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<title>茅屋为秋风所破歌</title>
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<a href="/2018/04/04/Java NIO学习笔记之Buffer/" class="thumbnail">
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<div class="date"><time datetime="2018-04-04T06:15:33.381Z" itemprop="datePublished">2018-04-04</time></div>
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<p class="article-excerpt">
Java NIO Buffer API缓冲区（Buffer）是 NIO 的一个重要概念，所有的网络数据，都需要Buffer 封装一层再出去。
10 <= 标记（mark） <= 位置(position) <= 限制（limit） <= 容量 (capacity)
首先搞清楚上面的概念，尤其是 limit 和 capacity 是不一样的
It is very import
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<div class="date"><time datetime="2017-07-07T14:56:15.000Z" itemprop="datePublished">2017-07-07</time></div>
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<h1 class="article-title" itemprop="name">
<a href="/2017/07/07/初涉Kubernetes三/">初涉 Kubernetes(三)： Overlay 与直接路由</a>
</h1>
<p class="article-excerpt">
Overview本篇主要是为了补充上一篇的网络专题。上一篇对很多技术有一个笼统的介绍，包括实现原理讲的都比较笼统。毕竟篇幅有限。这一篇主要从vxLan 和直接路由（无覆盖网络）两个角度来补充上一篇暂未提及的内容。
Further Reading
</p>
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<a href="/2017/07/03/初涉Kubernetes二/" class="thumbnail">
<span style="background-image:url(/2017/07/03/初涉Kubernetes二/Kubernetes.005.png)" alt="初涉 Kubernetes(二)：网络专题" class="thumbnail-image"></span>
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<div class="date"><time datetime="2017-07-03T14:56:15.000Z" itemprop="datePublished">2017-07-03</time></div>
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<h1 class="article-title" itemprop="name">
<a href="/2017/07/03/初涉Kubernetes二/">初涉 Kubernetes(二)：网络专题</a>
</h1>
<p class="article-excerpt">
Overview本篇探讨的是 Kubernetes 乃至 Docker 的一个重要话题，网络。我是开发出身，网络知识对我来说就是个新世界的大门。因此关于网络这块我会描述的更加基础，更加易懂，也是为了方便我自己理解。
Docker 的网络基础以下概念均是从 Linux 的角度来探讨。
网络命名空间处于不同命名空间的网络协议栈是完全隔离的，且彼此间无法通信，新创建的命名空间只有一个回环设备 lo。这样
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<a href="/2017/06/23/初涉Kubernetes一/" class="thumbnail">
<span style="background-image:url(/2017/06/23/初涉Kubernetes一/kube-architecture.png)" alt="初涉 Kubernetes(一)：组件认知" class="thumbnail-image"></span>
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<a class="article-category-link" href="/categories/云平台/">云平台</a>
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<div class="date"><time datetime="2017-06-23T07:56:15.000Z" itemprop="datePublished">2017-06-23</time></div>
</div>
<h1 class="article-title" itemprop="name">
<a href="/2017/06/23/初涉Kubernetes一/">初涉 Kubernetes(一)：组件认知</a>
</h1>
<p class="article-excerpt">
Overview
上一篇《Kubeadm 搭建 Kubernetes 集群》 简单交代了 Kubernetes 的集群搭建方法，这一篇算是基于安装好的集群，做了一个简单的入门总结。
API ServerAPI Server 是 Kubernetes 非常核心的一个组件，主要的作用就是提供了 Kubernetes 各类资源对象（Service、Pod、RC、Deployment 等） 的增删改查以及
</p>
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</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2017/06/15/Kubeadm搭建Kubernetes集群/" class="thumbnail">
<span style="background-image:url(/2017/06/15/Kubeadm搭建Kubernetes集群/monitor.jpeg)" alt="Kubeadm 搭建 Kubernetes 集群" class="thumbnail-image"></span>
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<a class="article-category-link" href="/categories/云平台/">云平台</a>
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<div class="date"><time datetime="2017-06-15T02:27:15.000Z" itemprop="datePublished">2017-06-15</time></div>
</div>
<h1 class="article-title" itemprop="name">
<a href="/2017/06/15/Kubeadm搭建Kubernetes集群/">Kubeadm 搭建 Kubernetes 集群</a>
</h1>
<p class="article-excerpt">
Overview
本文简单介绍如何利用 Kubeadm 搭建 Kubernetes 1.6.4 集群的方法，网络方案采用Flannel(Overlay)。本篇不对原理和架构有过多阐述和讲解，只阐述步骤。
废话少说，立马开始！
环境四台机器（CentOS7.1）1234192.168.80.23 (master)192.168.80.24192.168.80.25192.168.80.26
Doc
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<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2017/05/18/从LSM到HBase/" class="thumbnail">
<span style="background-image:url(/2017/05/18/从LSM到HBase/LSM_Tree.png)" alt="从 LSM Tree 到 HBase" class="thumbnail-image"></span>
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<a class="article-category-link" href="/categories/分布式理论/">分布式理论</a>
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<div class="date"><time datetime="2017-05-18T02:27:15.000Z" itemprop="datePublished">2017-05-18</time></div>
</div>
<h1 class="article-title" itemprop="name">
<a href="/2017/05/18/从LSM到HBase/">从 LSM Tree 到 HBase</a>
</h1>
<p class="article-excerpt">
之前的文章 《初涉 HBase》 初步介绍了 HBase 底层逻辑，包括 HBase 的基础架构、读写数据流程以及表设计要注意的一些要点。这篇文章着重从 LSM Tree 的角度介绍 LevelDB 的经典实现，并以此为切入点加深对 HBase 的认知。
LSM Tree 是什么所谓 LSM（The Log-Structured Merge-Tree），即日志结构合并树，是由两个或两个以上存储数据
</p>
</div>
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<div class="date"><time datetime="2017-04-27T09:00:15.000Z" itemprop="datePublished">2017-04-27</time></div>
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<a href="/2017/04/27/ElasticSearch内部机制浅析三/">ElasticSearch 内部机制浅析（三）</a>
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<p class="article-excerpt">
前言上篇从分布式的角度阐述了 ES 的分布式设计和思想，这一篇打算与 Lucene 结合起来，摸透一些 ES 的常遇到的概念，我们可以将了解到的这些东西应用到优化实践中去。
废话不多说，进入正题。
ShardShard 实际上是一个 Lucene 的一个实例（Lucene Index），但往往一个 Elastic Index 都是由多个 Shards （primary & replica）
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<div class="date"><time datetime="2017-04-20T09:00:15.000Z" itemprop="datePublished">2017-04-20</time></div>
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<a href="/2017/04/20/ElasticSearch内部机制浅析二/">ElasticSearch 内部机制浅析（二）</a>
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<p class="article-excerpt">
前言上篇大致介绍了 ElasticSearch CRUD 的数据走向和涉及到的 Gossip 算法和每一种节点扮演的角色。我们对 ES 有了初步的认知，这一篇着重从 CAP 的角度去解读 ES 的分布式思想。
Split Brain之前介绍过，对于去中心化的 ES 分布式系统来说，采用默认配置是无法避免脑裂问题的（可以参考前一篇文章的discovery.zen.minimum_master_nod
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<a href="/2017/04/15/ElasticSearch内部机制浅析一/">ElasticSearch 内部机制浅析（一）</a>
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<p class="article-excerpt">
前言ElasticSearch （以下简称为 ES）从名字上看是搜索引擎，实际上除了搜索的作用，ES 甚至还支持上千台服务器分布式部署以及 PB 级别的可靠性存储，适合构建高可用和可扩展的系统。本文从设计的角度探讨 ES 是如何运作且能够支撑如此庞大的数据量的检索和插入。
节点类型
Master Eligible Node （候选主节点）：设置成node.master=true (default)
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jps （JVM 进程状态工具）1jps -lv
通常这样就可以大致看清楚 JVM 应用的基本概况
-l : 输出main类或Jar的全名
-v : 输出携带的 JVM 参数
-m : 输出传入 main 方法的参数
-q : 静默模式，只输出进程号
jstack （查看 JVM 线程堆栈信息）123456789101112131415Usage: jstack [-l] <pi
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