leesf commented on a change in pull request #1006: [HUDI-276] Translate the
Configurations page into Chinese
URL: https://github.com/apache/incubator-hudi/pull/1006#discussion_r344466146
##########
File path: docs/configurations.cn.md
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@@ -1,48 +1,46 @@
---
-title: Configurations
+title: 配置
keywords: garbage collection, hudi, jvm, configs, tuning
sidebar: mydoc_sidebar
permalink: configurations.html
toc: true
-summary: "Here we list all possible configurations and what they mean"
+summary: 在这里，我们列出了所有可能的配置及其含义。
---
-This page covers the different ways of configuring your job to write/read Hudi
datasets.
-At a high level, you can control behaviour at few levels.
-
-- **[Spark Datasource Configs](#spark-datasource)** : These configs control
the Hudi Spark Datasource, providing ability to define keys/partitioning, pick
out the write operation, specify how to merge records or choosing view type to
read.
-- **[WriteClient Configs](#writeclient-configs)** : Internally, the Hudi
datasource uses a RDD based `HoodieWriteClient` api to actually perform writes
to storage. These configs provide deep control over lower level aspects like
- file sizing, compression, parallelism, compaction, write schema, cleaning
etc. Although Hudi provides sane defaults, from time-time these configs may
need to be tweaked to optimize for specific workloads.
-- **[RecordPayload Config](#PAYLOAD_CLASS_OPT_KEY)** : This is the lowest
level of customization offered by Hudi. Record payloads define how to produce
new values to upsert based on incoming new record and
- stored old record. Hudi provides default implementations such as
`OverwriteWithLatestAvroPayload` which simply update storage with the
latest/last-written record.
- This can be overridden to a custom class extending `HoodieRecordPayload`
class, on both datasource and WriteClient levels.
+该页面介绍了几种配置写入或读取Hudi数据集的作业的方法。
+简而言之，您可以在几个级别上控制行为。
+
+- **[Spark Datasource 配置](#spark-datasource)** : 这些配置控制Hudi Spark
Datasource，提供如下功能：
+ 定义键和分区、选择写操作、指定如何合并记录或选择要读取的视图类型。
+- **[WriteClient 配置](#writeclient-configs)** :
在内部，Hudi数据源使用基于RDD的`HoodieWriteClient` API
+ 真正执行对存储的写入。
这些配置可对文件大小、压缩（compression）、并行性、压缩（compaction）、写入模式、清理等底层方面进行完全控制。
+ 尽管Hudi提供了合理的默认设置，但在不同情形下，可能需要对这些配置进行调整以针对特定的工作负载进行优化。
+- **[RecordPayload 配置](#PAYLOAD_CLASS_OPT_KEY)** : 这是Hudi提供的最底层的定制。
+ RecordPayload定义了如何根据传入的新记录和存储的旧记录来产生新值以进行插入更新。
+ Hudi提供了诸如`OverwriteWithLatestAvroPayload`的默认实现，该实现仅使用最新或最后写入的记录来更新存储。
+ 在数据源和WriteClient级别，都可以将其重写为扩展`HoodieRecordPayload`类的自定义类。
-### Talking to Cloud Storage
+### 与云存储连接
-Immaterial of whether RDD/WriteClient APIs or Datasource is used, the
following information helps configure access
-to cloud stores.
+无论使用RDD/WriteClient API还是Datasource，以下信息有助于配置对云存储的访问。
* [AWS S3](s3_hoodie.html) <br/>
- Configurations required for S3 and Hudi co-operability.
+ S3和Hudi协同工作所需的配置。
* [Google Cloud Storage](gcs_hoodie.html) <br/>
- Configurations required for GCS and Hudi co-operability.
+ GCS和Hudi协同工作所需的配置。
-### Spark Datasource Configs {#spark-datasource}
+### Spark Datasource 配置 {#spark-datasource}
-Spark jobs using the datasource can be configured by passing the below options
into the `option(k,v)` method as usual.
-The actual datasource level configs are listed below.
+可以通过将以下选项传递到`option(k,v)`方法中来配置使用数据源的Spark作业。
+实际的数据源级别配置在下面列出。
+#### 写选项
-
-
-
-#### Write Options
-
-Additionally, you can pass down any of the WriteClient level configs directly
using `options()` or `option(k,v)` methods.
+另外，您可以使用`options()`或`option(k,v)`方法直接传递任何WriteClient级别的配置。
```
inputDF.write()
.format("org.apache.hudi")
-.options(clientOpts) // any of the Hudi client opts can be passed in as well
+.options(clientOpts) // 任何Hudi客户端选择都可以传入
Review comment:
选择 -> 选项？
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