MartijnVisser commented on code in PR #29837:
URL: https://github.com/apache/flink/pull/29837#discussion_r4535785059
##########
docs/content/docs/dev/table/data_stream_api.md:
##########
@@ -3410,44 +2595,6 @@ Table table = tableEnv.fromDataStream(stream, $("f1"));
// convert DataStream into Table with reordered and aliased fields "myString",
"myLong" (name-based)
Table table = tableEnv.fromDataStream(stream, $("f1").as("myString"),
$("f0").as("myLong"));
-```
-{{< /tab >}}
-{{< tab "Scala" >}}
-Flink supports Scala's built-in tuples.
-DataStreams of Scala's built-in tuples can be converted into tables.
-Fields can be renamed by providing names for all fields (mapping based on
position).
-If no field names are specified, the default field names are used.
-If the original field names (`_1`, `_2`, ... for Scala Tuples) are referenced,
the API assumes that the mapping is name-based instead of position-based.
-Name-based mapping allows for reordering fields and projection with alias
(`as`).
-
-```scala
-// get a TableEnvironment
-val tableEnv: StreamTableEnvironment = ... // see "Create a TableEnvironment"
section
-
-val stream: DataStream[(Long, String)] = ...
-
-// convert DataStream into Table with field names "myLong", "myString"
(position-based)
-val table: Table = tableEnv.fromDataStream(stream, $"myLong", $"myString")
-
-// convert DataStream into Table with reordered fields "_2", "_1" (name-based)
-val table: Table = tableEnv.fromDataStream(stream, $"_2", $"_1")
-
-// convert DataStream into Table with projected field "_2" (name-based)
-val table: Table = tableEnv.fromDataStream(stream, $"_2")
-
-// convert DataStream into Table with reordered and aliased fields "myString",
"myLong" (name-based)
-val table: Table = tableEnv.fromDataStream(stream, $"_2" as "myString", $"_1"
as "myLong")
-
-// define case class
-case class Person(name: String, age: Int)
-val streamCC: DataStream[Person] = ...
-
-// convert DataStream into Table with field names 'myName, 'myAge
(position-based)
-val table = tableEnv.fromDataStream(streamCC, $"myName", $"myAge")
-
-// convert DataStream into Table with reordered and aliased fields "myAge",
"myName" (name-based)
-val table: Table = tableEnv.fromDataStream(stream, $"age" as "myAge", $"name"
as "myName")
-
```
{{< /tab >}}
{{< tab "Python" >}}
Review Comment:
Renamed, also on zh.
##########
docs/content/docs/concepts/sql-table-concepts/time_attributes.md:
##########
@@ -207,62 +207,34 @@ There are two ways of defining the time attribute when
converting a `DataStream`
In either case, the event time timestamp field will hold the value of the
`DataStream` event time timestamp.
+In Java, declare the event time attribute in the `Schema` that you pass to
`fromDataStream`: the `rowtime`
+metadata column holds the timestamp of each stream record as
`TIMESTAMP_LTZ(3)`, and `SOURCE_WATERMARK()` reuses
+the watermarks of the `DataStream`. The two options above use the
expression-based conversion, which is
+deprecated in Java. See [DataStream API Integration]({{< ref
"docs/dev/table/data_stream_api" >}}) for details.
Review Comment:
Scoped it to the `.rowtime` expressions. On master those give `TIMESTAMP(3)`
and the `Schema` path gives `TIMESTAMP_LTZ(3)`.
##########
docs/content/docs/deployment/resource-providers/standalone/working_directory.md:
##########
@@ -54,21 +54,21 @@ If not explicitly configured, then it will be a random
value containing the host
Flink processes will use the working directory to store the following
artifacts:
* Blobs stored by the `BlobServer` and `BlobCache`
-* Local state if `state.backend.local-recovery` is enabled
+* Local state if `execution.state-recovery.from-local` is enabled
* RocksDB's working directory
## Local Recovery Across Process Restarts
The working directory can be used to enable local recovery across process
restarts ([FLIP-201](https://cwiki.apache.org/confluence/x/wJuqCw)).
This means that Flink does not have to recover state information from remote
storage.
-In order to use this feature, local recovery has to be enabled via
`state.backend.local-recovery`.
+In order to use this feature, local recovery has to be enabled via
`execution.state-recovery.from-local`.
Moreover, the TaskManager processes need to get a deterministic resource id
assigned via `taskmanager.resource-id`.
Last but not least, a failed TaskManager process needs to be restarted with
the same working directory.
```bash
process.working-dir: /path/to/working/dir/base
-state.backend.local-recovery: true
+execution.state-recovery.from-local: true
taskmanager.resource-id: TaskManager_1 # important: Change for every
TaskManager process
```
Review Comment:
Done, plus the same blocks in `zookeeper_ha.md` and the standalone overview.
##########
docs/content.zh/docs/deployment/memory/network_mem_tuning.md:
##########
@@ -155,17 +150,16 @@ As a rule of thumb, we don't recommend thinking about
increasing the buffer size
- 内存使用高
- 大量的 checkpoint 数据量（针对非对齐的 checkpoints）
- 漫长的 checkpoint 时间（针对对齐的 checkpoints）
-- `execution.buffer-timeout` 较小时内存分配使用率会比较低，因为缓冲区还没被塞满数据就被发送下去了。
+- `execution.buffer-timeout.interval` 较小时内存分配使用率会比较低，因为缓冲区还没被塞满数据就被发送下去了。
### 选择缓冲区的数量
-缓冲区的数量是通过 `taskmanager.network.memory.buffers-per-channel` 和
`taskmanager.network.memory.floating-buffers-per-gate` 来配置的。
-
-为了最好的吞吐率，我们建议使用独占缓冲区和流动缓冲区的默认值(except you have one of [limit cases]({{< ref
"docs/deployment/memory/network_mem_tuning"
>}}#limitations))。如果缓冲数据量存在问题，更建议打开[缓冲消胀]({{< ref
"docs/deployment/memory/network_mem_tuning"
>}}#the-buffer-debloating-mechanism)。
+从 Flink 2.0 开始，缓冲区的数量不再可配置：Flink 为每个通道使用 2 个独占缓冲区，为每个 gate 使用 8 个流动缓冲区。
+如果缓冲数据量存在问题，建议打开[缓冲消胀]({{< ref "docs/deployment/memory/network_mem_tuning"
>}}#the-buffer-debloating-mechanism)。
Review Comment:
Gave the zh heading the id `the-buffer-debloating-mechanism`, so it matches
the English page.
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