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......@@ -53,7 +53,7 @@ var context={
<p>
下图展示了使用Kafka Streams库的应用程序的解剖结构。让我们来看看一些细节。
</p>
<img class="centered" src="/{{version}}/images/streams-architecture-overview.jpg" style="width:750px">
<img class="centered" src="{{version}}/images/streams-architecture-overview.jpg" style="width:750px">
<h3><a id="streams_architecture_tasks" href="#streams_architecture_tasks">Stream Partitions and Tasks</a></h3>
......@@ -87,7 +87,7 @@ var context={
<p>
下图显示了两个任务，每个任务分配 input stream 的 一个 partition。
</p>
<img class="centered" src="/{{version}}/images/streams-architecture-tasks.jpg" style="width:400px">
<img class="centered" src="{{version}}/images/streams-architecture-tasks.jpg" style="width:400px">
<br>
<h3><a id="streams_architecture_threads" href="#streams_architecture_threads">Threading Model</a></h3>
......@@ -96,7 +96,7 @@ var context={
Kafka Streams 允许用户配置应用程序实例中可并行的<b>线程数量</b>。
每个线程都可以按照处理器拓扑结构独立执行一个或多个任务。 例如，下图显示了一个运行两个流任务的流线程。
</p>
<img class="centered" src="/{{version}}/images/streams-architecture-threads.jpg" style="width:400px">
<img class="centered" src="{{version}}/images/streams-architecture-threads.jpg" style="width:400px">
<p>
启动更多流线程或更多的应用程序实例仅仅意味着可以复制更多的拓扑结构来处理不同的Kafka分区子集，从而有效地并行处理。
......@@ -114,7 +114,7 @@ var context={
<p>
Kafka Streams 提供了所谓的<b> state stores </b>，它可以被流处理应用程序用来存储和查询数据，这是实现有状态操作时的一项重要功能。
例如， <a href="/{{version}}/documentation/streams/developer-guide#streams_dsl">Kafka Streams DSL</a> 会在您调用诸如<code> join（）</code>或<code> aggregate（）</code>等有状态运算符时，或者在窗口化一个流时自动创建和管理 state stores 。
例如， <a href="{{version}}/documentation/streams/developer-guide#streams_dsl">Kafka Streams DSL</a> 会在您调用诸如<code> join（）</code>或<code> aggregate（）</code>等有状态运算符时，或者在窗口化一个流时自动创建和管理 state stores 。
</p>
<p>
......@@ -125,7 +125,7 @@ var context={
<p>
下图中的两个流任务都具有专用的 local state stores 。
</p>
<img class="centered" src="/{{version}}/images/streams-architecture-states.jpg" style="width:400px">
<img class="centered" src="{{version}}/images/streams-architecture-states.jpg" style="width:400px">
<br>
<h3><a id="streams_architecture_recovery" href="#streams_architecture_recovery">Fault Tolerance</a></h3>
......@@ -139,7 +139,7 @@ var context={
<p>
此外，Kafka Streams 也确保 local state stores 的健壮性。对于每个 state store ，它都会维护一个可复制的 changelog Kafka topic 以便跟踪任何状态更新。
这些 changelog topics 也进行了分区，以便每个 local state store 实例以及访问这些 store 的任务都有其自己专用的 changelog topic partition 。
在 changelog topics 上会启用 <a href="/{{version}}/documentation/#compaction">日志压缩（Log compaction）</a>，以便可以安全地清除旧数据以防止 topic 无限增长。
在 changelog topics 上会启用 <a href="{{version}}/documentation/#compaction">日志压缩（Log compaction）</a>，以便可以安全地清除旧数据以防止 topic 无限增长。
如果任务在一台故障的服务器上运行，并在另一台服务器上重新启动，则 Kafka Streams 保证在另一台服务器启动需要恢复的任务之前，会回滚相应的 changelog topics ，将其关联的 state stores 恢复成失败前的内容。
因此，故障处理对最终用户来说是完全透明的。
</p>
......@@ -147,12 +147,12 @@ var context={
<p>
请注意，任务（重新）初始化的时间通常取决于恢复 state 的时间（主要是回滚 state stores 相关联的 changelog topics 的时间）。
为了尽可能缩短恢复时间，用户可以将应用程序配置为具有<b>备用副本（standby replicas）</b>的local states（即完全可复制的 state 副本）。
当发生任务迁移时，Kafka Streams 会尝试将任务分配给已存在备用副本的应用程序实例，以最大程度地缩短任务（重新）初始化时间。请在 <a href="/{{version}}/documentation/#streamsconfigs"><b>Kafka Streams Configs</b></a> 部分查看 <code>num.standby.replicas</code> 配置项。
当发生任务迁移时，Kafka Streams 会尝试将任务分配给已存在备用副本的应用程序实例，以最大程度地缩短任务（重新）初始化时间。请在 <a href="{{version}}/documentation/#streamsconfigs"><b>Kafka Streams Configs</b></a> 部分查看 <code>num.standby.replicas</code> 配置项。
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......@@ -101,11 +101,11 @@ var context={
注意：一个正常的处理器节点在处理记录的同时是可以访问其他远程系统。因此，它的处理结果既可以写入到其他远程系统，也可以回流到 Kafka 系统中。
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<img class="centered" src="{{version}}/images/streams-architecture-topology.jpg" style="width:400px">
<p>
Kafka Streams 提供两种定义流处理拓扑结构的方式： <a href="/{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams DSL</b></a> 提供
了一些常用的、开箱即用的数据转换操作，比如： <code>map</code>， <code>filter</code>， <code>join</code> 和 <code>aggregations</code> ；而底层的 <a href="/{{version}}/documentation/streams/developer-guide#streams_processor"><b>Processor API</b></a> 则允许
Kafka Streams 提供两种定义流处理拓扑结构的方式： <a href="{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams DSL</b></a> 提供
了一些常用的、开箱即用的数据转换操作，比如： <code>map</code>， <code>filter</code>， <code>join</code> 和 <code>aggregations</code> ；而底层的 <a href="{{version}}/documentation/streams/developer-guide#streams_processor"><b>Processor API</b></a> 则允许
开发者定义和连接自定义的处理器，并且可以与 <a href="#streams_state">state stores</a> 交互。
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......@@ -155,7 +155,7 @@ var context={
<p>
有些流处理程序不需要 state ，这意味着这些程序处理的消息与其他消息互相独立。
但是，能够维护状态使很多复杂的流处理应用的实现成为可能：我们可以连接输入流，分组和聚合数据。大量这种基于状态的操作由 <a href="/{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams DSL</b></a> 提供。
但是，能够维护状态使很多复杂的流处理应用的实现成为可能：我们可以连接输入流，分组和聚合数据。大量这种基于状态的操作由 <a href="{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams DSL</b></a> 提供。
</p>
<p>
Kafka Streams 提供一种能够被流处理应用程序用来保存和查询数据状态的功能，称为 <b>state stores</b> 。
......@@ -183,12 +183,12 @@ var context={
要详细了解如何在 Kafka Streams 内完成此操作，建议读者阅读 <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-129%3A+Streams+Exactly-Once+Semantics">KIP-129</a> 。
在运行 Kafka 流应用程序时，为了实现 exactly-once 语义，用户需要设置 <code>processing.guarantee</code> 参数的值为 <b>exactly_once</b> （默认值为 <b>at_least_once</b> ）。
更多细节请参考 <a href="/{{version}}/documentation#streamsconfigs"><b>Kafka Streams Configs</b></a> 部分.
更多细节请参考 <a href="{{version}}/documentation#streamsconfigs"><b>Kafka Streams Configs</b></a> 部分.
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......@@ -102,7 +102,7 @@ var context={
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......@@ -483,13 +483,13 @@ var context={
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<div class="right">
<a class="documentation__banner b-sticky-doc-banner" href="/documentation">
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......@@ -506,13 +506,13 @@ var context={
<span class="footer__legal__three">in the United States and other countries.</span>
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<div class="sub-nav-sticky">
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......@@ -338,8 +338,8 @@ summit 1
第二列显示从KTable的状态更新以及发送到Kafka output topic <b>streams-wordcount-output</b> 的更改记录。
</p>
<img src="/{{version}}/images/streams-table-updates-02.png" style="float: right; width: 25%;">
<img src="/{{version}}/images/streams-table-updates-01.png" style="float: right; width: 25%;">
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<img src="{{version}}/images/streams-table-updates-01.png" style="float: right; width: 25%;">
<p>
首先，当第一行文本 "all streams lead to kafka" 开始被处理。
......@@ -361,8 +361,8 @@ summit 1
<p>现在，你可以通过 <b>Ctrl + C</b> 按钮按顺序停止 consumer，producer，Wordcount程序， Kafka broker和ZooKeeper服务。</p>
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......@@ -464,7 +464,7 @@ summit 1
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......@@ -507,7 +507,7 @@ summit 1
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......@@ -531,14 +531,14 @@ summit 1
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......@@ -554,13 +554,13 @@ summit 1
<span class="footer__legal__three">in the United States and other countries.</span>
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......@@ -593,7 +593,7 @@ summit 1
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doc/kafka-100-doc-zh/documentation/streams/upgrade-guide.html
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......@@ -61,13 +61,13 @@ var context={
</p>
<p>
If you want to upgrade from 0.10.1.x to 0.10.2, see the <a href="/{{version}}/documentation/#upgrade_1020_streams"><b>Upgrade Section for 0.10.2</b></a>.
If you want to upgrade from 0.10.1.x to 0.10.2, see the <a href="{{version}}/documentation/#upgrade_1020_streams"><b>Upgrade Section for 0.10.2</b></a>.
It highlights incompatible changes you need to consider to upgrade your code and application.
See <a href="#streams_api_changes_0102">below</a> a complete list of 0.10.2 API and semantic changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
</p>
<p>
If you want to upgrade from 0.10.0.x to 0.10.1, see the <a href="/{{version}}/documentation/#upgrade_1010_streams"><b>Upgrade Section for 0.10.1</b></a>.
If you want to upgrade from 0.10.0.x to 0.10.1, see the <a href="{{version}}/documentation/#upgrade_1010_streams"><b>Upgrade Section for 0.10.1</b></a>.
It highlights incompatible changes you need to consider to upgrade your code and application.
See <a href="#streams_api_changes_0101">below</a> a complete list of 0.10.1 API changes that allow you to advance your application and/or simplify your code base, including the usage of new features.
</p>
......@@ -109,7 +109,7 @@ var context={
(and some more dependent classes) were added.
Those can be used to get a detailed description of the specified topology
and can be obtained by calling <code>Topology#describe()</code>.
An example using this new API is shown in the <a href="/{{version}}/documentation/streams/quickstart">quickstart section</a>.
An example using this new API is shown in the <a href="{{version}}/documentation/streams/quickstart">quickstart section</a>.
</p>
<p>
......@@ -167,7 +167,7 @@ var context={
<p>
The Processor API was extended to allow users to schedule <code>punctuate</code> functions either based on data-driven <b>stream time</b> or wall-clock time.
As a result, the original <code>ProcessorContext#schedule</code> is deprecated with a new overloaded function that accepts a user customizable <code>Punctuator</code> callback interface, which triggers its <code>punctuate</code> API method periodically based on the <code>PunctuationType</code>.
The <code>PunctuationType</code> determines what notion of time is used for the punctuation scheduling: either <a href="/{{version}}/documentation/streams/core-concepts#streams_time">stream time</a> or wall-clock time (by default, <b>stream time</b> is configured to represent event time via <code>TimestampExtractor</code>).
The <code>PunctuationType</code> determines what notion of time is used for the punctuation scheduling: either <a href="{{version}}/documentation/streams/core-concepts#streams_time">stream time</a> or wall-clock time (by default, <b>stream time</b> is configured to represent event time via <code>TimestampExtractor</code>).
In addition, the <code>punctuate</code> function inside <code>Processor</code> is also deprecated.
</p>
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......@@ -388,7 +388,7 @@ var context={
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浏览文件 @ c78f7437
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浏览文件 @ c78f7437
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<a href=""><img width="325" height="97" class="logo" src="images/logo.png"></a>
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......@@ -137,7 +137,7 @@
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......@@ -162,27 +162,27 @@
<!--#include virtual="includes/_nav.htm" -->
<div class="right right-home">
<div class="desc">
<a href="/documentation.html#producerapi" class="desc-item">
<a href="documentation.html#producerapi" class="desc-item">
<h2>发布 &amp; 订阅</h2>
<p>类似于一个消息系统，读写流式的数据.</p>
<span class="desc-item__cta">查看更多 &raquo;</span>
</a>
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<h2>处理</h2>
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<div class="callout">
<p>Kafka&reg; 用于构建实时的数据管道和流式的app.它可以水平扩展，高可用，速度快，并且已经运行在数千家公司的生产环境。</p>
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doc/kafka-100-doc-zh/intro.html
浏览文件 @ c78f7437
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<a href=""><img width="325" height="97" class="logo" src="images/logo.png"></a>
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......@@ -231,12 +231,12 @@ var context={
<p>Kafka有四个核心的API:</p>
<div style="overflow: hidden;">
<ul style="float: left; width: 40%;">
<li>The <a href="/documentation.html#producerapi">Producer API</a> 允许一个应用程序发布一串流式的数据到一个或者多个Kafka topic。
<li>The <a href="/documentation.html#consumerapi">Consumer API</a> 允许一个应用程序订阅一个或多个 topic ，并且对发布给他们的流式数据进行处理。
<li>The <a href="/documentation/streams">Streams API</a> 允许一个应用程序作为一个<i>流处理器</i>，消费一个或者多个topic产生的输入流，然后生产一个输出流到一个或多个topic中去，在输入输出流中进行有效的转换。
<li>The <a href="/documentation.html#connect">Connector API</a> 允许构建并运行可重用的生产者或者消费者，将Kafka topics连接到已存在的应用程序或者数据系统。比如，连接到一个关系型数据库，捕捉表（table）的所有变更内容。
<li>The <a href="documentation.html#producerapi">Producer API</a> 允许一个应用程序发布一串流式的数据到一个或者多个Kafka topic。
<li>The <a href="documentation.html#consumerapi">Consumer API</a> 允许一个应用程序订阅一个或多个 topic ，并且对发布给他们的流式数据进行处理。
<li>The <a href="documentation/streams">Streams API</a> 允许一个应用程序作为一个<i>流处理器</i>，消费一个或者多个topic产生的输入流，然后生产一个输出流到一个或多个topic中去，在输入输出流中进行有效的转换。
<li>The <a href="documentation.html#connect">Connector API</a> 允许构建并运行可重用的生产者或者消费者，将Kafka topics连接到已存在的应用程序或者数据系统。比如，连接到一个关系型数据库，捕捉表（table）的所有变更内容。
</ul>
<img src="/{{version}}/images/kafka-apis.png" style="float: right; width: 50%;">
<img src="{{version}}/images/kafka-apis.png" style="float: right; width: 50%;">
</div>
<p>
在Kafka中，客户端和服务器使用一个简单、高性能、支持多语言的 <a href="https://kafka.apache.org/protocol.html">TCP 协议</a>.此协议版本化并且向下兼容老版本， 我们为Kafka提供了Java客户端，也支持许多<a href="https://cwiki.apache.org/confluence/display/KAFKA/Clients">其他语言的客户端</a>。</p>
......@@ -245,14 +245,14 @@ var context={
<p>让我们首先深入了解下Kafka的核心概念:提供一串流式的记录&mdash; topic 。</p>
<p>Topic 就是数据主题，是数据记录发布的地方,可以用来区分业务系统。Kafka中的Topics总是多订阅者模式，一个topic可以拥有一个或者多个消费者来订阅它的数据。</p>
<p>对于每一个topic， Kafka集群都会维持一个分区日志，如下所示： </p>
<img class="centered" src="/{{version}}/images/log_anatomy.png">
<img class="centered" src="{{version}}/images/log_anatomy.png">
<p>每个分区都是有序且顺序不可变的记录集，并且不断地追加到结构化的commit log文件。分区中的每一个记录都会分配一个id号来表示顺序，我们称之为offset，<i>offset</i>用来唯一的标识分区中每一条记录。
</p>
<p>
Kafka 集群保留所有发布的记录&mdash;无论他们是否已被消费&mdash;并通过一个可配置的参数——保留期限来控制. 举个例子， 如果保留策略设置为2天，一条记录发布后两天内，可以随时被消费，两天过后这条记录会被抛弃并释放磁盘空间。Kafka的性能和数据大小无关，所以长时间存储数据没有什么问题.
</p>
<img class="centered" src="/{{version}}/images/log_consumer.png" style="width:400px">
<img class="centered" src="{{version}}/images/log_consumer.png" style="width:400px">
<p>
事实上，在每一个消费者中唯一保存的元数据是offset（偏移量）即消费在log中的位置.偏移量由消费者所控制:通常在读取记录后，消费者会以线性的方式增加偏移量，但是实际上，由于这个位置由消费者控制，所以消费者可以采用任何顺序来消费记录。例如，一个消费者可以重置到一个旧的偏移量，从而重新处理过去的数据；也可以跳过最近的记录，从"现在"开始消费。
</p>
......@@ -287,7 +287,7 @@ var context={
<p>
如果所有的消费者实例在不同的消费组中，每条消息记录会广播到所有的消费者进程.
</p>
<img class="centered" src="/{{version}}/images/consumer-groups.png">
<img class="centered" src="{{version}}/images/consumer-groups.png">
<p>
如图，这个 Kafka 集群有两台 server 的，四个分区(p0-p3)和两个消费者组。消费组A有两个消费者，消费组B有四个消费者。
</p>
......@@ -414,7 +414,7 @@ var context={
或者与离线系统进行交互，系统可间歇性地加载数据，也可在停机维护后再次加载数据。流处理功能使得数据可以在到达时转换数据。
</p>
<p>
有关Kafka提供的保证、API和功能的更多信息，请看<a href="/documentation.html">文档</a>的剩余部分。
有关Kafka提供的保证、API和功能的更多信息，请看<a href="documentation.html">文档</a>的剩余部分。
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......@@ -431,13 +431,13 @@ var context={
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<a class="btn" href="/downloads.html">下载</a>
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<h3 class="bullet">Bugs</h3>
<a class="pb-10" href="https://issues.apache.org/jira/browse/KAFKA" target="_blank">Follow current development and issues</a>
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doc/kafka-100-doc-zh/protocol.html
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......@@ -395,13 +395,13 @@ Size => int32
<span class="footer__legal__three">in the United States and other countries.</span>
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<a class="apache-feather" target="_blank" href="http://www.apache.org">
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doc/kafka-100-doc-zh/quickstart.html
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<a href=""><img width="325" height="97" class="logo" src="images/logo.png"></a>
</div>
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......@@ -102,31 +102,31 @@
<nav class="b-sticky-nav">
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<div class="nav__inner">
<a class="b-nav__home nav__item" href="/">主页</a>
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......@@ -471,7 +471,7 @@ bar
<p>
Kafka Streams是用于构建实时关键应用程序和微服务的客户端库，输入与输出数据存储在Kafka集群中。
Kafka Streams把客户端能够轻便地编写部署标准Java和Scala应用程序的优势与Kafka服务器端集群技术相结合，使这些应用程序具有高度伸缩性、弹性、容错性、分布式等特性。
本<a href="/{{version}}/documentation/streams/quickstart">快速入门示例</a>将演示如何运行一个基于该库编程的流式应用程序。
本<a href="{{version}}/documentation/streams/quickstart">快速入门示例</a>将演示如何运行一个基于该库编程的流式应用程序。
</p>
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doc/kafka-100-doc-zh/uses.html
浏览文件 @ c78f7437
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<a href=""><img width="325" height="97" class="logo" src="images/logo.png"></a>
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......@@ -217,7 +217,7 @@ Kafka 从这些日志文件中提取信息，并将其抽象为一个更加清
然后对这个内容进行标准化或者重复的内容，
并将处理完的文章内容发布到新的topic;
最终它会尝试将这些内容推荐给用户。
这种处理管道基于各个topic创建实时数据流图。从0.10.0.0开始，在Apache Kafka中，<a href="/documentation/streams">Kafka Streams</a>
这种处理管道基于各个topic创建实时数据流图。从0.10.0.0开始，在Apache Kafka中，<a href="documentation/streams">Kafka Streams</a>
可以用来执行上述的数据处理，它是一个轻量但功能强大的流处理库。除Kafka Streams外，可供替代的开源流处理工具还包括<a href="https://storm.apache.org/">Apache Storm</a>
和<a href="http://samza.apache.org/">Apache Samza</a>.
......@@ -228,7 +228,7 @@ Kafka 可以存储非常多的日志数据，为基于 event sourcing 的应用
<h4><a id="uses_commitlog" href="#uses_commitlog">提交日志</a></h4>
Kafka 可以从外部为分布式系统提供日志提交功能。
日志有助于记录节点和行为间的数据，采用重新同步机制可以从失败节点恢复数据。
Kafka的<a href="/documentation.html#compaction">日志压缩</a> 功能支持这一用法。
Kafka的<a href="documentation.html#compaction">日志压缩</a> 功能支持这一用法。
这一点与<a href="http://zookeeper.apache.org/bookkeeper/">Apache BookKeeper</a> 项目类似。
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......@@ -243,13 +243,13 @@ Kafka的<a href="/documentation.html#compaction">日志压缩</a> 功能支持
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