Spark 读取Hbase表数据并实现类似groupByKey操作

一、概述
程序运行环境很重要，本次测试基于：
hadoop-2.6.5
spark-1.6.2
hbase-1.2.4
zookeeper-3.4.6
jdk-1.8
废话不多说了，直接上需求

Andy column=baseINFO:age,  value=21Andy column=baseINFO:gender,  value=0 Andy column=baseINFO:telphone_number, value=110110110 Tom  column=baseINFO:age, value=18 Tom  column=baseINFO:gender, value=1 Tom  column=baseINFO:telphone_number, value=120120120

如上表所示，将之用spark进行分组，达到这样的效果：

[Andy,(21,0,110110110)]
[Tom,(18,1,120120120)]
需求比较简单，主要是熟悉一下程序运行过程

二、具体代码

package com.union.bigdata.spark.hbase;import org.apache.hadoop.hbase.HBaseConfiguration;import org.apache.hadoop.hbase.mapreduce.TableSplit;import org.apache.hadoop.hbase.util.Base64;import org.apache.hadoop.hbase.util.Bytes;import org.apache.spark.api.java.JavaSparkContext;import org.apache.spark.api.java.JavaRDD;import org.apache.spark.SparkConf;import org.apache.spark.api.java.function.Function;import org.apache.spark.api.java.function.Function2;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.hbase.client.Scan;import org.apache.hadoop.hbase.client.Result;import org.apache.hadoop.hbase.io.ImmutableBytesWritable;import org.apache.hadoop.hbase.mapreduce.TableInputFormat;import org.apache.spark.api.java.JavaPairRDD;import org.apache.hadoop.hbase.protobuf.ProtobufUtil;import org.apache.hadoop.hbase.protobuf.generated.ClientProtos;import org.apache.spark.api.java.function.PairFunction;import scala.Tuple10;import scala.Tuple2;import java.io.IOException;import java.util.ArrayList;import java.util.List;public class ReadHbase {    private static String appName = "ReadTable";    public static void main(String[] args) {        SparkConf sparkConf = new SparkConf();    //we can also run it at local:"local[3]"  the number 3 means 3 threads        sparkConf.setMaster("spark://master:7077").setAppName(appName);        JavaSparkContext jsc = new JavaSparkContext(sparkConf);        Configuration conf = HBaseConfiguration.create();        conf.set("hbase.zookeeper.quorum", "master");         conf.set("hbase.zookeeper.property.clientPort", "2181");         Scan scan = new Scan();        scan.addFamily(Bytes.toBytes("baseINFO"));        scan.addColumn(Bytes.toBytes("baseINFO"), Bytes.toBytes("telphone_number"));        scan.addColumn(Bytes.toBytes("baseINFO"), Bytes.toBytes("age"));        scan.addColumn(Bytes.toBytes("baseINFO"), Bytes.toBytes("gender"));        String scanToString = "";        try {            ClientProtos.Scan proto = ProtobufUtil.toScan(scan);            scanToString = Base64.encodeBytes(proto.toByteArray());        } catch (IOException io) {            System.out.println(io);        }        for (int i = 0; i < 2; i++) {            try {                String tableName = "VIPUSER";                conf.set(TableInputFormat.INPUT_TABLE, tableName);                conf.set(TableInputFormat.SCAN, scanToString);                //get the Result of query from the Table of Hbase                JavaPairRDD<ImmutableBytesWritable, Result> hBaseRDD = jsc.newAPIHadoopRDD(conf,                        TableInputFormat.class, ImmutableBytesWritable.class,                        Result.class);                //group by row key like : [(Andy,110,21,0),(Tom,120,18,1)]                JavaPairRDD<String, List<Integer>> art_scores = hBaseRDD.mapToPair(                        new PairFunction<Tuple2<ImmutableBytesWritable, Result>, String, List<Integer>>() {                            @Override                            public Tuple2<String, List<Integer>> call(Tuple2<ImmutableBytesWritable, Result> results) {                                List<Integer> list = new ArrayList<Integer>();                                byte[] telphone_number = results._2().getValue(Bytes.toBytes("baseINFO"), Bytes.toBytes("telphone_number"));                                byte[] age = results._2().getValue(Bytes.toBytes("baseINFO"), Bytes.toBytes("age"));                                byte[] gender = results._2().getValue(Bytes.toBytes("baseINFO"), Bytes.toBytes("gender"));                //the type of storage at Hbase is Byte Array,so we must let it be normal like Int,String and so on                                 list.add(Integer.parseInt(Bytes.toString(telphone_number)));                                list.add(Integer.parseInt(Bytes.toString(age)));                                list.add(Integer.parseInt(Bytes.toString(gender)));                                return new Tuple2<String, List<Integer>>(Bytes.toString(results._1().get()), list);                            }                        }                );                //switch to Cartesian product                 JavaPairRDD<Tuple2<String, List<Integer>>, Tuple2<String, List<Integer>>> cart = art_scores.cartesian(art_scores);                //use Row Key to delete the repetition from the last step "Cartesian product"                  JavaPairRDD<Tuple2<String, List<Integer>>, Tuple2<String, List<Integer>>> cart2 = cart.filter(                        new Function<Tuple2<Tuple2<String, List<Integer>>, Tuple2<String, List<Integer>>>, Boolean>() {                            public Boolean call(Tuple2<Tuple2<String, List<Integer>>, Tuple2<String, List<Integer>>> tuple2Tuple2Tuple2) throws Exception {                                return tuple2Tuple2Tuple2._1()._1().compareTo(tuple2Tuple2Tuple2._2()._1()) < 0;                            }                        }                );                System.out.println("Create the List 'collect'...");        //get the result we need                 List<Tuple2<Tuple2<String, List<Integer>>, Tuple2<String, List<Integer>>>> collect = cart2.collect();                 System.out.println("Done..");                 System.out.println(collect.size() > i ? collect.get(i):"STOP");                 if (collect.size() > i ) break;            } catch (Exception e) {                System.out.println(e);            }        }    }}

三、程序运行过程分析
1、spark自检以及Driver和excutor的启动过程
实例化一个SparkContext(若在spark2.x下，这里初始化的是一个SparkSession对象)，这时候启动SecurityManager线程去检查用户权限，OK之后创建sparkDriver线程，spark底层远程通信模块（akka框架实现）启动并监听sparkDriver,之后由sparkEnv对象来注册BlockManagerMaster线程，由它的实现类对象去监测运行资源
2、zookeeper与Hbase的自检和启动
第一步顺利完成之后由sparkContext对象去实例去启动程序访问Hbase的入口，触发之后zookeeper完成自己的一系列自检活动，包括用户权限、操作系统、数据目录等，一切OK之后初始化客户端连接对象，之后由Hbase的ClientCnxn对象来建立与master的完整连接
3、spark job 的运行
程序开始调用spark的action类方法，比如这里调用了collect，会触发job的执行，这个流程网上资料很详细，无非就是DAGScheduler搞的一大堆事情，连带着出现一大堆线程，比如TaskSetManager、TaskScheduler等等，最后完成job，返回结果集
4、结束程序
正确返回结果集之后，sparkContext利用反射调用stop()方法，这之后也会触发一系列的stop操作，主要线程有这些：BlockManager,ShutdownHookManager,后面还有释放actor的操作等等，最后一切结束，临时数据和目录会被删除，资源会被释放