项目实战从 0 到 1 学习之Flink （28）FlinkSql教程（二）

2022-01-18 05:10:08

从kafka到mysql

新建Java项目

最简单的方式是按照官网的方法，命令行执行curl https://flink.apache.org/q/quickstart.sh | bash -s 1.10.0，不过这种方法有些包还得自行添加，大家可以复制我的pom.xml，我已经将常用的包都放进去了，并且排除了冲突的包。注意的是，本地测试的时候，记得将scope注掉，不然会出现少包的情况。也可以在Run -> Edit Configurations中，勾选Include dependencies with "Provided" scope。最好在resources目录下丢个log4j的配置文件，这样有时候方便我们看日志找问题。
新建完项目之后，我们要做的第一件事，自然是写个Flink 版本的Hello World。所以，新建测试类，然后输入代码
```
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    DataStream dataStream = env.fromElements("Hello World");

    dataStream.print();
  
    env.execute("test");
```
看一下控制台
```
 Hello World
```
如愿以偿的得到了想要的结果，不过这个4>是什么玩应？其实这个4代表是第四个分区输出的结果。很多人可能会问，我也妹指定并发啊，数据怎么会跑到第四个分区呢？其实是因为本地模式的时候，会以匹配CPU的核数，启动对应数量的分区。只要我们在每个算子之后加上setParallelism(1)，就会只以一个分区来执行了。至此，我们的DataStream 版的Hellow World试验完毕，这里主要是为了验证一下环境是否正确，接下来才是我们今天的主题从kafka到mysql。另外，如果更想了解DataStream的内容，欢迎大家关注另一个系列Flink DataStream（不过目前还没开始写）

新建kafka数据源表

接下来咱们废话不多说，直接贴代码

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


public class FlinkSql02 {
    public static final String  KAFKA_TABLE_SOURCE_DDL = "" +
            "CREATE TABLE user_behavior (\n" +
            "    user_id BIGINT,\n" +
            "    item_id BIGINT,\n" +
            "    category_id BIGINT,\n" +
            "    behavior STRING,\n" +
            "    ts TIMESTAMP(3)\n" +
            ") WITH (\n" +
            "    ‘connector.type‘ = ‘kafka‘,  -- 指定连接类型是kafka\n" +
            "    ‘connector.version‘ = ‘0.11‘,  -- 与我们之前Docker安装的kafka版本要一致\n" +
            "    ‘connector.topic‘ = ‘mykafka‘, -- 之前创建的topic \n" +
            "    ‘connector.properties.group.id‘ = ‘flink-test-0‘, -- 消费者组，相关概念可自行百度\n" +
            "    ‘connector.startup-mode‘ = ‘earliest-offset‘,  --指定从最早消费\n" +
            "    ‘connector.properties.zookeeper.connect‘ = ‘localhost:2181‘,  -- zk地址\n" +
            "    ‘connector.properties.bootstrap.servers‘ = ‘localhost:9092‘,  -- broker地址\n" +
            "    ‘format.type‘ = ‘json‘  -- json格式，和topic中的消息格式保持一致\n" +
            ")";
    public static void main(String[] args) throws Exception {
        //构建StreamExecutionEnvironment 
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        
        //构建EnvironmentSettings 并指定Blink Planner
        EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        
        //构建StreamTableEnvironment 
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, bsSettings);
        
        //通过DDL，注册kafka数据源表
        tEnv.sqlUpdate(KAFKA_TABLE_SOURCE_DDL);
        
        //执行查询
        Table table = tEnv.sqlQuery("select * from user_behavior");
        
        //转回DataStream并输出
        tEnv.toAppendStream(table, Row.class).print().setParallelism(1);

        //任务启动，这行必不可少！
        env.execute("test");

    }
}

接下来就是激动人性的测试了，右击，run！查看控制台

543462,1715,1464116,pv,2017-11-26T01:00
543462,1715,1464116,pv,2017-11-26T01:00
543462,1715,1464116,pv,2017-11-26T01:00
543462,1715,1464116,pv,2017-11-26T01:00

嗯，跟我之前往kafka中丢的数据一样，没毛病！

如果大家在使用过程中遇到Caused by: org.apache.flink.table.api.NoMatchingTableFactoryException: Could not find a suitable table factory for ‘org.apache.flink.table.factories.TableSourceFactory‘ in这种异常，请仔细查看你的DDL语句，是否缺少或者用错了配置，这里大家可以参考一下Flink官网的手册，查看一下对应的配置。也可以在下方留言，一起交流。

新建mysql数据结果表

现在mysql中把表创建，毕竟flink现在还没法帮你自动建表，只能自己动手丰衣足食咯。

CREATE TABLE `user_behavior` (
  `user_id` bigint(20) DEFAULT NULL,
  `item_id` bigint(20) DEFAULT NULL,
  `behavior` varchar(255) DEFAULT NULL,
  `category_id` bigint(20) DEFAULT NULL,
  `ts` timestamp(6) NULL DEFAULT NULL
)

在mysql端创建完成后，回到我们的代码，注册mysql数据结果表，并将从kafka中读取到的数据，插入mysql结果表中。下面是完整代码，包含kafka数据源表的构建。

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.types.Row;


public class FlinkSql02 {
    public static final String  KAFKA_TABLE_SOURCE_DDL = "" +
            "CREATE TABLE user_behavior (\n" +
            "    user_id BIGINT,\n" +
            "    item_id BIGINT,\n" +
            "    category_id BIGINT,\n" +
            "    behavior STRING,\n" +
            "    ts TIMESTAMP(3)\n" +
            ") WITH (\n" +
            "    ‘connector.type‘ = ‘kafka‘,  -- 指定连接类型是kafka\n" +
            "    ‘connector.version‘ = ‘0.11‘,  -- 与我们之前Docker安装的kafka版本要一致\n" +
            "    ‘connector.topic‘ = ‘mykafka‘, -- 之前创建的topic \n" +
            "    ‘connector.properties.group.id‘ = ‘flink-test-0‘, -- 消费者组，相关概念可自行百度\n" +
            "    ‘connector.startup-mode‘ = ‘earliest-offset‘,  --指定从最早消费\n" +
            "    ‘connector.properties.zookeeper.connect‘ = ‘localhost:2181‘,  -- zk地址\n" +
            "    ‘connector.properties.bootstrap.servers‘ = ‘localhost:9092‘,  -- broker地址\n" +
            "    ‘format.type‘ = ‘json‘  -- json格式，和topic中的消息格式保持一致\n" +
            ")";

    public static final String MYSQL_TABLE_SINK_DDL=""+
            "CREATE TABLE `user_behavior_mysql` (\n" +
            "  `user_id` bigint  ,\n" +
            "  `item_id` bigint  ,\n" +
            "  `behavior` varchar  ,\n" +
            "  `category_id` bigint  ,\n" +
            "  `ts` timestamp(3)   \n" +
            ")WITH (\n" +
            "  ‘connector.type‘ = ‘jdbc‘, -- 连接方式\n" +
            "  ‘connector.url‘ = ‘jdbc:mysql://localhost:3306/mysql‘, -- jdbc的url\n" +
            "  ‘connector.table‘ = ‘user_behavior‘,  -- 表名\n" +
            "  ‘connector.driver‘ = ‘com.mysql.jdbc.Driver‘, -- 驱动名字，可以不填，会自动从上面的jdbc url解析 \n" +
            "  ‘connector.username‘ = ‘root‘, -- 顾名思义 用户名\n" +
            "  ‘connector.password‘ = ‘123456‘ , -- 密码\n" +
            "  ‘connector.write.flush.max-rows‘ = ‘5000‘, -- 意思是攒满多少条才触发写入 \n" +
            "  ‘connector.write.flush.interval‘ = ‘2s‘ -- 意思是攒满多少秒才触发写入；这2个参数，无论数据满足哪个条件，就会触发写入\n"+
            ")"



            ;
    public static void main(String[] args) throws Exception {
        //构建StreamExecutionEnvironment 
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        
        //构建EnvironmentSettings 并指定Blink Planner
        EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        
        //构建StreamTableEnvironment 
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, bsSettings);
        
        //通过DDL，注册kafka数据源表
        tEnv.sqlUpdate(KAFKA_TABLE_SOURCE_DDL);

        //通过DDL，注册mysql数据结果表
        tEnv.sqlUpdate(MYSQL_TABLE_SINK_DDL);
        
        //将从kafka中查到的数据，插入mysql中
        tEnv.sqlUpdate("insert into user_behavior_mysql select user_id,item_id,behavior,category_id,ts from user_behavior");
        
        //任务启动，这行必不可少！
        env.execute("test");

    }
}

打开我们的Navicat，看看我们的数据是否正确输入到mysql中。

user_id	item_id	behavior	category_id	ts
543462	1715	pv	1464116	2017-11-26 01:00:00.000
543462	1715	pv	1464116	2017-11-26 01:00:00.000
543462	1715	pv	1464116	2017-11-26 01:00:00.000
543462	1715	pv	1464116	2017-11-26 01:00:00.000

成功！并且数据和我们kafka中的数据也是一致，大家也可以通过上一章讲过的Java连接kafka来对比验证数据的一致性，此处就不再赘述。那么好了，本次的Flink Sql之旅就结束，下一章我们将带大家，在这次课程的基础上，完成常用聚合查询以及目前Flink Sql原生支持的维表Join。另外，有同学反映有些地方不知道为什么要这样做，不想只知其然而不知所以然，我们之后同样会有另外的专题讲述Flink 原理。

附录

pom.xml

    
    <properties>
        <flink.version>1.10.0</flink.version>
        <scala.binary.version>2.11</scala.binary.version>
    </properties>

    <dependencies>
        <!-- Flink modules -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>

            <scope>provided</scope>
            <exclusions>
                <exclusion>
                    <artifactId>scala-library</artifactId>
                    <groupId>org.scala-lang</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>slf4j-api</artifactId>
                    <groupId>org.slf4j</groupId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>1.10.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-jdbc_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- CLI dependencies -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
            <exclusions>
                <exclusion>
                    <artifactId>javassist</artifactId>
                    <groupId>org.javassist</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>scala-parser-combinators_2.11</artifactId>
                    <groupId>org.scala-lang.modules</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>slf4j-api</artifactId>
                    <groupId>org.slf4j</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>snappy-java</artifactId>
                    <groupId>org.xerial.snappy</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-clients -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.11.0.3</version>
            <exclusions>
                <exclusion>
                    <artifactId>slf4j-api</artifactId>
                    <groupId>org.slf4j</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.11_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
            <exclusions>
                <exclusion>
                    <artifactId>kafka-clients</artifactId>
                    <groupId>org.apache.kafka</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- https://mvnrepository.com/artifact/mysql/mysql-connector-java -->
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.37</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-redis_2.11</artifactId>
            <version>1.1.5</version>
            <exclusions>
                <exclusion>
                    <artifactId>force-shading</artifactId>
                    <groupId>org.apache.flink</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>slf4j-api</artifactId>
                    <groupId>org.slf4j</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-core</artifactId>
            <version>2.9.5</version>
        </dependency>

        <dependency>
            <groupId>io.lettuce</groupId>
            <artifactId>lettuce-core</artifactId>
            <version>5.0.5.RELEASE</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/com.alibaba/fastjson -->
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.46</version>
        </dependency>


        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.11</artifactId>
            <version>1.10.0</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>io.netty</groupId>
            <artifactId>netty-all</artifactId>
            <version>4.1.4.Final</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-jdbc -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-jdbc_2.11</artifactId>
            <version>1.10.0</version>
        </dependency>

    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.8.1</version>
                <configuration>
                    <encoding>UTF-8</encoding>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <filter>
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <artifactSet>
                                <excludes>
                                    <exclude>junit:junit</exclude>
                                </excludes>
                            </artifactSet>

                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

有点乱，懒得整理了，大家直接复制过去用就行。

log4j.xml

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE log4j:configuration SYSTEM "log4j.dtd">

<log4j:configuration xmlns:log4j=‘http://jakarta.apache.org/log4j/‘ >

    <appender name="myConsole" class="org.apache.log4j.ConsoleAppender">
        <layout class="org.apache.log4j.PatternLayout">
            <param name="ConversionPattern"
                   value="[%d{dd HH:mm:ss,SSS\} %-5p] [%t] %c{2\} - %m%n" />
        </layout>
        <!--过滤器设置输出的级别-->
        <filter class="org.apache.log4j.varia.LevelRangeFilter">
            <param name="levelMin" value="info" />
            <param name="levelMax" value="error" />
            <param name="AcceptOnMatch" value="true" />
        </filter>
    </appender>

    <!-- 指定logger的设置，additivity指示是否遵循缺省的继承机制-->
    <logger name="com.runway.bssp.activeXdemo" additivity="false">
        <appender-ref ref="myConsole" />
    </logger>

    <!-- 根logger的设置-->
    <root>
        <priority value ="debug"/>
        <appender-ref ref="myConsole"/>
    </root>
</log4j:configuration>

记得要放在resource目录下，别放错了。