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我的scala版本为2.12

<scala.binary.version>2.12</scala.binary.version>

我的Flink版本为1.13.6

<flink.version>1.13.6</flink.version>

FlinkSql读取kafka数据流需要如下依赖：

    <dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency>

我们首先建一个kafka主题person_test。然后建立一个scala类作为kafka的生产者，示例内容如下：

package cha01import org.apache.kafka.clients.producer.{KafkaProducer, ProducerConfig, ProducerRecord}import java.util.Properties
import Util._import scala.util.Randomobject FlinkSqlKafkaConnectorProducer {def main(args: Array[String]): Unit = {val producerConf = new Properties()producerConf.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"single01:9092")producerConf.setProperty(ProducerConfig.BATCH_SIZE_CONFIG,"10")producerConf.setProperty(ProducerConfig.LINGER_MS_CONFIG,"50")producerConf.setProperty(ProducerConfig.RETRIES_CONFIG,"2")producerConf.setProperty(ProducerConfig.ACKS_CONFIG,"1")producerConf.setProperty(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.IntegerSerializer")producerConf.setProperty(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer")val topic = "person_test"val producer:KafkaProducer[Integer,String] = new KafkaProducer(producerConf);val rand = new Random()for(i <- 1 to 2000){val line: String = s"$i,Origami$i,${rand.nextInt(30)+18},${if (rand.nextInt(10) >=8) "Male" else "Female"}"val record: ProducerRecord[Integer, String] =new ProducerRecord[Integer, String](topic, 0, System.currentTimeMillis(), i, line)producer.send(record)Thread.sleep(50+rand.nextInt(500))}producer.flush()producer.close()}
}

此kafka生产者会随机生产出一系列类似以下内容的数据，类型为csv：

id,name,age,gender
1,Origami1,25,Female
2,Origami2,30,Male
3,Origami3,22,Female

随后再在工程中建立一个scala类，内容示例如下：

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.EnvironmentSettings
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironmentobject CSVKafkaSystem {def main(args: Array[String]): Unit = {val settings = EnvironmentSettings.newInstance().inStreamingMode().build()val see: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironmentval tabEnv: StreamTableEnvironment = StreamTableEnvironment.create(see)tabEnv.executeSql("""|CREATE TABLE person(|id INT,|name STRING,|age INT,|gender STRING|) WITH (|'connector' = 'kafka',|'topic'= 'person_test',|'properties.bootstrap.servers' = 'single01:9092',|'properties.group.id' = 'person_test_group',|'scan.startup.mode' = 'earliest-offset',|'format' = 'csv',|'csv.ignore-parse-errors' = 'true',|'csv.field-delimiter' = ','|)|""".stripMargin)tabEnv.sqlQuery("SELECT * FROM person").execute().print()}
}

其中的一些参数解释如下：'

connector' = 'kafka'

指定连接器类型为kafka

'topic'= 'person_test'

指定要读取的kafka主题为person_test

'properties.bootstrap.servers' = 'single01:9092'

指定kafka所在的服务器的ip地址以及端口号

'properties.group.id' = 'person_test_group'

指定 Kafka 消费者组的 ID为person_test_group

'scan.startup.mode' = 'earliest-offset'

指定了控制 Flink 从 Kafka 中读取数据时的起始位置

• earliest-offset 表示从 Kafka 中每个分区的最早消息开始读取。
• latest-offset 表示从 Kafka 中每个分区的最新消息开始读取。
• group-offsets 表示使用 Kafka 消费者组的偏移量来恢复上次消费的位置

'format' = 'csv'

指定了 kafka 消息的格式为csv

'csv.ignore-parse-errors' = 'true'

指定了忽略解析异常的信息

'csv.field-delimiter' = '

指定 CSV 数据中字段的分隔符为,

可以看到最终结果如下，数据在源源不断的生成，flinkSQL也在源源不断的读取表内容