Flink怎么用sum统计
时间: 2023-07-03 19:05:04 浏览: 54
在Flink中,可以使用DataStream API中的sum函数对流数据进行求和统计。
例如,假设有一个包含整数的DataStream对象,可以使用以下方式进行求和统计:
```
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
DataStream<Integer> dataStream = ...; // 创建包含整数的DataStream对象
DataStream<Integer> sumStream = dataStream.sum(0); // 对整数进行求和统计
```
上述代码中,sum函数的参数指定了要对哪个字段进行求和统计。在本例中,由于整数位于DataStream对象的第一个位置,因此参数为0。执行上述代码后,会得到一个新的DataStream对象sumStream,其中包含了对整数的求和统计结果。
相关问题
flink词频统计scala
下面是使用 Flink 和 Scala 进行词频统计的示例代码:
```scala
import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
object WordCount {
def main(args: Array[String]): Unit = {
val env = StreamExecutionEnvironment.getExecutionEnvironment
// 读取文本文件
val textStream = env.readTextFile("path/to/your/text/file")
// 转换成单词流
val wordStream = textStream.flatMap(_.split("\\s+"))
// 统计单词数量
val countStream = wordStream.map((_, 1)).keyBy(0).sum(1)
// 打印结果
countStream.print()
env.execute("WordCount")
}
}
```
在上面的示例代码中,我们首先创建了一个 `StreamExecutionEnvironment` 对象,然后使用 `readTextFile` 方法从指定的文本文件中读取数据,接着使用 `flatMap` 方法将每行文本数据转换成单词流,再使用 `map` 方法将每个单词映射成一个 `(单词, 1)` 的键值对,最后使用 `keyBy` 方法将键值对按照键进行分组,再使用 `sum` 方法对值进行累加,得到每个单词的数量。最后,我们使用 `print` 方法将结果打印出来,并且调用 `env.execute` 方法启动任务的执行。
flink kafka 统计 代码
以下是使用Flink和Kafka进行统计的示例代码。
首先,需要导入相关的依赖:
```xml
<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-kafka_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
```
接下来,创建一个Flink的StreamExecutionEnvironment:
```java
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
```
然后,创建一个Kafka的消费者流:
```java
Properties props = new Properties();
props.setProperty("bootstrap.servers", "localhost:9092");
props.setProperty("group.id", "test");
FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("topic", new SimpleStringSchema(), props);
DataStream<String> stream = env.addSource(consumer);
```
接着,对数据流进行处理和转换,例如统计单词出现的次数:
```java
DataStream<Tuple2<String, Integer>> counts = stream
.flatMap((String line, Collector<Tuple2<String, Integer>> out) -> {
for (String word : line.split(" ")) {
out.collect(new Tuple2<>(word, 1));
}
})
.keyBy(0)
.sum(1);
```
最后,将结果写入到Kafka的一个新的Topic中:
```java
Properties producerProps = new Properties();
producerProps.setProperty("bootstrap.servers", "localhost:9092");
FlinkKafkaProducer<Tuple2<String, Integer>> producer = new FlinkKafkaProducer<>("output-topic", new Tuple2SerializationSchema(), producerProps, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
counts.addSink(producer);
```
完整的代码示例:
```java
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
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.util.serialization.SimpleStringSchema;
import org.apache.flink.util.Collector;
import java.util.Properties;
public class WordCount {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
Properties props = new Properties();
props.setProperty("bootstrap.servers", "localhost:9092");
props.setProperty("group.id", "test");
FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("input-topic", new SimpleStringSchema(), props);
DataStream<String> stream = env.addSource(consumer);
DataStream<Tuple2<String, Integer>> counts = stream
.flatMap((String line, Collector<Tuple2<String, Integer>> out) -> {
for (String word : line.split(" ")) {
out.collect(new Tuple2<>(word, 1));
}
})
.keyBy(0)
.sum(1);
Properties producerProps = new Properties();
producerProps.setProperty("bootstrap.servers", "localhost:9092");
FlinkKafkaProducer<Tuple2<String, Integer>> producer = new FlinkKafkaProducer<>("output-topic", new Tuple2SerializationSchema(), producerProps, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
counts.addSink(producer);
env.execute("Word Count");
}
private static class Tuple2SerializationSchema implements KafkaSerializationSchema<Tuple2<String, Integer>> {
@Override
public ProducerRecord<byte[], byte[]> serialize(Tuple2<String, Integer> element, @Nullable Long timestamp) {
return new ProducerRecord<>("output-topic", element.f0.getBytes(), element.f1.toString().getBytes());
}
}
}
```