Spark读取Hbase中的数据

大家可能都知道很熟悉Spark的两种常见的数据读取方式（存放到RDD中）：（1）、调用parallelize函数直接从集合中获取数据，并存入RDD中；Java版本如下：

JavaRDD<Integer> myRDD = sc.parallelize(Arrays.asList(1,2,3));

Scala版本如下：

val myRDD= sc.parallelize(List(1,2,3))

　　这种方式很简单，很容易就可以将一个集合中的数据变成RDD的初始化值；更常见的是（2）、从文本中读取数据到RDD中，这个文本可以是纯文本文件、可以是sequence文件；可以存放在本地(file://)、可以存放在HDFS（hdfs://）上，还可以存放在S3上。其实对文件来说，Spark支持Hadoop所支持的所有文件类型和文件存放位置。Java版如下：

/////////////////////////////////////////////////////////////////////

 User: 过往记忆

 Date: 14-6-29

 Time: 23:59

 bolg:

 本文地址：/archives/1051

 过往记忆博客，专注于hadoop、hive、spark、shark、flume的技术博客，大量的干货

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/////////////////////////////////////////////////////////////////////

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.api.java.JavaSparkContext;

 

SparkConf conf = new SparkConf().setAppName("Simple Application");

JavaSparkContext sc = new JavaSparkContext(conf);

sc.addFile("wyp.data");

JavaRDD<String> lines = sc.textFile(SparkFiles.get("wyp.data"));

Scala版本如下：

import org.apache.spark.SparkContext

import org.apache.spark.SparkConf

 

val conf = new SparkConf().setAppName("Simple Application")

val sc = new SparkContext(conf)

sc.addFile("spam.data")

val inFile = sc.textFile(SparkFiles.get("spam.data"))

　　在实际情况下，我们需要的数据可能不是简单的存放在HDFS文本中，我们需要的数据可能就存放在Hbase中，那么我们如何用Spark来读取Hbase中的数据呢？本文的所有测试是基于Hadoop 2.2.0、Hbase 0.98.2、Spark 0.9.1，不同版本可能代码的编写有点不同。本文只是简单地用Spark来读取Hbase中的数据，如果需要对Hbase进行更强的操作，本文可能不能帮你。话不多说，Spark操作Hbase的Java版本代码如下：

package com.iteblog.spark;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableInputFormat;
import org.apache.hadoop.hbase.protobuf.ProtobufUtil;
import org.apache.hadoop.hbase.protobuf.generated.ClientProtos;
import org.apache.hadoop.hbase.util.Base64;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Serializable;
import scala.Tuple2;

import java.io.IOException;
import java.util.List;

/**
 * User: iteblog
 * Date: 14-6-27
 * Time: 下午5:18
 *blog: http://www.iteblog.com
 *
 * Usage: bin/spark-submit --master yarn-cluster --class com.iteblog.spark.SparkFromHbase
 * --jars /home/q/hbase/hbase-0.96.0-hadoop2/lib/htrace-core-2.01.jar,
 * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-common-0.96.0-hadoop2.jar,
 * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-client-0.96.0-hadoop2.jar,
 * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-protocol-0.96.0-hadoop2.jar,
 * /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-server-0.96.0-hadoop2.jar
 * ./spark_2.10-1.0.jar
 */
public class SparkFromHbase implements Serializable {

    /**
     * copy from org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil
     *
     * @param scan
     * @return
     * @throws IOException
     */
    String convertScanToString(Scan scan) throws IOException {
        ClientProtos.Scan proto = ProtobufUtil.toScan(scan);
        return Base64.encodeBytes(proto.toByteArray());
    }

    public void start() {
        SparkConf sparkConf = new SparkConf();
        JavaSparkContext sc = new JavaSparkContext(sparkConf);


        Configuration conf = HBaseConfiguration.create();

        Scan scan = new Scan();
        //scan.setStartRow(Bytes.toBytes("195861-1035177490"));
        //scan.setStopRow(Bytes.toBytes("195861-1072173147"));
        scan.addFamily(Bytes.toBytes("cf"));
        scan.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("col_1"));

        try {

            String tableName = "wyp";
            conf.set(TableInputFormat.INPUT_TABLE, tableName);
            conf.set(TableInputFormat.SCAN, convertScanToString(scan));


            JavaPairRDD<ImmutableBytesWritable, Result> hBaseRDD = sc.newAPIHadoopRDD(conf,
                    TableInputFormat.class, ImmutableBytesWritable.class,
                    Result.class);

            JavaPairRDD<String, Integer> levels = hBaseRDD.mapToPair(
                    new PairFunction<Tuple2<ImmutableBytesWritable, Result>, String, Integer>() {
                        @Override
                        public Tuple2<String, Integer> call(Tuple2<ImmutableBytesWritable, Result> immutableBytesWritableResultTuple2) throws Exception {
                            byte[] o = immutableBytesWritableResultTuple2._2().getValue(Bytes.toBytes("cf"), Bytes.toBytes("col_1"));
                            if (o != null) {
                                return new Tuple2<String, Integer>(new String(o), 1);
                            }
                            return null;
                        }
                    });

            JavaPairRDD<String, Integer> counts = levels.reduceByKey(
                    new Function2<Integer, Integer, Integer>() {
                        @Override
                        public Integer call(Integer i1, Integer i2) {
                            return i1 + i2;
                        }
                    });

            List<Tuple2<String, Integer>> output = counts.collect();
            for (Tuple2 tuple : output) {
                System.out.println(tuple._1() + ": " + tuple._2());
            }

            sc.stop();

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

    public static void main(String[] args) throws InterruptedException {
        new SparkFromHbase().start();
        System.exit(0);
    }
}

这样本段代码段是从Hbase表名为flight_wap_order_log的数据库中读取cf列簇上的airName一列的数据，这样我们就可以对myRDD进行相应的操作：

System.out.println(myRDD.count());

本段代码需要在pom.xml文件加入以下依赖：

<dependency>

        <groupId>org.apache.spark</groupId>

        <artifactId>spark-core_2.10</artifactId>

        <version>0.9.1</version>

</dependency>

 

<dependency>

        <groupId>org.apache.hbase</groupId>

        <artifactId>hbase</artifactId>

        <version>0.98.2-hadoop2</version>

</dependency>

 

<dependency>

        <groupId>org.apache.hbase</groupId>

        <artifactId>hbase-client</artifactId>

        <version>0.98.2-hadoop2</version>

</dependency>

 

<dependency>

        <groupId>org.apache.hbase</groupId>

        <artifactId>hbase-common</artifactId>

        <version>0.98.2-hadoop2</version>

</dependency>

 

<dependency>

        <groupId>org.apache.hbase</groupId>

        <artifactId>hbase-server</artifactId>

        <version>0.98.2-hadoop2</version>

</dependency>

Scala版如下：

import org.apache.spark._

import org.apache.spark.rdd.NewHadoopRDD

import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor}

import org.apache.hadoop.hbase.client.HBaseAdmin

import org.apache.hadoop.hbase.mapreduce.TableInputFormat

 

/////////////////////////////////////////////////////////////////////

 User: 过往记忆

 Date: 14-6-29

 Time: 23:59

 bolg:

 本文地址：/archives/1051

 过往记忆博客，专注于hadoop、hive、spark、shark、flume的技术博客，大量的干货

 过往记忆博客微信公共帐号：iteblog_hadoop

/////////////////////////////////////////////////////////////////////

 

object HBaseTest {

  def main(args: Array[String]) {

    val sc = new SparkContext(args(0), "HBaseTest",

      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass))

 

    val conf = HBaseConfiguration.create()

    conf.set(TableInputFormat.INPUT_TABLE, args(1))

 

    val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],

      classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],

      classOf[org.apache.hadoop.hbase.client.Result])

 

    hBaseRDD.count()

 

    System.exit(0)

  }

}

我们需要在加入如下依赖：

libraryDependencies ++= Seq(

        "org.apache.spark" % "spark-core_2.10" % "0.9.1",

        "org.apache.hbase" % "hbase" % "0.98.2-hadoop2",

        "org.apache.hbase" % "hbase-client" % "0.98.2-hadoop2",

        "org.apache.hbase" % "hbase-common" % "0.98.2-hadoop2",

        "org.apache.hbase" % "hbase-server" % "0.98.2-hadoop2"

)

　　在测试的时候，需要配置好Hbase、Hadoop环境，否则程序会出现问题，特别是让程序找到Hbase-site.xml配置文件。

package com.iteblog.spark;
	import org.apache.hadoop.conf.Configuration;
	import org.apache.hadoop.hbase.HBaseConfiguration;
	import org.apache.hadoop.hbase.client.Result;
	import org.apache.hadoop.hbase.client.Scan;
	import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
	import org.apache.hadoop.hbase.mapreduce.TableInputFormat;
	import org.apache.hadoop.hbase.protobuf.ProtobufUtil;
	import org.apache.hadoop.hbase.protobuf.generated.ClientProtos;
	import org.apache.hadoop.hbase.util.Base64;
	import org.apache.hadoop.hbase.util.Bytes;
	import org.apache.spark.SparkConf;
	import org.apache.spark.api.java.JavaPairRDD;
	import org.apache.spark.api.java.JavaSparkContext;
	import org.apache.spark.api.java.function.Function2;
	import org.apache.spark.api.java.function.PairFunction;
	import scala.Serializable;
	import scala.Tuple2;
	import java.io.IOException;
	import java.util.List;
	/**
	* User: iteblog
	* Date: 14-6-27
	* Time: 下午5:18
	*blog: http://www.iteblog.com
	*
	* Usage: bin/spark-submit --master yarn-cluster --class com.iteblog.spark.SparkFromHbase
	* --jars /home/q/hbase/hbase-0.96.0-hadoop2/lib/htrace-core-2.01.jar,
	* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-common-0.96.0-hadoop2.jar,
	* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-client-0.96.0-hadoop2.jar,
	* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-protocol-0.96.0-hadoop2.jar,
	* /home/q/hbase/hbase-0.96.0-hadoop2/lib/hbase-server-0.96.0-hadoop2.jar
	* ./spark_2.10-1.0.jar
	*/
	public class SparkFromHbase implements Serializable {
	/**
	* copy from org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil
	*
	* @param scan
	* @return
	* @throws IOException
	*/
	String convertScanToString(Scan scan) throws IOException {
	ClientProtos.Scan proto = ProtobufUtil.toScan(scan);
	return Base64.encodeBytes(proto.toByteArray());
	}
	public void start() {
	SparkConf sparkConf = new SparkConf();
	JavaSparkContext sc = new JavaSparkContext(sparkConf);
	Configuration conf = HBaseConfiguration.create();
	Scan scan = new Scan();
	//scan.setStartRow(Bytes.toBytes("195861-1035177490"));
	//scan.setStopRow(Bytes.toBytes("195861-1072173147"));
	scan.addFamily(Bytes.toBytes("cf"));
	scan.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("col_1"));
	try {
	String tableName = "wyp";
	conf.set(TableInputFormat.INPUT_TABLE, tableName);
	conf.set(TableInputFormat.SCAN, convertScanToString(scan));
	JavaPairRDD<ImmutableBytesWritable, Result> hBaseRDD = sc.newAPIHadoopRDD(conf,
	TableInputFormat.class, ImmutableBytesWritable.class,
	Result.class);
	JavaPairRDD<String, Integer> levels = hBaseRDD.mapToPair(
	new PairFunction<Tuple2<ImmutableBytesWritable, Result>, String, Integer>() {
	@Override
	public Tuple2<String, Integer> call(Tuple2<ImmutableBytesWritable, Result> immutableBytesWritableResultTuple2) throws Exception {
	byte[] o = immutableBytesWritableResultTuple2._2().getValue(Bytes.toBytes("cf"), Bytes.toBytes("col_1"));
	if (o != null) {
	return new Tuple2<String, Integer>(new String(o), 1);
	}
	return null;
	}
	});
	JavaPairRDD<String, Integer> counts = levels.reduceByKey(
	new Function2<Integer, Integer, Integer>() {
	@Override
	public Integer call(Integer i1, Integer i2) {
	return i1 + i2;
	}
	});
	List<Tuple2<String, Integer>> output = counts.collect();
	for (Tuple2 tuple : output) {
	System.out.println(tuple._1() + ": " + tuple._2());
	}
	sc.stop();
	} catch (Exception e) {
	e.printStackTrace();
	}
	}
	public static void main(String[] args) throws InterruptedException {
	new SparkFromHbase().start();
	System.exit(0);
	}
	}