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案例一:实现 topic 之间的流传输
一、Kafka Java 代码
创立 maven 过程,导入以下依赖
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>2.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-streams</artifactId>
<version>2.0.0</version>
</dependency>
代码局部
public class MyStream {public static void main(String[] args) {Properties prop = new Properties();
prop.put(StreamsConfig.APPLICATION_ID_CONFIG,"mystream");
prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.247.201:9092");
prop.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
prop.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG,Serdes.String().getClass());
// 创立流结构器
StreamsBuilder builder = new StreamsBuilder();
// 构建好 builder 将 mystreamin topic 中的数据写入到 mystreamout topic 中
builder.stream("mystreamin").to("mystreamout");
final Topology topo = builder.build();
final KafkaStreams streams = new KafkaStreams(topo, prop);
final CountDownLatch latch = new CountDownLatch(1);
Runtime.getRuntime().addShutdownHook(new Thread("stream"){
@Override
public void run() {streams.close();
latch.countDown();}
});
try {streams.start();
latch.await();} catch (InterruptedException e) {e.printStackTrace();
}
System.exit(0);
}
}
二、Kafka Shell 命令
1、创立 Topic
`kafka-topics.sh --create --zookeeper 192.168.247.201:2181 --topic mystreamin --partitions 1 --replication-factor 1
kafka-topics.sh --create --zookeeper 192.168.247.201:2181 --topic mystreamout --partitions 1 --replication-factor 1`
* 1
* 2
查看 Topic
kafka-topics.sh --zookeeper 192.168.247.201:2181 --list
2、运行 Java 代码,执行以下步骤:
生产音讯
kafka-console-producer.sh --topic mystreamin --broker-list 127.0.0.1:9092
生产音讯
kafka-console-consumer.sh --topic mystreamout --bootstrap-server 127.0.0.1:9092 --from-beginning
案例二:WordCount Stream API
一、Kafka Java 代码
代码局部
public class WordCountStream {public static void main(String[] args) {Properties prop = new Properties();
prop.put(StreamsConfig.APPLICATION_ID_CONFIG,"wordcount");
prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.247.201:9092");
prop.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG,3000);
prop.put(ConsumerConfig.AUTO_OFFSET_RESET_DOC,"earliest"); // earliest latest
prop.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"false"); // 设置手动提交形式
prop.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
prop.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG,Serdes.String().getClass());
// 创立流结构器
// wordcount-input
// hello world
// hello java
StreamsBuilder builder = new StreamsBuilder();
KTable<String, Long> count = builder.stream("wordcount-input") // 从 kafka 中一条一条的取数据
.flatMapValues( // 返回压扁后的数据
(value) -> { // 对数据进行按空格切割,返回 List 汇合
String[] split = value.toString().split(" ");
List<String> strings = Arrays.asList(split);
return strings;
}) // key:null value:hello ,key:null value:world ,key:null value:hello ,key:null value:java
.map((k, v) -> {return new KeyValue<String, String>(v,"1");
}).groupByKey().count();
count.toStream().foreach((k,v) -> {System.out.println("key:"+k+"value:"+v);
});
count.toStream().map((x,y) -> {return new KeyValue<String,String>(x,y.toString());
}).to("wordcount-out");
final Topology topo = builder.build();
final KafkaStreams streams = new KafkaStreams(topo, prop);
final CountDownLatch latch = new CountDownLatch(1);
Runtime.getRuntime().addShutdownHook(new Thread("stream"){
@Override
public void run() {streams.close();
latch.countDown();}
});
try {streams.start();
latch.await();} catch (InterruptedException e) {e.printStackTrace();
}
System.exit(0);
}
}
二、Kafka Shell 命令
1、创立 Topic
kafka-topics.sh --create --zookeeper 192.168.247.201:2181 --topic wordcount-input --partitions 1 --replication-factor 1
kafka-topics.sh --create --zookeeper 192.168.247.201:2181 --topic wordcount-out --partitions 1 --replication-factor 1
**2、运行 Java 代码,执行以下步骤:
生产音讯 **
kafka-console-producer.sh --topic wordcount-input --broker-list 127.0.0.1:9092
生产音讯
kafka-console-consumer.sh --topic wordcount-out --bootstrap-server 127.0.0.1:9092 --from-beginning
显示 key 生产音讯
kafka-console-consumer.sh --topic wordcount-out --bootstrap-server 127.0.0.1:9092 --property print.key=true --from-beginning
案例三:利用 Kafka 流实现对输出数字的求和
一、Kafka Java 代码
public class SumStream {public static void main(String[] args) {Properties prop = new Properties();
prop.put(StreamsConfig.APPLICATION_ID_CONFIG,"sumstream");
prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.247.201:9092");
prop.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG,3000);
prop.put(ConsumerConfig.AUTO_OFFSET_RESET_DOC,"earliest"); // earliest latest
prop.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"false"); // 设置手动提交形式
prop.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
prop.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG,Serdes.String().getClass());
StreamsBuilder builder = new StreamsBuilder();
KStream<Object, Object> source = builder.stream("suminput");
source.map((key,value) ->
new KeyValue<String,String>("sum:",value.toString())
).groupByKey().reduce((x,y) ->{System.out.println("x:"+x+"y:"+y);
Integer sum = Integer.valueOf(x)+Integer.valueOf(y);
System.out.println("sum:"+sum);
return sum.toString();});
final Topology topo = builder.build();
final KafkaStreams streams = new KafkaStreams(topo, prop);
final CountDownLatch latch = new CountDownLatch(1);
Runtime.getRuntime().addShutdownHook(new Thread("stream"){
@Override
public void run() {streams.close();
latch.countDown();}
});
try {streams.start();
latch.await();} catch (InterruptedException e) {e.printStackTrace();
}
System.exit(0);
}
}
二、Kafka Shell 命令
1、创立 Topic
kafka-topics.sh --create --zookeeper 192.168.247.201:2181 --topic suminput --partitions 1 --replication-factor 1
**2、运行 Java 代码,执行以下步骤:
生产音讯 **
kafka-console-producer.sh --topic suminput --broker-list 127.0.0.1:9092
案例四:Kafka Stream 实现不同窗口的流解决
一、Kafka Java 代码
package cn.kgc.kb09;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.protocol.types.Field;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.*;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.SessionWindows;
import org.apache.kafka.streams.kstream.TimeWindows;
import java.time.Duration;
import java.util.Arrays;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;
/**
* @Qianchun
* @Date 2020/12/16
* @Description
*/
public class WindowStream {public static void main(String[] args) {Properties prop = new Properties();
// 不同的窗口流不能应用雷同的利用 ID
prop.put(StreamsConfig.APPLICATION_ID_CONFIG,"SessionWindow");
prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.247.201:9092");
prop.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG,3000);
prop.put(ConsumerConfig.AUTO_OFFSET_RESET_DOC,"earliest"); // earliest latest
prop.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"false"); // 设置手动提交形式
prop.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
prop.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG,Serdes.String().getClass());
StreamsBuilder builder = new StreamsBuilder();
KStream<Object, Object> source = builder.stream("windowdemo");
source.flatMapValues(value -> Arrays.asList(value.toString().split("s+")))
.map((x,y) -> {return new KeyValue<String, String>(y,"1");
}).groupByKey()
// 以下所有窗口的工夫均可通过下方参数调设
// Tumbling Time Window(窗口为 5 秒,5 秒内无效)
// .windowedBy(TimeWindows.of(Duration.ofSeconds(5).toMillis()))
// Hopping Time Window(窗口为 5 秒, 每次挪动 2 秒, 所以若 5 秒内只输出一次会呈现 5 /2+1= 3 次)
// .windowedBy(TimeWindows.of(Duration.ofSeconds(5).toMillis())
// .advanceBy(Duration.ofSeconds(2).toMillis()))
// Session Time Window(20 秒内只有输出 Session 就无效, 间隔下一次输出超过 20 秒 Session 生效, 所有从从新从 0 开始)
// .windowedBy(SessionWindows.with(Duration.ofSeconds(20).toMillis()))
.count().toStream().foreach((x,y) -> {System.out.println("x:"+x+"y:"+y);
});
final Topology topo = builder.build();
final KafkaStreams streams = new KafkaStreams(topo, prop);
final CountDownLatch latch = new CountDownLatch(1);
Runtime.getRuntime().addShutdownHook(new Thread("stream"){
@Override
public void run() {streams.close();
latch.countDown();}
});
try {streams.start();
latch.await();} catch (InterruptedException e) {e.printStackTrace();
}
System.exit(0);
}
}
二、Kafka Shell 命令
1、创立 Topic
kafka-topics.sh --create --zookeeper 192.168.247.201:2181 --topic windowdemo --partitions 1 --replication-factor 1
**2、运行 Java 代码,执行以下步骤:
生产音讯 **
kafka-console-producer.sh --topic windowdemo --broker-list 127.0.0.1:9092
留神:
-
ERROR:
- Exception in thread“sum-a3bbe4d0-4cc9-4812-a7a0-e650a8a60c9f-StreamThread-1”java.lang.IllegalArgumentException: Window endMs time cannot be smaller than window startMs time.
- 数组越界
-
解决方案:
- 大概率是窗口 ID 统一,请批改
prop.put(StreamsConfig.APPLICATION_ID_CONFIG, "sessionwindow");
的参数。
- 大概率是窗口 ID 统一,请批改
正文完