flink 多线程读取clickhouse中数据的java代码demo

以下是一个使用Flink多线程读取ClickHouse中数据的Java代码示例：

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.TypeExtractor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseIO;
import org.apache.flink.streaming.connectors.clickhouse.ClickHouseRow;
import org.apache.flink.streaming.connectors.clickhouse.serialization.ClickHouseRowDeserializationSchema;
import org.apache.flink.streaming.connectors.clickhouse.util.ClickHouseUtil;
import org.apache.flink.util.Collector;

import java.io.IOException;
import java.net.InetAddress;
import java.net.InetSocketAddress;
import java.net.SocketAddress;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;

public class ClickHouseDemo {

    private static final String[] FIELD_NAMES = new String[]{"id", "name", "age"};

    private static final TypeInformation<ClickHouseRow> TYPE_INFO = TypeExtractor.getForClass(ClickHouseRow.class);

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(2);

        Map<String, String> parameters = new HashMap<>();
        parameters.put(ClickHouseUtil.PARAM_HOST, "localhost");
        parameters.put(ClickHouseUtil.PARAM_PORT, "8123");
        parameters.put(ClickHouseUtil.PARAM_DATABASE, "test");
        parameters.put(ClickHouseUtil.PARAM_TABLE_NAME, "user");

        DataStream<ClickHouseRow> stream = env.addSource(createClickHouseSource(parameters));
        stream.flatMap(new FlatMapFunction<ClickHouseRow, String>() {
            @Override
            public void flatMap(ClickHouseRow value, Collector<String> out) throws Exception {
                for (int i = 0; i < FIELD_NAMES.length; i++) {
                    out.collect(FIELD_NAMES[i] + ":" + value.getField(i));
                }
            }
        }).print();

        env.execute("ClickHouseDemo");
    }

    private static ClickHouseIO<ClickHouseRow> createClickHouseSource(Map<String, String> parameters) {
        DeserializationSchema<ClickHouseRow> deserializationSchema = new ClickHouseRowDeserializationSchema(FIELD_NAMES, TYPE_INFO);

        return ClickHouseIO.<ClickHouseRow>source()
                .setHosts(createSocketAddress(parameters.get(ClickHouseUtil.PARAM_HOST), Integer.parseInt(parameters.get(ClickHouseUtil.PARAM_PORT))))
                .setDatabaseName(parameters.get(ClickHouseUtil.PARAM_DATABASE))
                .setTableName(parameters.get(ClickHouseUtil.PARAM_TABLE_NAME))
                .setUsername(parameters.get(ClickHouseUtil.PARAM_USER))
                .setPassword(parameters.get(ClickHouseUtil.PARAM_PASSWORD))
                .setDeserializationSchema(deserializationSchema)
                .setFetchSize(100)
                .setParallelism(2);
    }

    private static InetSocketAddress createSocketAddress(String host, int port) {
        try {
            InetAddress inetAddress = InetAddress.getByName(host);
            return new InetSocketAddress(inetAddress, port);
        } catch (IOException e) {
            throw new RuntimeException("Could not create socket address for " + host + ":" + port, e);
        }
    }

}

这个示例程序首先创建一个StreamExecutionEnvironment实例，然后设置并行度为2。

接下来，创建一个包含ClickHouse连接参数的Map，然后使用createClickHouseSource方法创建一个ClickHouseIO实例。

createClickHouseSource方法使用ClickHouse连接参数创建一个ClickHouseIO对象，并设置用于反序列化从ClickHouse读取的行的DeserializationSchema。然后，它还将并行度设置为2。

接下来，通过调用env.addSource方法创建一个DataStream实例，该实例从ClickHouse中读取数据。

最后，调用flatMap方法对读取的行进行扁平化处理并将其打印出来。

注意，这个示例程序是一个多线程读取ClickHouse中数据的示例，它将数据读取并行化到两个线程中，因此适用于大规模数据处理。