java Flink（四十）FlinkSql 简单Demo以及具体使用

原创已于 2023-06-12 17:52:40 修改 · 4.7k 阅读

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#java #flink #数学建模

于 2021-07-29 10:53:16 首次发布

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这篇博客介绍了如何在Flink中使用Flink SQL处理数据。首先引入flink-table-planner依赖，然后定义一个SensorReading POJO类来表示传感器温度数据。接着，从文本文件中读取数据并转换为DataStream<SensorReading>，创建StreamTableEnvironment，并将数据转换为表。通过SQL查询过滤特定ID的传感器数据，并打印结果。

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之前FlinkSql用的比较少，今天开始简单介绍一下。

首先导入依赖：

<!--        引入flink table相关依赖-->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-table-planner_2.12</artifactId>
    <version>1.10.1</version>
</dependency>

然后我们的数据源是读取文件：

POJO类：

package beans;

//传感器温度读数的数据类型
public class SensorReading {
    //属性 id,时间戳,温度值
    private String id;
    private Long timestamp;
    private Double temperature;

    public SensorReading() {
    }

    public SensorReading(String id, Long timestamp, Double temperature) {
        this.id = id;
        this.timestamp = timestamp;
        this.temperature = temperature;
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public Long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(Long timestamp) {
        this.timestamp = timestamp;
    }

    public Double getTemperature() {
        return temperature;
    }

    public void setTemperature(Double temperature) {
        this.temperature = temperature;
    }

    @Override
    public String toString() {
        return "SensorReading{" +
                "id='" + id + '\'' +
                ", timestamp=" + timestamp +
                ", temperature=" + temperature +
                '}';
    }
}

具体使用DEMO：

package table;

import beans.SensorReading;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

public class Table1 {
    public static void main(String[] args) throws Exception{

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        DataStreamSource<String> inputStream = env.readTextFile("D:\\idle\\FlinkTest\\src\\main\\resources\\sensor.txt");
        DataStream<SensorReading> dataStream = inputStream.map(line -> {
            String[] fields = line.split(",");
            return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
        });

        //创建表的执行环境
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        //创建表
        Table dataTable = tableEnv.fromDataStream(dataStream);
        //调用table API进行转换操作
        Table resultTable = dataTable.select("id, temperature")
                .where("id = 'sensor_1'");
        //执行sql
        //dataTable注册进环境
        tableEnv.createTemporaryView("sensor",dataTable);
        String sql = "select id, temperature from sensor where id='sensor_1'";
        Table resultSqlTable = tableEnv.sqlQuery(sql);

        //打印结果
        tableEnv.toAppendStream(resultTable, Row.class).print("result");
        tableEnv.toAppendStream(resultSqlTable, Row.class).print("sql");

        env.execute();


    }
}

查看结果