[大数据]从0到1Flink的成长之路(二十一)-Flink+Kafka：End-to-End Exactly-Once代码示例

Flink+Kafka：End-to-End Exactly-Once代码示例

analysis/

package xx.xxxxxx.flink.exactly;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
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.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.producer.ProducerRecord;
import javax.annotation.Nullable;
import java.nio.charset.StandardCharsets;
import java.util.Properties;
/**
* 1. Kafka Producer的容错-Kafka 0.9 and 0.10
* 如果Flink开启了checkpoint，针对FlinkKafkaProducer09 和FlinkKafkaProducer010 可以提供 at-least-once的语义，配置如下参数
* setLogFailuresOnly(false)
* setFlushOnCheckpoint(true)
* retries【这个参数的值默认是0】 //注意：建议修改kafka 生产者的重试次数
** 2. Kafka Producer的容错-Kafka 0.11+
* 如果Flink开启了checkpoint，针对FlinkKafkaProducer011+ 就可以提供 exactly-once的语义
* 但是需要选择具体的语义
* Semantic.NONE
* Semantic.AT_LEAST_ONCE【默认】
* Semantic.EXACTLY_ONCE
*/
public class StreamExactlyOnceKafkaDemo {
 /*
/export/server/kafka/bin/kafka-topics.sh --list --bootstrap-server node1.itcast.cn:9092
Source Kafka:
/export/server/kafka/bin/kafka-topics.sh --create \
--bootstrap-server node1.itcast.cn:9092 --replication-factor 1 --partitions 3 --topic flink-topic-source
/export/server/kafka/bin/kafka-console-producer.sh --broker-list node1.itcast.cn:9092 \
--topic flink-topic-source
Sink Kafka:
/export/server/kafka/bin/kafka-topics.sh --create --bootstrap-server node1.itcast.cn:9092 \
--replication-factor 1 --partitions 3 --topic flink-topic-sink
/export/server/kafka/bin/kafka-console-consumer.sh --bootstrap-server node1.itcast.cn:9092 \
--topic flink-topic-sink --from-beginning
*/
 public static void main(String[] args) throws Exception {
 // 1. 执行环境-env
 StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
 env.setParallelism(2);
 // 设置Checkpoint
 env.enableCheckpointing(5000);
 env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
 env.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
 env.getCheckpointConfig().enableExternalizedCheckpoints(
 CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION
 );
 // 2. 数据源-source
 // 2.1. Kafka Consumer 消费数据属性设置
 Properties sourceProps = new Properties();
 sourceProps.setProperty(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, "node1.itcast.cn:9092");
 sourceProps.setProperty("group.id","flink-1001");
 //如果有记录偏移量从记录的位置开始消费,如果没有从最新的数据开始消费
 sourceProps.setProperty("auto.offset.reset","latest");
 //开一个后台线程每隔5s检查Kafka的分区状态
 sourceProps.setProperty("flink.partition-discovery.interval-millis","5000");
 // 2.2. 实例化FlinkKafkaConsumer对象
 FlinkKafkaConsumer<String> kafkaSource = new FlinkKafkaConsumer<>(
 "flink-topic-source", new SimpleStringSchema(), sourceProps
 );
 // 从group offset记录的位置位置开始消费,如果没有该group信息，会根据"auto.offset.reset"的设置来决定从哪开始消费
 kafkaSource.setStartFromGroupOffsets();
 // Flink执行Checkpoint的时候提交偏移量(一份在Checkpoint中, 一份在Kafka主题中__comsumer_offsets(方便外部监控工具去看))
 kafkaSource.setCommitOffsetsOnCheckpoints(true) ;
 // 2.3. 添加数据源
 DataStreamSource<String> kafkaDataStream = env.addSource(kafkaSource);
 // 3. 数据转换-transformation
 // 4. 数据终端-sink
 // 4.1. Kafka Producer 生成者属性配置
 Properties sinkProps = new Properties();
 sinkProps.setProperty(CommonClientConfigs.BOOTSTRAP_SERVERS_CONFIG, "node1:9092");
 //设置事务超时时间，也可在kafka配置中设置
 sinkProps.setProperty("transaction.timeout.ms", 60000 * 15 + "");
 // 4.2. 创建序列化实例对象
 KafkaSerializationSchema<String> kafkaSchema = new KafkaSerializationSchema<String>(){
 @Override
 public ProducerRecord<byte[], byte[]> serialize(String element, @Nullable Long timestamp) {
 ProducerRecord<byte[], byte[]> record = new ProducerRecord<>(
 "flink-topic-sink", element.getBytes(StandardCharsets.UTF_8)
 );
 return record;
 }
 } ;
 // 4.3. 实例化FlinkKafkaProducer对象
 FlinkKafkaProducer<String> kafkaSink = new FlinkKafkaProducer<>(
 "flink-topic-sink",
 kafkaSchema,
 sinkProps,
 FlinkKafkaProducer.Semantic.EXACTLY_ONCE
 );
 // 4.4. 添加sink
 kafkaDataStream.addSink(kafkaSink) ;
 // 5. 触发执行-execute
 env.execute(StreamExactlyOnceKafkaDemo.class.getSimpleName());
  } }