reswqa commented on code in PR #25945:
URL: https://github.com/apache/flink/pull/25945#discussion_r1914028429
##########
docs/content/docs/dev/datastream/fault-tolerance/serialization/types_serialization.md:
##########
@@ -75,31 +75,12 @@ wordCounts.keyBy(value -> value.f0);
```
-
-{{< /tab >}}
-{{< tab "Scala" >}}
-
-Scala case classes (and Scala tuples which are a special case of case
classes), are composite types that contain a fixed number of fields with
various types. Tuple fields are addressed by their 1-offset names such as `_1`
for the first field. Case class fields are accessed by their name.
-
-```scala
-case class WordCount(word: String, count: Int)
-val input = env.fromElements(
- WordCount("hello", 1),
- WordCount("world", 2)) // Case Class Data Set
-
-input.keyBy(_.word)
-
-val input2 = env.fromElements(("hello", 1), ("world", 2)) // Tuple2 Data Set
-
-input2.keyBy(value => (value._1, value._2))
-```
-
{{< /tab >}}
Review Comment:
ditto.
##########
docs/content/docs/dev/datastream/fault-tolerance/serialization/types_serialization.md:
##########
@@ -143,33 +124,17 @@ DataStream<WordWithCount> wordCounts = env.fromElements(
wordCounts.keyBy(value -> value.word);
-```
-{{< /tab >}}
-{{< tab "Scala" >}}
-```scala
-class WordWithCount(var word: String, var count: Int) {
- def this() {
- this(null, -1)
- }
-}
-
-val input = env.fromElements(
- new WordWithCount("hello", 1),
- new WordWithCount("world", 2)) // Case Class Data Set
-
-input.keyBy(_.word)
-
```
{{< /tab >}}
Review Comment:
ditto.
##########
docs/content/docs/dev/datastream/fault-tolerance/serialization/types_serialization.md:
##########
@@ -366,20 +328,6 @@ TypeInformation<Tuple2<String, Double>> info =
TypeInformation.of(new TypeHint<T
Internally, this creates an anonymous subclass of the TypeHint that captures
the generic information to preserve it
until runtime.
{{< /tab >}}
-{{< tab "Scala" >}}
-In Scala, Flink uses *macros* that runs at compile time and captures all
generic type information while it is
-still available.
-```scala
-// important: this import is needed to access the 'createTypeInformation'
macro function
-import org.apache.flink.streaming.api.scala._
-
-val stringInfo: TypeInformation[String] = createTypeInformation[String]
-
-val tupleInfo: TypeInformation[(String, Double)] =
createTypeInformation[(String, Double)]
-```
-
-You can still use the same method as in Java as a fallback.
-{{< /tab >}}
Review Comment:
ditto.
##########
docs/content/docs/dev/datastream/fault-tolerance/serialization/custom_serialization.md:
##########
@@ -56,18 +56,6 @@ ListStateDescriptor<Tuple2<String, Integer>> descriptor =
checkpointedState = getRuntimeContext().getListState(descriptor);
```
{{< /tab >}}
-{{< tab "Scala" >}}
-```scala
-class CustomTypeSerializer extends TypeSerializer[(String, Integer)] {...}
-
-val descriptor = new ListStateDescriptor[(String, Integer)](
- "state-name",
- new CustomTypeSerializer)
-)
-
-checkpointedState = getRuntimeContext.getListState(descriptor)
-```
-{{< /tab >}}
Review Comment:
After remove scala part, we just have one tab. I suggest that we removing
the top-level `tab`. Maybe replace it with code format(e.g. ```java)
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