Update 1-kafka.md (#376)

Content duplication
apache · May 26, 2024 · 083c34a · 083c34a
1 parent efe0a63
commit 083c34a
Showing 1 changed file with 0 additions and 90 deletions.
diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/connector/1-kafka.md b/i18n/zh-CN/docusaurus-plugin-content-docs/current/connector/1-kafka.md
@@ -897,96 +897,6 @@ class JavaUser implements Serializable {
 </TabItem>
 </Tabs>
 
-### 指定SerializationSchema
-
-` Flink Kafka Producer` 需要知道如何将 Java/Scala 对象转化为二进制数据。 KafkaSerializationSchema 允许用户指定这样的schema, 相关操作方式和文档请参考[官网文档](https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/connectors/kafka.html#the-serializationschema)
-
-在`KafkaSink`里默认不指定序列化方式,采用的是`SimpleStringSchema`来进行序列化,这里开发者可以显示的指定一个自定义的序列化器,通过`serializationSchema`参数指定即可,例如,将`user`对象安装自定义的格式写入`kafka`
-
-<Tabs>
-<TabItem value="scala" label="Scala" default>
-
-```scala
-import org.apache.streampark.common.util.JsonUtils
-import org.apache.streampark.flink.core.scala.FlinkStreaming
-import org.apache.flink.api.common.serialization.SerializationSchema
-import org.apache.streampark.flink.core.scala.sink.JdbcSink
-import org.apache.streampark.flink.core.scala.source.KafkaSource
-import org.apache.flink.api.scala._
-
-object KafkaSinkApp extends FlinkStreaming {
-
-override def handle(): Unit = {
-val source = KafkaSource()
-.getDataStream[String]()
-.map(x => JsonUtils.read[User](x.value))
-
-    KafkaSink().sink[User](source, serialization = new SerializationSchema[User]() {
-      override def serialize(user: User): Array[Byte] = {
-        s"${user.name},${user.age},${user.gender},${user.address}".getBytes
-      }
-    })
-
-}
-
-}
-
-case class User(name: String, age: Int, gender: Int, address: String)
-```
-
-</TabItem>
-<TabItem value="Java" label="Java">
-
-```java
-import com.fasterxml.jackson.databind.ObjectMapper;
-import org.apache.streampark.flink.core.java.function.StreamEnvConfigFunction;
-import org.apache.streampark.flink.core.java.sink.KafkaSink;
-import org.apache.streampark.flink.core.java.source.KafkaSource;
-import org.apache.streampark.flink.core.scala.StreamingContext;
-import org.apache.streampark.flink.core.scala.source.KafkaRecord;
-import org.apache.streampark.flink.core.scala.util.StreamEnvConfig;
-import org.apache.flink.api.common.functions.FilterFunction;
-import org.apache.flink.api.common.functions.MapFunction;
-import org.apache.flink.api.common.serialization.SerializationSchema;
-import org.apache.flink.streaming.api.datastream.DataStream;
-
-import java.io.Serializable;
-
-public class kafkaSinkJavaApp {
-
-    public static void main(String[] args) {
-
-        StreamEnvConfig envConfig = new StreamEnvConfig(args, null);
-        StreamingContext context = new StreamingContext(envConfig);
-        ObjectMapper mapper = new ObjectMapper();
-
-        DataStream<JavaUser> source = new KafkaSource<String>(context)
-                .getDataStream()
-                .map((MapFunction<KafkaRecord<String>, JavaUser>) value ->
-                        mapper.readValue(value.value(), JavaUser.class));
-
-        new KafkaSink<JavaUser>(context)
-                .serializer(
-                        (SerializationSchema<JavaUser>) element ->
-                                String.format("%s,%d,%d,%s", element.name, element.age, element.gender, element.address).getBytes()
-                ).sink(source);
-
-        context.start();
-    }
-
-}
-
-class JavaUser implements Serializable {
-    String name;
-    Integer age;
-    Integer gender;
-    String address;
-}
-```
-
-</TabItem>
-</Tabs>
-
 
 ### 指定partitioner