怎么导入import java.util.function.Consumer;
时间: 2023-11-30 19:02:32 浏览: 38
在 Java 中,`Consumer` 是一个函数式接口,它可以用于接受一个参数并返回一个结果。要使用 `Consumer`,需要在代码中导入 `java.util.function.Consumer` 包。
你可以在 Java 代码中使用以下语句导入 `Consumer`:
```java
import java.util.function.Consumer;
```
然后就可以使用 `Consumer` 接口定义的方法了。例如,可以定义一个接受整数参数并打印出该整数的 `Consumer`,如下所示:
```java
Consumer<Integer> printInt = x -> System.out.println(x);
```
当你调用 `printInt.accept(10)` 时,它将打印输出 `10`。
相关问题
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。
java supplier consumer
在Java中,Supplier和Consumer都是函数式接口,用于函数式编程。Supplier接口用于提供数据,而Consumer接口用于消费数据。下面是一个简单的Java Supplier和Consumer演示:
```java
import java.util.function.Consumer;
import java.util.function.Supplier;
public class SupplierConsumerDemo {
public static void main(String[] args) {
// 定义Supplier接口实例
Supplier<String> supplier = () -> "Hello, world!";
// 定义Consumer接口实例
Consumer<String> consumer = (message) -> {
System.out.println("Message received: " + message);
};
// 使用Supplier提供数据,Consumer消费数据
consumer.accept(supplier.get());
}
}
```
在上面的代码中,我们首先定义了一个Supplier接口实例,返回一个字符串“Hello, world!”。然后我们定义了一个Consumer接口实例,用于接收一个字符串并打印出来。最后,我们使用Supplier提供数据,Consumer消费数据的方式,将字符串传递给Consumer实例,并打印出来。这个例子展示了如何在Java中使用Supplier和Consumer接口来提供和消费数据。