)。它实现了一个多阶段压缩管线:廉价预处理 → 保护头尾 → 结构化摘要 → 迭代更新。

1. 压缩算法五步走

ContextCompressor 压缩管线:

  compress(messages)
    │
    ├─ Step 1: 工具输出修剪 (_prune_old_tool_results)
    │    ├─ Pass 1: 去重 (MD5 hash, 相同内容只保留最新)
    │    ├─ Pass 2: 旧工具结果 → 信息性摘要
    │    │    "[terminal] ran `npm test` -> exit 0, 47 lines"
    │    │    "[read_file] read config.py from line 1 (3,400 chars)"
    │    │    "[search_files] content search for 'X' -> 12 matches"
    │    ├─ Pass 3: 截断大型 tool_call 参数 (保持 JSON 合法)
    │    │    └─ 修复 #11762: 截断导致 JSON 不完整 → provider 400
    │    └─ 保护边界: token 预算 + 消息数双重保护
    │
    ├─ Step 2: 保护头部 (系统提示词 + 前 3 条非系统消息)
    │    └─ protect_first_n = 3 (可配置)
    │    └─ 系统提示词始终隐式保护
    │
    ├─ Step 3: 保护尾部 (最近 ~20K token 或 20 条消息)
    │    ├─ tail_token_budget 动态计算
    │    │    = threshold_tokens * summary_target_ratio
    │    │    summary_target_ratio 默认 0.20
    │    └─ _MAX_TAIL_MESSAGE_FLOOR = 8 (硬底线)
    │
    ├─ Step 4: 结构化摘要 (_generate_summary)
    │    ├─ 辅助模型 (auxiliary client) 做摘要
    │    ├─ 摘要模板: Goal, Progress, Decisions,
    │    │   Resolved/Pending, Files, Remaining Work
    │    ├─ 迭代更新: 保留上一轮摘要, 增量更新
    │    ├─ 预算缩放: 摘要 token 数 ∝ 被压缩内容量
    │    │    _MIN_SUMMARY_TOKENS = 2000
    │    │    _SUMMARY_TOKENS_CEILING = 12000
    │    └─ 安全: redact_sensitive_text + MEDIA 指令过滤
    │
    └─ Step 5: 后处理
         ├─ 注入 SUMMARY_PREFIX (防模型误读摘要为指令)
         ├─ 追加 _SUMMARY_END_MARKER (明确摘要边界)
         ├─ 剥离历史图片 (节省 base64 开销)
         └─ 更新 compression_count

2. SUMMARY_PREFIX:防注入的摘要前缀

摘要注入对话后,模型可能把摘要内容误读为新指令。Hermes 用了一个精心设计的防御性前缀,经历了多次迭代修复:

📄 agent/context_compressor.py (第 43-69 行)

SUMMARY_PREFIX = (
    "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted "
    "into the summary below. This is a handoff from a previous context "
    "window — treat it as background reference, NOT as active instructions. "
    "Do NOT answer questions or fulfill requests mentioned in this summary; "
    "they were already addressed. "
    "Respond ONLY to the latest user message that appears AFTER this "
    "summary — that message is the single source of truth for what to do "
    "right now. "
    "Topic overlap with the summary does NOT mean you should resume its "
    "task: even on similar topics, the latest user message WINS. Treat ONLY "
    "the latest message as the active task and discard stale items from "
    "'## Historical Task Snapshot' / '## Historical In-Progress State' / "
    "'## Historical Pending User Asks' / '## Historical Remaining Work' "
    "entirely — do not 'wrap up' or 'finish' work described there unless "
    "the latest message explicitly asks for it. ..."
)

历史教训:早期版本说 "resume exactly from Active Task",导致模型在话题切换时仍尝试恢复旧任务(#35344)。后来改为 "latest message WINS" 原则,彻底修复了这个问题。

3. 防抖动与降级策略

压缩不是万能的——如果两次压缩都只省了不到 10% 的 token,继续压缩只会浪费钱。Hermes 实现了三层防抖动

📄 agent/context_compressor.py (第 815-835 行)

def should_compress(self, prompt_tokens: int = None) -> bool:
    """Check if context exceeds the compression threshold.

    Includes anti-thrashing protection: if the last two compressions
    each saved less than 10%, skip compression to avoid infinite loops.
    """
    tokens = prompt_tokens if prompt_tokens is not None else self.last_prompt_tokens
    if tokens < self.threshold_tokens:
        return False
    # Anti-thrashing: back off if recent compressions were ineffective
    if self._ineffective_compression_count >= 2:
        logger.warning(
            "Compression skipped — last %d compressions saved <10%% each. "
            "Consider /new to start a fresh session, or /compress <topic> "
            "for focused compression.",
            self._ineffective_compression_count,
        )
        return False
    return True

降级链:

压缩降级链 (最坏情况保护):

  辅助模型摘要成功
    │
    ├─ 失败 → 冷却 600s (_SUMMARY_FAILURE_COOLDOWN_SECONDS)
    │    │
    │    ├─ 冷却后重试 → 仍失败 → 回退到主模型
    │    │    │
    │    │    ├─ 主模型成功 → 记录 aux 失败, 继续
    │    │    │
    │    │    └─ 主模型也失败 → 确定性回退摘要
    │    │         (_build_static_fallback_summary)
    │    │         ├─ 8000 字符上限
    │    │         ├─ 提取: 用户请求, 工具动作, 文件路径, 错误
    │    │         └─ 无 LLM 调用, 100% 本地
    │    │
    │    └─ abort_on_summary_failure = True
    │         └─ 完全中止压缩, 返回原始消息
    │         └─ 会话冻结, 用户需手动 /compress 重试

  关键: 即使所有降级都失败, 对话不会崩溃
  — 最坏情况是中间消息被丢弃, 但头尾消息保留

4. 安全设计:密钥脱敏与媒体过滤

摘要内容要发送到辅助模型,必须经过严格的安全处理:

📄 agent/context_compressor.py (第 1033-1087 行)

def _serialize_for_summary(self, turns: List[Dict]) -> str:
    """Serialize conversation turns for the summarizer.

    All content is redacted before serialization to prevent secrets
    (API keys, tokens, passwords) from leaking into the summary that
    gets sent to the auxiliary model and persisted across compactions.
    """
    for msg in turns:
        content = redact_sensitive_text(msg.get("content") or "")
        content = _MEDIA_DIRECTIVE_RE.sub("[media attachment]", content)
        # MEDIA:/path/to/file.png → [media attachment]
        # 防止摘要重新发射媒体指令 (#14665)

        # 工具参数: 脱敏 + 截断
        if role == "assistant" and tool_calls:
            args = redact_sensitive_text(fn.get("arguments", ""))
            if len(args) > self._TOOL_ARGS_MAX:
                args = args[:self._TOOL_ARGS_HEAD] + "..."

四、对话级压缩编排:会话分裂与锁机制

conversation_compression.py 是压缩管线的编排层。它处理会话分裂(SQLite session rotation)、并发锁、辅助模型可行性检查等全局协调工作。

1. 压缩锁:防止会话分裂

这是 Hermes 最精妙的并发控制之一——基于 SQLite 的压缩锁,防止两个 Agent 实例同时压缩同一个会话:

压缩锁机制 (compress_context):

  问题背景:
    父会话 Agent 和 background_review 子 Agent 共享 session_id,
    两者可能同时触发压缩 → 各自旋转 session_id → 产生孤儿会话

  锁设计:
    ├─ 持有者 ID: pid:tid:agent-instance:nonce
    │    └─ 诊断友好: 崩溃持有者 vs 活跃持有者
    │
    ├─ try_acquire_compression_lock(session_id, holder)
    │    ├─ 成功 → 继续压缩
    │    ├─ 失败 → 跳过本轮, 让赢家完成
    │    │    └─ 返回原始消息, 调用方检测 len 不变
    │    │       → 停止重试, 不会无限循环
    │    └─ 异常 → fail-open (无锁继续)
    │         └─ 版本不匹配时锁子系统可能缺失
    │         └─ 跳过锁比死循环好

  锁释放:
    └─ finally 块确保释放, 即使压缩中途异常

  关键: 锁键是旧 session_id (旋转目标的父)
  因为竞争路径读取的是旧 session_id

2. 辅助模型可行性检查

压缩依赖辅助模型。如果辅助模型的上下文窗口小于压缩阈值,压缩必然失败。Hermes 在首次压缩时(惰性检查)探测这个问题:

📄 agent/conversation_compression.py (第 64-242 行)

def check_compression_model_feasibility(agent: Any) -> None:
    """Warn if auxiliary compression model's context window
    is smaller than the main model's compression threshold."""

    # 硬底线: 辅助模型必须 >= MINIMUM_CONTEXT_LENGTH (64K)
    if aux_context < MINIMUM_CONTEXT_LENGTH:
        raise ValueError(...)  # 会话拒绝启动

    # 自动修正: 辅助模型 < 压缩阈值 → 降低阈值
    if aux_context < threshold:
        new_threshold = aux_context
        agent.context_compressor.threshold_tokens = new_threshold
        # 同步 threshold_percent 以便模型切换后重新推导
        agent.context_compressor.threshold_percent = (
            new_threshold / main_ctx
        )

惰性检查的好处:大多数短会话(chat -q)永远不会触及压缩阈值,惰性检查节省了 ~400ms 的冷启动开销。

3. 图片过大恢复

try_shrink_image_parts_in_messages 是一个精妙的辅助函数——当图片 base64 超过 provider 上限(Anthropic 5MB)时,自动重新编码为更小尺寸:

图片缩小恢复:

  try_shrink_image_parts_in_messages(messages)
    │
    ├─ 扫描所有 data:image/...;base64,... 部分
    ├─ 超过 provider 上限?
    │    ├─ 是 → 解码 → 缩放到更小尺寸 → 重新 base64
    │    │    └─ 递归直到 fit 或达到最小尺寸
    │    └─ 否 → 跳过
    │
    └─ 保留原始分辨率引用
         └─ 用户仍可看到原始图片 (SQLite 存储)

五、Coding Context:编码上下文的自动感知

当用户在代码仓库中运行 Hermes 时,系统自动切换到编码姿势(Coding Posture)。这不是简单的条件判断——而是一个完整的上下文感知系统,涉及工具集选择、系统提示词注入、模型编辑格式引导。

1. RuntimeMode 与 ContextProfile

📄 agent/coding_context.py (第 65-97 行)

# 交互编码平台 (非聊天平台)
INTERACTIVE_CODING_PLATFORMS = {"cli", "tui", "acp", "desktop", ""}

# 项目根标记 (非 git repo 也识别)
_PROJECT_MARKERS = (
    "pyproject.toml", "setup.py", "package.json", "tsconfig.json",
    "Cargo.toml", "go.mod", "pom.xml", "build.gradle",
    "Gemfile", "CMakeLists.txt", "Makefile", "Dockerfile",
    "AGENTS.md", "CLAUDE.md", ".cursorrules",
)

# 激活模式 (config agent.coding_context):
#   auto   (默认) — 交互式编码表面 + 代码工作区
#   focus  — 同上, 但工具集收缩到 coding + MCP
#   on     — 强制在任何地方激活
#   off    — 完全禁用

2. 编码操作指南 (Operating Brief)

编码姿势注入一个精心设计的操作指南到系统提示词中,指导 Agent 像"谨慎的高级工程师"一样工作:

📄 agent/coding_context.py (第 162-200 行)

CODING_AGENT_GUIDANCE = (
    "You are a coding agent pairing with the user inside their codebase. "
    "Operate like a careful senior engineer.\n"
    "\n"
    "Gather context first:\n"
    "- Read the relevant files with `read_file` and locate code with "
    "`search_files` before changing anything.\n"
    "- Batch independent lookups: when several reads/searches don't "
    "depend on each other, issue them together in one turn.\n"
    "- Never invent files, symbols, APIs, or imports.\n"
    "\n"
    "Make changes through the tools, not the chat:\n"
    "- Edit with `patch`/`write_file`. Do NOT print code blocks "
    "as a substitute for editing.\n"
    "- Match the project's existing style and conventions.\n"
    "- If an edit fails, re-read the file before retrying.\n"
    "\n"
    "Verify, and know when to stop:\n"
    "- Use `terminal` for git, builds, tests, and inspection.\n"
    "- Fix root causes, not symptoms.\n"
    "- Stop after ~3 attempts on the same file and ask the user.\n"
)

3. 模型感知的编辑格式引导

不同模型对编辑格式的训练偏好不同。Hermes 根据模型家族自动引导编辑格式:

📄 agent/coding_context.py (第 116-132 行)

_EDIT_FORMAT_GUIDANCE: dict[str, tuple] = {
    "patch": (
        ("gpt", "codex"),  # GPT/Codex 家族
        "use `patch` with `mode='patch'` (V4A diff) — "
        "including single-file edits. It's the edit format "
        "you handle most reliably.",
    ),
    "replace": (
        ("claude", "sonnet", "opus", "haiku",
         "gemini", "gemma", "deepseek", "qwen", ...),
        "prefer `patch` in `mode='replace'` — match a unique "
        "snippet and swap it. Reach for `mode='patch'` (V4A) "
        "only when an edit genuinely spans several files.",
    ),
}
# 子字符串匹配模型 ID, 未知模型无引导

设计原理:GPT/Codex 在 Codex 中使用 V4A diff 作为唯一文件编辑器;而 Anthropic 模型和大多数开源模型在 RL 训练中更多接触 str_replace 风格编辑器。自动匹配训练格式减少错误。

4. 缓存安全保证

缓存安全设计

编码模式在提示词构建时解析一次,然后不可变。工作区快照(git 分支、dirty state)注入系统提示词的稳定层——绝不每轮重新探测(那会粉碎 prompt cache)。分支和 dirty state 在会话中漂移,所以操作指南告诉模型在行动前用 git 重新检查。这保证了 prompt cache 的稳定性。

六、Context References:@file/@url 内联引用

用户可以在消息中使用 @file:path/to/file.py@url:https://...@git:diff 等语法内联引用外部内容。系统自动解析并注入到对话中:

📄 agent/context_references.py (第 17-19 行)

REFERENCE_PATTERN = re.compile(
    rf"(?diff|staged)\b|"
    rf"(?Pfile|folder|git|url):"
    rf"(?P{_QUOTED_REFERENCE_VALUE}"
    rf"(?::\d+(?:-\d+)?)?|\S+))"
)

# 支持:
#   @file:path/to/file.py       — 读取文件
#   @file:path:10-20            — 读取文件特定行
#   @folder:path/to/dir         — 列出目录
#   @git:diff                   — git diff
#   @url:https://example.com    — 抓取网页
#   @diff                       — 简写
#   @staged                     — 暂存区 diff

安全设计:敏感路径(.ssh.aws.gnupg 等)和敏感文件(id_rsaauthorized_keys.netrc 等)被显式阻止。Token 预算控制注入量,防止引用膨胀。

七、Curator:技能库的后台编排器

Curator 是 Hermes 的技能库后台维护系统。它不依赖 cron daemon——而是空闲触发:当 Agent 空闲且距上次运行超过 interval_hours(默认 7 天),maybe_run_curator() 生成一个独立的 AIAgent 来做审查。

1. 生命周期自动转换

Curator 生命周期管理:

  技能状态机:
    active → stale (30 天无活动) → archived (90 天无活动)
     ↑          ↑
     └──────────┘ (再次使用 → 重新激活)

  硬规则:
    ├─ 只触碰 Agent 创建的技能 (非内置/非 Hub)
    ├─ 从不自动删除 — 只归档 (可恢复)
    ├─ 固定技能 (pinned) 跳过所有自动转换
    ├─ 使用辅助客户端, 不碰主会话的 prompt cache
    └─ 受保护的内置技能 (plan) 永不归档

  触发条件:
    ├─ curator.enabled == True (默认)
    ├─ not paused
    ├─ last_run_at 存在且超过 interval_hours
    └─ 首次安装: 不立即运行, 等待一个完整周期

2. 审查提示词:伞形合并策略

Curator 的审查提示词非常精妙——它要求 Agent 做伞形合并(Umbrella Consolidation),而不是简单的重复检测:

📄 agent/curator.py (第 344-400 行)

CURATOR_REVIEW_PROMPT = (
    "You are running as Hermes' background skill CURATOR. "
    "This is an UMBRELLA-BUILDING consolidation pass, "
    "not a passive audit and not a duplicate-finder.\n\n"
    "The goal of the skill collection is a LIBRARY OF CLASS-LEVEL "
    "INSTRUCTIONS AND EXPERIENTIAL KNOWLEDGE. A collection of hundreds "
    "of narrow skills where each one captures one session's specific "
    "bug is a FAILURE of the library — not a feature.\n\n"
    "The right target shape is CLASS-LEVEL skills with rich SKILL.md "
    "bodies + `references/`, `templates/`, and `scripts/` subfiles "
    "for session-specific detail — not one-session-one-skill "
    "micro-entries.\n\n"
    "How to work:\n"
    "1. Scan the full candidate list. Identify PREFIX CLUSTERS.\n"
    "2. For each cluster with 2+ members, ask 'what is the "
    "UMBRELLA CLASS these skills all serve?'\n"
    "3. Three ways to consolidate:\n"
    "   a. MERGE INTO EXISTING UMBRELLA\n"
    "   b. CREATE A NEW UMBRELLA SKILL.md\n"
    "   c. DO NOTHING (truly distinct skills)\n"
)

3. 持久化状态与回滚保护

Curator 状态管理:

  .curator_state 文件 (~/.hermes/skills/.curator_state):
    {
      "last_run_at": "2026-06-14T10:30:00Z",
      "last_run_duration_seconds": 127,
      "last_run_summary": "Archived 3, merged 2...",
      "paused": false,
      "run_count": 5
    }

  回滚保护 (curator_backup.py):
    ├─ 每次 Curator 运行前备份 .curator_state
    ├─ 归档操作前备份技能目录到 .archive/
    ├─ 支持 hermes curator restore 恢复
    └─ 原子写入 (atomic_json_write) 防损坏

  配置 (config.yaml):
    curator:
      enabled: true
      interval_hours: 168    # 7 天
      min_idle_hours: 2     # 最少空闲 2h
      stale_after_days: 30
      archive_after_days: 90
      prune_builtins: true  # 是否修剪内置技能

八、总结:上下文管理的架构哲学

这一讲我们深入了 Hermes 的上下文管理系统。几个核心设计哲学贯穿始终:

🔹 分层降级

压缩管线从最便宜的预处理开始(工具输出修剪),到中等成本的 LLM 摘要,最后到零成本的确定性回退。每一层失败都不会导致系统崩溃。

🔹 缓存优先

编码模式解析一次、摘要前缀防注入、会话锁防分裂——所有设计都围绕一个核心约束:不破坏 prompt cache。一次 cache 失效意味着用户支付完整 token 费用。

🔹 防御性编程

密钥脱敏、路径安全、JSON 合法性保持、并发锁、版本不匹配 fail-open——每一层都有多层防护。

🔹 可插拔架构

ContextEngine ABC 允许第三方引擎替换内置压缩器,Curator 的空闲触发设计不需要额外的 daemon 进程。扩展点在边缘,核心保持精简。

下一讲预告:MCP 集成、浏览器自动化与多模态处理

Hermes Agent 源码解析 · 第 8 讲 · 完

基本 文件 流程 错误 SQL 调试
  1. 请求信息 : 2026-06-21 15:13:50 HTTP/1.1 GET : https://www.yeyulingfeng.com/a/777183.html
  2. 运行时间 : 0.112177s [ 吞吐率:8.91req/s ] 内存消耗:4,729.54kb 文件加载:145
  3. 缓存信息 : 0 reads,0 writes
  4. 会话信息 : SESSION_ID=975452493c36ebb69b79e931b9b375d3
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  1. CONNECT:[ UseTime:0.000939s ] mysql:host=127.0.0.1;port=3306;dbname=wenku;charset=utf8mb4
  2. SHOW FULL COLUMNS FROM `fenlei` [ RunTime:0.001647s ]
  3. SELECT * FROM `fenlei` WHERE `fid` = 0 [ RunTime:0.000735s ]
  4. SELECT * FROM `fenlei` WHERE `fid` = 63 [ RunTime:0.000560s ]
  5. SHOW FULL COLUMNS FROM `set` [ RunTime:0.001342s ]
  6. SELECT * FROM `set` [ RunTime:0.000580s ]
  7. SHOW FULL COLUMNS FROM `article` [ RunTime:0.001387s ]
  8. SELECT * FROM `article` WHERE `id` = 777183 LIMIT 1 [ RunTime:0.001642s ]
  9. UPDATE `article` SET `lasttime` = 1782026030 WHERE `id` = 777183 [ RunTime:0.005676s ]
  10. SELECT * FROM `fenlei` WHERE `id` = 64 LIMIT 1 [ RunTime:0.000664s ]
  11. SELECT * FROM `article` WHERE `id` < 777183 ORDER BY `id` DESC LIMIT 1 [ RunTime:0.001022s ]
  12. SELECT * FROM `article` WHERE `id` > 777183 ORDER BY `id` ASC LIMIT 1 [ RunTime:0.000816s ]
  13. SELECT * FROM `article` WHERE `id` < 777183 ORDER BY `id` DESC LIMIT 10 [ RunTime:0.002017s ]
  14. SELECT * FROM `article` WHERE `id` < 777183 ORDER BY `id` DESC LIMIT 10,10 [ RunTime:0.002176s ]
  15. SELECT * FROM `article` WHERE `id` < 777183 ORDER BY `id` DESC LIMIT 20,10 [ RunTime:0.004685s ]
0.113900s