[incubator-hudi] branch asf-site updated: Travis CI build asf-site

Sat, 28 Mar 2020 20:32:00 -0700 

This is an automated email from the ASF dual-hosted git repository.

vinoth pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-hudi.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 96f3d74  Travis CI build asf-site
96f3d74 is described below

commit 96f3d746d491c06e67c494985452fd95d0b831ee
Author: CI <[email protected]>
AuthorDate: Sun Mar 29 03:30:47 2020 +0000

    Travis CI build asf-site
---
 test-content/assets/js/lunr/lunr-store.js     |   4 +-
 test-content/cn/docs/0.5.2-querying_data.html | 102 +++++++++++++++++++++++++-
 test-content/cn/docs/querying_data.html       | 102 +++++++++++++++++++++++++-
 test-content/docs/0.5.2-querying_data.html    |   3 +-
 test-content/docs/querying_data.html          |   3 +-
 5 files changed, 204 insertions(+), 10 deletions(-)

diff --git a/test-content/assets/js/lunr/lunr-store.js 
b/test-content/assets/js/lunr/lunr-store.js
index 1077cf3..9351ab6 100644
--- a/test-content/assets/js/lunr/lunr-store.js
+++ b/test-content/assets/js/lunr/lunr-store.js
@@ -435,7 +435,7 @@ var store = [{
         "url": "https://hudi.apache.org/docs/0.5.2-writing_data.html";,
         "teaser":"https://hudi.apache.org/assets/images/500x300.png"},{
         "title": "查询 Hudi 数据集",
-        "excerpt":"从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如之前所述。 数据集同步到Hive 
Metastore后，它将提供由Hudi的自定义输入格式支持的Hive外部表。一旦提供了适当的Hudi捆绑包， 
就可以通过Hive、Spark和Presto之类的常用查询引擎来查询数据集。 具体来说，在写入过程中传递了两个由table name命名的Hive表。 
例如，如果table name = hudi_tbl，我们得到 hudi_tbl 实现了由 HoodieParquetInputFormat 
支持的数据集的读优化视图，从而提供了纯列式数据。 hudi_tbl_rt 实现了由 HoodieParquetRealtimeInputFormat 
支持的数据集的实时视图，从而提供了基础数据和日志数据的合并视图。 如概念部分所述，增量处理所需要的 
一个关键原语是增量拉取（以从数据集中获取更改流/日志）。您可以增量提取Hudi数据集，这意味着自指定的即时时间起， 您可�
 �只获得全部更新和新行。 这与插入更新一起使用，对于构建某 [...]
+        "excerpt":"从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如之前所述。 数据集同步到Hive 
Metastore后，它将提供由Hudi的自定义输入格式支持的Hive外部表。一旦提供了适当的Hudi捆绑包， 
就可以通过Hive、Spark和Presto之类的常用查询引擎来查询数据集。 具体来说，在写入过程中传递了两个由table name命名的Hive表。 
例如，如果table name = hudi_tbl，我们得到 hudi_tbl 实现了由 HoodieParquetInputFormat 
支持的数据集的读优化视图，从而提供了纯列式数据。 hudi_tbl_rt 实现了由 HoodieParquetRealtimeInputFormat 
支持的数据集的实时视图，从而提供了基础数据和日志数据的合并视图。 如概念部分所述，增量处理所需要的 
一个关键原语是增量拉取（以从数据集中获取更改流/日志）。您可以增量提取Hudi数据集，这意味着自指定的即时时间起， 您可�
 �只获得全部更新和新行。 这与插入更新一起使用，对于构建某 [...]
         "tags": [],
         "url": "https://hudi.apache.org/cn/docs/0.5.2-querying_data.html";,
         "teaser":"https://hudi.apache.org/assets/images/500x300.png"},{
@@ -600,7 +600,7 @@ var store = [{
         "url": "https://hudi.apache.org/docs/writing_data.html";,
         "teaser":"https://hudi.apache.org/assets/images/500x300.png"},{
         "title": "查询 Hudi 数据集",
-        "excerpt":"从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如之前所述。 数据集同步到Hive 
Metastore后，它将提供由Hudi的自定义输入格式支持的Hive外部表。一旦提供了适当的Hudi捆绑包， 
就可以通过Hive、Spark和Presto之类的常用查询引擎来查询数据集。 具体来说，在写入过程中传递了两个由table name命名的Hive表。 
例如，如果table name = hudi_tbl，我们得到 hudi_tbl 实现了由 HoodieParquetInputFormat 
支持的数据集的读优化视图，从而提供了纯列式数据。 hudi_tbl_rt 实现了由 HoodieParquetRealtimeInputFormat 
支持的数据集的实时视图，从而提供了基础数据和日志数据的合并视图。 如概念部分所述，增量处理所需要的 
一个关键原语是增量拉取（以从数据集中获取更改流/日志）。您可以增量提取Hudi数据集，这意味着自指定的即时时间起， 您可�
 �只获得全部更新和新行。 这与插入更新一起使用，对于构建某 [...]
+        "excerpt":"从概念上讲，Hudi物理存储一次数据到DFS上，同时在其上提供三个逻辑视图，如之前所述。 数据集同步到Hive 
Metastore后，它将提供由Hudi的自定义输入格式支持的Hive外部表。一旦提供了适当的Hudi捆绑包， 
就可以通过Hive、Spark和Presto之类的常用查询引擎来查询数据集。 具体来说，在写入过程中传递了两个由table name命名的Hive表。 
例如，如果table name = hudi_tbl，我们得到 hudi_tbl 实现了由 HoodieParquetInputFormat 
支持的数据集的读优化视图，从而提供了纯列式数据。 hudi_tbl_rt 实现了由 HoodieParquetRealtimeInputFormat 
支持的数据集的实时视图，从而提供了基础数据和日志数据的合并视图。 如概念部分所述，增量处理所需要的 
一个关键原语是增量拉取（以从数据集中获取更改流/日志）。您可以增量提取Hudi数据集，这意味着自指定的即时时间起， 您可�
 �只获得全部更新和新行。 这与插入更新一起使用，对于构建某 [...]
         "tags": [],
         "url": "https://hudi.apache.org/cn/docs/querying_data.html";,
         "teaser":"https://hudi.apache.org/assets/images/500x300.png"},{
diff --git a/test-content/cn/docs/0.5.2-querying_data.html 
b/test-content/cn/docs/0.5.2-querying_data.html
index 0f4a441..5d337d6 100644
--- a/test-content/cn/docs/0.5.2-querying_data.html
+++ b/test-content/cn/docs/0.5.2-querying_data.html
@@ -335,6 +335,12 @@
           <nav class="toc">
             <header><h4 class="nav__title"><i class="fas fa-file-alt"></i> IN 
THIS PAGE</h4></header>
             <ul class="toc__menu">
+  <li><a href="#查询引擎支持列表">查询引擎支持列表</a>
+    <ul>
+      <li><a href="#读优化表">读优化表</a></li>
+      <li><a href="#实时表">实时表</a></li>
+    </ul>
+  </li>
   <li><a href="#hive">Hive</a>
     <ul>
       <li><a href="#hive-ro-view">读优化表</a></li>
@@ -352,7 +358,7 @@
   <li><a href="#presto">Presto</a></li>
   <li><a href="#impala此功能还未正式发布">Impala(此功能还未正式发布)</a>
     <ul>
-      <li><a href="#读优化表">读优化表</a></li>
+      <li><a href="#读优化表-1">读优化表</a></li>
     </ul>
   </li>
 </ul>
@@ -377,6 +383,94 @@
 并与其他表（数据集/维度）结合以<a 
href="/cn/docs/0.5.2-writing_data.html">写出增量</a>到目标Hudi数据集。增量视图是通过查询上表之一实现的，并具有特殊配置，
 该特殊配置指示查询计划仅需要从数据集中获取增量数据。</p>
 
+<h2 id="查询引擎支持列表">查询引擎支持列表</h2>
+
+<p>下面的表格展示了各查询引擎是否支持Hudi格式</p>
+
+<h3 id="读优化表">读优化表</h3>
+
+<table>
+  <thead>
+    <tr>
+      <th>查询引擎</th>
+      <th>实时视图</th>
+      <th>增量拉取</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Hive</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark SQL</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark Datasource</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Presto</strong></td>
+      <td>Y</td>
+      <td>N</td>
+    </tr>
+    <tr>
+      <td><strong>Impala</strong></td>
+      <td>Y</td>
+      <td>N</td>
+    </tr>
+  </tbody>
+</table>
+
+<h3 id="实时表">实时表</h3>
+
+<table>
+  <thead>
+    <tr>
+      <th>查询引擎</th>
+      <th>实时视图</th>
+      <th>增量拉取</th>
+      <th>读优化表</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Hive</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark SQL</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark Datasource</strong></td>
+      <td>N</td>
+      <td>N</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Presto</strong></td>
+      <td>N</td>
+      <td>N</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Impala</strong></td>
+      <td>N</td>
+      <td>N</td>
+      <td>Y</td>
+    </tr>
+  </tbody>
+</table>
+
 <p>接下来，我们将详细讨论在每个查询引擎上如何访问所有三个视图。</p>
 
 <h2 id="hive">Hive</h2>
@@ -542,7 +636,9 @@ Upsert实用程序（<code 
class="highlighter-rouge">HoodieDeltaStreamer</code>
              <span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">VIEW_TYPE_INCREMENTAL_OPT_VAL</span><span 
class="o">())</span>
      <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">BEGIN_INSTANTTIME_OPT_KEY</span><span 
class="o">(),</span>
             <span class="o">&lt;</span><span 
class="n">beginInstantTime</span><span class="o">&gt;)</span>
-     <span class="o">.</span><span class="na">load</span><span 
class="o">(</span><span class="n">tablePath</span><span class="o">);</span> 
<span class="c1">// For incremental view, pass in the root/base path of 
dataset</span>
+     <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">INCR_PATH_GLOB_OPT_KEY</span><span 
class="o">(),</span>
+            <span class="s">"/year=2020/month=*/day=*"</span><span 
class="o">)</span> <span class="c1">// 可选，从指定的分区增量拉取</span>
+     <span class="o">.</span><span class="na">load</span><span 
class="o">(</span><span class="n">tablePath</span><span class="o">);</span> 
<span class="c1">// 用数据集的最底层路径</span>
 </code></pre></div></div>
 
 <p>请参阅<a 
href="/cn/docs/0.5.2-configurations.html#spark-datasource">设置</a>部分，以查看所有数据源选项。</p>
@@ -579,7 +675,7 @@ Upsert实用程序（<code 
class="highlighter-rouge">HoodieDeltaStreamer</code>
 
 <h2 id="impala此功能还未正式发布">Impala(此功能还未正式发布)</h2>
 
-<h3 id="读优化表">读优化表</h3>
+<h3 id="读优化表-1">读优化表</h3>
 
 <p>Impala可以在HDFS上查询Hudi读优化表，作为一种 <a 
href="https://docs.cloudera.com/documentation/enterprise/6/6.3/topics/impala_tables.html#external_tables";>EXTERNAL
 TABLE</a> 的形式。<br />
 可以通过以下方式在Impala上建立Hudi读优化表:</p>
diff --git a/test-content/cn/docs/querying_data.html 
b/test-content/cn/docs/querying_data.html
index 835ab9e..290ef18 100644
--- a/test-content/cn/docs/querying_data.html
+++ b/test-content/cn/docs/querying_data.html
@@ -335,6 +335,12 @@
           <nav class="toc">
             <header><h4 class="nav__title"><i class="fas fa-file-alt"></i> IN 
THIS PAGE</h4></header>
             <ul class="toc__menu">
+  <li><a href="#查询引擎支持列表">查询引擎支持列表</a>
+    <ul>
+      <li><a href="#读优化表">读优化表</a></li>
+      <li><a href="#实时表">实时表</a></li>
+    </ul>
+  </li>
   <li><a href="#hive">Hive</a>
     <ul>
       <li><a href="#hive-ro-view">读优化表</a></li>
@@ -352,7 +358,7 @@
   <li><a href="#presto">Presto</a></li>
   <li><a href="#impala此功能还未正式发布">Impala(此功能还未正式发布)</a>
     <ul>
-      <li><a href="#读优化表">读优化表</a></li>
+      <li><a href="#读优化表-1">读优化表</a></li>
     </ul>
   </li>
 </ul>
@@ -377,6 +383,94 @@
 并与其他表（数据集/维度）结合以<a 
href="/cn/docs/writing_data.html">写出增量</a>到目标Hudi数据集。增量视图是通过查询上表之一实现的，并具有特殊配置，
 该特殊配置指示查询计划仅需要从数据集中获取增量数据。</p>
 
+<h2 id="查询引擎支持列表">查询引擎支持列表</h2>
+
+<p>下面的表格展示了各查询引擎是否支持Hudi格式</p>
+
+<h3 id="读优化表">读优化表</h3>
+
+<table>
+  <thead>
+    <tr>
+      <th>查询引擎</th>
+      <th>实时视图</th>
+      <th>增量拉取</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Hive</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark SQL</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark Datasource</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Presto</strong></td>
+      <td>Y</td>
+      <td>N</td>
+    </tr>
+    <tr>
+      <td><strong>Impala</strong></td>
+      <td>Y</td>
+      <td>N</td>
+    </tr>
+  </tbody>
+</table>
+
+<h3 id="实时表">实时表</h3>
+
+<table>
+  <thead>
+    <tr>
+      <th>查询引擎</th>
+      <th>实时视图</th>
+      <th>增量拉取</th>
+      <th>读优化表</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Hive</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark SQL</strong></td>
+      <td>Y</td>
+      <td>Y</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Spark Datasource</strong></td>
+      <td>N</td>
+      <td>N</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Presto</strong></td>
+      <td>N</td>
+      <td>N</td>
+      <td>Y</td>
+    </tr>
+    <tr>
+      <td><strong>Impala</strong></td>
+      <td>N</td>
+      <td>N</td>
+      <td>Y</td>
+    </tr>
+  </tbody>
+</table>
+
 <p>接下来，我们将详细讨论在每个查询引擎上如何访问所有三个视图。</p>
 
 <h2 id="hive">Hive</h2>
@@ -542,7 +636,9 @@ Upsert实用程序（<code 
class="highlighter-rouge">HoodieDeltaStreamer</code>
              <span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">VIEW_TYPE_INCREMENTAL_OPT_VAL</span><span 
class="o">())</span>
      <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">BEGIN_INSTANTTIME_OPT_KEY</span><span 
class="o">(),</span>
             <span class="o">&lt;</span><span 
class="n">beginInstantTime</span><span class="o">&gt;)</span>
-     <span class="o">.</span><span class="na">load</span><span 
class="o">(</span><span class="n">tablePath</span><span class="o">);</span> 
<span class="c1">// For incremental view, pass in the root/base path of 
dataset</span>
+     <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">INCR_PATH_GLOB_OPT_KEY</span><span 
class="o">(),</span>
+            <span class="s">"/year=2020/month=*/day=*"</span><span 
class="o">)</span> <span class="c1">// 可选，从指定的分区增量拉取</span>
+     <span class="o">.</span><span class="na">load</span><span 
class="o">(</span><span class="n">tablePath</span><span class="o">);</span> 
<span class="c1">// 用数据集的最底层路径</span>
 </code></pre></div></div>
 
 <p>请参阅<a 
href="/cn/docs/configurations.html#spark-datasource">设置</a>部分，以查看所有数据源选项。</p>
@@ -579,7 +675,7 @@ Upsert实用程序（<code 
class="highlighter-rouge">HoodieDeltaStreamer</code>
 
 <h2 id="impala此功能还未正式发布">Impala(此功能还未正式发布)</h2>
 
-<h3 id="读优化表">读优化表</h3>
+<h3 id="读优化表-1">读优化表</h3>
 
 <p>Impala可以在HDFS上查询Hudi读优化表，作为一种 <a 
href="https://docs.cloudera.com/documentation/enterprise/6/6.3/topics/impala_tables.html#external_tables";>EXTERNAL
 TABLE</a> 的形式。<br />
 可以通过以下方式在Impala上建立Hudi读优化表:</p>
diff --git a/test-content/docs/0.5.2-querying_data.html 
b/test-content/docs/0.5.2-querying_data.html
index 69482b3..5be58d9 100644
--- a/test-content/docs/0.5.2-querying_data.html
+++ b/test-content/docs/0.5.2-querying_data.html
@@ -471,7 +471,7 @@ with special configurations that indicates to query 
planning that only increment
       <td><strong>Impala</strong></td>
       <td>N</td>
       <td>N</td>
-      <td>N</td>
+      <td>Y</td>
     </tr>
   </tbody>
 </table>
@@ -628,6 +628,7 @@ The following snippet shows how to obtain all records 
changed after <code class=
      <span class="o">.</span><span class="na">format</span><span 
class="o">(</span><span class="s">"org.apache.hudi"</span><span 
class="o">)</span>
      <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">QUERY_TYPE_OPT_KEY</span><span 
class="o">(),</span> <span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">QUERY_TYPE_INCREMENTAL_OPT_VAL</span><span 
class="o">())</span>
      <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">BEGIN_INSTANTTIME_OPT_KEY</span><span 
class="o">(),</span> <span class="o">&lt;</span><span 
class="n">beginInstantTime</span><span class="o">&gt;)</span>
+     <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">INCR_PATH_GLOB_OPT_KEY</span><span 
class="o">(),</span> <span class="s">"/year=2020/month=*/day=*"</span><span 
class="o">)</span> <span class="c1">// Optional, use glob pattern if querying 
certain partitions</span>
      <span class="o">.</span><span class="na">load</span><span 
class="o">(</span><span class="n">tablePath</span><span class="o">);</span> 
<span class="c1">// For incremental query, pass in the root/base path of 
table</span>
      
 <span class="n">hudiIncQueryDF</span><span class="o">.</span><span 
class="na">createOrReplaceTempView</span><span class="o">(</span><span 
class="s">"hudi_trips_incremental"</span><span class="o">)</span>
diff --git a/test-content/docs/querying_data.html 
b/test-content/docs/querying_data.html
index f25f28b..70e9d8f 100644
--- a/test-content/docs/querying_data.html
+++ b/test-content/docs/querying_data.html
@@ -471,7 +471,7 @@ with special configurations that indicates to query 
planning that only increment
       <td><strong>Impala</strong></td>
       <td>N</td>
       <td>N</td>
-      <td>N</td>
+      <td>Y</td>
     </tr>
   </tbody>
 </table>
@@ -628,6 +628,7 @@ The following snippet shows how to obtain all records 
changed after <code class=
      <span class="o">.</span><span class="na">format</span><span 
class="o">(</span><span class="s">"org.apache.hudi"</span><span 
class="o">)</span>
      <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">QUERY_TYPE_OPT_KEY</span><span 
class="o">(),</span> <span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">QUERY_TYPE_INCREMENTAL_OPT_VAL</span><span 
class="o">())</span>
      <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">BEGIN_INSTANTTIME_OPT_KEY</span><span 
class="o">(),</span> <span class="o">&lt;</span><span 
class="n">beginInstantTime</span><span class="o">&gt;)</span>
+     <span class="o">.</span><span class="na">option</span><span 
class="o">(</span><span class="nc">DataSourceReadOptions</span><span 
class="o">.</span><span class="na">INCR_PATH_GLOB_OPT_KEY</span><span 
class="o">(),</span> <span class="s">"/year=2020/month=*/day=*"</span><span 
class="o">)</span> <span class="c1">// Optional, use glob pattern if querying 
certain partitions</span>
      <span class="o">.</span><span class="na">load</span><span 
class="o">(</span><span class="n">tablePath</span><span class="o">);</span> 
<span class="c1">// For incremental query, pass in the root/base path of 
table</span>
      
 <span class="n">hudiIncQueryDF</span><span class="o">.</span><span 
class="na">createOrReplaceTempView</span><span class="o">(</span><span 
class="s">"hudi_trips_incremental"</span><span class="o">)</span>

Reply via email to