You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@hudi.apache.org by vi...@apache.org on 2019/10/09 01:52:20 UTC

[incubator-hudi] branch asf-site updated: [docs][chinese] revert the PR(#900) (#946)

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 73a162d  [docs][chinese] revert the PR(#900) (#946)
73a162d is described below

commit 73a162d5e3c37cc77e619c5229d7a2b4c31f46e5
Author: leesf <49...@qq.com>
AuthorDate: Wed Oct 9 09:52:15 2019 +0800

    [docs][chinese] revert the PR(#900) (#946)
---
 docs/quickstart.cn.md | 2 +-
 docs/use_cases.cn.md  | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/quickstart.cn.md b/docs/quickstart.cn.md
index cb109ff..75bcec0 100644
--- a/docs/quickstart.cn.md
+++ b/docs/quickstart.cn.md
@@ -4,7 +4,7 @@ keywords: hudi, quickstart
 tags: [quickstart]
 sidebar: mydoc_sidebar
 toc: false
-permalink: /cn/quickstart.html
+permalink: quickstart.html
 ---
 <br/>
 为快速了解Hudi的功能,我们制作了一个基于Docker设置、所有依赖系统都在本地运行的[演示视频](https://www.youtube.com/watch?V=vhngusxdrd0)。
diff --git a/docs/use_cases.cn.md b/docs/use_cases.cn.md
index 8cd88f4..681d9ca 100644
--- a/docs/use_cases.cn.md
+++ b/docs/use_cases.cn.md
@@ -2,7 +2,7 @@
 title: Use Cases
 keywords: hudi, data ingestion, etl, real time, use cases
 sidebar: mydoc_sidebar
-permalink: /cn/use_cases.html
+permalink: use_cases.html
 toc: false
 summary: "以下是一些使用Hudi的示例,说明了加快处理速度和提高效率的好处"
 
@@ -65,4 +65,4 @@ Hadoop的一个流行用例是压缩数据,然后将其分发回在线服务
 例如,一个Spark管道可以[确定Hadoop上的紧急制动事件](https://eng.uber.com/telematics/)并将它们加载到服务存储层(如ElasticSearch)中,供Uber应用程序使用以增加安全驾驶。这种用例中,通常架构会在Hadoop和服务存储之间引入`队列`,以防止目标服务存储被压垮。
 对于队列的选择,一种流行的选择是Kafka,这个模型经常导致__在DFS上存储相同数据的冗余(用于计算结果的离线分析)和Kafka(用于分发)__
 
-通过将每次运行的Spark管道更新插入的输出转换为Hudi数据集,Hudi可以再次有效地解决这个问题,然后可以以增量方式获取尾部数据(就像Kafka主题一样)然后写入服务存储层。
\ No newline at end of file
+通过将每次运行的Spark管道更新插入的输出转换为Hudi数据集,Hudi可以再次有效地解决这个问题,然后可以以增量方式获取尾部数据(就像Kafka主题一样)然后写入服务存储层。