将scala set 转为 java.util.set
时间: 2023-06-18 17:03:04 浏览: 50
您可以使用Scala的JavaConverters来将Scala Set转换为Java Set。以下是一个示例:
```scala
import scala.collection.JavaConverters._
val scalaSet = Set("apple", "banana", "orange")
val javaSet = scalaSet.asJava
```
现在,您可以将`javaSet`传递给使用`java.util.Set`的Java方法。
相关问题
将scala set 转为 java set
可以使用Scala自带的JavaConverters工具将Scala Set转换为Java Set,示例如下:
```scala
import scala.collection.JavaConverters._
import java.util.{HashSet => JHashSet}
val scalaSet = Set(1, 2, 3)
val javaSet = new JHashSet[Int](scalaSet.asJava)
```
这里我们先导入JavaConverters,将Scala Set转换为Java Set时需要使用asJava方法,最终得到的javaSet是一个Java HashSet。
java flink mysql binlog 代码 带pom.xml
以下是一个使用Java和Apache Flink连接MySQL并读取binlog的代码,并且使用了Maven进行依赖管理。请注意,这里仅提供代码示例,具体的数据库连接信息和binlog配置需要根据实际情况进行修改。
pom.xml文件内容如下:
```xml
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-core</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>${mysql.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
</dependencies>
```
Java代码实现如下:
```java
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSink;
import org.apache.flink.streaming.connectors.kafka.KafkaTopicPartition;
import org.apache.flink.streaming.connectors.kafka.internals.KeyedSerializationSchemaWrapper;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionState;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionStateSentinel;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFetcher;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFetcherThread;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.internals.LegacyFlinkKafkaConsumerBase;
import org.apache.flink.streaming.connectors.kafka.internals.metrics.KafkaConsumerMetricConstants;
import org.apache.flink.streaming.connectors.kafka.internals.metrics.KafkaConsumerMetricWrapper;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.serialization.StringSerializer;
import java.util.Properties;
public class FlinkKafkaConnector {
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//设置kafka相关参数
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
properties.setProperty("group.id", "test");
//创建KafkaSource
FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("test", new SimpleStringSchema(), properties);
//将KafkaSource添加到Flink环境中
DataStream<String> stream = env.addSource(kafkaConsumer);
//将数据转换成大写字母
DataStream<String> upperStream = stream.map(new MapFunction<String, String>() {
@Override
public String map(String s) throws Exception {
return s.toUpperCase();
}
});
//将数据写回到Kafka
Properties producerProperties = new Properties();
producerProperties.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>("test", new KeyedSerializationSchemaWrapper<>(new SimpleStringSchema()), producerProperties, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
upperStream.addSink(kafkaProducer);
//执行任务
env.execute("Flink Kafka Connector Example");
}
}
```
需要注意的是,这里使用了Flink Kafka Connector,因此需要在pom.xml文件中加入相应的依赖。此外,还需要根据实际情况修改Kafka和MySQL的连接信息。