[GitHub] [incubator-hudi] leesf commented on a change in pull request #1010: [HUDI-277] Translate the Performance page into Chinese

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leesf commented on a change in pull request #1010: [HUDI-277] Translate the Performance page into Chinese
URL: https://github.com/apache/incubator-hudi/pull/1010#discussion_r345532446
 
 

 ##########
 File path: docs/performance.cn.md
 ##########
 @@ -1,50 +1,47 @@
 ---
-title: Performance
+title: 性能
 keywords: hudi, index, storage, compaction, cleaning, implementation
 sidebar: mydoc_sidebar
 toc: false
 permalink: performance.html
 ---
 
-In this section, we go over some real world performance numbers for Hudi upserts, incremental pull and compare them against
-the conventional alternatives for achieving these tasks. 
+在本节中，我们将介绍一些有关Hudi插入更新、增量提取的实际性能数据，并将其与实现这些任务的其它传统工具进行比较。
 
-## Upserts
+## 插入更新
 
-Following shows the speed up obtained for NoSQL database ingestion, from incrementally upserting on a Hudi dataset on the copy-on-write storage,
-on 5 tables ranging from small to huge (as opposed to bulk loading the tables)
+下面显示了从NoSQL数据库摄取获得的速度提升，这些速度提升数据是通过在写入时复制存储上的Hudi数据集上插入更新而获得的，
+数据集包括5个从小到大的表（相对于批量加载表）。
 
 <figure>
     <img class="docimage" src="/images/hudi_upsert_perf1.png" alt="hudi_upsert_perf1.png" style="max-width: 1000px" />
 </figure>
 
-Given Hudi can build the dataset incrementally, it opens doors for also scheduling ingesting more frequently thus reducing latency, with
-significant savings on the overall compute cost.
+由于Hudi可以通过增量构建数据集，它也为更频繁地调度摄取提供了可能性，从而减少了延迟，并显著节省了总体计算成本。
 
 <figure>
     <img class="docimage" src="/images/hudi_upsert_perf2.png" alt="hudi_upsert_perf2.png" style="max-width: 1000px" />
 </figure>
 
-Hudi upserts have been stress tested upto 4TB in a single commit across the t1 table. 
-See [here](https://cwiki.apache.org/confluence/display/HUDI/Tuning+Guide) for some tuning tips.
+Hudi插入更新在t1表的一次提交中就进行了高达4TB的压力测试。
+有关一些调优技巧，请参见[这里](https://cwiki.apache.org/confluence/display/HUDI/Tuning+Guide)。
 
-## Indexing
+## 索引
 
-In order to efficiently upsert data, Hudi needs to classify records in a write batch into inserts & updates (tagged with the file group 
-it belongs to). In order to speed this operation, Hudi employs a pluggable index mechanism that stores a mapping between recordKey and 
-the file group id it belongs to. By default, Hudi uses a built in index that uses file ranges and bloom filters to accomplish this, with
-upto 10x speed up over a spark join to do the same. 
+为了有效地插入更新数据，Hudi需要将要写入的批量数据中的记录分类为插入和更新（并标记它所属的文件组）。
+为了加快此操作的速度，Hudi采用了可插入索引机制，该机制存储了recordKey和它所属的文件组ID之间的映射。
 
 Review comment:
   可插入 -> 可插拔？

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