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flink自定义写入clickhouse

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首先 在pom.xml中放入所需依赖，我这里整理了一份mysql、reduis和clickhouse的所有依赖

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.atguigu.flink</groupId>
    <artifactId>flink-first</artifactId>
    <version>0.1</version>

    <properties>
        <flink.version>1.13.0</flink.version>
        <java.version>1.8</java.version>
        <scala.binary.version>2.12</scala.binary.version>
        <slf4j.version>1.7.30</slf4j.version>
    </properties>

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

        <dependency>
            <groupId>org.apache.bahir</groupId>
            <artifactId>flink-connector-redis_2.11</artifactId>
            <version>1.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.47</version>
            <!--            <scope>provided</scope>-->
        </dependency>
        <!-->flink-connector-jdbc flink版本需在1.11.0之后<!-->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-common</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-csv</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-cep_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
<!--   clickhouse的连接     ======-->

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala-bridge_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <!-->clickhouse jdbc连接<!-->
        <dependency>
            <groupId>ru.yandex.clickhouse</groupId>
            <artifactId>clickhouse-jdbc</artifactId>
            <version>0.1.55</version>
        </dependency>

<!--        ==========-->
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-to-slf4j</artifactId>
            <version>2.14.0</version>
        </dependency>
    </dependencies>

<!--    打包操作的相关依赖-->
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.3.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>8</source>
                    <target>8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>


</project>

具体flink代码如下：

```java
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
//写入clickhouse
public class IntoClickhouse {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env
                .addSource(new Example1.ClickSource())
                .addSink(new RichSinkFunction<Example1.Event>() {
                    private Connection conn;
                    private PreparedStatement insertStmt;
                    @Override
                    public void open(Configuration parameters) throws Exception {
                        super.open(parameters);
                        conn = DriverManager.getConnection(
                                "jdbc:clickhouse://hadoop102:8123/test"
                        );
                        insertStmt = conn.prepareStatement("INSERT INTO click (user, url) VALUES (?, ?)");
                    }


                    // 来一条数据触发调用一次
                    @Override
                    public void invoke(Example1.Event value, Context context) throws Exception {
                            insertStmt.setString(1, value.user);
                            insertStmt.setString(2, value.url);
                            insertStmt.execute();

                    }

                    @Override
                    public void close() throws Exception {
                        super.close();
                        insertStmt.close();
                        conn.close();
                    }
                });


        env.execute();
    }
}

里面用到的自定义数据源：

```java
 public static class Event{
        public String user;
        public String url;
        public Long timestamp;

        public Event() {
        }

        public Event(String user, String url, Long timestamp) {
            this.user = user;
            this.url = url;
            this.timestamp = timestamp;
        }

        @Override
        public String toString() {
            return "Event{" +
                    "user='" + user + '\'' +
                    ", url='" + url + '\'' +
                    ", timestamp=" + new Timestamp(timestamp) +
                    '}';
        }
    }

    // 自定义数据源
    // SourceFunction<T>, T是数据流中的元素类型
    // SourceFunction只能用来产生并行度为1的数据源
    // ParallelSourceFunction可以产生并行数据源
    public static class ClickSource implements SourceFunction<Event> {
        private boolean running=true;
        private Random random=new Random();
        private String[] userArr={"Mary", "Bob", "Alice", "Liz"};
        private String[] urlArr={"./home", "./cart", "./fav", "./prod?id=1", "./prod?id=2"};

        // 任务开始时触发run的调用
        @Override
        public void run(SourceContext<Event> sourceContext) throws Exception {
            while (running){
                String user=userArr[random.nextInt(userArr.length)];
                String url=urlArr[random.nextInt(urlArr.length)];
                long timestamp = Calendar.getInstance().getTimeInMillis();// 毫秒时间戳(机器时间)
                Event event=new Event(user,url,timestamp);
                // 使用collect方法发射数据
                sourceContext.collect(event);
                Thread.sleep(100L);

            }
        }

        // 在取消任务时触发调用cancel，例如在web ui点击任务的cancel按钮
        @Override
        public void cancel() {
            running=false;
        }
    }