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從kafka到mysql

新建Java項目

最簡單的方式是按照官網的方法，命令行執(zhí)行curl https://flink.apache.org/q/quickstart.sh | bash -s 1.10.0，不過這種方法有些包還得自行添加，大家可以復制我的pom.xml，我已經將常用的包都放進去了，并且排除了沖突的包。注意的是，本地測試的時候，記得將scope注掉，不然會出現少包的情況。也可以在Run -> Edit Configurations中，勾選Include dependencies with "Provided" scope。最好在resources目錄下丟個log4j的配置文件，這樣有時候方便我們看日志找問題。
新建完項目之后，我們要做的第一件事，自然是寫個Flink 版本的Hello World。所以，新建測試類，然后輸入代碼
```
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

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

    dataStream.print();
  
    env.execute("test");
```
看一下控制臺
```
    4> Hello World
```
如愿以償的得到了想要的結果，不過這個4>是什么玩應？其實這個4代表是第四個分區(qū)輸出的結果。很多人可能會問，我也妹指定并發(fā)啊，數據怎么會跑到第四個分區(qū)呢？其實是因為本地模式的時候，會以匹配CPU的核數，啟動對應數量的分區(qū)。只要我們在每個算子之后加上setParallelism(1)，就會只以一個分區(qū)來執(zhí)行了。至此，我們的DataStream 版的Hellow World試驗完畢，這里主要是為了驗證一下環(huán)境是否正確，接下來才是我們今天的主題從kafka到mysql。另外，如果更想了解DataStream的內容，歡迎大家關注另一個系列Flink DataStream（不過目前還沒開始寫）

新建kafka數據源表

接下來咱們廢話不多說，直接貼代碼

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',  -- 指定連接類型是kafka\n" +
            "    'connector.version' = '0.11',  -- 與我們之前Docker安裝的kafka版本要一致\n" +
            "    'connector.topic' = 'mykafka', -- 之前創(chuàng)建的topic \n" +
            "    'connector.properties.group.id' = 'flink-test-0', -- 消費者組，相關概念可自行百度\n" +
            "    'connector.startup-mode' = 'earliest-offset',  --指定從最早消費\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 {
        //構建StreamExecutionEnvironment 
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        
        //構建EnvironmentSettings 并指定Blink Planner
        EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        
        //構建StreamTableEnvironment 
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, bsSettings);
        
        //通過DDL，注冊kafka數據源表
        tEnv.sqlUpdate(KAFKA_TABLE_SOURCE_DDL);
        
        //執(zhí)行查詢
        Table table = tEnv.sqlQuery("select * from user_behavior");
        
        //轉回DataStream并輸出
        tEnv.toAppendStream(table, Row.class).print().setParallelism(1);

        //任務啟動，這行必不可少！
        env.execute("test");

    }
}

接下來就是激動人性的測試了，右擊，run！查看控制臺

    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中丟的數據一樣，沒毛??！

如果大家在使用過程中遇到Caused by: org.apache.flink.table.api.NoMatchingTableFactoryException: Could not find a suitable table factory for 'org.apache.flink.table.factories.TableSourceFactory' in這種異常，請仔細查看你的DDL語句，是否缺少或者用錯了配置，這里大家可以參考一下Flink官網的手冊，查看一下對應的配置。也可以在下方留言，一起交流。

新建mysql數據結果表

現在mysql中把表創(chuàng)建，畢竟flink現在還沒法幫你自動建表，只能自己動手豐衣足食咯。

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端創(chuàng)建完成后，回到我們的代碼，注冊mysql數據結果表，并將從kafka中讀取到的數據，插入mysql結果表中。下面是完整代碼，包含kafka數據源表的構建。

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',  -- 指定連接類型是kafka\n" +
            "    'connector.version' = '0.11',  -- 與我們之前Docker安裝的kafka版本要一致\n" +
            "    'connector.topic' = 'mykafka', -- 之前創(chuàng)建的topic \n" +
            "    'connector.properties.group.id' = 'flink-test-0', -- 消費者組，相關概念可自行百度\n" +
            "    'connector.startup-mode' = 'earliest-offset',  --指定從最早消費\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', -- 連接方式\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', -- 驅動名字，可以不填，會自動從上面的jdbc url解析 \n" +
            "  'connector.username' = 'root', -- 顧名思義 用戶名\n" +
            "  'connector.password' = '123456' , -- 密碼\n" +
            "  'connector.write.flush.max-rows' = '5000', -- 意思是攢滿多少條才觸發(fā)寫入 \n" +
            "  'connector.write.flush.interval' = '2s' -- 意思是攢滿多少秒才觸發(fā)寫入；這2個參數，無論數據滿足哪個條件，就會觸發(fā)寫入\n"+
            ")"



            ;
    public static void main(String[] args) throws Exception {
        //構建StreamExecutionEnvironment 
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        
        //構建EnvironmentSettings 并指定Blink Planner
        EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
        
        //構建StreamTableEnvironment 
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env, bsSettings);
        
        //通過DDL，注冊kafka數據源表
        tEnv.sqlUpdate(KAFKA_TABLE_SOURCE_DDL);

        //通過DDL，注冊mysql數據結果表
        tEnv.sqlUpdate(MYSQL_TABLE_SINK_DDL);
        
        //將從kafka中查到的數據，插入mysql中
        tEnv.sqlUpdate("insert into user_behavior_mysql select user_id,item_id,behavior,category_id,ts from user_behavior");
        
        //任務啟動，這行必不可少！
        env.execute("test");

    }
}

打開我們的Navicat，看看我們的數據是否正確輸入到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！并且數據和我們kafka中的數據也是一致，大家也可以通過上一章講過的Java連接kafka來對比驗證數據的一致性，此處就不再贅述。那么好了，本次的Flink Sql之旅就結束，下一章我們將帶大家，在這次課程的基礎上，完成常用聚合查詢以及目前Flink Sql原生支持的維表Join。另外，有同學反映有些地方不知道為什么要這樣做，不想只知其然而不知所以然，我們之后同樣會有另外的專題講述Flink 原理。

附錄

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>

有點亂，懶得整理了，大家直接復制過去用就行。

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>
        <!--過濾器設置輸出的級別-->
        <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的設置，additivity指示是否遵循缺省的繼承機制-->
    <logger name="com.runway.bssp.activeXdemo" additivity="false">
        <appender-ref ref="myConsole" />
    </logger>

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

記得要放在resource目錄下，別放錯了。

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Flink Sql教程（2）

Flink Sql教程（2）

從kafka到mysql

新建Java項目

新建kafka數據源表

新建mysql數據結果表

附錄

pom.xml

log4j.xml

相關閱讀更多精彩內容

友情鏈接更多精彩內容

色偷偷精品伊人,欧洲久久精品,欧美综合婷婷骚逼,国产AV主播,国产最新探花在线,九色在线视频一区,伊人大交九 欧美,1769亚洲,黄色成人av

Flink Sql教程（2）

從kafka到mysql

新建Java項目

新建kafka數據源表

新建mysql數據結果表

附錄

pom.xml

log4j.xml

相關閱讀更多精彩內容

友情鏈接更多精彩內容

色偷偷精品伊人,欧洲久久精品,欧美综合婷婷骚逼,国产AV主播,国产最新探花在线,九色在线视频一区,伊人大交九欧美,1769亚洲,黄色成人av