flink写入hbase的代码
时间: 2023-11-07 21:57:03 浏览: 72
下面是一个示例代码,演示如何使用Apache Flink将数据写入HBase:
```
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.util.Bytes;
public class FlinkHBaseExample {
public static void main(String[] args) throws Exception {
// 创建Flink的执行环境
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
// 加载数据集
DataSet<Tuple2<String, String>> data = env.fromElements(
new Tuple2<>("row1", "value1"),
new Tuple2<>("row2", "value2"),
new Tuple2<>("row3", "value3")
);
// 配置HBase连接信息
Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost");
config.set("hbase.zookeeper.property.clientPort", "2181");
Connection connection = ConnectionFactory.createConnection(config);
Table table = connection.getTable(TableName.valueOf("mytable"));
// 将数据写入HBase
data.map(new MapFunction<Tuple2<String, String>, Put>() {
@Override
public Put map(Tuple2<String, String> value) throws Exception {
Put put = new Put(Bytes.toBytes(value.f0));
put.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("col1"), Bytes.toBytes(value.f1));
return put;
}
}).output(new HBaseOutputFormat(table));
// 执行程序
env.execute();
}
}
```
在上面的代码中,我们首先创建了Flink的执行环境,并加载了一个数据集。接下来,我们使用HBaseConfiguration类来配置HBase连接信息,然后创建一个HBase表对象。最后,我们使用map函数将数据转换为Put对象,并将其输出到HBase中。在output方法中,我们使用了一个自定义的HBaseOutputFormat对象,该对象用于将Put对象写入HBase表中。
需要注意的是,上面的代码中没有包含所有必要的依赖项。在实际使用中,您需要在项目中添加以下依赖项:
```
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-core</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-hbase</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hbase-client</artifactId>
<version>${hbase.version}</version>
</dependency>
```
其中,${flink.version}、${hadoop.version}和${hbase.version}是您选择的Flink、Hadoop和HBase版本的占位符。
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