使用java语言flink实时读取kafka写入hbase数据库

好的，以下是使用Java语言实现Flink实时读取Kafka并将数据写入HBase数据库的示例代码：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema;
import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.TableDescriptorBuilder;
import org.apache.hadoop.hbase.client.TableDescriptor;
import org.apache.hadoop.hbase.client.ColumnFamilyDescriptorBuilder;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;

import java.util.Properties;

public class KafkaToHbaseExample {

    public static void main(String[] args) throws Exception {

        // 设置Kafka消费者配置
        String kafkaBroker = "localhost:9092";
        String kafkaTopic = "test";
        String kafkaGroupId = "flink-kafka-hbase";
        Properties kafkaProps = new Properties();
        kafkaProps.setProperty("bootstrap.servers", kafkaBroker);
        kafkaProps.setProperty("group.id", kafkaGroupId);

        // 设置HBase表格的配置
        String hbaseTableName = "test_table";
        String hbaseColumnFamily = "cf";
        Configuration hbaseConfig = HBaseConfiguration.create();
        hbaseConfig.set(TableOutputFormat.OUTPUT_TABLE, hbaseTableName);
        hbaseConfig.set("hbase.zookeeper.quorum", "localhost");
        hbaseConfig.set("hbase.zookeeper.property.clientPort", "2181");
        Connection hbaseConnection = ConnectionFactory.createConnection(hbaseConfig);
        Admin hbaseAdmin = hbaseConnection.getAdmin();
        TableDescriptor hbaseTableDescriptor = TableDescriptorBuilder.newBuilder(TableName.valueOf(hbaseTableName))
                .setColumnFamily(ColumnFamilyDescriptorBuilder.newBuilder(Bytes.toBytes(hbaseColumnFamily)).build())
                .build();
        if (!hbaseAdmin.tableExists(TableName.valueOf(hbaseTableName))) {
            hbaseAdmin.createTable(hbaseTableDescriptor);
        }
        hbaseAdmin.close();
        hbaseConnection.close();

        // 创建Flink执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 创建Kafka数据流
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>(kafkaTopic, new SimpleStringSchema(), kafkaProps);
        DataStream<String> kafkaStream = env.addSource(kafkaConsumer);

        // 将Kafka数据流转换为HBase数据流
        DataStream<Put> hbaseStream = kafkaStream.map(new MapFunction<String, Put>() {
            @Override
            public Put map(String value) throws Exception {
                Put put = new Put(Bytes.toBytes("row key"));
                put.addColumn(Bytes.toBytes(hbaseColumnFamily), Bytes.toBytes("column"), Bytes.toBytes(value));
                return put;
            }
        });

        // 将HBase数据流写入表格
        FlinkKafkaProducer<Put> hbaseSink = new FlinkKafkaProducer<>(kafkaBroker, hbaseTableName, new KeyedSerializationSchema<Put>() {
            @Override
            public byte[] serializeKey(Put element) {
                return null;
            }

            @Override
            public byte[] serializeValue(Put element) {
                return element.toByteArray();
            }

            @Override
            public String getTargetTopic(Put element) {
                return null;
            }
        }, kafkaProps, FlinkKafkaProducer.Semantic.EXACTLY_ONCE);
        hbaseStream.addSink(hbaseSink);

        // 执行Flink任务
        env.execute("Read from Kafka and write to HBase");
    }
}

在上面的代码中，我们首先设置了Kafka消费者和HBase表格的配置。接下来，我们创建了一个HBase表格，并使用Flink的Kafka源将Kafka数据流读取到Flink中。然后，我们将Kafka数据流转换为HBase数据流，并在每个记录上创建一个Put对象，该对象包含HBase表格的行键和列。

最后，我们将HBase数据流写入Kafka中，以便在后续流程中将其写入HBase表格。请注意，因为我们使用了Flink的Kafka生产者，需要实现KeyedSerializationSchema接口来序列化Put对象。