|NO.Z.00027|——————————|BigDataEnd|——|Hadoop&Flink.V11|——|Flink.v11|API详解|Flink Table API|SQL_API|

一、Flink Table API和SQL_API

### --- Flink Table API Flink SQL_API

~~~     Apache Flink提供了两种顶层的关系型API，分别为Table API和SQL，
~~~     Flink通过Table API&SQL实现了批流统一。
~~~     其中Table API是用于Scala和Java的语言集成查询API，
~~~     它允许以非常直观的方式组合关系运算符（例如select，where和join）的查询。
~~~     Flink SQL基于Apache Calcite 实现了标准的SQL，用户可以使用标准的SQL处理数据集。
~~~     Table API和SQL与Flink的DataStream和DataSet API紧密集成在一起，用户可以实现相互转化，
~~~     比如可以将DataStream或者DataSet注册为table进行操作数据。
~~~     值得注意的是，Table API and SQL目前尚未完全完善，还在积极的开发中，
~~~     所以并不是所有的算子操作都可以通过其实现。

二、导入相关依赖

### --- 在pom.xml下导入依赖环境

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table</artifactId>
            <version>1.11.1</version>
            <type>pom</type>
            <scope>provided</scope>
        </dependency>

        <!-- Either... -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.12</artifactId>
            <version>1.11.1</version>
            <scope>provided</scope>
        </dependency>
        <!-- or... -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala-bridge_2.12</artifactId>
            <version>1.11.1</version>
            <scope>provided</scope>
        </dependency>


        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_2.12</artifactId>
            <version>1.11.1</version>
            <scope>provided</scope>
        </dependency>

三、编程代码实现

### --- 编程代码实现：基于TableAPI的案例：

package com.yanqi.table;


import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.core.execution.PipelineExecutor;
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.streaming.api.functions.source.SourceFunction;
import org.apache.flink.table.api.*;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.descriptors.*;
import org.apache.flink.table.types.DataType;
import org.apache.flink.types.Row;
import org.apache.flink.util.CloseableIterator;

import static org.apache.flink.table.api.Expressions.$;

public class TableApiDemo {
    public static void main(String[] args) throws Exception {
        //Flink执行环境env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //用env，做出Table环境tEnv
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        //table环境的重载方法：
        /*EnvironmentSettings settings = EnvironmentSettings.newInstance()
                .useBlinkPlanner()
//                .inBatchMode()
                .inStreamingMode()
                .build();
        StreamTableEnvironment.create(env,settings);*/

        //获取流式数据源
       /*DataStreamSource<Tuple2<String, Integer>> data = env.addSource(new SourceFunction<Tuple2<String, Integer>>() {
            @Override
            public void run(SourceContext<Tuple2<String, Integer>> ctx) throws Exception {
                int num = 0;
                while (true) {
                    num++;
                    ctx.collect(new Tuple2<>("name"+num, num));
                    Thread.sleep(1000);
                }
            }

            @Override
            public void cancel() {

            }
        });*/

       //从kafka上获取数据
        /*ConnectTableDescriptor descriptor = tEnv.connect(
                // declare the external system to connect to
                new Kafka()
                        .version("universal")
                        .topic("animal")
                        .startFromEarliest()
                        .property("bootstrap.servers", "hdp-2:9092")
        )

                // declare a format for this system
                .withFormat(
//                        new Json()
                        new Csv()
                )

                // declare the schema of the table
                .withSchema(
                        new Schema()
//                                .field("rowtime", DataTypes.TIMESTAMP(3))
//                                .rowtime(new Rowtime()
//                                        .timestampsFromField("timestamp")
//                                        .watermarksPeriodicBounded(60000)
//                                )
//                                .field("user", DataTypes.BIGINT())
                                .field("message", DataTypes.STRING())
                );
        // create a table with given name
        descriptor.createTemporaryTable("MyUserTable");

        Table table1 = tEnv.sqlQuery("select * from MyUserTable");
        DataStream<Tuple2<Boolean, Row>> tuple2DataStream = tEnv.toRetractStream(table1, Row.class);
        tuple2DataStream.print();*/

        tEnv.connect(new FileSystem().path("d:\\data\\input"))// 定义表数据来源，外部连接
                .withFormat(new Csv()) // 定义从外部系统读取数据之后的格式化方法
                .withSchema(new Schema()
                        .field("id", DataTypes.STRING())
                        .field("name",DataTypes.STRING())
//                        .field("timestamp", DataTypes.BIGINT())
//                        .field("temperature", DataTypes.DOUBLE())
                ) // 定义表结构
                .createTemporaryTable("inputTable"); // 创建临时表
        Table resultTable = tEnv.sqlQuery("select * from inputTable");
        DataStream<Tuple2<Boolean, Row>> result = tEnv.toRetractStream(resultTable, Row.class);
        result.print();

//        PipelineExecutor

        //Table方式
        /*//将流式数据源做成Table
        Table table = tEnv.fromDataStream(data, $("name"), $("age"));
        //对Table中的数据做查询
        Table name = table.select($("name"));
        Table filtered = table.select($("name"), $("age")).filter($("age").mod(2).isEqual(0));*/

        //将处理结果输出到控制台
//        DataStream<Tuple2<Boolean, Row>> result = tEnv.toRetractStream(filtered, Row.class);

//        Table mingzi = table.select($("name").as("mingzi"));
//        DataStream<Tuple2<Boolean, Row>> result = tEnv.toRetractStream(mingzi, Row.class);

//        GroupedTable groupedTable = table.select($("name"), $("age")).groupBy($("age").mod(2).as("jiou"));
//        tEnv.toRetractStream(groupedTable,Row.class);

        //SQL方式：
        /*tEnv.createTemporaryView("userss",data, $("name"), $("age"));
        String s = "select name,age from userss where mod(age,2)=0";
        Table table = tEnv.sqlQuery(s);
        DataStream<Tuple2<Boolean, Row>> result = tEnv.toRetractStream(table, Row.class);*/

       /* tEnv.connect(new FileSystem().path("D:\\data\\out.txt"))
                .withFormat(new Csv())
                .withSchema(new Schema().field("name", DataTypes.STRING()).field("age",DataTypes.INT()))
                .createTemporaryTable("outputTable");
        filtered.executeInsert("outputTable");*/


       //往kafka上输出表
        /*DataStreamSource<String> data = env.addSource(new SourceFunction<String>() {
            @Override
            public void run(SourceContext<String> ctx) throws Exception {
                int num = 0;
                while (true) {
                    num++;
                    ctx.collect("name"+num);
                    Thread.sleep(1000);
                }
            }

            @Override
            public void cancel() {

            }
        });

        Table name = tEnv.fromDataStream(data, $("name"));

        ConnectTableDescriptor descriptor = tEnv.connect(
                // declare the external system to connect to
                new Kafka()
                        .version("universal")
                        .topic("animal")
                        .startFromEarliest()
                        .property("bootstrap.servers", "hdp-2:9092")
        )

                // declare a format for this system
                .withFormat(
//                        new Json()
                        new Csv()
                )

                // declare the schema of the table
                .withSchema(
                        new Schema()
//                                .field("rowtime", DataTypes.TIMESTAMP(3))
//                                .rowtime(new Rowtime()
//                                        .timestampsFromField("timestamp")
//                                        .watermarksPeriodicBounded(60000)
//                                )
//                                .field("user", DataTypes.BIGINT())
                                .field("message", DataTypes.STRING())
                );
        // create a table with given name
        descriptor.createTemporaryTable("MyUserTable");

        name.executeInsert("MyUserTable");*/

        //往mysql输出表数据
        DataStreamSource<String> data = env.addSource(new SourceFunction<String>() {
            @Override
            public void run(SourceContext<String> ctx) throws Exception {
                int num = 0;
                while (true) {
                    num++;
                    ctx.collect("name"+num);
                    Thread.sleep(1000);
                }
            }

            @Override
            public void cancel() {

            }
        });

        Table name = tEnv.fromDataStream(data, $("name"));

        String sql = "create table jdbcOutputTable (name varchar(20) not null) with ('connector.type' = 'jdbc','connector.url' = 'jdbc:mysql://hadoop03:3306/bigdata?useUnicode=true&characterEncoding=UTF-8&serverTimezone=UTC','connector.table' = 'name','connector.driver' = 'com.mysql.cj.jdbc.Driver','connector.username' = 'root','connector.password' = '12345678')";

        tEnv.sqlUpdate(sql);
//        TableResult tableResult = tEnv.executeSql(sql);
//        tableResult.
        name.executeInsert("jdbcOutputTable");

//        result.print();
        env.execute();

    }
}

### --- 编译打印

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com.yanqi.table.TableApiDemo

~~~ 输出参数：1秒中打印一个name出来
1> (true,name)
2> (true,name)
3> (true,name)
4> (true,name)
5> (true,name)

Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

——W.S.Landor