clickhouse split

splitByChar是Clickhouse的一个字符串拆分函数，它可以将字符串根据指定的分隔符进行切分，并返回一个数组。比如，在执行select splitByChar('_', '123_456_142354_23543__') AS arr；之后，得到的结果将会是一个包含多个元素的数组。你可以通过数组的下标来获取具体的元素。希望这个解答对你有帮助。

相关问题

flink 写入clickhouse

可以使用 Flink 的 JDBC Sink 将数据写入 ClickHouse 数据库。具体步骤如下：

1. 在 pom.xml 中添加 ClickHouse JDBC 驱动的依赖：

<dependency>
    <groupId>ru.yandex.clickhouse</groupId>
    <artifactId>clickhouse-jdbc</artifactId>
    <version>0.3.0</version>
</dependency>

2. 在 Flink 程序中创建 ClickHouse JDBC Sink：

import java.sql.PreparedStatement;
import java.sql.SQLException;

import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import ru.yandex.clickhouse.ClickHouseConnection;
import ru.yandex.clickhouse.ClickHouseDataSource;

public class ClickHouseSink extends RichSinkFunction<String> {

    private static final long serialVersionUID = 1L;
    private static final Logger LOG = LoggerFactory.getLogger(ClickHouseSink.class);

    private ClickHouseConnection connection;
    private PreparedStatement statement;

    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);

        // 初始化 ClickHouse 连接
        ClickHouseDataSource dataSource = new ClickHouseDataSource("jdbc:clickhouse://<clickhouse-host>:<clickhouse-port>/<clickhouse-database>");
        connection = dataSource.getConnection();
        statement = connection.prepareStatement("INSERT INTO <clickhouse-table> (col1, col2, ...) VALUES (?, ?, ...)");
    }

    @Override
    public void invoke(String value, Context context) throws Exception {
        String[] fields = value.split(",");

        // 设置 PreparedStatement 的参数
        statement.setString(1, fields[0]);
        statement.setInt(2, Integer.parseInt(fields[1]));
        ...

        // 执行插入操作
        statement.executeUpdate();
    }

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

        // 关闭 ClickHouse 连接
        if (statement != null) {
            statement.close();
        }
        if (connection != null) {
            connection.close();
        }
    }

}

3. 在 Flink 程序中使用 ClickHouse JDBC Sink 输出数据：

DataStream<String> dataStream = ... // 获取数据流
dataStream.addSink(new ClickHouseSink());

其中 `<clickhouse-host>`、`<clickhouse-port>`、`<clickhouse-database>` 和 `<clickhouse-table>` 分别表示 ClickHouse 数据库的主机名、端口号、数据库名称和数据表名称。在执行插入操作时，需要根据实际情况设置 PreparedStatement 的参数。

Flink ClickHouse 代码

以下是使用Flink将数据写入ClickHouse的Java代码示例：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseSink;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseSinkFunction;
import org.apache.flink.streaming.connectors.clickhouse.common.ClickHouseRowConverter;
import org.apache.flink.streaming.connectors.clickhouse.common.container.ClickHouseClientProvider;
import org.apache.flink.streaming.connectors.clickhouse.common.container.JDBCDriverContainer;
import org.apache.flink.streaming.connectors.clickhouse.common.enums.InsertMode;

import java.sql.PreparedStatement;
import java.sql.SQLException;
import java.util.Properties;

public class FlinkClickHouseExample {
    public static void main(String[] args) throws Exception {
        // set up the execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // configure the properties for the ClickHouse sink
        Properties properties = new Properties();
        properties.setProperty(ClickHouseSinkOptions.TARGET_TABLE_NAME, "test");
        properties.setProperty(ClickHouseSinkOptions.INSERT_MODE, InsertMode.REPLACE.name());
        properties.setProperty(ClickHouseSinkOptions.USERNAME, "user");
        properties.setProperty(ClickHouseSinkOptions.PASSWORD, "password");
        properties.setProperty(ClickHouseSinkOptions.DATABASE_NAME, "test");
        properties.setProperty(ClickHouseSinkOptions.TARGET_TABLE_SCHEMA, "id Int32, name String");

        // create a ClickHouseSink with the properties and a row converter
        ClickHouseRowConverter rowConverter = new ClickHouseRowConverter.SimpleClickHouseRowConverter();
        ClickHouseSinkFunction clickHouseSinkFunction = new ClickHouseSink(properties, rowConverter, new JDBCDriverContainer(), new ClickHouseClientProvider());

        // create a data stream from a source, map it to a ClickHouse row, and add the ClickHouse sink
        DataStream<String> source = env.socketTextStream("localhost", 9000);
        DataStream<ClickHouseRow> clickHouseRows = source.map(new MapFunction<String, ClickHouseRow>() {
            @Override
            public ClickHouseRow map(String value) throws Exception {
                String[] fields = value.split(",");
                return new ClickHouseRow(fields[0], fields[1]);
            }
        });
        clickHouseRows.addSink(clickHouseSinkFunction);

        // execute the Flink job
        env.execute("Flink ClickHouse Example");
    }

    public static class ClickHouseRow {
        private int id;
        private String name;

        public ClickHouseRow(String id, String name) {
            this.id = Integer.parseInt(id);
            this.name = name;
        }

        public int getId() {
            return id;
        }

        public String getName() {
            return name;
        }
    }
}

在此代码中，我们首先配置了ClickHouse sink的属性，并创建了一个ClickHouseSinkFunction。接下来，我们从数据源创建了一个数据流，并将其映射为一个ClickHouse行。最后，我们将ClickHouse行添加到ClickHouse sink并执行Flink作业。