Flink-SQL 设置水位线（Watermark）和开窗（window）示例

主代码 *App

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

/**
 * @author zyj
 * @Date 2022/1/19 10:08
 */
public class ProvinceStatsSqlApp {
    public static void main(String[] args) throws Exception {
        // TODO 1.环境准备
        // 1.1 流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 2.2 表执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        // 2.3 设置并行度
        env.setParallelism(4);

        // TODO 2.设置检查点
        //2.1 开启检查点
        env.enableCheckpointing(5000L, CheckpointingMode.EXACTLY_ONCE);
        //2.2 设置检查点超时时间
        env.getCheckpointConfig().setCheckpointTimeout(60000);
        //2.3 设置重启策略
        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3,3000L));
        //2.4 设置job取消后，检查点是否保留
        env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        //2.5 设置状态后端   内存|文件系统|RocksDB
        env.setStateBackend(new FsStateBackend("hdfs:///ck/gmall"));
        //2.6 指定操作HDFS的用户
        System.setProperty("HADOOP_USER_NAME","zyj");

        // TODO 3.从指定的数据源读取数据  转换为动态表
        String topic = "dwm_order_wide";
        String groupId = "province_stats_app_group";

        tableEnv.executeSql("CREATE TABLE order_wide (" +
                " province_id BIGINT, " +
                " province_name STRING," +
                " province_area_code STRING," +
                " province_iso_code STRING," +
                " province_3166_2_code STRING," +
                " order_id STRING," +
                " split_total_amount DOUBLE," +
                " create_time STRING," +
                " rowtime as TO_TIMESTAMP(create_time) ," +                     // 创建水位线固定格式
                " WATERMARK FOR rowtime AS rowtime - INTERVAL '3' SECOND " +    // 创建水位线固定格式
                ") WITH (" + MyKafkaUtil.getKafkaDDL(topic, groupId) + ")");

        //TODO 4.分组、开窗、聚合计算
        Table provinceStatTable = tableEnv.sqlQuery("select " +
                " DATE_FORMAT(TUMBLE_START(rowtime, INTERVAL '10' SECOND),'yyyy-MM-dd HH:mm:ss') as stt," +
                " DATE_FORMAT(TUMBLE_END(rowtime, INTERVAL '10' SECOND),'yyyy-MM-dd HH:mm:ss') as edt," +
                " province_id," +
                " province_name," +
                " province_area_code area_code," +
                " province_iso_code iso_code ," +
                " province_3166_2_code iso_3166_2," +
                " count(distinct order_id) order_count," +
                " sum(split_total_amount) order_amount," +
                " UNIX_TIMESTAMP() * 1000 as ts " +

                " from " +
                " order_wide " +
                " group by " +
                " TUMBLE(rowtime, INTERVAL '10' SECOND)," +    // 开窗固定格式
                " province_id,province_name,province_area_code,province_iso_code,province_3166_2_code");

        // TODO 5.将动态表转换为流
        DataStream provinceStatsDS = tableEnv.toAppendStream(provinceStatTable, ProvinceStats.class);

        provinceStatsDS.print(">>>>");

        // TODO 6.将流中的数据写到 clickhouse中
        provinceStatsDS.addSink(
                ClickHouseUtil.getJdbcSink("insert into  province_stats_0224  values(?,?,?,?,?,?,?,?,?,?)")
        );

        env.execute();
    }
}

工具类 MyKafkaUtil 

import org.apache.flink.api.common.serialization.SimpleStringSchema;
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.consumer.ConsumerConfig;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;

import javax.annotation.Nullable;
import java.util.Properties;

/**
 * @author zyj
 * @Date 2022/1/6 15:29
 * 操作 kafka 的工具类
 */
public class MyKafkaUtil {
    private static final String KAFKA_SERVER = "hadoop101:9092,hadoop102:9092,hadoop103:9092";
    private static final String DEFAULT_TOPIC = "default_topic";

    // 获取kafka的消费者
    public static FlinkKafkaConsumer getKafkaSource(String topic, String groupId) {
        Properties props = new Properties();
        props.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, KAFKA_SERVER);
        props.setProperty(ConsumerConfig.GROUP_ID_CONFIG, groupId);

        return new FlinkKafkaConsumer(topic, new SimpleStringSchema(), props);
    }

    // 获取kafka的生产者（String数据类型）
   /*
   注意：下面这中实现只能保证数据不丢，不能保证精准一次性
   public static FlinkKafkaProducer getKafkaSink(String topic) {
        return new FlinkKafkaProducer(
                KAFKA_SERVER,
                topic,
                new SimpleStringSchema()
        );
    }*/
    public static FlinkKafkaProducer getKafkaSink(String topic) {
        Properties props = new Properties();
        // 指定kafka服务端ip地址
        props.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, KAFKA_SERVER);
        // 设置生产超时时间
        props.setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, 1000 * 60 * 15 + "");

        // 创建kafka生产者，自定义序列化器 KafkaSerializationSchema 这里是指定String类型
        return new FlinkKafkaProducer(DEFAULT_TOPIC, new KafkaSerializationSchema() {
            @Override
            public ProducerRecord serialize(String str, @Nullable Long timestamp) {
                return new ProducerRecord(topic, str.getBytes());
            }
        }, props, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
    }

    // 获取kafka的生产者（通用数据类型）
    public static  FlinkKafkaProducer getKafkaSinkSchema(KafkaSerializationSchema kafkaSerializationSchema) {
        Properties props = new Properties();
        // 指定kafka服务端ip地址
        props.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, KAFKA_SERVER);
        // 设置生产超时时间
        props.setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG, 1000 * 60 * 15 + "");

        return new FlinkKafkaProducer(DEFAULT_TOPIC, kafkaSerializationSchema, props, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
    }

    public static String getKafkaDDL(String topic, String groupId) {
        String ddl = "'connector' = 'kafka'," +
                "'topic' = '" + topic + "'," +
                "'properties.bootstrap.servers' = '" + KAFKA_SERVER + "'," +
                "'properties.group.id' = '" + groupId + "'," +
                "'scan.startup.mode' = 'latest-offset'," +
                "'format' = 'json'";
        return ddl;
    }
}

工具类  ClickHouseUtil

import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcExecutionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.connector.jdbc.JdbcStatementBuilder;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;

import java.lang.reflect.Field;
import java.sql.PreparedStatement;
import java.sql.SQLException;

/**
 * @author zyj
 * @Date 2022/1/18 9:15
 */
public class ClickHouseUtil {
    public static  SinkFunction getJdbcSink(String sql) {
        // insert into visitor_stats_0224 values(?,?,?,?,?,?,?,?,?,?,?,?)
        SinkFunction sinkFunction = JdbcSink.sink(
                sql,
                new JdbcStatementBuilder() {
                    // 获取流中对象obj的属性值，赋值给问号占位符?  参数 T obj  就是流中的一条数据
                    @Override
                    public void accept(PreparedStatement ps, T obj) throws SQLException {
                        // 获取流中对象所属类的属性
                        Field[] fields = obj.getClass().getDeclaredFields();
                        // 对属性数组进行遍历
                        int skipNum = 0;
                        for (int i = 0; i < fields.length; i++) {
                            // 获取每一个属性
                            Field field = fields[i];

                            // 判断该属性是否有@TransientSink注解
                            TransientSink transientSink = field.getAnnotation(TransientSink.class);
                            if (transientSink != null) {
                                skipNum++;
                                continue;
                            }

                            // 设置私有属性的访问权限
                            field.setAccessible(true);

                            // 获取对象的属性值
                            try {
                                Object fieldValue = field.get(obj);
                                // 将属性的值  赋值给问号占位符
                                ps.setObject(i + 1 - skipNum, fieldValue);
                            } catch (IllegalAccessException e) {
                                e.printStackTrace();
                            }
                        }

                    }
                },
                new JdbcExecutionOptions.Builder()
                        // 批量插入(5条插一次)  每个并行度上每到5条数据就写出
                        .withBatchSize(5)
                        // 插入超时时间
                        .withBatchIntervalMs(2000)
                        // 插入失败重试次数
                        .withMaxRetries(3)
                        .build(),
                new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
                        // 驱动
                        .withDriverName("ru.yandex.clickhouse.ClickHouseDriver")
                        // clickhouse的ip和端口地址
                        .withUrl(GmallConfig.CLICKHOUSE_URL)
                        .build()
        );
        return sinkFunction;
    }
}