From 42e1d6614ba82e602d4842f365fe6ee57c7edb67 Mon Sep 17 00:00:00 2001 From: cjc7373 Date: Wed, 11 Oct 2023 11:36:44 +0000 Subject: [PATCH] deploy: 73f8aee4d18b95da73a404a1318c9822a19d0638 --- categories/distributed-system/index.html | 4 +- categories/distributed-system/index.xml | 7 +- categories/index.html | 2 +- categories/index.xml | 2 +- index.html | 8 +- index.xml | 213 +++++++++------- page/2/index.html | 8 +- page/2/index.xml | 156 ++++++------ page/3/index.html | 8 +- page/3/index.xml | 231 ++++++------------ page/4/index.html | 6 +- page/4/index.xml | 159 +++++++++++- .../git_\345\205\245\351\227\250/index.html" | 2 +- posts/2023/mit_6.824_1_gfs/index.html | 2 +- .../image-20230907175938021.png | Bin 0 -> 74609 bytes .../image-20230907175951530.png | Bin 0 -> 67795 bytes .../image-20230907180012141.png | Bin 0 -> 61174 bytes .../image-20230908182459520.png | Bin 0 -> 408069 bytes .../image-20230909175357672.png | Bin 0 -> 102823 bytes .../image-20230928111923370.png | Bin 0 -> 49438 bytes .../image-20230928141311077.png | Bin 0 -> 54231 bytes .../image-20231001151607479.png | Bin 0 -> 53155 bytes .../image-20231001151618755.png | Bin 0 -> 45888 bytes posts/2023/mit_6.824_2_raft/index.html | 23 ++ posts/index.html | 2 +- posts/index.xml | 7 +- posts/page/2/index.html | 2 +- posts/page/3/index.html | 2 +- posts/page/4/index.html | 2 +- sitemap.xml | 2 +- 30 files changed, 505 insertions(+), 343 deletions(-) create mode 100644 posts/2023/mit_6.824_2_raft/image-20230907175938021.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20230907175951530.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20230907180012141.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20230908182459520.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20230909175357672.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20230928111923370.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20230928141311077.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20231001151607479.png create mode 100644 posts/2023/mit_6.824_2_raft/image-20231001151618755.png create mode 100644 posts/2023/mit_6.824_2_raft/index.html diff --git a/categories/distributed-system/index.html b/categories/distributed-system/index.html index 199d65f..7c4b950 100644 --- a/categories/distributed-system/index.html +++ b/categories/distributed-system/index.html @@ -8,7 +8,9 @@ menu Coherence's Blog
Categories / Distributed System
MIT 6.824 学习笔记(一) GFS
2023-09-05 +dark_mode
Coherence's Blog
存档 标签 diff --git a/categories/distributed-system/index.xml b/categories/distributed-system/index.xml index 8bb4ec7..5470c4e 100644 --- a/categories/distributed-system/index.xml +++ b/categories/distributed-system/index.xml @@ -1,4 +1,9 @@ -Distributed System on Coherence's Bloghttps://blog.coherence.codes/categories/distributed-system/Recent content in Distributed System on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Tue, 05 Sep 2023 00:00:00 +0000MIT 6.824 学习笔记(一) GFShttps://blog.coherence.codes/posts/2023/mit_6.824_1_gfs/Tue, 05 Sep 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/mit_6.824_1_gfs/存储系统通常是一个分布式系统的基石，通常应用可以是无状态的，而所有状态便由存储系统来管理。 +Distributed System on Coherence's Bloghttps://blog.coherence.codes/categories/distributed-system/Recent content in Distributed System on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000MIT 6.824 学习笔记(二) Rafthttps://blog.coherence.codes/posts/2023/mit_6.824_2_raft/Sun, 01 Oct 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/mit_6.824_2_raft/Note: 为了保持准确性，我会尽量使用英文术语。 +为了解决单点故障问题，Raft 采用了 Majority Vote，基本上任何操作都需要得到多数确认才能够执行。为了避免 tie，Raft 的节点数量必须为奇数。因此在一个 2f+1 个节点的系统中，最多允许 f 个节点故障，f+1 即为法定人数（quorum）。 +如果 server 不出错，网络也很稳定，共识算法是很简单的，比如在 Raft 中，leader 被选举出来，client 的请求由 leader 处理并转发给 followers，所有 server 都和 leader 保持一致，这个过程是很简单并且直观的。这些算法真正处理的、细节繁杂的地方是 server 出错、RPC 受网络影响（包括包的延迟，重复，顺序改变，丢失，网络分区）的时候。 +Introduction 在过去十年, Leslie Lamport 的 Paxos 几乎成了共识的同义词.. Paxos 首先定义了一种协议来对单个决定达成共识, 比如一条单个的 log entry, 这被称为 single-decree Paxos. 其支持多个决定的版本 (比如 log) 被称为 muti-Paxos。然而，Paxos 的缺点是难以理解，并且没有提供一个良好的基础来构建可行的实现。 +相较于 Paxos，Raft 的目标是易于理解且符合直觉。为了使 Raft 易于理解，作者采取了解耦 (Raft 将共识问题分解成几个子问题 leader election, log replication, safety, and membership changes) 和缩减状态空间的方式。 +Raft 和已有的共识算法类似（尤其是 Viewstamped Replication），但它有一些新特性。Raft 采取了强 leader 的设计，例如 log entry 只会从 leader 向其他节点分发。这可能是为了性能考虑（比无 leader 要更快，RPC 也更少）Raft 采用基于随机计时器的 leader 选举, 从而用一种简单的方法来解决冲突。另外还有处理成员变更方面的改进。MIT 6.824 学习笔记(一) GFShttps://blog.coherence.codes/posts/2023/mit_6.824_1_gfs/Tue, 05 Sep 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/mit_6.824_1_gfs/存储系统通常是一个分布式系统的基石，通常应用可以是无状态的，而所有状态便由存储系统来管理。 Google 文件系统（The Google File System, GFS）在 2003 年于一篇同名论文中被提出，发表在系统领域顶会 SOSP 上，是 Google 大数据三驾马车之一1。GFS 是一个成功的系统，在 00 年代早期，人们对于分布式文件系统已经有很好的理解了，但是尚未有一个可以扩展到上千个节点的系统被实现出来。很多 GFS 的设计被 HDFS 等后来的分布式文件系统上。 GFS 基于以下几个方面的观察： 节点故障是常见的，因为其运行在大量普通机器（commodity components）上 存储的文件以大文件（数百 MB 到数 GB）为主，小文件应当被支持，但不应对其优化 对文件的大多数写入操作是追加 （append）而不是覆盖/随机写，大多数读取操作是顺序读 （1 MB 或更多） 所以系统必须有良好的并发追加操作的语义 工作负载通常是批处理任务，所以高吞吐量比低延时重要 同时设计应用和文件系统 API 有利于整个系统的灵活性 架构 GFS 采用 Master/Slave 架构，集群中存在一个 master 和多个 chunkservers，并且被多个 clients 访问。（由于单 master 的存在，GFS 存在单点故障的可能性，虽然 master 同样有备份，但恢复可能需要人工干预）一个文件将被分割成多个 chunk。chunk 的特性如下： diff --git a/categories/index.html b/categories/index.html index c17880c..e5cc949 100644 --- a/categories/index.html +++ b/categories/index.html @@ -9,7 +9,7 @@ Coherence's Blog
Coherence's Blog
存档 标签 分类 diff --git a/categories/index.xml b/categories/index.xml index b0acda3..26ba58e 100644 --- a/categories/index.xml +++ b/categories/index.xml @@ -1 +1 @@ -Categories on Coherence's Bloghttps://blog.coherence.codes/categories/Recent content in Categories on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Tue, 05 Sep 2023 00:00:00 +0000Distributed Systemhttps://blog.coherence.codes/categories/distributed-system/Tue, 05 Sep 2023 00:00:00 +0000https://blog.coherence.codes/categories/distributed-system/Windowshttps://blog.coherence.codes/categories/windows/Fri, 02 Nov 2018 18:23:47 +0000https://blog.coherence.codes/categories/windows/ \ No newline at end of file +Categories on Coherence's Bloghttps://blog.coherence.codes/categories/Recent content in Categories on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000Distributed Systemhttps://blog.coherence.codes/categories/distributed-system/Sun, 01 Oct 2023 00:00:00 +0000https://blog.coherence.codes/categories/distributed-system/Windowshttps://blog.coherence.codes/categories/windows/Fri, 02 Nov 2018 18:23:47 +0000https://blog.coherence.codes/categories/windows/ \ No newline at end of file diff --git a/index.html b/index.html index aeaf3f7..f4e9ff3 100644 --- a/index.html +++ b/index.html @@ -1,4 +1,4 @@ -Coherence's Blog +Coherence's Blog
存档 标签 @@ -8,7 +8,9 @@ menu Coherence's Blog
git 入门
2023-09-18 +dark_mode
Coherence's Blog
存档 标签 分类 diff --git a/index.xml b/index.xml index ecffebe..0c9a0b3 100644 --- a/index.xml +++ b/index.xml @@ -1,4 +1,129 @@ -Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Mon, 18 Sep 2023 00:00:00 +0000git 入门https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/Mon, 18 Sep 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/Coherence's Blog https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/ -<p>本文的大部分内容来自于 <a href="https://git-scm.com/book/en/v2">Pro Git</a> 这本书。</p> +Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000MIT 6.824 学习笔记(二) Rafthttps://blog.coherence.codes/posts/2023/mit_6.824_2_raft/Sun, 01 Oct 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/mit_6.824_2_raft/Coherence's Blog https://blog.coherence.codes/posts/2023/mit_6.824_2_raft/ -<blockquote> +<p>Note: 为了保持准确性，我会尽量使用英文术语。</p> +</blockquote> +<p>为了解决单点故障问题，Raft 采用了 Majority Vote，基本上任何操作都需要得到多数确认才能够执行。为了避免 tie，Raft 的节点数量必须为奇数。因此在一个 2f+1 个节点的系统中，最多允许 f 个节点故障，f+1 即为法定人数（quorum）。</p> +<p>如果 server 不出错，网络也很稳定，共识算法是很简单的，比如在 Raft 中，leader 被选举出来，client 的请求由 leader 处理并转发给 followers，所有 server 都和 leader 保持一致，这个过程是很简单并且直观的。这些算法真正处理的、细节繁杂的地方是 server 出错、RPC 受网络影响（包括包的延迟，重复，顺序改变，丢失，网络分区）的时候。</p> +<h2 id="introduction">Introduction</h2> +<p>在过去十年, Leslie Lamport 的 Paxos 几乎成了共识的同义词.. Paxos 首先定义了一种协议来对单个决定达成共识, 比如一条单个的 log entry, 这被称为 single-decree Paxos. 其支持多个决定的版本 (比如 log) 被称为 muti-Paxos。然而，Paxos 的缺点是难以理解，并且没有提供一个良好的基础来构建可行的实现。</p> +<p>相较于 Paxos，Raft 的目标是易于理解且符合直觉。为了使 Raft 易于理解，作者采取了解耦 (Raft 将共识问题分解成几个子问题 leader election, log replication, safety, and membership changes) 和缩减状态空间的方式。</p> +<p>Raft 和已有的共识算法类似（尤其是 Viewstamped Replication），但它有一些新特性。Raft 采取了强 leader 的设计，例如 log entry 只会从 leader 向其他节点分发。这可能是为了性能考虑（比无 leader 要更快，RPC 也更少）Raft 采用基于随机计时器的 leader 选举, 从而用一种简单的方法来解决冲突。另外还有处理成员变更方面的改进。</p> +<h2 id="replicated-state-machines-复制状态机">Replicated state machines (复制状态机)</h2> +<p>通常实现主从复制有两种方法：</p> +<ul> +<li>状态转移（State trasnsfer），主服务器将存储的所有状态都发送给从服务器</li> +<li>复制状态机（Replicated State Machine），将服务器视为确定性的状态机，主服务器将所有输入发送给从服务器，那么他们在任意时刻的状态都是相同的</li> +</ul> +<p><img src="./image-20230907175938021.png" alt="image-20230907175938021"></p> +<p>Replicated state machines 通常使用 replicated log 实现. 每个节点存储一份 log (一系列指令), 其状态机按顺序执行它们. 每个状态机获得的 log 都是完全一致的, 所以它们是确定性的, 存储有完全相同的状态. Replicated log 的一致性由共识算法保证, 一个节点上的共识模块从 client 接收指令, 并把它们加到 log 中, 它和其他节点的共识模块通信以保证 log 存储了相同顺序的指令。在 Raft 中，状态机由上层应用负责处理。</p> +<p>共识算法需要具有以下属性:</p> +<ul> +<li>在非拜占庭错误 (包括 network delays, partitions, and packet loss, duplication, and reordering) 下保证安全性 (safety), 即不会返回不正确的结果</li> +<li>只要多数 (即超过一半) 节点工作, 服务就是可用的</li> +<li>不依赖时间来确保一致性, 错误的时间或是极端情况下的延迟只会导致可用性问题</li> +<li>只要多数节点回应, 一条命令就能够完成, 少数 slow servers 不会影响整个系统的性能</li> +</ul> +<h2 id="raft-共识算法">Raft 共识算法</h2> +<p><img src="./image-20230908182459520.png" alt="image-20230908182459520"></p> +<p>这张图（论文中的 Figure 2）总结了 Raft 协议（除了成员变更和 log 压缩），我们在实现 Raft 协议时会反复看这张图。</p> +<p>Raft 主要由两个 RPC 构成：</p> +<ul> +<li><code>RequestVote RPC</code>，由 candidate 请求，用于收集投票</li> +<li><code>AppendEntries RPC</code>，由 leader 请求，用于心跳和复制 log entries</li> +</ul> +<p>Raft 首先将选举一个 leader, leader 的存在能够简化对 replicated log 的管理，比如 leader 能够独自决定把新的 entry 放在哪里，数据自然地由 leader 流向其他 servers。leader 拥有完全管理 replicated log 的权力，leader 从 clients 接受 log entries, 分发给其他节点, 并告诉它们何时能应用这些 log entries 到它们自己的状态机。</p> +<p>关键性质:</p> +<ul> +<li>Election Safety: 一个任期中只有一个 leader</li> +<li>Leader Append-Only: leader 不会覆盖或删除自己的 log entry, 只会 append</li> +<li>Log Matching: 如果两份 log 中某一个 entry 的 index 和 term 相同, 那么两份 log 从头开始直到该 entry 都是相同的</li> +<li>Leader Completeness: 如果一个 log entry 在某一 term 中 commit 了, 这个 entry 会存在于任何更高任期的 leader 的 log 中</li> +<li>State Machine Safety: 如果一个 server 将一个指定 index 的 log entry 应用到了 state machine 中（i.e. commit 了这个 entry）, 没有其他 server 会在那个 index 上会应用其他的 entry</li> +</ul> +<h3 id="leader-选举">leader 选举</h3> +<p>任何一个节点处于以下三种状态: leader, follower, candidate</p> +<p><img src="./image-20230907175951530.png" alt="image-20230907175951530"></p> +<p>上图描述了 server 所有的状态变化，下面描述几个关键的步骤：</p> +<ul> +<li>Follower -&gt; Candidate：开始选举，follower 递增 term 并进入 candidate 状态，它接着给自己投票并向其他 server 发送 RequestVote RPC。每个 server 在一个 term 只能给一个 candidate 投票，基于先来先得的原则</li> +<li>Candidate -&gt; Leader：当 candidate 接收到了多数票（majority）之后将赢得选举，接着它会马上发送心跳包宣告自己的权威并阻止新的选举。leader 会一直保持 leader 状态，除非 a). 它 fail 了 b). 由于丢包等原因导致某个 follower 的选举计时器超时了</li> +<li>Candidate -&gt; Follower：如果 candidate 收到其他 server 的 AppendEntries RPC 宣称是 leader，并且其 term 不小于 candidate 的当前 term，则该 leader 合法，candidate 回退到 follower 状态</li> +<li>Candidate -&gt; Candidate：如果没有产生多数票，在超时后选举将重复进行，为了打破僵局 Raft 使用随机的选举超时时间</li> +<li>无论何时，candidate 和 leader 只要收到一个 term 比自己大的 RPC，它必须转变为 follower</li> +</ul> +<h3 id="log-复制-replication">log 复制 (replication)</h3> +<p>log 由 entry 组成，每个 entry 都有一个唯一递增的 log index（可以理解为在数组中的 index）。entry 中包含 term 和给状态机的指令。</p> +<p>当一个 log entry 能够被应用到状态机上时 (i.e. 当 leader 知道这个 entry 已经被 replicate 到多数 server 上时，但下文会说明一种例外情况), 我们称这个 entry 为 committed. leader 决定哪些 entry 能够 commit，并在下一次 AppendEntries RPC 中将这个状态告知 followers. Raft 保证了 commited entries 是持久的并<strong>最终</strong>会应用到所有 servers 上。</p> +<p>leader 维护 <code>commitIndex</code>，即最高的 committed entry index，并且会将这个 index 包含在 AppendEntries RPC 中。</p> +<p>Raft 协议中的 Log Matching 性质实际上由两部分组成：</p> +<ul> +<li>如果两份 log 中某一个 entry 的 index 和 term 相同, 那么这两个 entry 相同 +<ul> +<li>这是因为一个 term 只有一个 leader，而一个 leader 对于给定 log index 只能创建最多一个 entry，并且 log entry 从不改变它们在 log 中的位置</li> +</ul> +</li> +<li>如果两份 log 中某一个 entry 的 index 和 term 相同, 它们之前的 entries 都是相同的 +<ul> +<li>这个性质由 AppendEntries 的一个一致性检查保证。leader 会把新 entries 的前一个 entry 的 index 和 term 包含在 AppendEntries RPC 中（<code>prevLogIndex</code> 和 <code>prevLogTerm</code>），这样如果 follower 找不到这样一个 entry，它就会拒绝新的 entries</li> +</ul> +</li> +</ul> +<p><img src="./image-20230907180012141.png" alt="image-20230907180012141"></p> +<p>正常情况下所有 server 的 log 会保持一致，然而当 leader crash 时便会产生不一致。follower 可能会缺一些 entries，也可能多一些，也可能两者都有。leader 通过强制 follower 的 log 和自己同步来解决不一致。leader 为每个 follower 维护一个 <code>nextIndex</code>，即下一个将会被发给 follower 的 log entry。在一开始 leader 会初始化 <code>nextIndex</code> 为它最新一个 log entry 的下一个位置，当某个 follower 拒绝了一次 AppendEntries 之后，leader 递减 <code>nextIndex</code> 并重试 AppendEntries RPC。最终 <code>nextIndex</code> 会到达一个 leader 和 follower 都一致的位置，follower 在那个位置之后的所有 entries 都会被 leader 的覆盖。上述描述同时也符合了 Leader Append-Only 这个性质。</p> +<h3 id="安全性">安全性</h3> +<p>如果 Figure 7 中的 (f) 被选举为了下一个 leader，那么 term 4 及之后的更改岂不是都丢失了？因此 Raft 限制了谁能够被选为 leader，以便满足 Leader Completeness 性质。如果 candidate 的 log 至少和多数 log 一样新（at least as up-to-date as any other log in the majority）那么它包含了所有 committed entries。如果 log 的最后一个 entry term 更大，那么它就更新。如果 term 相同，那么更长的 log 更新。在 RequestVote RPC 中，candidate 会包含 <code>lastLogIndex</code> 和 <code>lastLogTerm</code>，只有当 candidate 的 log 更新时一个 server 才会投票给它。</p> +<p><img src="./image-20230909175357672.png" alt="image-20230909175357672"></p> +<p>当 leader commit 一个来自它之前 term 的 entry 时，可能会遭遇上图的问题，在 (c) 中，来自 term 2 的 entry 已经被复制到多数 server 上了，然而如果 (d) 发生了，它仍然会被来自 term 3 的 entry 覆盖。因此 Raft 将 commit 定义为只有来时当前 term 的 entry 才能够通过 replica 计数来 commit，而来自之前任期的 entry 只能够通过在它之后的 entry 被 commit 来间接 commit（Log Matching 性质）。</p> +<p>我们可以用反证法来证明这么做的安全性（非严格证明），假设节点数量为 2f+1，log index m 已经被 commit 了，并且位于 f+1 个 server 上。此时一个不包含 m 的 server x 想要竞选 leader。为了得到多数票，至少有一个包含 m 的 server y需要投票给 x，此时需要 x 比 y 更新。根据 up-to-date 的定义，可能有以下两种情况：TODO</p> +<ul> +<li>x 和 y 的最后一个 log entry term 相同，且 x 的 log 至少和 y 一样长</li> +<li>x 的最后一个 log entry term 更大</li> +</ul> +<h3 id="时机和可用性">时机和可用性</h3> +<p>在 leader 选举中，时机（Timing）是很关键的，如果 RPC 用时过长，那么 candidate 在超时之前将无法赢得选举。Raft 需要满足以下时机约束：$broadcastTime ≪ electionTimeout ≪ MTBF$，broadcastTime 是平均一次 RPC 的时间，electionTimeout 是选举超时时间，MTBF（Mean Time Between Failures）是发生两次 server 故障的平均间隔时间。</p> +<h2 id="集群成员变更">集群成员变更</h2> +<p>这里指的是增减 server。虽然我们总是能够通过下线整个集群、更新配置、上线集群的方式来增减 server，但这样就减少了可用性。为了使配置变更是安全的，必须保证同一 term 不可能有两个 leader 被选举出来。然而，任何直接由旧配置转变为新配置的做法都是不安全的，如下图所示：</p> +<p><img src="./image-20230928111923370.png" alt="image-20230928111923370"></p> +<p>为了保证安全性，Raft 采取了一种两阶段的策略，集群会切换到一个过渡的配置，称为 joint consensus。论文没有给出这个过程的具体实现，我对这部分也理解得不是很清楚。</p> +<h2 id="log-压缩compaction">log 压缩（compaction）</h2> +<p>如果没有压缩，log 的大小会无限增长，并且在 server 故障重启后重新应用 log 的时间会非常长，这显然是不划算的。</p> +<p>快照（snapshot）是最简单的压缩方式。在一次快照中，当前的系统状态被写入到非易失存储中，然后到这个时刻的所有 log 将被丢弃。</p> +<p><img src="./image-20230928141311077.png" alt="image-20230928141311077"></p> +<p>然而，快照带来了新的复杂度。如果一个 leader 的 log 如上图所示，在一次快照之后 log 中的第一个 entry 为 6，但是如果一个 follower 的 log 还停留在最后一个 entry 为 4，那么这个 follower 永远无法获得 entry 5 了。Raft 的做法是引入了一个新的 InstallSnapshot RPC，用于把 leader 的当前 snapshot 发送给 follower。</p> +<p>snapshot 的创建过程依赖于上层应用，因为 Raft 对于状态机里的状态知之甚少。但是何时创建 snapshot 由 Raft 决定，一个简单的策略是当 log 达到固定大小时创建。</p> +<p>每个 server 能够独立创建 snapshots，因而其偏离了 Raft 的强 leader 原则，但是由于在创建 snapshot 时共识已经达成了，所以这不会有问题。如果只有 leader 能创建 snapshot，那么有两个缺点 a) 每次发送 snapshot 都会浪费网络带宽 b) leader 的实现会变得复杂，因为它必须同时发送 snapshot 和新的 entries。</p> +<h2 id="客户端交互">客户端交互</h2> +<p>client 只会和 leader 交互，在 client 刚启动时，它可能会连接到一个随机的 server，若该 server 不为 leader，则 server 会拒绝这个请求并提供 leader 的信息。</p> +<p>Raft 的目标是提供可线性化（linearizable）的语义，即每个操作会在调用和回复之间的某个时间立刻执行执行一次（exactly once）。然而，Raft 可能会执行一个操作多次，如果 leader 在 commit log entry 之后回复 client 之前崩溃了，client 会重试请求，导致一个操作执行了两次。这个问题的解决方案是对每个操作分配一个独特的序列号，如果 leader 收到了一个请求包含已经被执行的序列号，那么它就不会再次执行这个请求。</p> +<p>如果没有额外的措施，读请求可能会读到过时的（stale）数据，如由于网络分区当前 leader 已经被新的 leader 取代了，但是当前 leader 并不知道这一点。为了保证不会读到过时的数据，在 leader 刚当选时它必须发送一个 no-op entry，以便找出哪些 entry 已经 commit 了。其次 leader 必须检查它是否被新的 leader 取代了，这可以通过发送心跳包来检查是否有多数 server 回应来实现。</p> +<h2 id="notes">Notes</h2> +<ul> +<li> +<p>Raft 是强一致性的协议，因为 commit 过的 log 总是一致的，如果出现网络分区则只有多数区能够达成共识，所以其符合了 CAP 原则中的 CP。另一种应对 split-brain 的方式是允许 split-brain，在网络恢复之后通过一些机制来调谐分叉的状态（reconcile the diverging state），通常这被称为最终一致性。</p> +</li> +<li> +<p>Q：一个 Raft 集群通常位于一个物理位置还是说可以部署在地理上不同的数据中心里？</p> +<p>A：典型的部署方式是一个数据中心。我们会在之后看到一些运行 Paxos 的系统（如 Spanner）能够跨数据中心，其通常是一种无 leader 的设计，以便一个 client 能够同一个本地 server 通信。</p> +<p>Google 的 Chubby 论文报告了它们的 Chubby 部署通常是在一个数据中心里，除了 root Chubby 是跨数据中心的（chubby 并未使用 raft，而是用了基于 Paxos 的一个内部库）</p> +</li> +</ul> +<h2 id="可线性化linearizability">可线性化（Linearizability）</h2> +<p>并发和不确定性的存在，导致分布式系统很容易存在一些微妙的 bug。除非使用形式化方法，否则我们只能依赖测试来验证系统的正确性。</p> +<p>通过 Linearizability 这样一种一致性模型，我们可以基于一种顺序的规格形式地确定并发操作的正确性。在一个 linearizable 的系统中，每个操作看起来是在调用和回复之间的某个时间点原子、瞬间执行。可线性化可以被视作强一致性的同义词。</p> +<blockquote> +<p>Definition of linearizability:</p> +<p>Execution history is linearizable if there exists a total order of the operations in the history that matches real time for non-concurrent requests. Each read sees the value of the most recent write in order.</p> +</blockquote> +<p>下图所示的执行历史是可线性化的：</p> +<p><img src="./image-20231001151607479.png" alt="image-20231001151607479"></p> +<p>而下图则不是：</p> +<p><img src="./image-20231001151618755.png" alt="image-20231001151618755"></p> +<h2 id="heading"></h2> +<h2 id="参考">参考</h2> +<ul> +<li>Raft 论文</li> +<li><a href="https://pdos.csail.mit.edu/6.824/papers/raft-faq.txt">6.824 Raft FAQ</a></li> +</ul> +- https://blog.coherence.codes/posts/2023/mit_6.824_2_raft/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议git 入门https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/Mon, 18 Sep 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/Coherence's Blog https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/ -<p>本文的大部分内容来自于 <a href="https://git-scm.com/book/en/v2">Pro Git</a> 这本书。</p> <h2 id="objects">Objects</h2> <p>git 中最基础的元素是 object，每个 object 由一个 object ID (OID) 唯一标识，OID 是一个 160 bits （用 hex 字符串表示为四十个字符） 的 SHA-1 哈希。具体而言，git 中主要存在三种 objects：</p> <ul> @@ -227,7 +352,7 @@ <p>当应用写入的区域很大时，GFS client 会将其切分成一个一个 chunk，并分为多个写入操作来执行，此时如果有并发操作，则会出现一致但不确定的状态。</p> <h3 id="记录追加record-append">记录追加（Record Append）</h3> <p>此操作中 client 只指定文件而不指定偏移，GFS 保证该操作在 GFS 指定的偏移处原子地完成至少一次，并向客户端返回这个偏移。GFS 的应用中有很多是多生产者/单消费者模型，很适合使用记录追加操作。</p> -<p>记录追加操作和写入大致相同，惟 primary 会检查要追加的数据是否会超出当前 chunk，如果会的话，它将 pad 这个 chunk 到 64MB，指示其他 replicas 也这么操作，并指示客户端重试。（记录追加操作限制最大数据大小为四分之一个 最大 chunk size 以便控制碎片的大小）如果没有超出，则按写入的流程进行，此时 primary 会选择一个 offset （即它所认为的文件末尾）告知其他 replicas 进行写入。如果任何一个 replica 失败，客户端会重试操作。这种情况下部分 replica 将会出现重复的记录，但是数据肯定以某一偏移写入了所有 replicas。</p> +<p>记录追加操作和写入大致相同，惟 primary 会检查要追加的数据是否会超出当前 chunk，如果会的话，它将 pad 这个 chunk 到 64MB，指示其他 replicas 也这么操作，并指示客户端重试。（记录追加操作限制最大数据大小为四分之一个最大 chunk size 以便控制碎片的大小）如果没有超出，则按写入的流程进行，此时 primary 会选择一个 offset （即它所认为的文件末尾）告知其他 replicas 进行写入。如果任何一个 replica 失败，客户端会重试操作。这种情况下部分 replica 将会出现重复的记录，但是数据肯定以某一偏移写入了所有 replicas。</p> <h2 id="问题">问题</h2> <ul> <li>如果 chunk 的版本号只在分配租约是增加的话，假设一个 replica （非 primary）在某一时刻挂了，因此错过了几个写入，如果版本号此时不改变的话，在那个 replica 恢复之后，client 就会读到过时数据了。论文里好像也没提这种情况怎么处理..</li> @@ -688,86 +813,4 @@ WebAssembly 是一种低级的类汇编语言, 能够达到和原生机器代码 <p>我并没有理解这个 PEP 的好处在哪.. 目前的实现在回归测试中导致了 ~2% 的性能损失..</p> <h2 id="尾巴">尾巴</h2> <p>本次会议很大程度上讨论了 Python 的性能改善, 然而大多数变化是琐碎且渐进的, 想要看到某个改动带来的性能飞跃似乎不太可能 <del>(除了 GIL)</del>. 我们希望去年 Language Summit 上 Guido 吹下的牛── 4 年里将 CPython 的性能提升 5 倍──能够实现吧.</p> -- https://blog.coherence.codes/posts/2022/the-2022-python-language-summit/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议民族主义自媒体是如何消费我们的https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/Fri, 29 Apr 2022 00:00:00 +0000https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/Coherence's Blog https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/ -<p>目前的中文互联网上存在着大量的鼓吹民族主义, 制造中西方对立的自媒体, 令人震惊的是, 他们中大部分不但充斥着情绪输出, 甚至连大部分事实都是错误的. 我偶然间读到了这样的一篇文章, 并对其中列出的事实进行了查证, 然后感觉我的智商受到了侮辱..</p> -<p>原文: <a href="https://www.zhihu.com/question/428451991/answer/2431916538">西方媒体是怎样给西方人洗脑的？ - 远方青木的回答 - 知乎</a>, <a href="http://web.archive.org/web/20220429053052/https://www.zhihu.com/question/428451991/answer/2431916538">archive</a></p> -<hr> -<blockquote> -<p>这几年欧美一直在向中国发动舆论战，我见过最离谱的一个理论甚至说什么“中国人每吃一口肉，亚马逊热带雨林就会冒出一缕白烟”。</p> -<p>我本以为这套歪理邪说就是欧美造谣生事的想象力极限，没想到欧美的底线可以永无止尽的被突破。</p> -</blockquote> -<p>来源: <a href="https://www.nytimes.com/2020/01/07/business/fake-pork-china.html">纽约时报 中国人会购买人造肉吗？</a>, 另见<a href="https://world.huanqiu.com/article/9CaKrnKoNzC">环球网报道</a></p> -<p>评论: 虽然文章很有&quot;反华&quot;的倾向性, 但文章主要讲的是人造肉在中国难以推广. 肉类消费对于气候的影响是不争的事实. 所以&quot;中国人每吃一口肉，亚马逊热带雨林就会冒出一缕白烟&quot;这句话也不能算错, 但应该把主语换成全世界人. -要求中国牺牲国民营养而减少肉类消费是不合理的, 但是推广人造肉是合理的. 作者在这里显然有断章取义之嫌.</p> -<hr> -<blockquote> -<p>《<a href="https://web.archive.org/web/20220429053052mp_/https://www.zhihu.com/search?q=%E5%8D%8E%E7%9B%9B%E9%A1%BF%E9%82%AE%E6%8A%A5&amp;search_source=Entity&amp;hybrid_search_source=Entity&amp;hybrid_search_extra=%7B%22sourceType%22%3A%22answer%22%2C%22sourceId%22%3A2431916538%7D">华盛顿邮报</a>》在报道中声称**“中国每年强迫种植数亿棵新树”，这简直是在“犯罪”！**</p> -<p><img src="./e577e26ef47585f1faae27fb45123efd.png" alt="e577e26ef47585f1faae27fb45123efd.png"></p> -<p>我捋一捋他们的思路，<strong>大概意思就是说中国每年种数亿棵新树，但把这些新树种下去之前并没有征求这些树的同意，这数亿棵树是“不一定同意自己被种植”的，但全部被中国强迫种在地里了。</strong></p> -<p><strong>因此，中国侵犯了树“自由被种植”的权力，强迫树木劳动，这属于犯罪，因此中国需要反思自己这种野蛮的行为。</strong></p> -<p>逻辑好像有那么一点点道理哦，但我怎么就觉得那么奇怪和荒谬呢。</p> -<p>中国人种树怎么了？</p> -<p>种树不是在保护环境吗？</p> -<p>怎么拐了一圈之后种树居然成犯罪行为了？</p> -</blockquote> -<p>来源: 没有找到来源.</p> -<p>评论: 据 <a href="https://checkyourfact.com/2022/04/18/fact-check-washington-post-china-trees-article/">checkyourfact</a>, 该截图为杜撰, 最初流传于 Facebook 和 Twitter.</p> -<hr> -<blockquote> -<p>更荒谬的是，同一家西方媒体，前几年报道中国的标题是**《中国对一次性筷子的成瘾正在摧毁森林》。**</p> -<p>首先，一次性筷子不是用森林做出来的，一次性筷子的主要原料是一个月能长3米的竹子，以及速生经济林的边角废木料。</p> -<p>用森林里的好木头确实可以做一次性筷子，但没人这么做，因为成本太高商家会亏本，只有速生林边角废料和竹子的低廉价格才能让商家赚到钱。</p> -<p>其次，抛开筷子的真实原材料不谈，这家西方媒体的主要立场好像也是森林是宝贵的，森林对人类有益，并以此来指责中国。</p> -<p><strong>既然如此，为什么中国种植森林也能被指责一番。是不是中国人干什么都不行，连呼吸都是错的？</strong></p> -</blockquote> -<p>来源: <a href="https://www.washingtonpost.com/news/worldviews/wp/2013/03/14/chinas-disposable-chopstick-addiction-is-destroying-its-forests/">华盛顿邮报《中国对一次性筷子的成瘾正在摧毁森林》</a></p> -<p>评论: 华盛顿邮报 2013 年的报道, 主要援引的是时任吉林森工集团董事长柏广新的一句话: &ldquo;我国每年生产800亿双一次性筷子，相当于要砍伐2000万棵生长20年的大树&rdquo;. <a href="http://www.gov.cn/2013lh/content_2349497.htm">中国政府网</a>和<a href="http://cpc.people.com.cn/pinglun/n/2013/0312/c78779-20759994.html">光明网</a>均能找到这句话以及相关对一次性筷子的批评. 报道中均指出了木头和竹子均是一次性筷子的原料.</p> -<hr> -<blockquote> -<p>几年前，欧美媒体报道说根据科学家的研究，中国人种植水稻会产生更多的温室气体，因此中国人吃大米正在毁灭全球气候，进而导致人类灭亡。</p> -</blockquote> -<p>来源: 我都不知道怎么找..</p> -<p>评论: 类似的研究肯定有, 但我不觉得哪家媒体会报道吃大米导致人类灭亡&hellip;</p> -<hr> -<blockquote> -<p>2019年，国际顶级科学期刊《自然》杂志发表了一篇文章，说根据科学家的研究成果，中国政府治理沙漠，大力推进的荒漠绿化工程，种树过多，可能导致人类水资源枯竭，因此会对地球自然生态造成极大影响。</p> -<p>《自然》杂志本是人类最顶级的科学杂志，但写这篇文章的“科学家”逻辑推导却很简单，算一算中国政府要消灭荒漠需要种多少棵树，种这些树需要消耗多少水资源，<strong>因此得出结论中国种树会破坏自然环境。</strong></p> -<p>别笑，这是真事。</p> -<p><strong>中国人花费几代心血把沙漠变成了绿洲，对人类如此巨大的贡献居然招来西方期刊的指责？</strong></p> -<p><strong>治理沙漠居然都能成为破坏环境的罪证。</strong></p> -</blockquote> -<p>来源: <a href="https://www.nature.com/articles/d41586-019-02789-w">nature 文章 China&rsquo;s tree-planting drive could falter in a warming world</a></p> -<p>评论: 但凡看一下文章就会觉得文章的中立性是没问题的. 文章警告了非原产地树木对当地水资源的大量消耗, 可能会导致干旱地区的扩散. 也引用了多位中国科学家的意见.</p> -<hr> -<blockquote> -<p>巴西烧毁森林种地的时候，西方批判，说这样会造成水土流失，土地荒漠化，从而破坏人类环境。</p> -<p>中国种树消灭荒漠的时候，西方又批判，说这是在破坏人类环境，还不如让土地继续荒漠化。</p> -<p>澳大利亚和美国森林大火，烧毁无数森林，漫天黑烟，西方说这都是很正常的行为，对人类环境没有丝毫损害。</p> -</blockquote> -<p>来源: 不知道怎么找.</p> -<p>评论: 因果倒置, 是气候变化导致了森林火灾的可能性增加</p> -<hr> -<blockquote> -<p>但没有西方人说那群在荒漠里植树造林的中国人有功劳，反而把一个从来不种树的嘴炮少女捧为“环保公主”。</p> -</blockquote> -<p>评论: 讽刺 Greta Thunberg 也是传统艺能了, 不过我个人对她的评价还是比较积极的, 她组织了贯穿整个欧洲的学生罢课, 多次在 COP 上演讲. 她的<a href="https://en.wikipedia.org/wiki/Greta_Thunberg">维基百科页面</a>也是比较正面的. 她频繁提到过 IPCC 的 <a href="https://www.ipcc.ch/site/assets/uploads/sites/2/2019/09/IPCC-Special-Report-1.5-SPM_zh.pdf">全球升温 1.5°C</a>, 我也推荐大家去读一读, 这算是为什么要应对气候变化的科学基础.</p> -<hr> -<blockquote> -<p>欧美人种一点点树就是天大的功劳，但中国不是，不仅无功，反而有罪。</p> -<p>类似的双标相当常见，当初意大利封城，那就是**“冒着经济风险保欧洲”<strong>，而中国封城则是</strong>“以牺牲人民自由为代价”**，两篇文章不仅在同一家媒体《纽约时报》发布，而且时间居然只差了20分钟。</p> -<p><img src="./9d2d417242ceec2596a90beaaebb71a8.png" alt="9d2d417242ceec2596a90beaaebb71a8.png"></p> -</blockquote> -<p>来源: <a href="https://twitter.com/nytimes/status/1236479276586807296">https://twitter.com/nytimes/status/1236479276586807296</a>, <a href="https://twitter.com/nytimes/status/1236484352965521408">https://twitter.com/nytimes/status/1236484352965521408</a></p> -<p>评论: 单看这两条推特确实是西方媒体的偏见..</p> -<hr> -<blockquote> -<p>另一招，就是在报道中国正面的同时，再加上一句，就是鼎鼎大名的**“但代价是什么”。**</p> -<p>组合起来的效果给大家展示一下，比如说**“中国也许有效控制了疫情，但代价惨重”。**</p> -<p>比如说，<strong>中国正在变得智能，但代价是什么？</strong></p> -<p><img src="./246b296018db3460d935bb2e9d121731.png" alt="246b296018db3460d935bb2e9d121731.png"></p> -<p>这，就是欧美媒体对中国报道的常规格式。</p> -<p>把中国的好事变坏简直太简单了，<strong>你这个好事似乎存在，但代价是什么？</strong></p> -</blockquote> -<p>评论: 虚空打靶.. 别只看个标题啊, 你倒是具体说说西方媒体说了啥啊.</p> -<hr> -<p>总结: 看这种自媒体真的降智商.. 以后要少看..</p> -- https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议 \ No newline at end of file +- https://blog.coherence.codes/posts/2022/the-2022-python-language-summit/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议 \ No newline at end of file diff --git a/page/2/index.html b/page/2/index.html index b6777e5..1a54e3a 100644 --- a/page/2/index.html +++ b/page/2/index.html @@ -1,4 +1,4 @@ -Coherence's Blog +Coherence's Blog
存档 标签 @@ -8,7 +8,9 @@ menu Coherence's Blog
我的手机变迁史
2022-04-07 +dark_mode
Coherence's Blog
存档 标签 分类 diff --git a/page/2/index.xml b/page/2/index.xml index 80e4c01..a912d06 100644 --- a/page/2/index.xml +++ b/page/2/index.xml @@ -1,4 +1,86 @@ -Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Mon, 18 Sep 2023 00:00:00 +0000我的手机变迁史https://blog.coherence.codes/posts/2022/%E6%88%91%E7%9A%84%E6%89%8B%E6%9C%BA%E5%8F%98%E8%BF%81%E5%8F%B2/Thu, 07 Apr 2022 00:00:00 +0000https://blog.coherence.codes/posts/2022/%E6%88%91%E7%9A%84%E6%89%8B%E6%9C%BA%E5%8F%98%E8%BF%81%E5%8F%B2/Coherence's Blog https://blog.coherence.codes/posts/2022/%E6%88%91%E7%9A%84%E6%89%8B%E6%9C%BA%E5%8F%98%E8%BF%81%E5%8F%B2/ -<p>本文纯属回忆, 描述可能会有偏差.</p> +Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000民族主义自媒体是如何消费我们的https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/Fri, 29 Apr 2022 00:00:00 +0000https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/Coherence's Blog https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/ -<p>目前的中文互联网上存在着大量的鼓吹民族主义, 制造中西方对立的自媒体, 令人震惊的是, 他们中大部分不但充斥着情绪输出, 甚至连大部分事实都是错误的. 我偶然间读到了这样的一篇文章, 并对其中列出的事实进行了查证, 然后感觉我的智商受到了侮辱..</p> +<p>原文: <a href="https://www.zhihu.com/question/428451991/answer/2431916538">西方媒体是怎样给西方人洗脑的？ - 远方青木的回答 - 知乎</a>, <a href="http://web.archive.org/web/20220429053052/https://www.zhihu.com/question/428451991/answer/2431916538">archive</a></p> +<hr> +<blockquote> +<p>这几年欧美一直在向中国发动舆论战，我见过最离谱的一个理论甚至说什么“中国人每吃一口肉，亚马逊热带雨林就会冒出一缕白烟”。</p> +<p>我本以为这套歪理邪说就是欧美造谣生事的想象力极限，没想到欧美的底线可以永无止尽的被突破。</p> +</blockquote> +<p>来源: <a href="https://www.nytimes.com/2020/01/07/business/fake-pork-china.html">纽约时报 中国人会购买人造肉吗？</a>, 另见<a href="https://world.huanqiu.com/article/9CaKrnKoNzC">环球网报道</a></p> +<p>评论: 虽然文章很有&quot;反华&quot;的倾向性, 但文章主要讲的是人造肉在中国难以推广. 肉类消费对于气候的影响是不争的事实. 所以&quot;中国人每吃一口肉，亚马逊热带雨林就会冒出一缕白烟&quot;这句话也不能算错, 但应该把主语换成全世界人. +要求中国牺牲国民营养而减少肉类消费是不合理的, 但是推广人造肉是合理的. 作者在这里显然有断章取义之嫌.</p> +<hr> +<blockquote> +<p>《<a href="https://web.archive.org/web/20220429053052mp_/https://www.zhihu.com/search?q=%E5%8D%8E%E7%9B%9B%E9%A1%BF%E9%82%AE%E6%8A%A5&amp;search_source=Entity&amp;hybrid_search_source=Entity&amp;hybrid_search_extra=%7B%22sourceType%22%3A%22answer%22%2C%22sourceId%22%3A2431916538%7D">华盛顿邮报</a>》在报道中声称**“中国每年强迫种植数亿棵新树”，这简直是在“犯罪”！**</p> +<p><img src="./e577e26ef47585f1faae27fb45123efd.png" alt="e577e26ef47585f1faae27fb45123efd.png"></p> +<p>我捋一捋他们的思路，<strong>大概意思就是说中国每年种数亿棵新树，但把这些新树种下去之前并没有征求这些树的同意，这数亿棵树是“不一定同意自己被种植”的，但全部被中国强迫种在地里了。</strong></p> +<p><strong>因此，中国侵犯了树“自由被种植”的权力，强迫树木劳动，这属于犯罪，因此中国需要反思自己这种野蛮的行为。</strong></p> +<p>逻辑好像有那么一点点道理哦，但我怎么就觉得那么奇怪和荒谬呢。</p> +<p>中国人种树怎么了？</p> +<p>种树不是在保护环境吗？</p> +<p>怎么拐了一圈之后种树居然成犯罪行为了？</p> +</blockquote> +<p>来源: 没有找到来源.</p> +<p>评论: 据 <a href="https://checkyourfact.com/2022/04/18/fact-check-washington-post-china-trees-article/">checkyourfact</a>, 该截图为杜撰, 最初流传于 Facebook 和 Twitter.</p> +<hr> +<blockquote> +<p>更荒谬的是，同一家西方媒体，前几年报道中国的标题是**《中国对一次性筷子的成瘾正在摧毁森林》。**</p> +<p>首先，一次性筷子不是用森林做出来的，一次性筷子的主要原料是一个月能长3米的竹子，以及速生经济林的边角废木料。</p> +<p>用森林里的好木头确实可以做一次性筷子，但没人这么做，因为成本太高商家会亏本，只有速生林边角废料和竹子的低廉价格才能让商家赚到钱。</p> +<p>其次，抛开筷子的真实原材料不谈，这家西方媒体的主要立场好像也是森林是宝贵的，森林对人类有益，并以此来指责中国。</p> +<p><strong>既然如此，为什么中国种植森林也能被指责一番。是不是中国人干什么都不行，连呼吸都是错的？</strong></p> +</blockquote> +<p>来源: <a href="https://www.washingtonpost.com/news/worldviews/wp/2013/03/14/chinas-disposable-chopstick-addiction-is-destroying-its-forests/">华盛顿邮报《中国对一次性筷子的成瘾正在摧毁森林》</a></p> +<p>评论: 华盛顿邮报 2013 年的报道, 主要援引的是时任吉林森工集团董事长柏广新的一句话: &ldquo;我国每年生产800亿双一次性筷子，相当于要砍伐2000万棵生长20年的大树&rdquo;. <a href="http://www.gov.cn/2013lh/content_2349497.htm">中国政府网</a>和<a href="http://cpc.people.com.cn/pinglun/n/2013/0312/c78779-20759994.html">光明网</a>均能找到这句话以及相关对一次性筷子的批评. 报道中均指出了木头和竹子均是一次性筷子的原料.</p> +<hr> +<blockquote> +<p>几年前，欧美媒体报道说根据科学家的研究，中国人种植水稻会产生更多的温室气体，因此中国人吃大米正在毁灭全球气候，进而导致人类灭亡。</p> +</blockquote> +<p>来源: 我都不知道怎么找..</p> +<p>评论: 类似的研究肯定有, 但我不觉得哪家媒体会报道吃大米导致人类灭亡&hellip;</p> +<hr> +<blockquote> +<p>2019年，国际顶级科学期刊《自然》杂志发表了一篇文章，说根据科学家的研究成果，中国政府治理沙漠，大力推进的荒漠绿化工程，种树过多，可能导致人类水资源枯竭，因此会对地球自然生态造成极大影响。</p> +<p>《自然》杂志本是人类最顶级的科学杂志，但写这篇文章的“科学家”逻辑推导却很简单，算一算中国政府要消灭荒漠需要种多少棵树，种这些树需要消耗多少水资源，<strong>因此得出结论中国种树会破坏自然环境。</strong></p> +<p>别笑，这是真事。</p> +<p><strong>中国人花费几代心血把沙漠变成了绿洲，对人类如此巨大的贡献居然招来西方期刊的指责？</strong></p> +<p><strong>治理沙漠居然都能成为破坏环境的罪证。</strong></p> +</blockquote> +<p>来源: <a href="https://www.nature.com/articles/d41586-019-02789-w">nature 文章 China&rsquo;s tree-planting drive could falter in a warming world</a></p> +<p>评论: 但凡看一下文章就会觉得文章的中立性是没问题的. 文章警告了非原产地树木对当地水资源的大量消耗, 可能会导致干旱地区的扩散. 也引用了多位中国科学家的意见.</p> +<hr> +<blockquote> +<p>巴西烧毁森林种地的时候，西方批判，说这样会造成水土流失，土地荒漠化，从而破坏人类环境。</p> +<p>中国种树消灭荒漠的时候，西方又批判，说这是在破坏人类环境，还不如让土地继续荒漠化。</p> +<p>澳大利亚和美国森林大火，烧毁无数森林，漫天黑烟，西方说这都是很正常的行为，对人类环境没有丝毫损害。</p> +</blockquote> +<p>来源: 不知道怎么找.</p> +<p>评论: 因果倒置, 是气候变化导致了森林火灾的可能性增加</p> +<hr> +<blockquote> +<p>但没有西方人说那群在荒漠里植树造林的中国人有功劳，反而把一个从来不种树的嘴炮少女捧为“环保公主”。</p> +</blockquote> +<p>评论: 讽刺 Greta Thunberg 也是传统艺能了, 不过我个人对她的评价还是比较积极的, 她组织了贯穿整个欧洲的学生罢课, 多次在 COP 上演讲. 她的<a href="https://en.wikipedia.org/wiki/Greta_Thunberg">维基百科页面</a>也是比较正面的. 她频繁提到过 IPCC 的 <a href="https://www.ipcc.ch/site/assets/uploads/sites/2/2019/09/IPCC-Special-Report-1.5-SPM_zh.pdf">全球升温 1.5°C</a>, 我也推荐大家去读一读, 这算是为什么要应对气候变化的科学基础.</p> +<hr> +<blockquote> +<p>欧美人种一点点树就是天大的功劳，但中国不是，不仅无功，反而有罪。</p> +<p>类似的双标相当常见，当初意大利封城，那就是**“冒着经济风险保欧洲”<strong>，而中国封城则是</strong>“以牺牲人民自由为代价”**，两篇文章不仅在同一家媒体《纽约时报》发布，而且时间居然只差了20分钟。</p> +<p><img src="./9d2d417242ceec2596a90beaaebb71a8.png" alt="9d2d417242ceec2596a90beaaebb71a8.png"></p> +</blockquote> +<p>来源: <a href="https://twitter.com/nytimes/status/1236479276586807296">https://twitter.com/nytimes/status/1236479276586807296</a>, <a href="https://twitter.com/nytimes/status/1236484352965521408">https://twitter.com/nytimes/status/1236484352965521408</a></p> +<p>评论: 单看这两条推特确实是西方媒体的偏见..</p> +<hr> +<blockquote> +<p>另一招，就是在报道中国正面的同时，再加上一句，就是鼎鼎大名的**“但代价是什么”。**</p> +<p>组合起来的效果给大家展示一下，比如说**“中国也许有效控制了疫情，但代价惨重”。**</p> +<p>比如说，<strong>中国正在变得智能，但代价是什么？</strong></p> +<p><img src="./246b296018db3460d935bb2e9d121731.png" alt="246b296018db3460d935bb2e9d121731.png"></p> +<p>这，就是欧美媒体对中国报道的常规格式。</p> +<p>把中国的好事变坏简直太简单了，<strong>你这个好事似乎存在，但代价是什么？</strong></p> +</blockquote> +<p>评论: 虚空打靶.. 别只看个标题啊, 你倒是具体说说西方媒体说了啥啊.</p> +<hr> +<p>总结: 看这种自媒体真的降智商.. 以后要少看..</p> +- https://blog.coherence.codes/posts/2022/%E6%B0%91%E6%97%8F%E4%B8%BB%E4%B9%89%E8%87%AA%E5%AA%92%E4%BD%93%E6%98%AF%E5%A6%82%E4%BD%95%E6%B6%88%E8%B4%B9%E6%88%91%E4%BB%AC%E7%9A%84/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议我的手机变迁史https://blog.coherence.codes/posts/2022/%E6%88%91%E7%9A%84%E6%89%8B%E6%9C%BA%E5%8F%98%E8%BF%81%E5%8F%B2/Thu, 07 Apr 2022 00:00:00 +0000https://blog.coherence.codes/posts/2022/%E6%88%91%E7%9A%84%E6%89%8B%E6%9C%BA%E5%8F%98%E8%BF%81%E5%8F%B2/Coherence's Blog https://blog.coherence.codes/posts/2022/%E6%88%91%E7%9A%84%E6%89%8B%E6%9C%BA%E5%8F%98%E8%BF%81%E5%8F%B2/ -<p>本文纯属回忆, 描述可能会有偏差.</p> <p>小学时, 为方便联系, 我爸给了我一个 ZTE 的功能机, 里面只有一个打砖块的小游戏.</p> <p>后来这手机被我在路上掉了, 于是我换成了一个 LG 的功能机. 这个 LG 的神奇之处在于它自带了一个浏览器, 于是我可以用 2G 上网去起点, 纵横看小说. 另外它自带电信的一个应用商店, 里面有一些免费的小游戏可以下 (不知道是啥技术, 估计是 java..), 因为我当时并没有流量套餐, 后来还因为话费异常被我爸查到并追问我在上网干什么, 我记得我用看新闻蒙混过去了. 这个手机陪伴我度过了初中.</p> <p>考上省中后, 爸妈为了奖励我, 给我换了个新手机, 这时我爸被中国电信忽悠买了个酷派 K1 (酷派 5952).</p> @@ -573,74 +655,4 @@ ROM 之家这种网站也是时代的眼泪了</p> <p>思路大概是这样：按年份归类，然后图片统一放到当前目录下的<code>images</code>文件夹里。</p> <h2 id="实践">实践</h2> <p>脚本的逻辑大概是对每一篇博文，寻找其元数据的 date 字段，将其移动到对应的文件夹下，同时对同名的文件夹也进行递归移动。由于图片都是相对引用的，所以不会出问题。（即使出了咱还有 git）</p> -- https://blog.coherence.codes/posts/2020_and_before/write_a_script_to_classify_posts/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议Kickstart 2020 Round A 题解https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/Thu, 26 Mar 2020 19:00:00 +0000https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/ -<p>第一次打Kickstart，体验还是很不错的。</p> -<!-- raw HTML omitted --> -<p><a href="https://codingcompetitions.withgoogle.com/kickstart/round/000000000019ffc7">比赛链接</a></p> -<p>迟了几分钟进比赛，发现前十已经两题AC了。</p> -<h2 id="allocation">Allocation</h2> -<p><code>1/0 Accepted</code></p> -<p>签到题，排序后从大到小输出即可。</p> -<h2 id="plates">Plates</h2> -<p><code>2/0 TLE, WA</code></p> -<p>N叠盘子，每叠K个，每个盘子有一个beauty值，从中取P个，但对于每叠只能从上到下取，求beauty值最大为多少。</p> -<p>先写了一个每次取最大的，WA，发现不对，然后去做第三题了。后又写了个dfs，TLE了。当时心态有点崩，因为TOP 10基本是两分钟AC，想到DP，但没有深入想。</p> -<p>官方题解是对于每一叠盘子，先预处理前n个的beauty值和sum，然后对于每一个状态<code>dp[i][j]</code>，即在前i叠盘子和取j个盘子时能取到的最大值，有状态转移方程<code>dp[i][j] = max(dp[i][j], sum[i][x]+dp[i-1][j-x])</code>。循环求dp即可。</p> -<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span> -</span></span><span style="display:flex;"><span><span style="color:#66d9ef">if</span> __name__<span style="color:#f92672">==</span><span style="color:#e6db74">&#39;__main__&#39;</span>: -</span></span><span style="display:flex;"><span> t <span style="color:#f92672">=</span> int(input()) -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, t<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>): -</span></span><span style="display:flex;"><span> out <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Case #</span><span style="color:#e6db74">{}</span><span style="color:#e6db74">: &#34;</span><span style="color:#f92672">.</span>format(i) -</span></span><span style="display:flex;"><span> n, k, p <span style="color:#f92672">=</span> [int(i) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> input()<span style="color:#f92672">.</span>split()] -</span></span><span style="display:flex;"><span> a <span style="color:#f92672">=</span> [] -</span></span><span style="display:flex;"><span> sum <span style="color:#f92672">=</span> [] -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n): -</span></span><span style="display:flex;"><span> a<span style="color:#f92672">.</span>append([int(i) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> input()<span style="color:#f92672">.</span>split()]) -</span></span><span style="display:flex;"><span> sum<span style="color:#f92672">.</span>append([<span style="color:#ae81ff">0</span>]) -</span></span><span style="display:flex;"><span> s <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span> -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> j <span style="color:#f92672">in</span> a[i]: -</span></span><span style="display:flex;"><span> s <span style="color:#f92672">+=</span> j -</span></span><span style="display:flex;"><span> sum[i]<span style="color:#f92672">.</span>append(s) -</span></span><span style="display:flex;"><span> cur <span style="color:#f92672">=</span> [<span style="color:#ae81ff">0</span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n)] -</span></span><span style="display:flex;"><span> dp <span style="color:#f92672">=</span> [[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">*</span> (p<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n)] -</span></span><span style="display:flex;"><span> dp[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> sum[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">+</span> [<span style="color:#ae81ff">0</span>] <span style="color:#f92672">*</span> (p<span style="color:#f92672">-</span>k) -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, n): -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> j <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, p<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>): -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> x <span style="color:#f92672">in</span> range(min(j<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>, k<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>)): -</span></span><span style="display:flex;"><span> dp[i][j] <span style="color:#f92672">=</span> max(dp[i][j], sum[i][x] <span style="color:#f92672">+</span> dp[i<span style="color:#f92672">-</span><span style="color:#ae81ff">1</span>][j<span style="color:#f92672">-</span>x]) -</span></span><span style="display:flex;"><span> ans <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span> -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n): -</span></span><span style="display:flex;"><span> ans <span style="color:#f92672">=</span> max(ans, dp[i][p]) -</span></span><span style="display:flex;"><span> out <span style="color:#f92672">+=</span> str(ans) -</span></span><span style="display:flex;"><span> print(out) -</span></span></code></pre></div><h2 id="workout">Workout</h2> -<p><code>2/1 Accepted</code></p> -<p>给一个递增的数列，插入K个值，求每两个值的差的最小值。</p> -<p>这道题和我校2017新生赛的一题类似，对结果二分即可。</p> -<h2 id="bundling">Bundling</h2> -<p><code>Not Attempted</code></p> -<p>把N个字符串分成K组，每组的分数为最长公共前缀的长度，求全局最高分。</p> -<p>直接进行分组比较困难，我们可以考虑每组的分数同时也是共同前缀的个数。那么对于每个前缀，若有p个单词拥有这个前缀，则这个前缀会给结果贡献<code>p // K</code>分。由此求字符串中的每个前缀和拥有这个前缀的字符串数即可。</p> -<p>使用前缀树可很容易求得。一开始我的做法是递归这棵树，用了两种写法都RE了，Google了一圈找到测试数据会导致爆栈，故采用了list来模拟栈遍历。</p> -<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#66d9ef">if</span> __name__<span style="color:#f92672">==</span><span style="color:#e6db74">&#39;__main__&#39;</span>: -</span></span><span style="display:flex;"><span> t <span style="color:#f92672">=</span> int(input()) -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, t<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>): -</span></span><span style="display:flex;"><span> out <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Case #</span><span style="color:#e6db74">{}</span><span style="color:#e6db74">: &#34;</span><span style="color:#f92672">.</span>format(i) -</span></span><span style="display:flex;"><span> n, k <span style="color:#f92672">=</span> [int(i) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> input()<span style="color:#f92672">.</span>split()] -</span></span><span style="display:flex;"><span> tree <span style="color:#f92672">=</span> {<span style="color:#e6db74">&#39;pre_num&#39;</span>: <span style="color:#ae81ff">0</span>} -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n): -</span></span><span style="display:flex;"><span> word <span style="color:#f92672">=</span> input() -</span></span><span style="display:flex;"><span> cur <span style="color:#f92672">=</span> tree -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> c <span style="color:#f92672">in</span> word: -</span></span><span style="display:flex;"><span> cur <span style="color:#f92672">=</span> cur<span style="color:#f92672">.</span>setdefault(c, {<span style="color:#e6db74">&#39;pre_num&#39;</span>: <span style="color:#ae81ff">0</span>}) -</span></span><span style="display:flex;"><span> cur[<span style="color:#e6db74">&#39;pre_num&#39;</span>] <span style="color:#f92672">+=</span> <span style="color:#ae81ff">1</span> -</span></span><span style="display:flex;"><span> stack <span style="color:#f92672">=</span> [tree] -</span></span><span style="display:flex;"><span> s <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span> -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">while</span> len(stack): -</span></span><span style="display:flex;"><span> node <span style="color:#f92672">=</span> stack<span style="color:#f92672">.</span>pop() -</span></span><span style="display:flex;"><span> s <span style="color:#f92672">+=</span> node[<span style="color:#e6db74">&#39;pre_num&#39;</span>] <span style="color:#f92672">//</span> k -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> node: -</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">if</span> i <span style="color:#f92672">==</span> <span style="color:#e6db74">&#39;pre_num&#39;</span>: <span style="color:#66d9ef">continue</span> -</span></span><span style="display:flex;"><span> stack<span style="color:#f92672">.</span>append(node[i]) -</span></span><span style="display:flex;"><span> out <span style="color:#f92672">+=</span> str(s) -</span></span><span style="display:flex;"><span> print(out) -</span></span></code></pre></div>- https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议 \ No newline at end of file +- https://blog.coherence.codes/posts/2020_and_before/write_a_script_to_classify_posts/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议 \ No newline at end of file diff --git a/page/3/index.html b/page/3/index.html index 13bdbe2..98d26c8 100644 --- a/page/3/index.html +++ b/page/3/index.html @@ -1,4 +1,4 @@ -Coherence's Blog +Coherence's Blog
存档 标签 @@ -8,7 +8,9 @@ menu Coherence's Blog
2019年终总结&2020展望
2020-02-01 +dark_mode
Coherence's Blog
存档 标签 分类 diff --git a/page/3/index.xml b/page/3/index.xml index f94fd14..7af43a5 100644 --- a/page/3/index.xml +++ b/page/3/index.xml @@ -1,4 +1,74 @@ -Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Mon, 18 Sep 2023 00:00:00 +00002019年终总结&2020展望https://blog.coherence.codes/posts/2020_and_before/2019summary_2020plan/Sat, 01 Feb 2020 19:00:00 +0000https://blog.coherence.codes/posts/2020_and_before/2019summary_2020plan/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/2019summary_2020plan/ -<p>又是一年一度的年终总结时间了，进入二十一世纪的第二个十年，我的心情十分激动……</p> +Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000Kickstart 2020 Round A 题解https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/Thu, 26 Mar 2020 19:00:00 +0000https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/ -<p>第一次打Kickstart，体验还是很不错的。</p> +<!-- raw HTML omitted --> +<p><a href="https://codingcompetitions.withgoogle.com/kickstart/round/000000000019ffc7">比赛链接</a></p> +<p>迟了几分钟进比赛，发现前十已经两题AC了。</p> +<h2 id="allocation">Allocation</h2> +<p><code>1/0 Accepted</code></p> +<p>签到题，排序后从大到小输出即可。</p> +<h2 id="plates">Plates</h2> +<p><code>2/0 TLE, WA</code></p> +<p>N叠盘子，每叠K个，每个盘子有一个beauty值，从中取P个，但对于每叠只能从上到下取，求beauty值最大为多少。</p> +<p>先写了一个每次取最大的，WA，发现不对，然后去做第三题了。后又写了个dfs，TLE了。当时心态有点崩，因为TOP 10基本是两分钟AC，想到DP，但没有深入想。</p> +<p>官方题解是对于每一叠盘子，先预处理前n个的beauty值和sum，然后对于每一个状态<code>dp[i][j]</code>，即在前i叠盘子和取j个盘子时能取到的最大值，有状态转移方程<code>dp[i][j] = max(dp[i][j], sum[i][x]+dp[i-1][j-x])</code>。循环求dp即可。</p> +<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span> +</span></span><span style="display:flex;"><span><span style="color:#66d9ef">if</span> __name__<span style="color:#f92672">==</span><span style="color:#e6db74">&#39;__main__&#39;</span>: +</span></span><span style="display:flex;"><span> t <span style="color:#f92672">=</span> int(input()) +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, t<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>): +</span></span><span style="display:flex;"><span> out <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Case #</span><span style="color:#e6db74">{}</span><span style="color:#e6db74">: &#34;</span><span style="color:#f92672">.</span>format(i) +</span></span><span style="display:flex;"><span> n, k, p <span style="color:#f92672">=</span> [int(i) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> input()<span style="color:#f92672">.</span>split()] +</span></span><span style="display:flex;"><span> a <span style="color:#f92672">=</span> [] +</span></span><span style="display:flex;"><span> sum <span style="color:#f92672">=</span> [] +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n): +</span></span><span style="display:flex;"><span> a<span style="color:#f92672">.</span>append([int(i) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> input()<span style="color:#f92672">.</span>split()]) +</span></span><span style="display:flex;"><span> sum<span style="color:#f92672">.</span>append([<span style="color:#ae81ff">0</span>]) +</span></span><span style="display:flex;"><span> s <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span> +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> j <span style="color:#f92672">in</span> a[i]: +</span></span><span style="display:flex;"><span> s <span style="color:#f92672">+=</span> j +</span></span><span style="display:flex;"><span> sum[i]<span style="color:#f92672">.</span>append(s) +</span></span><span style="display:flex;"><span> cur <span style="color:#f92672">=</span> [<span style="color:#ae81ff">0</span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n)] +</span></span><span style="display:flex;"><span> dp <span style="color:#f92672">=</span> [[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">*</span> (p<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n)] +</span></span><span style="display:flex;"><span> dp[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">=</span> sum[<span style="color:#ae81ff">0</span>] <span style="color:#f92672">+</span> [<span style="color:#ae81ff">0</span>] <span style="color:#f92672">*</span> (p<span style="color:#f92672">-</span>k) +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, n): +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> j <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, p<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>): +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> x <span style="color:#f92672">in</span> range(min(j<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>, k<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>)): +</span></span><span style="display:flex;"><span> dp[i][j] <span style="color:#f92672">=</span> max(dp[i][j], sum[i][x] <span style="color:#f92672">+</span> dp[i<span style="color:#f92672">-</span><span style="color:#ae81ff">1</span>][j<span style="color:#f92672">-</span>x]) +</span></span><span style="display:flex;"><span> ans <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span> +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n): +</span></span><span style="display:flex;"><span> ans <span style="color:#f92672">=</span> max(ans, dp[i][p]) +</span></span><span style="display:flex;"><span> out <span style="color:#f92672">+=</span> str(ans) +</span></span><span style="display:flex;"><span> print(out) +</span></span></code></pre></div><h2 id="workout">Workout</h2> +<p><code>2/1 Accepted</code></p> +<p>给一个递增的数列，插入K个值，求每两个值的差的最小值。</p> +<p>这道题和我校2017新生赛的一题类似，对结果二分即可。</p> +<h2 id="bundling">Bundling</h2> +<p><code>Not Attempted</code></p> +<p>把N个字符串分成K组，每组的分数为最长公共前缀的长度，求全局最高分。</p> +<p>直接进行分组比较困难，我们可以考虑每组的分数同时也是共同前缀的个数。那么对于每个前缀，若有p个单词拥有这个前缀，则这个前缀会给结果贡献<code>p // K</code>分。由此求字符串中的每个前缀和拥有这个前缀的字符串数即可。</p> +<p>使用前缀树可很容易求得。一开始我的做法是递归这棵树，用了两种写法都RE了，Google了一圈找到测试数据会导致爆栈，故采用了list来模拟栈遍历。</p> +<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#66d9ef">if</span> __name__<span style="color:#f92672">==</span><span style="color:#e6db74">&#39;__main__&#39;</span>: +</span></span><span style="display:flex;"><span> t <span style="color:#f92672">=</span> int(input()) +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(<span style="color:#ae81ff">1</span>, t<span style="color:#f92672">+</span><span style="color:#ae81ff">1</span>): +</span></span><span style="display:flex;"><span> out <span style="color:#f92672">=</span> <span style="color:#e6db74">&#34;Case #</span><span style="color:#e6db74">{}</span><span style="color:#e6db74">: &#34;</span><span style="color:#f92672">.</span>format(i) +</span></span><span style="display:flex;"><span> n, k <span style="color:#f92672">=</span> [int(i) <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> input()<span style="color:#f92672">.</span>split()] +</span></span><span style="display:flex;"><span> tree <span style="color:#f92672">=</span> {<span style="color:#e6db74">&#39;pre_num&#39;</span>: <span style="color:#ae81ff">0</span>} +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> range(n): +</span></span><span style="display:flex;"><span> word <span style="color:#f92672">=</span> input() +</span></span><span style="display:flex;"><span> cur <span style="color:#f92672">=</span> tree +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> c <span style="color:#f92672">in</span> word: +</span></span><span style="display:flex;"><span> cur <span style="color:#f92672">=</span> cur<span style="color:#f92672">.</span>setdefault(c, {<span style="color:#e6db74">&#39;pre_num&#39;</span>: <span style="color:#ae81ff">0</span>}) +</span></span><span style="display:flex;"><span> cur[<span style="color:#e6db74">&#39;pre_num&#39;</span>] <span style="color:#f92672">+=</span> <span style="color:#ae81ff">1</span> +</span></span><span style="display:flex;"><span> stack <span style="color:#f92672">=</span> [tree] +</span></span><span style="display:flex;"><span> s <span style="color:#f92672">=</span> <span style="color:#ae81ff">0</span> +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">while</span> len(stack): +</span></span><span style="display:flex;"><span> node <span style="color:#f92672">=</span> stack<span style="color:#f92672">.</span>pop() +</span></span><span style="display:flex;"><span> s <span style="color:#f92672">+=</span> node[<span style="color:#e6db74">&#39;pre_num&#39;</span>] <span style="color:#f92672">//</span> k +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">for</span> i <span style="color:#f92672">in</span> node: +</span></span><span style="display:flex;"><span> <span style="color:#66d9ef">if</span> i <span style="color:#f92672">==</span> <span style="color:#e6db74">&#39;pre_num&#39;</span>: <span style="color:#66d9ef">continue</span> +</span></span><span style="display:flex;"><span> stack<span style="color:#f92672">.</span>append(node[i]) +</span></span><span style="display:flex;"><span> out <span style="color:#f92672">+=</span> str(s) +</span></span><span style="display:flex;"><span> print(out) +</span></span></code></pre></div>- https://blog.coherence.codes/posts/2020_and_before/kickstart_2020_round_a/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议2019年终总结&2020展望https://blog.coherence.codes/posts/2020_and_before/2019summary_2020plan/Sat, 01 Feb 2020 19:00:00 +0000https://blog.coherence.codes/posts/2020_and_before/2019summary_2020plan/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/2019summary_2020plan/ -<p>又是一年一度的年终总结时间了，进入二十一世纪的第二个十年，我的心情十分激动……</p> <!-- raw HTML omitted --> <p>好吧，激动是假的。时空是连续的，进入2020和平常的一天没有任何区别。转眼间已经来到了庚子鼠年的大年初八，我恍然记得我的年终总结还没有写，于是准备把这个传统艺能发扬下去，也看一看2019定的目标完成了多少。</p> <h2 id="2019-总结">2019 总结</h2> @@ -881,161 +951,4 @@ Linux 是小萝莉，懵懂无知，你怎么调教就怎么跟你玩，但是 <h2 id="后记">后记</h2> <p>这篇文章写了三天才写完，写的也比较杂乱。事实上在一开始我就没有一个整体的构思。</p> <p>2019年已经快过去了两个月，我才堪堪写完年初计划。这些计划（或者说我未来的目标）虽有个模糊的大方向，却又不甚明了，或许我还需要更深层次的思考吧。</p> -- https://blog.coherence.codes/posts/2020_and_before/2018summary_2019plan/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议DOMjudge 配置/踩坑指南https://blog.coherence.codes/posts/2020_and_before/domjudge-config/Wed, 19 Dec 2018 20:12:51 +0000https://blog.coherence.codes/posts/2020_and_before/domjudge-config/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/domjudge-config/ -<p>我校新生赛也能用上 World Final 同款评测系统啦~</p> -<p>DOMjudge 提供了详尽的<a href="https://www.domjudge.org/documentation">官方文档</a>，然而对于博主这种英文弱鸡来说读的很痛苦。前前后后折腾了有两个星期吧，以此文作为避（踩）坑指南。</p> -<!-- raw HTML omitted --> -<h2 id="简介">简介</h2> -<p>DOMjudge，作为一款开源的 CCS(<a href="https://clics.ecs.baylor.edu/index.php?title=Contest_Control_System">Contest Control System</a>)，现已被ICPC钦定为官方的比赛评测系统。比 pc^2 这种闭源而含有诸多 Bug 的 CCS 高到不知道哪里去了啊。</p> -<p><img src="./1545218741334.png" alt="1545218741334"></p> -<p>根据官方文档的描述，DOMjudge 的主要特性有：</p> -<ul> -<li>分布式且自动评测的 judge host</li> -<li>Web 用户界面</li> -<li>模块化的系统，方面添加不同的语言/编译器</li> -</ul> -<p>整个 DOMjudge 主要分为两个部分，Domserver 和 Judgehost，据其名字即可得出它们的功能。需要注意的是，DOMjudge 使用数据库来存放几乎一切数据，所以我们还需配置 MySQL 或者 MariaDB。</p> -<h2 id="安装">安装</h2> -<p>DOMjudge 只能运行在 Linux 环境下。博主的运行环境是 Ubuntu Server 18.04 LTS，以及 DOMjudge 7.1.1。</p> -<p>除了传统的安装方式外，DOMjudge 还提供了 Docker 镜像以供安装，这样就大大简化了安装的困难程度。</p> -<p>关于 Docker 的使用这里不作详细描述。（其实是博主也一知半解）</p> -<p>然而<a href="https://hub.docker.com/r/domjudge/domserver">官方的 Docker 文档中</a>仅使用了命令来启动容器，为了进一步简化安装，也为了方便修改配置，我决定采用 Docker Compose 。以下是 <code>docker-compose.yml </code>文件：</p> -<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-yaml" data-lang="yaml"><span style="display:flex;"><span><span style="color:#f92672">version</span>: <span style="color:#e6db74">&#34;3&#34;</span> -</span></span><span style="display:flex;"><span><span style="color:#f92672">services</span>: -</span></span><span style="display:flex;"><span> <span style="color:#f92672">mariadb</span>: -</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">mariadb</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">dj-mariadb</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_ROOT_PASSWORD=rootpw</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_USER=domjudge</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_PASSWORD=djpw</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_DATABASE=domjudge</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">volumes</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">$PWD/data:/var/lib/mysql</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">ports</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">13306</span>:<span style="color:#ae81ff">3306</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">command</span>: -</span></span><span style="display:flex;"><span> - --<span style="color:#ae81ff">max-connections=1000</span> -</span></span><span style="display:flex;"><span> - --<span style="color:#ae81ff">innodb-log-file-size=480M</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">domserver</span>: -</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">domjudge/domserver:latest</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">domserver</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_HOST=mariadb</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_USER=domjudge</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_DATABASE=domjudge</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_PASSWORD=djpw</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_ROOT_PASSWORD=rootpw</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">CONTAINER_TIMEZONE=Asia/Shanghai</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">restart</span>: <span style="color:#ae81ff">always</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">depends_on</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">phpmyadmin</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">mariadb</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">ports</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">80</span>:<span style="color:#ae81ff">80</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">links</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">mariadb</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">judgehost</span>: -</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">domjudge/judgehost:latest</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">judgedaemon-0</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">privileged</span>: <span style="color:#66d9ef">true</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">CONTAINER_TIMEZONE=Asia/Shanghai</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">DAEMON_ID=0</span> -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">JUDGEDAEMON_PASSWORD=whatthehell</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">depends_on</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">domserver</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">links</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">domserver</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">restart</span>: <span style="color:#ae81ff">always</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">volumes</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">/sys/fs/cgroup:/sys/fs/cgroup:ro</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">phpmyadmin</span>: -</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">phpmyadmin/phpmyadmin</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">myadmin</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">ports</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">8080</span>:<span style="color:#ae81ff">80</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">PMA_HOST=mariadb</span> -</span></span><span style="display:flex;"><span> <span style="color:#f92672">links</span>: -</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">mariadb:mariadb</span> -</span></span></code></pre></div><p>然后运行 <code>docker-compose up -d</code> 即可完成部署。</p> -<p>Judgehost 利用了 Linux 内核的 Cgroup 特性，所以需进行一步额外设置（似乎部分内核较新的系统不需要）。编辑 <code>/etc/default/grub</code> 文件，将默认的命令改为：</p> -<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-sh" data-lang="sh"><span style="display:flex;"><span>GRUB_CMDLINE_LINUX_DEFAULT<span style="color:#f92672">=</span><span style="color:#e6db74">&#34;quiet cgroup_enable=memory swapaccount=1&#34;</span> -</span></span><span style="display:flex;"><span>GRUB_CMDLINE_LINUX<span style="color:#f92672">=</span><span style="color:#e6db74">&#34;quiet cgroup_enable=memory swapaccount=1&#34;</span> -</span></span></code></pre></div><p>然后运行 <code>update-grub</code> 并重启。</p> -<p>需要多核判题的话创建多个 Judgehost 容器并修改 DAEMON_ID 为不同的值即可。</p> -<p>目前存在的问题：</p> -<ul> -<li>judgehost 用户的初始密码不定，需要手动去 web 端更改并添加 <code>JUDGEDAEMON_PASSWORD</code> 的环境变量。（默认环境变量的密码是 <code>password</code>）</li> -<li>整个系统对我来说是个黑盒&hellip;</li> -</ul> -<p>除了 MariaDB , DOMserver , Judgehost 这三个必要的容器外，我还使用了 phpMyAdmin 来进行图形化的数据库管理（备份)。数据库（即 <code>mariadb</code> 容器的数据卷）创建在了当前目录的<code>data</code>文件夹下，方便备份。</p> -<p>其中，Domserver 映射到了主机的 80 端口， phpMyAdmin 映射到了 8080 端口。由于是内网环境，故不准备采用 HTTPS 加密。</p> -<p>需要注意的是，DOMjudge 提供的 Judgehost 镜像中仅包含 C, C++, Java 三种语言的运行环境。</p> -<h2 id="配置">配置</h2> -<p>Domserver 部署成功后，即可通过 Web 端来访问了。默认的管理员账号用户名 <code>admin</code> ，默认密码被打印在domserver启动时的日志中，同时也可以用<code>docker exec -it domserver cat /opt/domjudge/domserver/etc/initial_admin_password.secret</code>来获取 。</p> -<p>可以在 Config Checker 中进行配置检查。</p> -<p>C++ 的默认编译选项没有 C++11， 需手动添加。</p> -<h3 id="题目导入">题目导入</h3> -<p>DOMjudge 支持用 zip 格式的压缩包导入/导出题目。</p> -<p>具体的文件结构是：</p> -<pre tabindex="0"><code>│ domjudge-problem.ini -│ -├─data -│ ├─sample -│ └─secret -│ -└─problem_statement -problem.{pdf,html,txt} -</code></pre><p>在 <code>domjudge-problem.ini</code> 中定义了如下信息：</p> -<p><img src="./1545219324330.png" alt="1545219324330"></p> -<p>（懒得翻译了，有时间再补）</p> -<p>在 secret 文件夹中存放测试数据，在 sample 文件夹中存放样例数据， 在 problem_statement 存放题面。</p> -<p>（不知道为什么博主在压缩包内上传题面没有成功过，只能通过 Web 端来上传）</p> -<p>测试数据的扩展名为 <code>.in</code> 和 <code>.ans</code>。</p> -<h3 id="队伍导入">队伍导入</h3> -<p>队伍及用户导入采用 tsv(<em>tab-separated values</em>) 格式，即信息之间采用制表符（TAB）分隔。事实上，文件的扩展名并不需要采用<code>.tsv</code>。我们可以直接在Excel中创建一个表格然后将其后复制至一个文本文件中。</p> -<p>格式由<a href="https://clics.ecs.baylor.edu/index.php?title=Contest_Control_System_Requirements#Appendix:_File_formats"> ICPC 官方定义</a>。</p> -<p>teams 格式：</p> -<pre tabindex="0"><code>teams 1 -team_id external_ID group_id team_name institution_name institution_short_name country_code -</code></pre><p>参考<a href="https://cubercsl.cn/notes/DOMjudge-Note.html">CSL的博客</a>发现：</p> -<blockquote> -<p>在导入teams表的时候，在<code>Country Code</code>后追加一列，填写<code>Institution External ID</code>。 -这步是阅读<a href="https://github.com/DOMjudge/domjudge/blob/master/lib/lib.impexp.php#L108">源代码</a>后猜测的，当时的实际操作是直接在数据库中的<code>team_affiliation</code>用SQL语句更新<code>ExternalID</code>的。</p> -</blockquote> -<p>博主采用了这种方式，并在导入队伍后通过 Web 界面手动添加 Affiliation 信息。</p> -<p>事实上，institution_name institution_short_name country_code这三个字段都不是必要的，只需要有External ID就可以。而如果没有External ID的话，会创建和队伍数量一样多的Affiliation。</p> -<p>accounts 格式：</p> -<pre tabindex="0"><code>accounts 1 -account_type fullname username password -</code></pre><p>一种（可能）简单的方式是先导入队伍再导入用户，并且用户 username 的后缀数字与 team_id 相同。不需要的字段可以直接留空。（不能缺少分隔符）</p> -<p>tsv 文件可以采用 Excel 生成。在 Excel 中输入完数据后选择另存为文本文件（制表符分隔）即可。</p> -<p>如需使用<code>institution_name institution_short_name country_code</code> 三项需在 Team Affiliations 中提前创建相应的条目。</p> -<h3 id="api">API</h3> -<p>参考官方的 API 说明，DOMjudge 包含<a href="https://clics.ecs.baylor.edu/index.php/Contest_API"> ICPC 定义的 API </a>和<a href="https://www.domjudge.org/demoweb/api/doc">自有的 API</a>。</p> -<p><img src="./1545220336183.png" alt="1545220336183"></p> -<h3 id="滚榜">滚榜</h3> -<p>尝试使用官方的 <a href="https://icpc.baylor.edu/icpctools/">ICPC tools</a>，发现总是提示比赛未结束，查阅日志认为是 DOMjudge 的 event feed 格式与 ICPC tools 提供的 resolver 工具的格式不兼容。</p> -<p>尝试 DOMjura 发现同样不能正确读取 event-feed 。</p> -<hr> -<p>2019年校赛补充：滚榜采用了 Github 上的<a href="https://github.com/hex539/scoreboard">这个项目</a>。这比官方的滚榜工具好用多了，但是也有坑。</p> -<p>首先它使用bazel作为构建工具，但是bazel对于Windows很不友好（至少对于这个项目而言），会有奇怪的报错。所以我使用了Ubuntu。其次，由于构建过程中大部分源都在国外，对于国内的网络环境来说很不友好。我的解决办法是使用proxychains代理bazel，实测无需任何配置即可支持Domjudge 7.1.1（2019/12/7）。</p> -<h2 id="2018冬季新生赛概况">2018冬季新生赛概况</h2> -<p>闲扯几句。</p> -<p>热身赛一切良好。出题人数据出锅，被批判了一个多小时2333。</p> -<p>正式赛赛前比赛账号导入的时候，有个人是后来添加的，和之前的版本没有对应上，导致部分队伍登录到了后一个队伍的账号，重新导入 team 后发现无法登录，还需导入 accounts。这导致比赛推迟了 5 分钟。</p> -<p>由于 Submission 的输出数据是保留的，而赛前我的服务器硬盘空间本来就不多了，比赛时当服务器的硬盘占用达到 90% 时，所有 judgehost 就都被关了。我只能胡乱删几个软件包腾出空间。还好没有造成大的影响。</p> -<p>然后直到最后还是没有搞出滚榜。</p> -<p>GG。总体来说还是挺顺利的，服务器没有崩，甚至平均 CPU load 只有 1.0 左右，就是感觉比较吃 I/O，还好我用的是固态（<del>先见之明</del>）。</p> -<p>总结一下，DOMjudge 体验极佳，可以在校赛再推广一波。</p> -<h2 id="2019冬季新生赛概况">2019冬季新生赛概况</h2> -<p>由于今年懒得去嫖学校的服务器了，所以打算用公网服务器。本来在GCP和阿里云之间纠结用哪个，给我恰好滑稽给我提供了他在镇江的独服，于是建了一个16C，16G内存的虚拟机用作评测服务器（顺带送了个域名）。</p> -<p>这台服务器的网络结构比较复杂，首先，他是Windows Server，通过一个软件把某些端口的流量转发至一台Debian虚拟机中（为了方便管理证书等），然后由Debian上的Nginx反代至Domjudge的虚拟机中。这样我自己就不用配置证书也能有HTTPS。</p> -<p>但是Windows有一个老生常谈的坑：时间问题。Windows的硬件时钟是本地时间，而Linux为UTC时间，这导致了一开始Domjudge里的时间是假的，需要手动设置。</p> -<p>本次Domjudge的配置基本与之前相同，用Docker-compose一键部署。</p> -<p>之前一直没有做过压力测试一直是我的遗憾，这次在研究了Domjudge的<a href="https://www.domjudge.org/demoweb/api/doc">API文档</a>之后，写了一个<a href="Domjudge-config/submit.py">自动交题的脚本</a>，并且用Locust辅助做压力测试，然后评测机不负众望炸了。。</p> -<p><img src="./image-20191208184458561.png" alt="image-20191208184458561"></p> -<p>其实还不止这个错，还有各种奇怪的错误。幸好在正式比赛中评测机表现得很稳健，并没有出锅，应该是压力还不够大。。</p> -<p>比赛时还碰到一个bug，在一道题重测后这道题的一血变更了，但是榜单上的一血是错的，重新刷新榜单也未解决。查看数据库表发现有一张叫<code>scorecache</code>的表，推测该表即为榜单的缓存，于是更改<code>is_first_solved</code>字段后，成功解决。</p> -- https://blog.coherence.codes/posts/2020_and_before/domjudge-config/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议 \ No newline at end of file +- https://blog.coherence.codes/posts/2020_and_before/2018summary_2019plan/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议 \ No newline at end of file diff --git a/page/4/index.html b/page/4/index.html index 3b81375..400b9ce 100644 --- a/page/4/index.html +++ b/page/4/index.html @@ -1,4 +1,4 @@ -Coherence's Blog +Coherence's Blog
存档 标签 @@ -8,7 +8,9 @@ menu Coherence's Blog
使用KMS激活Visio
2018-12-01 +dark_mode
DOMjudge 配置/踩坑指南
2018-12-19 +  + 
使用KMS激活Visio
2018-12-01    
Markdown简介
2018-11-08   diff --git a/page/4/index.xml b/page/4/index.xml index e4c78fe..642b53f 100644 --- a/page/4/index.xml +++ b/page/4/index.xml @@ -1,4 +1,161 @@ -Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Mon, 18 Sep 2023 00:00:00 +0000使用KMS激活Visiohttps://blog.coherence.codes/posts/2020_and_before/visio-kms-activate/Sat, 01 Dec 2018 23:11:38 +0000https://blog.coherence.codes/posts/2020_and_before/visio-kms-activate/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/visio-kms-activate/ -<h2 id="题记">题记</h2> +Coherence's Bloghttps://blog.coherence.codes/Recent content on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000DOMjudge 配置/踩坑指南https://blog.coherence.codes/posts/2020_and_before/domjudge-config/Wed, 19 Dec 2018 20:12:51 +0000https://blog.coherence.codes/posts/2020_and_before/domjudge-config/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/domjudge-config/ -<p>我校新生赛也能用上 World Final 同款评测系统啦~</p> +<p>DOMjudge 提供了详尽的<a href="https://www.domjudge.org/documentation">官方文档</a>，然而对于博主这种英文弱鸡来说读的很痛苦。前前后后折腾了有两个星期吧，以此文作为避（踩）坑指南。</p> +<!-- raw HTML omitted --> +<h2 id="简介">简介</h2> +<p>DOMjudge，作为一款开源的 CCS(<a href="https://clics.ecs.baylor.edu/index.php?title=Contest_Control_System">Contest Control System</a>)，现已被ICPC钦定为官方的比赛评测系统。比 pc^2 这种闭源而含有诸多 Bug 的 CCS 高到不知道哪里去了啊。</p> +<p><img src="./1545218741334.png" alt="1545218741334"></p> +<p>根据官方文档的描述，DOMjudge 的主要特性有：</p> +<ul> +<li>分布式且自动评测的 judge host</li> +<li>Web 用户界面</li> +<li>模块化的系统，方面添加不同的语言/编译器</li> +</ul> +<p>整个 DOMjudge 主要分为两个部分，Domserver 和 Judgehost，据其名字即可得出它们的功能。需要注意的是，DOMjudge 使用数据库来存放几乎一切数据，所以我们还需配置 MySQL 或者 MariaDB。</p> +<h2 id="安装">安装</h2> +<p>DOMjudge 只能运行在 Linux 环境下。博主的运行环境是 Ubuntu Server 18.04 LTS，以及 DOMjudge 7.1.1。</p> +<p>除了传统的安装方式外，DOMjudge 还提供了 Docker 镜像以供安装，这样就大大简化了安装的困难程度。</p> +<p>关于 Docker 的使用这里不作详细描述。（其实是博主也一知半解）</p> +<p>然而<a href="https://hub.docker.com/r/domjudge/domserver">官方的 Docker 文档中</a>仅使用了命令来启动容器，为了进一步简化安装，也为了方便修改配置，我决定采用 Docker Compose 。以下是 <code>docker-compose.yml </code>文件：</p> +<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-yaml" data-lang="yaml"><span style="display:flex;"><span><span style="color:#f92672">version</span>: <span style="color:#e6db74">&#34;3&#34;</span> +</span></span><span style="display:flex;"><span><span style="color:#f92672">services</span>: +</span></span><span style="display:flex;"><span> <span style="color:#f92672">mariadb</span>: +</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">mariadb</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">dj-mariadb</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_ROOT_PASSWORD=rootpw</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_USER=domjudge</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_PASSWORD=djpw</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_DATABASE=domjudge</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">volumes</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">$PWD/data:/var/lib/mysql</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">ports</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">13306</span>:<span style="color:#ae81ff">3306</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">command</span>: +</span></span><span style="display:flex;"><span> - --<span style="color:#ae81ff">max-connections=1000</span> +</span></span><span style="display:flex;"><span> - --<span style="color:#ae81ff">innodb-log-file-size=480M</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">domserver</span>: +</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">domjudge/domserver:latest</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">domserver</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_HOST=mariadb</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_USER=domjudge</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_DATABASE=domjudge</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_PASSWORD=djpw</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">MYSQL_ROOT_PASSWORD=rootpw</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">CONTAINER_TIMEZONE=Asia/Shanghai</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">restart</span>: <span style="color:#ae81ff">always</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">depends_on</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">phpmyadmin</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">mariadb</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">ports</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">80</span>:<span style="color:#ae81ff">80</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">links</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">mariadb</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">judgehost</span>: +</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">domjudge/judgehost:latest</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">judgedaemon-0</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">privileged</span>: <span style="color:#66d9ef">true</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">CONTAINER_TIMEZONE=Asia/Shanghai</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">DAEMON_ID=0</span> +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">JUDGEDAEMON_PASSWORD=whatthehell</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">depends_on</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">domserver</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">links</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">domserver</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">restart</span>: <span style="color:#ae81ff">always</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">volumes</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">/sys/fs/cgroup:/sys/fs/cgroup:ro</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">phpmyadmin</span>: +</span></span><span style="display:flex;"><span> <span style="color:#f92672">image</span>: <span style="color:#ae81ff">phpmyadmin/phpmyadmin</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">container_name</span>: <span style="color:#ae81ff">myadmin</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">ports</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">8080</span>:<span style="color:#ae81ff">80</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">environment</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">PMA_HOST=mariadb</span> +</span></span><span style="display:flex;"><span> <span style="color:#f92672">links</span>: +</span></span><span style="display:flex;"><span> - <span style="color:#ae81ff">mariadb:mariadb</span> +</span></span></code></pre></div><p>然后运行 <code>docker-compose up -d</code> 即可完成部署。</p> +<p>Judgehost 利用了 Linux 内核的 Cgroup 特性，所以需进行一步额外设置（似乎部分内核较新的系统不需要）。编辑 <code>/etc/default/grub</code> 文件，将默认的命令改为：</p> +<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-sh" data-lang="sh"><span style="display:flex;"><span>GRUB_CMDLINE_LINUX_DEFAULT<span style="color:#f92672">=</span><span style="color:#e6db74">&#34;quiet cgroup_enable=memory swapaccount=1&#34;</span> +</span></span><span style="display:flex;"><span>GRUB_CMDLINE_LINUX<span style="color:#f92672">=</span><span style="color:#e6db74">&#34;quiet cgroup_enable=memory swapaccount=1&#34;</span> +</span></span></code></pre></div><p>然后运行 <code>update-grub</code> 并重启。</p> +<p>需要多核判题的话创建多个 Judgehost 容器并修改 DAEMON_ID 为不同的值即可。</p> +<p>目前存在的问题：</p> +<ul> +<li>judgehost 用户的初始密码不定，需要手动去 web 端更改并添加 <code>JUDGEDAEMON_PASSWORD</code> 的环境变量。（默认环境变量的密码是 <code>password</code>）</li> +<li>整个系统对我来说是个黑盒&hellip;</li> +</ul> +<p>除了 MariaDB , DOMserver , Judgehost 这三个必要的容器外，我还使用了 phpMyAdmin 来进行图形化的数据库管理（备份)。数据库（即 <code>mariadb</code> 容器的数据卷）创建在了当前目录的<code>data</code>文件夹下，方便备份。</p> +<p>其中，Domserver 映射到了主机的 80 端口， phpMyAdmin 映射到了 8080 端口。由于是内网环境，故不准备采用 HTTPS 加密。</p> +<p>需要注意的是，DOMjudge 提供的 Judgehost 镜像中仅包含 C, C++, Java 三种语言的运行环境。</p> +<h2 id="配置">配置</h2> +<p>Domserver 部署成功后，即可通过 Web 端来访问了。默认的管理员账号用户名 <code>admin</code> ，默认密码被打印在domserver启动时的日志中，同时也可以用<code>docker exec -it domserver cat /opt/domjudge/domserver/etc/initial_admin_password.secret</code>来获取 。</p> +<p>可以在 Config Checker 中进行配置检查。</p> +<p>C++ 的默认编译选项没有 C++11， 需手动添加。</p> +<h3 id="题目导入">题目导入</h3> +<p>DOMjudge 支持用 zip 格式的压缩包导入/导出题目。</p> +<p>具体的文件结构是：</p> +<pre tabindex="0"><code>│ domjudge-problem.ini +│ +├─data +│ ├─sample +│ └─secret +│ +└─problem_statement +problem.{pdf,html,txt} +</code></pre><p>在 <code>domjudge-problem.ini</code> 中定义了如下信息：</p> +<p><img src="./1545219324330.png" alt="1545219324330"></p> +<p>（懒得翻译了，有时间再补）</p> +<p>在 secret 文件夹中存放测试数据，在 sample 文件夹中存放样例数据， 在 problem_statement 存放题面。</p> +<p>（不知道为什么博主在压缩包内上传题面没有成功过，只能通过 Web 端来上传）</p> +<p>测试数据的扩展名为 <code>.in</code> 和 <code>.ans</code>。</p> +<h3 id="队伍导入">队伍导入</h3> +<p>队伍及用户导入采用 tsv(<em>tab-separated values</em>) 格式，即信息之间采用制表符（TAB）分隔。事实上，文件的扩展名并不需要采用<code>.tsv</code>。我们可以直接在Excel中创建一个表格然后将其后复制至一个文本文件中。</p> +<p>格式由<a href="https://clics.ecs.baylor.edu/index.php?title=Contest_Control_System_Requirements#Appendix:_File_formats"> ICPC 官方定义</a>。</p> +<p>teams 格式：</p> +<pre tabindex="0"><code>teams 1 +team_id external_ID group_id team_name institution_name institution_short_name country_code +</code></pre><p>参考<a href="https://cubercsl.cn/notes/DOMjudge-Note.html">CSL的博客</a>发现：</p> +<blockquote> +<p>在导入teams表的时候，在<code>Country Code</code>后追加一列，填写<code>Institution External ID</code>。 +这步是阅读<a href="https://github.com/DOMjudge/domjudge/blob/master/lib/lib.impexp.php#L108">源代码</a>后猜测的，当时的实际操作是直接在数据库中的<code>team_affiliation</code>用SQL语句更新<code>ExternalID</code>的。</p> +</blockquote> +<p>博主采用了这种方式，并在导入队伍后通过 Web 界面手动添加 Affiliation 信息。</p> +<p>事实上，institution_name institution_short_name country_code这三个字段都不是必要的，只需要有External ID就可以。而如果没有External ID的话，会创建和队伍数量一样多的Affiliation。</p> +<p>accounts 格式：</p> +<pre tabindex="0"><code>accounts 1 +account_type fullname username password +</code></pre><p>一种（可能）简单的方式是先导入队伍再导入用户，并且用户 username 的后缀数字与 team_id 相同。不需要的字段可以直接留空。（不能缺少分隔符）</p> +<p>tsv 文件可以采用 Excel 生成。在 Excel 中输入完数据后选择另存为文本文件（制表符分隔）即可。</p> +<p>如需使用<code>institution_name institution_short_name country_code</code> 三项需在 Team Affiliations 中提前创建相应的条目。</p> +<h3 id="api">API</h3> +<p>参考官方的 API 说明，DOMjudge 包含<a href="https://clics.ecs.baylor.edu/index.php/Contest_API"> ICPC 定义的 API </a>和<a href="https://www.domjudge.org/demoweb/api/doc">自有的 API</a>。</p> +<p><img src="./1545220336183.png" alt="1545220336183"></p> +<h3 id="滚榜">滚榜</h3> +<p>尝试使用官方的 <a href="https://icpc.baylor.edu/icpctools/">ICPC tools</a>，发现总是提示比赛未结束，查阅日志认为是 DOMjudge 的 event feed 格式与 ICPC tools 提供的 resolver 工具的格式不兼容。</p> +<p>尝试 DOMjura 发现同样不能正确读取 event-feed 。</p> +<hr> +<p>2019年校赛补充：滚榜采用了 Github 上的<a href="https://github.com/hex539/scoreboard">这个项目</a>。这比官方的滚榜工具好用多了，但是也有坑。</p> +<p>首先它使用bazel作为构建工具，但是bazel对于Windows很不友好（至少对于这个项目而言），会有奇怪的报错。所以我使用了Ubuntu。其次，由于构建过程中大部分源都在国外，对于国内的网络环境来说很不友好。我的解决办法是使用proxychains代理bazel，实测无需任何配置即可支持Domjudge 7.1.1（2019/12/7）。</p> +<h2 id="2018冬季新生赛概况">2018冬季新生赛概况</h2> +<p>闲扯几句。</p> +<p>热身赛一切良好。出题人数据出锅，被批判了一个多小时2333。</p> +<p>正式赛赛前比赛账号导入的时候，有个人是后来添加的，和之前的版本没有对应上，导致部分队伍登录到了后一个队伍的账号，重新导入 team 后发现无法登录，还需导入 accounts。这导致比赛推迟了 5 分钟。</p> +<p>由于 Submission 的输出数据是保留的，而赛前我的服务器硬盘空间本来就不多了，比赛时当服务器的硬盘占用达到 90% 时，所有 judgehost 就都被关了。我只能胡乱删几个软件包腾出空间。还好没有造成大的影响。</p> +<p>然后直到最后还是没有搞出滚榜。</p> +<p>GG。总体来说还是挺顺利的，服务器没有崩，甚至平均 CPU load 只有 1.0 左右，就是感觉比较吃 I/O，还好我用的是固态（<del>先见之明</del>）。</p> +<p>总结一下，DOMjudge 体验极佳，可以在校赛再推广一波。</p> +<h2 id="2019冬季新生赛概况">2019冬季新生赛概况</h2> +<p>由于今年懒得去嫖学校的服务器了，所以打算用公网服务器。本来在GCP和阿里云之间纠结用哪个，给我恰好滑稽给我提供了他在镇江的独服，于是建了一个16C，16G内存的虚拟机用作评测服务器（顺带送了个域名）。</p> +<p>这台服务器的网络结构比较复杂，首先，他是Windows Server，通过一个软件把某些端口的流量转发至一台Debian虚拟机中（为了方便管理证书等），然后由Debian上的Nginx反代至Domjudge的虚拟机中。这样我自己就不用配置证书也能有HTTPS。</p> +<p>但是Windows有一个老生常谈的坑：时间问题。Windows的硬件时钟是本地时间，而Linux为UTC时间，这导致了一开始Domjudge里的时间是假的，需要手动设置。</p> +<p>本次Domjudge的配置基本与之前相同，用Docker-compose一键部署。</p> +<p>之前一直没有做过压力测试一直是我的遗憾，这次在研究了Domjudge的<a href="https://www.domjudge.org/demoweb/api/doc">API文档</a>之后，写了一个<a href="Domjudge-config/submit.py">自动交题的脚本</a>，并且用Locust辅助做压力测试，然后评测机不负众望炸了。。</p> +<p><img src="./image-20191208184458561.png" alt="image-20191208184458561"></p> +<p>其实还不止这个错，还有各种奇怪的错误。幸好在正式比赛中评测机表现得很稳健，并没有出锅，应该是压力还不够大。。</p> +<p>比赛时还碰到一个bug，在一道题重测后这道题的一血变更了，但是榜单上的一血是错的，重新刷新榜单也未解决。查看数据库表发现有一张叫<code>scorecache</code>的表，推测该表即为榜单的缓存，于是更改<code>is_first_solved</code>字段后，成功解决。</p> +- https://blog.coherence.codes/posts/2020_and_before/domjudge-config/ - Coherence. 本站遵循 CC BY-NC-SA 4.0 协议使用KMS激活Visiohttps://blog.coherence.codes/posts/2020_and_before/visio-kms-activate/Sat, 01 Dec 2018 23:11:38 +0000https://blog.coherence.codes/posts/2020_and_before/visio-kms-activate/Coherence's Blog https://blog.coherence.codes/posts/2020_and_before/visio-kms-activate/ -<h2 id="题记">题记</h2> <p>如果你懒的话当然可以用KMSpico等工具一键激活，但博主不想电脑里多个启动项，并且博主的电脑预装了Office2013家庭与学生版，用此类工具可能出现不可预知的后果，所以打算自己动手激活Visio。</p> <!-- raw HTML omitted --> <h2 id="转换visio版本">转换Visio版本</h2> diff --git "a/posts/2023/git_\345\205\245\351\227\250/index.html" "b/posts/2023/git_\345\205\245\351\227\250/index.html" index 2991446..817e1f0 100644 --- "a/posts/2023/git_\345\205\245\351\227\250/index.html" +++ "b/posts/2023/git_\345\205\245\351\227\250/index.html" @@ -114,7 +114,7 @@ [master 2aa02d0] 2 Date: Tue Sep 19 16:32:32 2023 +0800 1 file changed, 1 insertion(+) -

本质上是 diff 2f6c412 和 2f6c412 的 parent，然后在当前分支重新应用这个 diff。rebase 本质上是自动的 cherry-pick。

Simple divergent history

在 experiment 分支执行 git rebase master 则会找到 experiment 和 master 的共同祖先 C2，然后发现 experiment 分支有一个新的 commit C4，git 会在 master 重做这个 commit。

一个 fsck 导致的问题

某日我在检查软件更新时，发现 KeePassDX f-droid 版没有随着上游更新，进而发现了这个 issue，问题的原因是 KeePassDX 的仓库中有几个 tree object 不合法，运行 git fsck 会报错 error in tree b7a0b493966f9077f6fcd7de37244699bb73615e: duplicateEntries: contains duplicate file entries。

git 不允许一个 tree 中含有两个相同的文件名，但是不知为何这个错误发生了（包含这个 tree 的 commit 可以追溯到 2009 年，可能是由于 Windows 不区分文件名大小写且在上古时期 git 处理这个的逻辑有问题）。而 f-droid 在几个月前启用了 git clone 时的 fsck 检查（transfer.fsckObjects 等配置），导致 build 时 clone 仓库失败。顺便一提 Github push 现在也有 git fsck 检查，所以把这个仓库往另一个仓库 push 也会失败。

fsck 失败的问题在多个仓库出现过，包括highlight.js, Behat, requests，无一例外都能追溯到某些上古 commit，目前看来除了重写所有 commits 并没有什么办法来修复这些错误。我尝试了 replace 的魔法发现并不能够使其通过 git fsck（毕竟出错的 tree 还在）。

回到 KeePassDX 的问题只能让 f-droid 在 clone 的时候做一些 workaround 来绕过了…

最后修改于 2023-09-18

本质上是 diff 2f6c412 和 2f6c412 的 parent，然后在当前分支重新应用这个 diff。rebase 本质上是自动的 cherry-pick。

Simple divergent history

在 experiment 分支执行 git rebase master 则会找到 experiment 和 master 的共同祖先 C2，然后发现 experiment 分支有一个新的 commit C4，git 会在 master 重做这个 commit。

一个 fsck 导致的问题

某日我在检查软件更新时，发现 KeePassDX f-droid 版没有随着上游更新，进而发现了这个 issue，问题的原因是 KeePassDX 的仓库中有几个 tree object 不合法，运行 git fsck 会报错 error in tree b7a0b493966f9077f6fcd7de37244699bb73615e: duplicateEntries: contains duplicate file entries。

git 不允许一个 tree 中含有两个相同的文件名，但是不知为何这个错误发生了（包含这个 tree 的 commit 可以追溯到 2009 年，可能是由于 Windows 不区分文件名大小写且在上古时期 git 处理这个的逻辑有问题）。而 f-droid 在几个月前启用了 git clone 时的 fsck 检查（transfer.fsckObjects 等配置），导致 build 时 clone 仓库失败。顺便一提 Github push 现在也有 git fsck 检查，所以把这个仓库往另一个仓库 push 也会失败。

fsck 失败的问题在多个仓库出现过，包括highlight.js, Behat, requests，无一例外都能追溯到某些上古 commit，目前看来除了重写所有 commits 并没有什么办法来修复这些错误。我尝试了 replace 的魔法发现并不能够使其通过 git fsck（毕竟出错的 tree 还在）。

回到 KeePassDX 的问题只能让 f-droid 在 clone 的时候做一些 workaround 来绕过了…

最后修改于 2023-09-18

Coherence's Blog
存档 标签 分类 diff --git a/posts/2023/mit_6.824_1_gfs/index.html b/posts/2023/mit_6.824_1_gfs/index.html index adb365d..0807a9f 100644 --- a/posts/2023/mit_6.824_1_gfs/index.html +++ b/posts/2023/mit_6.824_1_gfs/index.html @@ -23,7 +23,7 @@ type Master struct { Files map[Filename][]ChunkHandle } -

除此之外，还需要 log 和 checkpoint 机制来持久化元数据。元数据中 Handle, ReplicaNum 和 Version 应该是非易失的（non-volatile)。没有持久化 ChunkServers 字段可能是因为 chunkserver 会经常变动。

一致性模型

GFS 采用了一种宽松的一致性模型，不同于 Raft 等强一致性协议，其允许不一致的情况出现。不一致的情况将由应用处理。这实际上是为了性能而牺牲了一些一致性。

image-20230901182949527

对于一个文件区域，GFS 定义了几种状态：

  • 一致的（consistent）：无论从哪个 replica 读，所有 clients 都会看到同样的数据
  • 确定的（defined）：在一次写入之后，数据是一致的，并且 clients 都能完整看到这次写入写了什么。
  • 不一致的（inconsistent）：不同的 client 能够读到不一样的结果

这三种状态发生于以下情况：

  • 当没有并发写的写入成功后，这块区域就是确定的
  • 当若干写入操作并发成功后，这块文件区域是不确定但一致的：所有 clients 都能看到同样的数据，但这块可能不能反映出其中任何一次写入的结果（通常是这几次写入的结果交织在一起）
  • 失败的写入使得这块区域不一致

GFS 中的写入（mutation）分为指定偏移量的写入（Write）和记录末尾追加（Record Append），record append 能够保证原子的至少一次（at least once）追加，但是有小概率会出现 padding 或者重复数据。GFS 的应用可以通过在每条记录后加入校验来处理不完整的记录，还可以通过加入唯一的标识符来处理重复记录。GFS 的客户端库中包含了这些通用的处理不一致的功能。

实现

GFS 使用租约（lease）来维护一个一致的写入顺序（consistent mutation order）。租约就是在一段时间内，master 让某一 replica 成为 primary，master 将保证在这段时间内不会分配另一个 primary，而 primary 将保证在租约过期后不再履行 primary 的职责。primary 将指定一个 chunk 的写入顺序，其他的 replica 则跟随 primary 的顺序。

写入

image-20230905110612114

client 首先向 master 请求 primary 和其他 replicas 的位置。如果尚未有一个 primary，master 会首先增大版本号并通知所有 replicas，然后选定一个 primary 并向其分配租约。client 会缓存这些元数据，以减少 master 的负载。

client 随后向所有 replica 推送数据，数据被存储在 chunkserver 的本地缓存中等待被使用。数据流和控制流是分离的，这是为了更有效地利用网络。数据流采用了一种流水线的形式，首先 client 向距离其最近的 chunkserver 推送数据，这个 chunkserver 再向其最近的 chunkserver 转发，链式地发送数据，转发不会等待 chunk 传输完而是立即开始的。

当所有 repicas 都 ACK 收到了数据之后，client 向 primary 请求写入。由于可能存在并发，primary 为所有写入分配一个序列号，在本地应用这些写入，并转发写入请求到其他 replicas。所有 replicas 都 ACK 之后，primary 向 client 回复。如果任何一个 replica 出错，那么被修改的区域就处于不一致的状态，此时由 client 来负责重试失败的写入。（如果在 primary 就出错的话，那么它不会转发写入请求，此时数据应该仍然处于一致的状态）

当应用写入的区域很大时，GFS client 会将其切分成一个一个 chunk，并分为多个写入操作来执行，此时如果有并发操作，则会出现一致但不确定的状态。

记录追加（Record Append）

此操作中 client 只指定文件而不指定偏移，GFS 保证该操作在 GFS 指定的偏移处原子地完成至少一次，并向客户端返回这个偏移。GFS 的应用中有很多是多生产者/单消费者模型，很适合使用记录追加操作。

记录追加操作和写入大致相同，惟 primary 会检查要追加的数据是否会超出当前 chunk，如果会的话，它将 pad 这个 chunk 到 64MB，指示其他 replicas 也这么操作，并指示客户端重试。（记录追加操作限制最大数据大小为四分之一个 最大 chunk size 以便控制碎片的大小）如果没有超出，则按写入的流程进行，此时 primary 会选择一个 offset （即它所认为的文件末尾）告知其他 replicas 进行写入。如果任何一个 replica 失败，客户端会重试操作。这种情况下部分 replica 将会出现重复的记录，但是数据肯定以某一偏移写入了所有 replicas。

问题

  • 如果 chunk 的版本号只在分配租约是增加的话，假设一个 replica （非 primary）在某一时刻挂了，因此错过了几个写入，如果版本号此时不改变的话，在那个 replica 恢复之后，client 就会读到过时数据了。论文里好像也没提这种情况怎么处理..

总结

以上并未包含论文的完整内容。GFS 我早在去年夏天就开始学了，然后就一直搁置在一旁，直到这两天才重新拿起来…

参考

本文的内容来源于：

  1. 另外两篇论文是 MapReduce: Simplified Data Processing on Large Clusters 和 Bigtable: A Distributed Storage System for Structured Data ↩︎

最后修改于 2023-09-05

除此之外，还需要 log 和 checkpoint 机制来持久化元数据。元数据中 Handle, ReplicaNum 和 Version 应该是非易失的（non-volatile)。没有持久化 ChunkServers 字段可能是因为 chunkserver 会经常变动。

一致性模型

GFS 采用了一种宽松的一致性模型，不同于 Raft 等强一致性协议，其允许不一致的情况出现。不一致的情况将由应用处理。这实际上是为了性能而牺牲了一些一致性。

image-20230901182949527

对于一个文件区域，GFS 定义了几种状态：

  • 一致的（consistent）：无论从哪个 replica 读，所有 clients 都会看到同样的数据
  • 确定的（defined）：在一次写入之后，数据是一致的，并且 clients 都能完整看到这次写入写了什么。
  • 不一致的（inconsistent）：不同的 client 能够读到不一样的结果

这三种状态发生于以下情况：

  • 当没有并发写的写入成功后，这块区域就是确定的
  • 当若干写入操作并发成功后，这块文件区域是不确定但一致的：所有 clients 都能看到同样的数据，但这块可能不能反映出其中任何一次写入的结果（通常是这几次写入的结果交织在一起）
  • 失败的写入使得这块区域不一致

GFS 中的写入（mutation）分为指定偏移量的写入（Write）和记录末尾追加（Record Append），record append 能够保证原子的至少一次（at least once）追加，但是有小概率会出现 padding 或者重复数据。GFS 的应用可以通过在每条记录后加入校验来处理不完整的记录，还可以通过加入唯一的标识符来处理重复记录。GFS 的客户端库中包含了这些通用的处理不一致的功能。

实现

GFS 使用租约（lease）来维护一个一致的写入顺序（consistent mutation order）。租约就是在一段时间内，master 让某一 replica 成为 primary，master 将保证在这段时间内不会分配另一个 primary，而 primary 将保证在租约过期后不再履行 primary 的职责。primary 将指定一个 chunk 的写入顺序，其他的 replica 则跟随 primary 的顺序。

写入

image-20230905110612114

client 首先向 master 请求 primary 和其他 replicas 的位置。如果尚未有一个 primary，master 会首先增大版本号并通知所有 replicas，然后选定一个 primary 并向其分配租约。client 会缓存这些元数据，以减少 master 的负载。

client 随后向所有 replica 推送数据，数据被存储在 chunkserver 的本地缓存中等待被使用。数据流和控制流是分离的，这是为了更有效地利用网络。数据流采用了一种流水线的形式，首先 client 向距离其最近的 chunkserver 推送数据，这个 chunkserver 再向其最近的 chunkserver 转发，链式地发送数据，转发不会等待 chunk 传输完而是立即开始的。

当所有 repicas 都 ACK 收到了数据之后，client 向 primary 请求写入。由于可能存在并发，primary 为所有写入分配一个序列号，在本地应用这些写入，并转发写入请求到其他 replicas。所有 replicas 都 ACK 之后，primary 向 client 回复。如果任何一个 replica 出错，那么被修改的区域就处于不一致的状态，此时由 client 来负责重试失败的写入。（如果在 primary 就出错的话，那么它不会转发写入请求，此时数据应该仍然处于一致的状态）

当应用写入的区域很大时，GFS client 会将其切分成一个一个 chunk，并分为多个写入操作来执行，此时如果有并发操作，则会出现一致但不确定的状态。

记录追加（Record Append）

此操作中 client 只指定文件而不指定偏移，GFS 保证该操作在 GFS 指定的偏移处原子地完成至少一次，并向客户端返回这个偏移。GFS 的应用中有很多是多生产者/单消费者模型，很适合使用记录追加操作。

记录追加操作和写入大致相同，惟 primary 会检查要追加的数据是否会超出当前 chunk，如果会的话，它将 pad 这个 chunk 到 64MB，指示其他 replicas 也这么操作，并指示客户端重试。（记录追加操作限制最大数据大小为四分之一个最大 chunk size 以便控制碎片的大小）如果没有超出，则按写入的流程进行，此时 primary 会选择一个 offset （即它所认为的文件末尾）告知其他 replicas 进行写入。如果任何一个 replica 失败，客户端会重试操作。这种情况下部分 replica 将会出现重复的记录，但是数据肯定以某一偏移写入了所有 replicas。

问题

  • 如果 chunk 的版本号只在分配租约是增加的话，假设一个 replica （非 primary）在某一时刻挂了，因此错过了几个写入，如果版本号此时不改变的话，在那个 replica 恢复之后，client 就会读到过时数据了。论文里好像也没提这种情况怎么处理..

总结

以上并未包含论文的完整内容。GFS 我早在去年夏天就开始学了，然后就一直搁置在一旁，直到这两天才重新拿起来…

参考

本文的内容来源于：

  1. 另外两篇论文是 MapReduce: Simplified Data Processing on Large Clusters 和 Bigtable: A Distributed Storage System for Structured Data ↩︎

最后修改于 2023-09-05

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MIT 6.824 学习笔记(二) Raft
+folder +[Distributed System] + 

Note: 为了保持准确性，我会尽量使用英文术语。

为了解决单点故障问题，Raft 采用了 Majority Vote，基本上任何操作都需要得到多数确认才能够执行。为了避免 tie，Raft 的节点数量必须为奇数。因此在一个 2f+1 个节点的系统中，最多允许 f 个节点故障，f+1 即为法定人数（quorum）。

如果 server 不出错，网络也很稳定，共识算法是很简单的，比如在 Raft 中，leader 被选举出来，client 的请求由 leader 处理并转发给 followers，所有 server 都和 leader 保持一致，这个过程是很简单并且直观的。这些算法真正处理的、细节繁杂的地方是 server 出错、RPC 受网络影响（包括包的延迟，重复，顺序改变，丢失，网络分区）的时候。

Introduction

在过去十年, Leslie Lamport 的 Paxos 几乎成了共识的同义词.. Paxos 首先定义了一种协议来对单个决定达成共识, 比如一条单个的 log entry, 这被称为 single-decree Paxos. 其支持多个决定的版本 (比如 log) 被称为 muti-Paxos。然而，Paxos 的缺点是难以理解，并且没有提供一个良好的基础来构建可行的实现。

相较于 Paxos，Raft 的目标是易于理解且符合直觉。为了使 Raft 易于理解，作者采取了解耦 (Raft 将共识问题分解成几个子问题 leader election, log replication, safety, and membership changes) 和缩减状态空间的方式。

Raft 和已有的共识算法类似（尤其是 Viewstamped Replication），但它有一些新特性。Raft 采取了强 leader 的设计，例如 log entry 只会从 leader 向其他节点分发。这可能是为了性能考虑（比无 leader 要更快，RPC 也更少）Raft 采用基于随机计时器的 leader 选举, 从而用一种简单的方法来解决冲突。另外还有处理成员变更方面的改进。

Replicated state machines (复制状态机)

通常实现主从复制有两种方法：

  • 状态转移（State trasnsfer），主服务器将存储的所有状态都发送给从服务器
  • 复制状态机（Replicated State Machine），将服务器视为确定性的状态机，主服务器将所有输入发送给从服务器，那么他们在任意时刻的状态都是相同的

image-20230907175938021

Replicated state machines 通常使用 replicated log 实现. 每个节点存储一份 log (一系列指令), 其状态机按顺序执行它们. 每个状态机获得的 log 都是完全一致的, 所以它们是确定性的, 存储有完全相同的状态. Replicated log 的一致性由共识算法保证, 一个节点上的共识模块从 client 接收指令, 并把它们加到 log 中, 它和其他节点的共识模块通信以保证 log 存储了相同顺序的指令。在 Raft 中，状态机由上层应用负责处理。

共识算法需要具有以下属性:

  • 在非拜占庭错误 (包括 network delays, partitions, and packet loss, duplication, and reordering) 下保证安全性 (safety), 即不会返回不正确的结果
  • 只要多数 (即超过一半) 节点工作, 服务就是可用的
  • 不依赖时间来确保一致性, 错误的时间或是极端情况下的延迟只会导致可用性问题
  • 只要多数节点回应, 一条命令就能够完成, 少数 slow servers 不会影响整个系统的性能

Raft 共识算法

image-20230908182459520

这张图（论文中的 Figure 2）总结了 Raft 协议（除了成员变更和 log 压缩），我们在实现 Raft 协议时会反复看这张图。

Raft 主要由两个 RPC 构成：

  • RequestVote RPC，由 candidate 请求，用于收集投票
  • AppendEntries RPC，由 leader 请求，用于心跳和复制 log entries

Raft 首先将选举一个 leader, leader 的存在能够简化对 replicated log 的管理，比如 leader 能够独自决定把新的 entry 放在哪里，数据自然地由 leader 流向其他 servers。leader 拥有完全管理 replicated log 的权力，leader 从 clients 接受 log entries, 分发给其他节点, 并告诉它们何时能应用这些 log entries 到它们自己的状态机。

关键性质:

  • Election Safety: 一个任期中只有一个 leader
  • Leader Append-Only: leader 不会覆盖或删除自己的 log entry, 只会 append
  • Log Matching: 如果两份 log 中某一个 entry 的 index 和 term 相同, 那么两份 log 从头开始直到该 entry 都是相同的
  • Leader Completeness: 如果一个 log entry 在某一 term 中 commit 了, 这个 entry 会存在于任何更高任期的 leader 的 log 中
  • State Machine Safety: 如果一个 server 将一个指定 index 的 log entry 应用到了 state machine 中（i.e. commit 了这个 entry）, 没有其他 server 会在那个 index 上会应用其他的 entry

leader 选举

任何一个节点处于以下三种状态: leader, follower, candidate

image-20230907175951530

上图描述了 server 所有的状态变化，下面描述几个关键的步骤：

  • Follower -> Candidate：开始选举，follower 递增 term 并进入 candidate 状态，它接着给自己投票并向其他 server 发送 RequestVote RPC。每个 server 在一个 term 只能给一个 candidate 投票，基于先来先得的原则
  • Candidate -> Leader：当 candidate 接收到了多数票（majority）之后将赢得选举，接着它会马上发送心跳包宣告自己的权威并阻止新的选举。leader 会一直保持 leader 状态，除非 a). 它 fail 了 b). 由于丢包等原因导致某个 follower 的选举计时器超时了
  • Candidate -> Follower：如果 candidate 收到其他 server 的 AppendEntries RPC 宣称是 leader，并且其 term 不小于 candidate 的当前 term，则该 leader 合法，candidate 回退到 follower 状态
  • Candidate -> Candidate：如果没有产生多数票，在超时后选举将重复进行，为了打破僵局 Raft 使用随机的选举超时时间
  • 无论何时，candidate 和 leader 只要收到一个 term 比自己大的 RPC，它必须转变为 follower

log 复制 (replication)

log 由 entry 组成，每个 entry 都有一个唯一递增的 log index（可以理解为在数组中的 index）。entry 中包含 term 和给状态机的指令。

当一个 log entry 能够被应用到状态机上时 (i.e. 当 leader 知道这个 entry 已经被 replicate 到多数 server 上时，但下文会说明一种例外情况), 我们称这个 entry 为 committed. leader 决定哪些 entry 能够 commit，并在下一次 AppendEntries RPC 中将这个状态告知 followers. Raft 保证了 commited entries 是持久的并最终会应用到所有 servers 上。

leader 维护 commitIndex，即最高的 committed entry index，并且会将这个 index 包含在 AppendEntries RPC 中。

Raft 协议中的 Log Matching 性质实际上由两部分组成：

  • 如果两份 log 中某一个 entry 的 index 和 term 相同, 那么这两个 entry 相同
    • 这是因为一个 term 只有一个 leader，而一个 leader 对于给定 log index 只能创建最多一个 entry，并且 log entry 从不改变它们在 log 中的位置
  • 如果两份 log 中某一个 entry 的 index 和 term 相同, 它们之前的 entries 都是相同的
    • 这个性质由 AppendEntries 的一个一致性检查保证。leader 会把新 entries 的前一个 entry 的 index 和 term 包含在 AppendEntries RPC 中（prevLogIndex 和 prevLogTerm），这样如果 follower 找不到这样一个 entry，它就会拒绝新的 entries

image-20230907180012141

正常情况下所有 server 的 log 会保持一致，然而当 leader crash 时便会产生不一致。follower 可能会缺一些 entries，也可能多一些，也可能两者都有。leader 通过强制 follower 的 log 和自己同步来解决不一致。leader 为每个 follower 维护一个 nextIndex，即下一个将会被发给 follower 的 log entry。在一开始 leader 会初始化 nextIndex 为它最新一个 log entry 的下一个位置，当某个 follower 拒绝了一次 AppendEntries 之后，leader 递减 nextIndex 并重试 AppendEntries RPC。最终 nextIndex 会到达一个 leader 和 follower 都一致的位置，follower 在那个位置之后的所有 entries 都会被 leader 的覆盖。上述描述同时也符合了 Leader Append-Only 这个性质。

安全性

如果 Figure 7 中的 (f) 被选举为了下一个 leader，那么 term 4 及之后的更改岂不是都丢失了？因此 Raft 限制了谁能够被选为 leader，以便满足 Leader Completeness 性质。如果 candidate 的 log 至少和多数 log 一样新（at least as up-to-date as any other log in the majority）那么它包含了所有 committed entries。如果 log 的最后一个 entry term 更大，那么它就更新。如果 term 相同，那么更长的 log 更新。在 RequestVote RPC 中，candidate 会包含 lastLogIndex 和 lastLogTerm，只有当 candidate 的 log 更新时一个 server 才会投票给它。

image-20230909175357672

当 leader commit 一个来自它之前 term 的 entry 时，可能会遭遇上图的问题，在 (c) 中，来自 term 2 的 entry 已经被复制到多数 server 上了，然而如果 (d) 发生了，它仍然会被来自 term 3 的 entry 覆盖。因此 Raft 将 commit 定义为只有来时当前 term 的 entry 才能够通过 replica 计数来 commit，而来自之前任期的 entry 只能够通过在它之后的 entry 被 commit 来间接 commit（Log Matching 性质）。

我们可以用反证法来证明这么做的安全性（非严格证明），假设节点数量为 2f+1，log index m 已经被 commit 了，并且位于 f+1 个 server 上。此时一个不包含 m 的 server x 想要竞选 leader。为了得到多数票，至少有一个包含 m 的 server y需要投票给 x，此时需要 x 比 y 更新。根据 up-to-date 的定义，可能有以下两种情况：TODO

  • x 和 y 的最后一个 log entry term 相同，且 x 的 log 至少和 y 一样长
  • x 的最后一个 log entry term 更大

时机和可用性

在 leader 选举中，时机（Timing）是很关键的，如果 RPC 用时过长，那么 candidate 在超时之前将无法赢得选举。Raft 需要满足以下时机约束：$broadcastTime ≪ electionTimeout ≪ MTBF$，broadcastTime 是平均一次 RPC 的时间，electionTimeout 是选举超时时间，MTBF（Mean Time Between Failures）是发生两次 server 故障的平均间隔时间。

集群成员变更

这里指的是增减 server。虽然我们总是能够通过下线整个集群、更新配置、上线集群的方式来增减 server，但这样就减少了可用性。为了使配置变更是安全的，必须保证同一 term 不可能有两个 leader 被选举出来。然而，任何直接由旧配置转变为新配置的做法都是不安全的，如下图所示：

image-20230928111923370

为了保证安全性，Raft 采取了一种两阶段的策略，集群会切换到一个过渡的配置，称为 joint consensus。论文没有给出这个过程的具体实现，我对这部分也理解得不是很清楚。

log 压缩（compaction）

如果没有压缩，log 的大小会无限增长，并且在 server 故障重启后重新应用 log 的时间会非常长，这显然是不划算的。

快照（snapshot）是最简单的压缩方式。在一次快照中，当前的系统状态被写入到非易失存储中，然后到这个时刻的所有 log 将被丢弃。

image-20230928141311077

然而，快照带来了新的复杂度。如果一个 leader 的 log 如上图所示，在一次快照之后 log 中的第一个 entry 为 6，但是如果一个 follower 的 log 还停留在最后一个 entry 为 4，那么这个 follower 永远无法获得 entry 5 了。Raft 的做法是引入了一个新的 InstallSnapshot RPC，用于把 leader 的当前 snapshot 发送给 follower。

snapshot 的创建过程依赖于上层应用，因为 Raft 对于状态机里的状态知之甚少。但是何时创建 snapshot 由 Raft 决定，一个简单的策略是当 log 达到固定大小时创建。

每个 server 能够独立创建 snapshots，因而其偏离了 Raft 的强 leader 原则，但是由于在创建 snapshot 时共识已经达成了，所以这不会有问题。如果只有 leader 能创建 snapshot，那么有两个缺点 a) 每次发送 snapshot 都会浪费网络带宽 b) leader 的实现会变得复杂，因为它必须同时发送 snapshot 和新的 entries。

客户端交互

client 只会和 leader 交互，在 client 刚启动时，它可能会连接到一个随机的 server，若该 server 不为 leader，则 server 会拒绝这个请求并提供 leader 的信息。

Raft 的目标是提供可线性化（linearizable）的语义，即每个操作会在调用和回复之间的某个时间立刻执行执行一次（exactly once）。然而，Raft 可能会执行一个操作多次，如果 leader 在 commit log entry 之后回复 client 之前崩溃了，client 会重试请求，导致一个操作执行了两次。这个问题的解决方案是对每个操作分配一个独特的序列号，如果 leader 收到了一个请求包含已经被执行的序列号，那么它就不会再次执行这个请求。

如果没有额外的措施，读请求可能会读到过时的（stale）数据，如由于网络分区当前 leader 已经被新的 leader 取代了，但是当前 leader 并不知道这一点。为了保证不会读到过时的数据，在 leader 刚当选时它必须发送一个 no-op entry，以便找出哪些 entry 已经 commit 了。其次 leader 必须检查它是否被新的 leader 取代了，这可以通过发送心跳包来检查是否有多数 server 回应来实现。

Notes

  • Raft 是强一致性的协议，因为 commit 过的 log 总是一致的，如果出现网络分区则只有多数区能够达成共识，所以其符合了 CAP 原则中的 CP。另一种应对 split-brain 的方式是允许 split-brain，在网络恢复之后通过一些机制来调谐分叉的状态（reconcile the diverging state），通常这被称为最终一致性。

  • Q：一个 Raft 集群通常位于一个物理位置还是说可以部署在地理上不同的数据中心里？

    A：典型的部署方式是一个数据中心。我们会在之后看到一些运行 Paxos 的系统（如 Spanner）能够跨数据中心，其通常是一种无 leader 的设计，以便一个 client 能够同一个本地 server 通信。

    Google 的 Chubby 论文报告了它们的 Chubby 部署通常是在一个数据中心里，除了 root Chubby 是跨数据中心的（chubby 并未使用 raft，而是用了基于 Paxos 的一个内部库）

可线性化（Linearizability）

并发和不确定性的存在，导致分布式系统很容易存在一些微妙的 bug。除非使用形式化方法，否则我们只能依赖测试来验证系统的正确性。

通过 Linearizability 这样一种一致性模型，我们可以基于一种顺序的规格形式地确定并发操作的正确性。在一个 linearizable 的系统中，每个操作看起来是在调用和回复之间的某个时间点原子、瞬间执行。可线性化可以被视作强一致性的同义词。

Definition of linearizability:

Execution history is linearizable if there exists a total order of the operations in the history that matches real time for non-concurrent requests. Each read sees the value of the most recent write in order.

下图所示的执行历史是可线性化的：

image-20231001151607479

而下图则不是：

image-20231001151618755

参考

最后修改于 2023-10-01

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存档 标签 分类 关于 diff --git a/posts/index.xml b/posts/index.xml index 31ef015..6bf1e43 100644 --- a/posts/index.xml +++ b/posts/index.xml @@ -1,4 +1,9 @@ -Posts on Coherence's Bloghttps://blog.coherence.codes/posts/Recent content in Posts on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Mon, 18 Sep 2023 00:00:00 +0000git 入门https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/Mon, 18 Sep 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/本文的大部分内容来自于 Pro Git 这本书。 +Posts on Coherence's Bloghttps://blog.coherence.codes/posts/Recent content in Posts on Coherence's BlogHugo -- gohugo.iozh-cnCoherence. 本站遵循 CC BY-NC-SA 4.0 协议Sun, 01 Oct 2023 00:00:00 +0000MIT 6.824 学习笔记(二) Rafthttps://blog.coherence.codes/posts/2023/mit_6.824_2_raft/Sun, 01 Oct 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/mit_6.824_2_raft/Note: 为了保持准确性，我会尽量使用英文术语。 +为了解决单点故障问题，Raft 采用了 Majority Vote，基本上任何操作都需要得到多数确认才能够执行。为了避免 tie，Raft 的节点数量必须为奇数。因此在一个 2f+1 个节点的系统中，最多允许 f 个节点故障，f+1 即为法定人数（quorum）。 +如果 server 不出错，网络也很稳定，共识算法是很简单的，比如在 Raft 中，leader 被选举出来，client 的请求由 leader 处理并转发给 followers，所有 server 都和 leader 保持一致，这个过程是很简单并且直观的。这些算法真正处理的、细节繁杂的地方是 server 出错、RPC 受网络影响（包括包的延迟，重复，顺序改变，丢失，网络分区）的时候。 +Introduction 在过去十年, Leslie Lamport 的 Paxos 几乎成了共识的同义词.. Paxos 首先定义了一种协议来对单个决定达成共识, 比如一条单个的 log entry, 这被称为 single-decree Paxos. 其支持多个决定的版本 (比如 log) 被称为 muti-Paxos。然而，Paxos 的缺点是难以理解，并且没有提供一个良好的基础来构建可行的实现。 +相较于 Paxos，Raft 的目标是易于理解且符合直觉。为了使 Raft 易于理解，作者采取了解耦 (Raft 将共识问题分解成几个子问题 leader election, log replication, safety, and membership changes) 和缩减状态空间的方式。 +Raft 和已有的共识算法类似（尤其是 Viewstamped Replication），但它有一些新特性。Raft 采取了强 leader 的设计，例如 log entry 只会从 leader 向其他节点分发。这可能是为了性能考虑（比无 leader 要更快，RPC 也更少）Raft 采用基于随机计时器的 leader 选举, 从而用一种简单的方法来解决冲突。另外还有处理成员变更方面的改进。git 入门https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/Mon, 18 Sep 2023 00:00:00 +0000https://blog.coherence.codes/posts/2023/git_%E5%85%A5%E9%97%A8/本文的大部分内容来自于 Pro Git 这本书。 Objects git 中最基础的元素是 object，每个 object 由一个 object ID (OID) 唯一标识，OID 是一个 160 bits （用 hex 字符串表示为四十个字符） 的 SHA-1 哈希。具体而言，git 中主要存在三种 objects： blobs: 存储文件内容，OID 即为文件内容的哈希 trees: 一个文件名（path entries）的有序列表，OID 为这个列表的哈希。子目录同样是 trees。项目的根目录即为 root tree。所有的 trees 构成了一棵 Merkle tree，所以 git 就是区块链（即答。下图中三角代表 trees，方块为 blobs diff --git a/posts/page/2/index.html b/posts/page/2/index.html index a9ec9e0..36caabe 100644 --- a/posts/page/2/index.html +++ b/posts/page/2/index.html @@ -8,7 +8,7 @@ menu Coherence's Blog
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