MrWhiteSike commented on a change in pull request #18718:
URL: https://github.com/apache/flink/pull/18718#discussion_r808717415
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File path: docs/content.zh/docs/connectors/datastream/filesystem.md
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@@ -811,61 +809,63 @@ input.sinkTo(sink)
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-The `SequenceFileWriterFactory` supports additional constructor parameters to
specify compression settings.
+`SequenceFileWriterFactory` 提供额外的构造参数去设置是否开启压缩功能。
+
+<a name="bucket-assignment"></a>
+
+### 桶分配
-### Bucket Assignment
+桶的逻辑定义了如何将数据分配到基本输出目录内的子目录中。
-The bucketing logic defines how the data will be structured into
subdirectories inside the base output directory.
+Row-encoded Format 和 Bulk-encoded Format (参考 [Format
Types](#sink-format-types)) 使用了 `DateTimeBucketAssigner` 作为默认的分配器。
+默认的分配器 `DateTimeBucketAssigner` 会基于使用了格式为 `yyyy-MM-dd--HH` 的系统默认时区来创建小时桶。日期格式（
*即* 桶大小）和时区都可以手动配置。
-Both row and bulk formats (see [File Formats](#file-formats)) use the
`DateTimeBucketAssigner` as the default assigner.
-By default the `DateTimeBucketAssigner` creates hourly buckets based on the
system default timezone
-with the following format: `yyyy-MM-dd--HH`. Both the date format (*i.e.*
bucket size) and timezone can be
-configured manually.
+我们可以在格式化构造器中通过调用 `.withBucketAssigner(assigner)` 方法去指定自定义的 `BucketAssigner`。
-We can specify a custom `BucketAssigner` by calling
`.withBucketAssigner(assigner)` on the format builders.
+Flink 内置了两种 BucketAssigners：
-Flink comes with two built-in BucketAssigners:
+- `DateTimeBucketAssigner` ： 默认的基于时间的分配器
+- `BasePathBucketAssigner` ： 分配所有文件存储在基础路径上（单个全局桶）
-- `DateTimeBucketAssigner` : Default time based assigner
-- `BasePathBucketAssigner` : Assigner that stores all part files in the base
path (single global bucket)
+<a name="rolling-policy"></a>
-### Rolling Policy
+### 滚动策略
-The `RollingPolicy` defines when a given in-progress part file will be closed
and moved to the pending and later to finished state.
-Part files in the "finished" state are the ones that are ready for viewing and
are guaranteed to contain valid data that will not be reverted in case of
failure.
-In `STREAMING` mode, the Rolling Policy in combination with the checkpointing
interval (pending files become finished on the next checkpoint) control how
quickly
-part files become available for downstream readers and also the size and
number of these parts. In `BATCH` mode, part-files become visible at the end of
the job but
-the rolling policy can control their maximum size.
+`RollingPolicy` 定义了何时关闭给定的进行中的文件，并将其转换为挂起状态，然后在转换为完成状态。
+完成状态的文件，可供查看并且可以保证数据的有效性，在出现故障时不会恢复。
+在 `STREAMING` 模式下，滚动策略结合 Checkpoint 间隔（到下一个 Checkpoint 完成时挂起状态的文件变成完成状态）共同控制
Part 文件对下游 readers 是否可见以及这些文件的大小和数量。在 `BATCH` 模式下，Part 文件在 Job
最后对下游才变得可见，滚动策略只控制最大的 Part 文件大小。
-Flink comes with two built-in RollingPolicies:
+Flink 内置了两种 RollingPolicies：
- `DefaultRollingPolicy`
- `OnCheckpointRollingPolicy`
-### Part file lifecycle
+<a name="part-file-lifecycle"></a>
-In order to use the output of the `FileSink` in downstream systems, we need to
understand the naming and lifecycle of the output files produced.
+### Part 文件生命周期
-Part files can be in one of three states:
-1. **In-progress** : The part file that is currently being written to is
in-progress
-2. **Pending** : Closed (due to the specified rolling policy) in-progress
files that are waiting to be committed
-3. **Finished** : On successful checkpoints (`STREAMING`) or at the end of
input (`BATCH`) pending files transition to "Finished"
+为了在下游使用 `FileSink` 作为输出，我们需要了解生成的输出文件的命名和生命周期。
-Only finished files are safe to read by downstream systems as those are
guaranteed to not be modified later.
+Part 文件可以处于以下三种状态中的任意一种：
+1. **In-progress** ：当前正在被写入的 Part 文件处于 in-progress 状态
+2. **Pending** : （由于指定的滚动策略）关闭 in-progress 状态的文件，并且等待提交
+3. **Finished** : 流模式(`STREAMING`)下的成功的 Checkpoint
或者批模式(`BATCH`)下输入结束，挂起状态文件转换为完成状态
-Each writer subtask will have a single in-progress part file at any given time
for every active bucket, but there can be several pending and finished files.
+只有完成状态下的文件被下游读取时才是安全的，并且保证不会被修改。
-**Part file example**
+对于每个活动的桶，在任何给定时间每个写入 Subtask 中都有一个正在进行的 Part 文件，但可能有多个挂起和完成的文件。
-To better understand the lifecycle of these files let's look at a simple
example with 2 sink subtasks:
+**Part 文件示例**
Review comment:
No, it isn't a link tag.
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