本篇将演示如何使用 Flink SQL 实现上一篇demo5的功能，上一篇传送门 Apache Flink 学习笔记（三）

Flink SQl 是无限接近关系型数据库sql语句的抽象模块，SQL和Table API查询可以无缝混合，SQL查询是使用sqlQuery()方法指定的TableEnvironment，该方法返回SQL查询的结果为Table。

直接上代码demo6

import com.alibaba.fastjson.JSONObject;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer09;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import org.apache.flink.util.Collector;

import java.util.Date;

/**
 * Flink SQL
 */
public class Demo6 {
    private static final String APP_NAME = "app_name";

    public static void main(String[] args) {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.getConfig().enableSysoutLogging();
        env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime); //设置窗口的时间单位为process time
        env.setParallelism(1);//全局并发数

        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "kafka bootstrap.servers");
        //设置topic和 app name
        //FlinkKafkaManager 源码见笔记二
        FlinkKafkaManager manager = new FlinkKafkaManager("kafka.topic", APP_NAME, properties);
        FlinkKafkaConsumer09<JSONObject> consumer = manager.build(JSONObject.class);
        consumer.setStartFromLatest();

        //获取DataStream，并转成Bean3
        DataStream<Bean3> stream = env.addSource(consumer).map(new FlatMap());

        final StreamTableEnvironment tableEnvironment = StreamTableEnvironment.getTableEnvironment(env);
        tableEnvironment.registerDataStream("common", stream, "timestamp,appId,module,tt.proctime");//注册表名

        //module 必须加``，否则报错
        String sql = "SELECT appId, COUNT(`module`) AS totals FROM common WHERE appId = '100007336' OR appId = '100013668' GROUP BY TUMBLE(tt, INTERVAL '10' SECOND),appId";
        Table query = tableEnvironment.sqlQuery(sql);

        DataStream<Row> result = tableEnvironment.toAppendStream(query, Row.class);
        result.process(new ProcessFunction<Row, Object>() {
            @Override
            public void processElement(Row value, Context ctx, Collector<Object> out) throws Exception {
                System.out.println(String.format("AppId:%s, Module Count:%s", value.getField(0).toString(), value.getField(1).toString()));
            }
        });

        try {
            env.execute(APP_NAME);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    public static class FlatMap implements MapFunction<JSONObject, Bean3> {
        @Override
        public Bean3 map(JSONObject jsonObject) throws Exception {
            return new Bean3(new Date().getTime(), jsonObject.getString("appId"), jsonObject.getString("module"));
        }
    }
}

除了StreamTableEnvironment处理不一样，其余代码几乎没有改变，这里需要注意的是，Table API中的window函数在SQL里表现为TUMBLE(tt, INTERVAL '10' SECOND)（针对滚动窗口）

更多细节部分，参考官网文档即可（我自己也没怎么看，特殊需求特殊对待，哈哈）。