Flink ClickHouse Sink - 使用 Class.forName 导致卡死问题解决方案

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Flink ClickHouse Sink - 使用 Class.forName 导致卡死问题解决方案

在处理大数据时,Apache Flink 是一个非常流行的分布式流处理框架,而 ClickHouse 则是一种快速、可扩展的列式数据库。在使用 Flink 将数据写入 ClickHouse 时,我们通常会使用 Flink 的 ClickHouse Sink。然而,有时候在配置 ClickHouse Sink 时,使用 Class.forName 方法加载 ClickHouse 驱动程序可能会导致任务卡死,本文将探讨这个问题的原因并提供解决方案。

问题描述:
在配置 Flink 的 ClickHouse Sink 时,我们使用 Class.forName 加载 ClickHouse 驱动程序,类似于以下代码:

Class.forName("ru.yandex.clickhouse.ClickHouseDriver")
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。
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