[GitHub] [incubator-hudi] yihua commented on a change in pull request #1006: [HUDI-276] Translate the Configurations page into Chinese

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yihua 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_r344858236
 
 

 ##########
 File path: docs/configurations.cn.md
 ##########
 @@ -51,385 +49,419 @@ inputDF.write()
 .save(basePath);
 ```
 
-Options useful for writing datasets via `write.format.option(...)`
+用于通过`write.format.option(...)`写入数据集的选项
 
 ##### TABLE_NAME_OPT_KEY {#TABLE_NAME_OPT_KEY}
-  Property: `hoodie.datasource.write.table.name` [Required]<br/>
-  <span style="color:grey">Hive table name, to register the dataset into.</span>
+  属性：`hoodie.datasource.write.table.name` [必须]<br/>
+  <span style="color:grey">Hive表名，用于将数据集注册到其中。</span>
   
 ##### OPERATION_OPT_KEY {#OPERATION_OPT_KEY}
-  Property: `hoodie.datasource.write.operation`, Default: `upsert`<br/>
-  <span style="color:grey">whether to do upsert, insert or bulkinsert for the write operation. Use `bulkinsert` to load new data into a table, and there on use `upsert`/`insert`. 
-  bulk insert uses a disk based write path to scale to load large inputs without need to cache it.</span>
+  属性：`hoodie.datasource.write.operation`, 默认值：`upsert`<br/>
+  <span style="color:grey">是否为写操作进行插入更新、插入或批量插入。使用`bulkinsert`将新数据加载到表中，之后使用`upsert`或`insert`。
+  批量插入使用基于磁盘的写入路径来扩展以加载大量输入，而无需对其进行缓存。</span>
   
 ##### STORAGE_TYPE_OPT_KEY {#STORAGE_TYPE_OPT_KEY}
-  Property: `hoodie.datasource.write.storage.type`, Default: `COPY_ON_WRITE` <br/>
-  <span style="color:grey">The storage type for the underlying data, for this write. This can't change between writes.</span>
+  属性：`hoodie.datasource.write.storage.type`, 默认值：`COPY_ON_WRITE` <br/>
+  <span style="color:grey">此写入的基础数据的存储类型。两次写入之间不能改变。</span>
   
 ##### PRECOMBINE_FIELD_OPT_KEY {#PRECOMBINE_FIELD_OPT_KEY}
-  Property: `hoodie.datasource.write.precombine.field`, Default: `ts` <br/>
-  <span style="color:grey">Field used in preCombining before actual write. When two records have the same key value,
-we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..)</span>
+  属性：`hoodie.datasource.write.precombine.field`, 默认值：`ts` <br/>
+  <span style="color:grey">实际写入之前在preCombining中使用的字段。
+  当两个记录具有相同的键值时，我们将使用Object.compareTo(..)从precombine字段中选择一个值最大的记录。</span>
 
 ##### PAYLOAD_CLASS_OPT_KEY {#PAYLOAD_CLASS_OPT_KEY}
-  Property: `hoodie.datasource.write.payload.class`, Default: `org.apache.hudi.OverwriteWithLatestAvroPayload` <br/>
-  <span style="color:grey">Payload class used. Override this, if you like to roll your own merge logic, when upserting/inserting. 
-  This will render any value set for `PRECOMBINE_FIELD_OPT_VAL` in-effective</span>
+  属性：`hoodie.datasource.write.payload.class`, 默认值：`org.apache.hudi.OverwriteWithLatestAvroPayload` <br/>
+  <span style="color:grey">使用的有效载荷类。如果您想在插入更新或插入时使用自己的合并逻辑，请重写此方法。
+  这将使为`PRECOMBINE_FIELD_OPT_VAL`设置的任何值无效</span>
   
 ##### RECORDKEY_FIELD_OPT_KEY {#RECORDKEY_FIELD_OPT_KEY}
-  Property: `hoodie.datasource.write.recordkey.field`, Default: `uuid` <br/>
-  <span style="color:grey">Record key field. Value to be used as the `recordKey` component of `HoodieKey`. Actual value
-will be obtained by invoking .toString() on the field value. Nested fields can be specified using
-the dot notation eg: `a.b.c`</span>
+  属性：`hoodie.datasource.write.recordkey.field`, 默认值：`uuid` <br/>
+  <span style="color:grey">记录键字段。用作`HoodieKey`中`recordKey`部分的值。
+  实际值将通过在字段值上调用.toString()来获得。可以使用点符号指定嵌套字段，例如：`a.b.c`</span>
 
 ##### PARTITIONPATH_FIELD_OPT_KEY {#PARTITIONPATH_FIELD_OPT_KEY}
-  Property: `hoodie.datasource.write.partitionpath.field`, Default: `partitionpath` <br/>
-  <span style="color:grey">Partition path field. Value to be used at the `partitionPath` component of `HoodieKey`.
-Actual value ontained by invoking .toString()</span>
+  属性：`hoodie.datasource.write.partitionpath.field`, 默认值：`partitionpath` <br/>
+  <span style="color:grey">分区路径字段。用作`HoodieKey`中`partitionPath`部分的值。
+  通过调用.toString()获得实际的值</span>
 
 ##### KEYGENERATOR_CLASS_OPT_KEY {#KEYGENERATOR_CLASS_OPT_KEY}
-  Property: `hoodie.datasource.write.keygenerator.class`, Default: `org.apache.hudi.SimpleKeyGenerator` <br/>
-  <span style="color:grey">Key generator class, that implements will extract the key out of incoming `Row` object</span>
+  属性：`hoodie.datasource.write.keygenerator.class`, 默认值：`org.apache.hudi.SimpleKeyGenerator` <br/>
+  <span style="color:grey">键生成器类，实现从输入的`Row`对象中提取键</span>
   
 ##### COMMIT_METADATA_KEYPREFIX_OPT_KEY {#COMMIT_METADATA_KEYPREFIX_OPT_KEY}
-  Property: `hoodie.datasource.write.commitmeta.key.prefix`, Default: `_` <br/>
-  <span style="color:grey">Option keys beginning with this prefix, are automatically added to the commit/deltacommit metadata.
-This is useful to store checkpointing information, in a consistent way with the hudi timeline</span>
+  属性：`hoodie.datasource.write.commitmeta.key.prefix`, 默认值：`_` <br/>
+  <span style="color:grey">以该前缀开头的选项键会自动添加到提交/增量提交的元数据中。
+  这对于以与hudi时间轴一致的方式存储检查点信息很有用</span>
 
 ##### INSERT_DROP_DUPS_OPT_KEY {#INSERT_DROP_DUPS_OPT_KEY}
-  Property: `hoodie.datasource.write.insert.drop.duplicates`, Default: `false` <br/>
-  <span style="color:grey">If set to true, filters out all duplicate records from incoming dataframe, during insert operations. </span>
+  属性：`hoodie.datasource.write.insert.drop.duplicates`, 默认值：`false` <br/>
+  <span style="color:grey">如果设置为true，则在插入操作期间从传入数据帧中过滤掉所有重复记录。</span>
   
 ##### HIVE_SYNC_ENABLED_OPT_KEY {#HIVE_SYNC_ENABLED_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.enable`, Default: `false` <br/>
-  <span style="color:grey">When set to true, register/sync the dataset to Apache Hive metastore</span>
+  属性：`hoodie.datasource.hive_sync.enable`, 默认值：`false` <br/>
+  <span style="color:grey">设置为true时，将数据集注册并同步到Apache Hive Metastore</span>
   
 ##### HIVE_DATABASE_OPT_KEY {#HIVE_DATABASE_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.database`, Default: `default` <br/>
-  <span style="color:grey">database to sync to</span>
+  属性：`hoodie.datasource.hive_sync.database`, 默认值：`default` <br/>
+  <span style="color:grey">要同步到的数据库</span>
   
 ##### HIVE_TABLE_OPT_KEY {#HIVE_TABLE_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.table`, [Required] <br/>
-  <span style="color:grey">table to sync to</span>
+  属性：`hoodie.datasource.hive_sync.table`, [Required] <br/>
+  <span style="color:grey">要同步到的表</span>
   
 ##### HIVE_USER_OPT_KEY {#HIVE_USER_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.username`, Default: `hive` <br/>
-  <span style="color:grey">hive user name to use</span>
+  属性：`hoodie.datasource.hive_sync.username`, 默认值：`hive` <br/>
+  <span style="color:grey">要使用的Hive用户名</span>
   
 ##### HIVE_PASS_OPT_KEY {#HIVE_PASS_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.password`, Default: `hive` <br/>
-  <span style="color:grey">hive password to use</span>
+  属性：`hoodie.datasource.hive_sync.password`, 默认值：`hive` <br/>
+  <span style="color:grey">要使用的Hive密码</span>
   
 ##### HIVE_URL_OPT_KEY {#HIVE_URL_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.jdbcurl`, Default: `jdbc:hive2://localhost:10000` <br/>
+  属性：`hoodie.datasource.hive_sync.jdbcurl`, 默认值：`jdbc:hive2://localhost:10000` <br/>
   <span style="color:grey">Hive metastore url</span>
   
 ##### HIVE_PARTITION_FIELDS_OPT_KEY {#HIVE_PARTITION_FIELDS_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.partition_fields`, Default: ` ` <br/>
-  <span style="color:grey">field in the dataset to use for determining hive partition columns.</span>
+  属性：`hoodie.datasource.hive_sync.partition_fields`, 默认值：` ` <br/>
+  <span style="color:grey">数据集中用于确定Hive分区的字段。</span>
   
 ##### HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY {#HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.partition_extractor_class`, Default: `org.apache.hudi.hive.SlashEncodedDayPartitionValueExtractor` <br/>
-  <span style="color:grey">Class used to extract partition field values into hive partition columns.</span>
+  属性：`hoodie.datasource.hive_sync.partition_extractor_class`, 默认值：`org.apache.hudi.hive.SlashEncodedDayPartitionValueExtractor` <br/>
+  <span style="color:grey">用于将分区字段值提取到配置单元分区列中的类。</span>
   
 ##### HIVE_ASSUME_DATE_PARTITION_OPT_KEY {#HIVE_ASSUME_DATE_PARTITION_OPT_KEY}
-  Property: `hoodie.datasource.hive_sync.assume_date_partitioning`, Default: `false` <br/>
-  <span style="color:grey">Assume partitioning is yyyy/mm/dd</span>
+  属性：`hoodie.datasource.hive_sync.assume_date_partitioning`, 默认值：`false` <br/>
+  <span style="color:grey">假设分区格式是yyyy/mm/dd</span>
 
-#### Read Options
+#### 读选项
 
-Options useful for reading datasets via `read.format.option(...)`
+用于通过`read.format.option(...)`读取数据集的选项
 
 ##### VIEW_TYPE_OPT_KEY {#VIEW_TYPE_OPT_KEY}
-Property: `hoodie.datasource.view.type`, Default: `read_optimized` <br/>
-<span style="color:grey">Whether data needs to be read, in incremental mode (new data since an instantTime)
-(or) Read Optimized mode (obtain latest view, based on columnar data)
-(or) Real time mode (obtain latest view, based on row & columnar data)</span>
+属性：`hoodie.datasource.view.type`, 默认值：`read_optimized` <br/>
+<span style="color:grey">是否需要以某种模式读取数据，增量模式（自InstantTime以来的新数据）
+（或）读优化模式（基于列数据获取最新视图）
+（或）实时模式（基于行和列数据获取最新视图）</span>
 
 ##### BEGIN_INSTANTTIME_OPT_KEY {#BEGIN_INSTANTTIME_OPT_KEY} 
-Property: `hoodie.datasource.read.begin.instanttime`, [Required in incremental mode] <br/>
-<span style="color:grey">Instant time to start incrementally pulling data from. The instanttime here need not
-necessarily correspond to an instant on the timeline. New data written with an
- `instant_time > BEGIN_INSTANTTIME` are fetched out. For e.g: '20170901080000' will get
- all new data written after Sep 1, 2017 08:00AM.</span>
+属性：`hoodie.datasource.read.begin.instanttime`, [在增量模式下必须] <br/>
+<span style="color:grey">开始增量提取数据的即时时间。这里的instanttime不必一定与时间轴上的即时相对应。
+取出以`instant_time > BEGIN_INSTANTTIME`写入的新数据。
+例如：'20170901080000'将获取2017年9月1日08:00 AM之后写入的所有新数据。</span>
  
 ##### END_INSTANTTIME_OPT_KEY {#END_INSTANTTIME_OPT_KEY}
-Property: `hoodie.datasource.read.end.instanttime`, Default: latest instant (i.e fetches all new data since begin instant time) <br/>
-<span style="color:grey"> Instant time to limit incrementally fetched data to. New data written with an
-`instant_time <= END_INSTANTTIME` are fetched out.</span>
+属性：`hoodie.datasource.read.end.instanttime`, 默认值：latest instant (i.e fetches all new data since begin instant time) <br/>
 
 Review comment:
   Good catch... fixed in the latest commit

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