authuir commented on a change in pull request #12420:
URL: https://github.com/apache/flink/pull/12420#discussion_r458270785
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File path: docs/dev/table/streaming/joins.zh.md
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@@ -300,25 +285,26 @@ FROM
ON r.currency = o.currency
{% endhighlight %}
-Each record from the probe side will be joined with the current version of the
build side table. In our example, the query is using the processing-time
notion, so a newly appended order would always be joined with the most recent
version of `LatestRates` when executing the operation. Note that the result is
not deterministic for processing-time.
+探针侧表中的每个记录都将与构建侧表的当前版本所关联。 在此示例中，查询使用 `processing-time` 作为处理时间，因而新增订单将始终与表
`LatestRates` 的最新汇率执行 Join 操作。 注意，结果对于处理时间来说不是确定的。
+
+与[常规 Join](#regular-joins) 相比，尽管构建侧表的数据发生了变化，但时态表 Join 的变化前结果不会随之变化。而且时态表 Join
运算非常轻量级且不会保留任何状态。
-In contrast to [regular joins](#regular-joins), the previous results of the
temporal table join will not be affected despite the changes on the build side.
Also, the temporal table join operator is very lightweight and does not keep
any state.
+与[时间区间 Join](#interval-joins) 相比，时态表 Join 没有定义决定哪些记录将被 Join 的时间窗口。
+探针侧的记录将总是与构建侧在对应 `processing time` 时间的最新数据执行 Join。因而构建侧的数据可能是任意旧的。
-Compared to [interval joins](#interval-joins), temporal table joins do not
define a time window within which the records will be joined.
-Records from the probe side are always joined with the build side's latest
version at processing time. Thus, records on the build side might be
arbitrarily old.
+[时态表函数 Join](#join-with-a-temporal-table-function) 和时态表 Join都有类似的功能，但是有不同的 SQL
语法和 runtime 实现：
Review comment:
Done
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