利用Spark Rdd生成Hfile直接导入到Hbase

针对大批量插入Hbase的场景，如果单条记录插入的时候效率比较低下，如果可以利用Rdd生成Hfile的话，然后利用Bulk Load导入Hfile的话，则会大大提升导入的速度，废话不说，直接上代码：

1.利用Create创建表blog：create 'blog' ,'article'

2.创建数据文件 blog.txt

 

 3.上传文件至hdfs

备注：因为之前文件已经上传了

4.Java版本代码

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapred.TableOutputFormat;
import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat;
import org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.mapreduce.Job;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.sql.*;
import org.apache.spark.sql.hive.HiveContext;
import org.apache.spark.sql.types.DataType;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import scala.Tuple2;

import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.TimeUnit;

/**
 * Created by WangLiang on 2015/11/30.
 */
public class Test {
    private static Logger log = Logger.getLogger(HelloWorld.class);
    public static void main(String[] args) {
        try {
            System.setProperty("javax.xml.parsers.DocumentBuilderFactory",
                    "com.sun.org.apache.xerces.internal.jaxp.DocumentBuilderFactoryImpl");
            System.setProperty("javax.xml.parsers.SAXParserFactory",
                    "com.sun.org.apache.xerces.internal.jaxp.SAXParserFactoryImpl");
	    //项目内部自己的配置类，可以忽略，其实就是设置sparkConf，然后获取到JavaSparkContext
            String sparkMaster = Configure.instance.get("sparkMaster");
            String sparkJarAddress = Configure.instance.get("sparkJarAddress");
            String sparkExecutorMemory = Configure.instance.get("sparkExecutorMemory");
            String sparkCoresMax = Configure.instance.get("sparkCoresMax");
            String sparkLocalDir = Configure.instance.get("sparkLocalDir");
            log.info("initialize parameters");
            log.info("sparkMaster:" + sparkMaster);
            log.info("sparkJarAddress:" + sparkJarAddress);
            log.info("sparkExecutorMemory:" + sparkExecutorMemory);
            log.info("sparkCoresMax:" + sparkCoresMax);
            log.info("sparkLocalDir:" + sparkLocalDir);
            SparkConf sparkConf = new SparkConf().setAppName("dse load application in Java");
            sparkConf.setMaster(sparkMaster);
            if (!sparkJarAddress.isEmpty() && !sparkMaster.contains("local")) {
                sparkConf.set("spark.executor.memory", sparkExecutorMemory); // 16g
                sparkConf.set("spark.scheduler.mode", "FAIR");
                sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
                sparkConf.set("spark.kryo.registrator", "com.dahua.dse3.driver.dataset.DseKryoRegistrator");
                sparkConf.set("spark.cores.max", sparkCoresMax);
                sparkConf.set("spark.akka.threads", "12");
                sparkConf.set("spark.local.dir", sparkLocalDir);
                sparkConf.set("spark.shuffle.manager", "SORT");
                sparkConf.set("spark.network.timeout", "120");
                sparkConf.set("spark.rpc.lookupTimeout", "120");
                sparkConf.set("spark.executor.extraClassPath", "/usr/dahua/spark/executelib/hbase-protocol-0.98.3-hadoop2.jar");
                sparkConf.set("spark.executor.extraJavaOptions", "-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps");
                sparkConf.set("spark.driver.extraJavaOptions", "-XX:PermSize=256M -XX:MaxPermSize=512M");
                sparkConf.set("spark.network.timeout", "120");

            }
            JavaSparkContext jsc = new JavaSparkContext(sparkConf);
            if (!sparkJarAddress.isEmpty() && !sparkMaster.contains("local")) {
                jsc.addJar(sparkJarAddress);
            }

            Configuration conf = HBaseConfiguration.create();
            String zk = "172.25.3.160,172.25.3.161,172.25.3.162";
            String tableName = "blog";
            conf.set("hbase.zookeeper.quorum", zk);
            HTable table = new HTable(conf, tableName);
            conf.set(TableOutputFormat.OUTPUT_TABLE, tableName);
            Job job = Job.getInstance(conf);
            job.setMapOutputKeyClass(ImmutableBytesWritable.class);
            job.setMapOutputValueClass(KeyValue.class);
            HFileOutputFormat.configureIncrementalLoad(job, table);
            String hdfsPath = "hdfs://mycluster/raw/hfile/blog.txt";
            JavaRDD<String> lines = jsc.textFile(hdfsPath);
            JavaPairRDD<ImmutableBytesWritable,KeyValue> hfileRdd = lines.mapToPair(new PairFunction<String, ImmutableBytesWritable, KeyValue>() {
                public Tuple2<ImmutableBytesWritable, KeyValue> call(String v1) throws Exception {
                    String[] tokens = v1.split(" ");
                    String rowkey = tokens[0];
                    String content = tokens[1];
                    KeyValue keyValue = new KeyValue(Bytes.toBytes(rowkey), Bytes.toBytes("article"), Bytes.toBytes("value"), Bytes.toBytes(content));
                    return new Tuple2<ImmutableBytesWritable, KeyValue>(new ImmutableBytesWritable(Bytes.toBytes(rowkey)), keyValue);
                }
            });
            String hfilePath = "hdfs://mycluster/hfile/blog.hfile";
            hfileRdd.saveAsNewAPIHadoopFile(hfilePath, ImmutableBytesWritable.class, KeyValue.class, HFileOutputFormat.class, conf);
			
			//利用bulk load hfile
            LoadIncrementalHFiles bulkLoader = new LoadIncrementalHFiles(conf);
            bulkLoader.doBulkLoad(new Path(hfilePath), table);

        }catch(Exception e){
            e.printStackTrace();
        }finally {
            ;
        }
    }

}

5.scan blog表，数据已经入库

参考文章链接如下：http://www.openkb.info/2015/01/how-to-use-scala-on-spark-to-load-data.html