Flink ClickHouse 代码
时间: 2024-01-14 14:04:11 浏览: 24
以下是使用Flink将数据写入ClickHouse的Java代码示例:
```java
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。