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Flink 消费kafka数据写入hbase

一.前提准备

1.创建Hbase表

create 'test_20191122','info'

2.pom.xml文件中要添加依赖

<dependency>
	<groupId>org.apache.hbase</groupId>
	<artifactId>hbase-client</artifactId>
	<version>${hbase.version}</version>
</dependency>
<dependency>
	<groupId>org.apache.hbase</groupId>
	<artifactId>hbase-server</artifactId>
	<version>${hbase.version}</version>
</dependency>

二.直接上代码

package pulsar;

import com.hoperun.flink.sink.hbase.connector.*;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;
import org.apache.flink.util.Collector;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.HashMap;
import java.util.Map;
import java.util.Properties;

public class FlinkConsumeKafkaWriteHbase {
    private static final Logger LOG = LoggerFactory.getLogger(FlinkConsumeKafkaWriteHbase.class);
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.enableCheckpointing(5000);
        // kafka 参数
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "cdh01.com:9092,cdh02.com:9092,cdh03.com:9092");
        properties.setProperty("group.id", "test227");
        properties.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
        properties.setProperty("max.poll.records","1000");
        properties.setProperty("max.partition.fetch.bytes","5242880");
        // kafka消费者
        FlinkKafkaConsumer011<String> consumer = new FlinkKafkaConsumer011<>(
                "testtopic",
                new SimpleStringSchema(),
                properties);
        consumer.setStartFromLatest();
        // 生成DateStream ,并做简单的wordcount计算
        DataStreamSource<String> text = env.addSource(consumer,"Kafka").setParallelism(1);
        DataStream<Tuple2<String, Integer>> sum = text.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public void flatMap(String str, Collector<Tuple2<String, Integer>> collector) throws Exception {
                String[] arr = str.split(" ");
                for (String s : arr) {
                    collector.collect(new Tuple2<>(s, 1));
                }
            }
        }).keyBy(0).timeWindow(Time.seconds(3)).sum(1);

        sum.print();

        // 写入 hbase
        Map<String, String> userConfig = new HashMap<>();
        userConfig.put("hbase.zookeeper.quorum","cdh01.com,cdh02.com,cdh03.com");
        userConfig.put("hbase.zookeeper.property.clientPort","2181");
        userConfig.put("bulk.flush.interval.ms","1000");
        sum.addSink(new HBaseSink<>(userConfig,
                new HBaseSinkFunction<Tuple2<String, Integer>>() {
                    @Override
                    public void process(Tuple2<String, Integer> element, RuntimeContext ctx, RequestPutter putter) {
                        byte[] rowKey = Bytes.toBytes(element.f0);
                        byte[] cf = Bytes.toBytes("info");
                        byte[] qualifier = Bytes.toBytes("num");

                        Put put = new Put(rowKey);
                        put.addColumn(cf, qualifier, Bytes.toBytes(element.f1.toString()));
                        put.addColumn(cf,Bytes.toBytes("test") , Bytes.toBytes("227"));

                        putter.add(new PutRequest(TableName.valueOf("test_20191122"),put));
                    }
                },
                new PutRequestFailureHandler() {
                    @Override
                    public void onFailure(PutRequest entity, Throwable failure, int restStatusCode, RequestPutter putter) throws Throwable {
                        try {
                            String tableName = entity.getTableName().getNameAsString();
                            for (Cell cell : entity.getPut().getFamilyCellMap().get(Bytes.toBytes("info"))) {
                                String rowKey = Bytes.toString(CellUtil.cloneRow(cell));
                                String qualifier = Bytes.toString(CellUtil.cloneQualifier(cell));
                                String value = Bytes.toString(CellUtil.cloneValue(cell));
                                if(value != null){
                                    LOG.error("HBase数据写入异常。Table:{}, RowKey:{}, Qualifier:{}, value:{}", tableName, rowKey, qualifier, value);
                                    LOG.error("异常消息：", failure);
                                } else {
                                    return;
                                }
                            }
                        } catch (Exception e) {
                            LOG.error("HBase错误处理异常:", e);
                            LOG.error("异常处理数据:", entity);
                        }
                    }
                }));
        env.execute("FlinkConsumeKafkaWriteHbase");
    }
}

三.结果展示

hbase(main):005:0> scan 'test_20191122'
ROW                      COLUMN+CELL
 aaa                     column=info:num, timestamp=1574390105876, value=4
 aaa                     column=info:test, timestamp=1574390105876, value=227
 bbb                     column=info:num, timestamp=1574390105876, value=2
 bbb                     column=info:test, timestamp=1574390105876, value=227
 ccc                     column=info:num, timestamp=1574390105876, value=2
 ccc                     column=info:test, timestamp=1574390105876, value=227
 ddd                     column=info:num, timestamp=1574390105876, value=2
 ddd                     column=info:test, timestamp=1574390105876, value=227
4 row(s) in 0.0380 seconds

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