[GitHub] [incubator-hudi] yihua commented on a change in pull request #985: [HUDI-275] Translate the Querying Data page into Chinese documentation

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yihua commented on a change in pull request #985: [HUDI-275] Translate the Querying Data page into Chinese documentation
URL: https://github.com/apache/incubator-hudi/pull/985#discussion_r340756611
 
 

 ##########
 File path: docs/querying_data.cn.md
 ##########
 @@ -1,122 +1,122 @@
 ---
-title: Querying Hudi Datasets
+title: 查询 Hudi 数据集
 keywords: hudi, hive, spark, sql, presto
 sidebar: mydoc_sidebar
 permalink: querying_data.html
 toc: false
-summary: In this page, we go over how to enable SQL queries on Hudi built tables.
+summary: 在这一页里，我们介绍了如何在Hudi构建的表上启用SQL查询。
 ---
 
-Conceptually, Hudi stores data physically once on DFS, while providing 3 logical views on top, as explained [before](concepts.html#views). 
-Once the dataset is synced to the Hive metastore, it provides external Hive tables backed by Hudi's custom inputformats. Once the proper hudi
-bundle has been provided, the dataset can be queried by popular query engines like Hive, Spark and Presto.
+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](concepts.html#views)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。
 
-Specifically, there are two Hive tables named off [table name](configurations.html#TABLE_NAME_OPT_KEY) passed during write. 
-For e.g, if `table name = hudi_tbl`, then we get  
+具体来说，在写入过程中传递了两个由[table name](configurations.html#TABLE_NAME_OPT_KEY)命名的Hive表。
+例如，如果`table name = hudi_tbl`，我们得到
 
- - `hudi_tbl` realizes the read optimized view of the dataset backed by `HoodieParquetInputFormat`, exposing purely columnar data.
- - `hudi_tbl_rt` realizes the real time view of the dataset  backed by `HoodieParquetRealtimeInputFormat`, exposing merged view of base and log data.
+ - `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。
 
-As discussed in the concepts section, the one key primitive needed for [incrementally processing](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop),
-is `incremental pulls` (to obtain a change stream/log from a dataset). Hudi datasets can be pulled incrementally, which means you can get ALL and ONLY the updated & new rows 
-since a specified instant time. This, together with upserts, are particularly useful for building data pipelines where 1 or more source Hudi tables are incrementally pulled (streams/facts),
-joined with other tables (datasets/dimensions), to [write out deltas](writing_data.html) to a target Hudi dataset. Incremental view is realized by querying one of the tables above, 
-with special configurations that indicates to query planning that only incremental data needs to be fetched out of the dataset. 
+如概念部分所述，[增量处理](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop)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，
+您可以只获得全部更新和新行。 这与插入更新一起使用，对于构建某些数据管道尤其有用，包括将1个或多个源Hudi表（数据流/事实）以增量方式拉出（流/事实）
+并与其他表（数据集/维度）结合以[写出增量](write_data.html)到目标Hudi数据集。增量视图是通过查询上表之一实现的，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。
 
-In sections, below we will discuss in detail how to access all the 3 views on each query engine.
+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。
 
 ## Hive
 
-In order for Hive to recognize Hudi datasets and query correctly, the HiveServer2 needs to be provided with the `hudi-hadoop-mr-bundle-x.y.z-SNAPSHOT.jar` 
-in its [aux jars path](https://www.cloudera.com/documentation/enterprise/5-6-x/topics/cm_mc_hive_udf.html#concept_nc3_mms_lr). This will ensure the input format 
-classes with its dependencies are available for query planning & execution. 
-
-### Read Optimized table {#hive-ro-view}
-In addition to setup above, for beeline cli access, the `hive.input.format` variable needs to be set to the  fully qualified path name of the 
-inputformat `org.apache.hudi.hadoop.HoodieParquetInputFormat`. For Tez, additionally the `hive.tez.input.format` needs to be set 
-to `org.apache.hadoop.hive.ql.io.HiveInputFormat`
-
-### Real time table {#hive-rt-view}
-In addition to installing the hive bundle jar on the HiveServer2, it needs to be put on the hadoop/hive installation across the cluster, so that
-queries can pick up the custom RecordReader as well.
-
-### Incremental Pulling {#hive-incr-pull}
-
-`HiveIncrementalPuller` allows incrementally extracting changes from large fact/dimension tables via HiveQL, combining the benefits of Hive (reliably process complex SQL queries) and 
-incremental primitives (speed up query by pulling tables incrementally instead of scanning fully). The tool uses Hive JDBC to run the hive query and saves its results in a temp table.
-that can later be upserted. Upsert utility (`HoodieDeltaStreamer`) has all the state it needs from the directory structure to know what should be the commit time on the target table.
-e.g: `/app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}`.The Delta Hive table registered will be of the form `{tmpdb}.{source_table}_{last_commit_included}`.
-
-The following are the configuration options for HiveIncrementalPuller
-
-| **Config** | **Description** | **Default** |
-|hiveUrl| Hive Server 2 URL to connect to |  |
-|hiveUser| Hive Server 2 Username |  |
-|hivePass| Hive Server 2 Password |  |
-|queue| YARN Queue name |  |
-|tmp| Directory where the temporary delta data is stored in DFS. The directory structure will follow conventions. Please see the below section.  |  |
-|extractSQLFile| The SQL to execute on the source table to extract the data. The data extracted will be all the rows that changed since a particular point in time. |  |
-|sourceTable| Source Table Name. Needed to set hive environment properties. |  |
-|targetTable| Target Table Name. Needed for the intermediate storage directory structure.  |  |
-|sourceDataPath| Source DFS Base Path. This is where the Hudi metadata will be read. |  |
-|targetDataPath| Target DFS Base path. This is needed to compute the fromCommitTime. This is not needed if fromCommitTime is specified explicitly. |  |
-|tmpdb| The database to which the intermediate temp delta table will be created | hoodie_temp |
-|fromCommitTime| This is the most important parameter. This is the point in time from which the changed records are pulled from.  |  |
-|maxCommits| Number of commits to include in the pull. Setting this to -1 will include all the commits from fromCommitTime. Setting this to a value > 0, will include records that ONLY changed in the specified number of commits after fromCommitTime. This may be needed if you need to catch up say 2 commits at a time. | 3 |
-|help| Utility Help |  |
-
-
-Setting fromCommitTime=0 and maxCommits=-1 will pull in the entire source dataset and can be used to initiate backfills. If the target dataset is a Hudi dataset,
-then the utility can determine if the target dataset has no commits or is behind more than 24 hour (this is configurable),
-it will automatically use the backfill configuration, since applying the last 24 hours incrementally could take more time than doing a backfill. The current limitation of the tool
-is the lack of support for self-joining the same table in mixed mode (normal and incremental modes).
-
-**NOTE on Hive queries that are executed using Fetch task:**
-Since Fetch tasks invoke InputFormat.listStatus() per partition, Hoodie metadata can be listed in
-every such listStatus() call. In order to avoid this, it might be useful to disable fetch tasks
-using the hive session property for incremental queries: `set hive.fetch.task.conversion=none;` This
-would ensure Map Reduce execution is chosen for a Hive query, which combines partitions (comma
-separated) and calls InputFormat.listStatus() only once with all those partitions.
+为了使Hive能够识别Hudi数据集并正确查询，
+HiveServer2需要在其[辅助jars路径](https://www.cloudera.com/documentation/enterprise/5-6-x/topics/cm_mc_hive_udf.html#concept_nc3_mms_lr)中提供`hudi-hadoop-mr-bundle-x.y.z-SNAPSHOT.jar`。 
+这将确保输入格式类及其依赖项可用于查询计划和执行。
+
+### 读优化表 {#hive-ro-view}
+除了上述设置之外，对于beeline cli访问，还需要将`hive.input.format`变量设置为`org.apache.hudi.hadoop.HoodieParquetInputFormat`输入格式的完全限定路径名。
+对于Tez，还需要将`hive.tez.input.format`设置为`org.apache.hadoop.hive.ql.io.HiveInputFormat`。
+
+### 实时表 {#hive-rt-view}
+除了在HiveServer2上安装Hive捆绑jars之外，还需要将其放在整个集群的hadoop/hive安装中，这样查询也可以使用自定义RecordReader。
+
+### 增量拉取 {#hive-incr-pull}
+
+`HiveIncrementalPuller`允许通过HiveQL从大型事实/维表中增量提取更改，
+结合了Hive（可靠地处理复杂的SQL查询）和增量原语的好处（通过增量拉取而不是完全扫描来加快查询速度）。
+该工具使用Hive JDBC运行hive查询并将其结果保存在临时表中，这个表可以被插入更新。
+Upsert实用程序（`HoodieDeltaStreamer`）具有目录结构所需的所有状态，以了解目标表上的提交时间应为多少。
+例如：`/app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}`。
+已注册的Delta Hive表的格式为`{tmpdb}.{source_table}_{last_commit_included}`。
+
+以下是HiveIncrementalPuller的配置选项
+
+| **配置** | **描述** | **默认值** |
+|hiveUrl| 要连接的Hive Server 2的URL |  |
+|hiveUser| Hive Server 2 用户名 |  |
+|hivePass| Hive Server 2 密码 |  |
+|queue| YARN 队列名称 |  |
+|tmp| DFS中存储临时增量数据的目录。目录结构将遵循约定。请参阅以下部分。  |  |
+|extractSQLFile| 在源表上要执行的提取数据的SQL。提取的数据将是自特定时间点以来已更改的所有行。 |  |
+|sourceTable| 源表名称。在Hive环境属性中需要设置。 |  |
+|targetTable| 目标表名称。中间存储目录结构需要。  |  |
+|sourceDataPath| 源DFS基本路径。这是读取Hudi元数据的地方。 |  |
+|targetDataPath| 目标DFS基本路径。 这是计算fromCommitTime所必需的。 如果显式指定了fromCommitTime，则不需要设置这个参数。 |  |
+|tmpdb| 用来创建中间临时增量表的数据库 | hoodie_temp |
+|fromCommitTime| 这是最重要的参数。 这是从中提取更改的记录的时间点。 |  |
+|maxCommits| 要包含在拉取中的提交数。将此设置为-1将包括从fromCommitTime开始的所有提交。将此设置为大于0的值，将包括在fromCommitTime之后仅更改指定提交次数的记录。如果您需要一次赶上两次提交，则可能需要这样做。| 3 |
+|help| 实用程序帮助 |  |
+
+
+设置fromCommitTime=0和maxCommits=-1将提取整个源数据集，可用于启动回填。
 
 Review comment:
   我看"Backfill"有翻译成”回填“。要么不译直接放”Backfill“？

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