流计算中的流式SQL是什么？请解释其作用和用途。

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

import java.util.Properties;

public class StreamSQLExample {
    public static void main(String[] args) throws Exception {
        // 设置流式执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 创建Kafka消费者
        Properties props = new Properties();
        props.setProperty("bootstrap.servers", "localhost:9092");
        props.setProperty("group.id", "test-consumer-group");
        FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("topic", new SimpleStringSchema(), props);

        // 从Kafka读取数据流
        DataStream<String> stream = env.addSource(consumer);

        // 创建流式表环境
        EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, settings);

        // 将数据流转换为表
        Table table = tableEnv.fromDataStream(stream, "value");

        // 执行流式SQL查询
        Table result = tableEnv.sqlQuery("SELECT value, COUNT(*) AS count FROM table GROUP BY value");

        // 将查询结果转换为数据流
        DataStream<Tuple2<Boolean, Row>> resultStream = tableEnv.toRetractStream(result, Row.class);

        // 将结果数据流输出到控制台
        resultStream.map(new MapFunction<Tuple2<Boolean, Row>, String>() {
            @Override
            public String map(Tuple2<Boolean, Row> value) throws Exception {
                return value.f1.toString();
            }
        }).print();

        // 执行流式计算
        env.execute("Stream SQL Example");
    }
}