于Spark它是一个计算框架，于Spark环境，不仅支持单个文件操作，HDFS档，同时也可以使用Spark对Hbase操作。

从企业的数据源HBase取出。这涉及阅读hbase数据，在本文中尽快为了尽可能地让我们可以实践和操作Hbase。Spark Shell 来进行Hbase操作。

一、环境：

Haoop2.2.0

Hbase版本号0.96.2-hadoop2, r1581096

Spark1.0.0

本文如果环境已经搭建好，Spark环境搭建可见Spark Haoop集群搭建

Hadoop2.2.0要注意和Hbase的版本号兼容，这里Hbase採用0.96.2

二、原理

Spark操作HBase事实上是和java client操作HBase的原理是一致的：

scala和java都是基于jvm的语言。仅仅要将hbase的类载入到classpath内，就可以调用操作，其他框架相似。

同样点：即都是当作client来连接HMaster，然后利用hbase的API来对Hbase进行操作。

不同点：唯一不同的是：Spark能够将Hbase的数据来当作RDD处理，从而利用Spark来进行并行计算。

三、实践

1、首先检查依赖jar包。在这之前如果hbase的jar包不在spark-shell的classpath里。则须要加入进来。
设置方法： 在Spark-evn.sh里加入SPARK_CLASSPATH=/home/victor/software/hbase/lib/*
这样再再启动启动bin/spark-shell， 启动完成而且Worker成功注冊上之后。import jar 包。 

2、操作hbase

2.1 Hbase中数据

hbase里有张score表，里面有2个CF。分别为course和grade。数据例如以下：
hbase(main):001:0> scan 'scores'

ROW                                    COLUMN+CELL                                                                                                     

 Jim                                   column=course:art, timestamp=1404142440676, value=67                                                            

 Jim                                   column=course:math, timestamp=1404142434405, value=77                                                           

 Jim                                   column=grade:, timestamp=1404142422653, value=3                                                                 

 Tom                                   column=course:art, timestamp=1404142407018, value=88                                                            

 Tom                                   column=course:math, timestamp=1404142398986, value=97                                                           

 Tom                                   column=grade:, timestamp=1404142383206, value=5                                                                 

 shengli                               column=course:art, timestamp=1404142468266, value=17                                                            

 shengli                               column=course:math, timestamp=1404142461952, value=27                                                           

 shengli                               column=grade:, timestamp=1404142452157, value=8                                                                 

3 row(s) in 0.3230 seconds

2.1  初始化连接參数

scala> import org.apache.spark._

import org.apache.spark._

scala> import org.apache.spark.rdd.NewHadoopRDD

import org.apache.spark.rdd.NewHadoopRDD

scala> import org.apache.hadoop.conf.Configuration;  

import org.apache.hadoop.conf.Configuration

scala> import org.apache.hadoop.hbase.HBaseConfiguration;  

import org.apache.hadoop.hbase.HBaseConfiguration

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

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

scala> val configuration = HBaseConfiguration.create();  //初始化配置

configuration: org.apache.hadoop.conf.Configuration = Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hbase-default.xml, hbase-site.xml

scala> configuration.set("hbase.zookeeper.property.clientPort", "2181"); //设置zookeeper client端口

scala> configuration.set("hbase.zookeeper.quorum", "localhost");  //设置zookeeper quorum

scala> configuration.set("hbase.master", "localhost:60000");  //设置hbase master

scala> configuration.addResource("/home/victor/software/hbase/conf/hbase-site.xml")  //将hbase的配置载入

scala> configuration.set(TableInputFormat.INPUT_TABLE, "scores")


scala> import org.apache.hadoop.hbase.client.HBaseAdminimport org.apache.hadoop.hbase.client.HBaseAdmin
scala> val hadmin = new HBaseAdmin(configuration); //实例化hbase管理

2014-07-01 00:39:24,649 INFO  [main] zookeeper.ZooKeeper (ZooKeeper.java:<init>(438)) - Initiating client connection, connectString=localhost:2181 sessionTimeout=90000 watcher=hconnection-0xc7eea5, quorum=localhost:2181, baseZNode=/hbase

2014-07-01 00:39:24,707 INFO  [main] zookeeper.RecoverableZooKeeper (RecoverableZooKeeper.java:<init>(120)) - Process identifier=hconnection-0xc7eea5 connecting to ZooKeeper ensemble=localhost:2181

2014-07-01 00:39:24,753 INFO  [main-SendThread(localhost:2181)] zookeeper.ClientCnxn (ClientCnxn.java:logStartConnect(966)) - Opening socket connection to server localhost/127.0.0.1:2181. Will not attempt to authenticate using SASL (unknown error)

2014-07-01 00:39:24,755 INFO  [main-SendThread(localhost:2181)] zookeeper.ClientCnxn (ClientCnxn.java:primeConnection(849)) - Socket connection established to localhost/127.0.0.1:2181, initiating session

2014-07-01 00:39:24,938 INFO  [main-SendThread(localhost:2181)] zookeeper.ClientCnxn (ClientCnxn.java:onConnected(1207)) - Session establishment complete on server localhost/127.0.0.1:2181, sessionid = 0x146ed61c4ef0015, negotiated timeout = 40000

hadmin: org.apache.hadoop.hbase.client.HBaseAdmin = org.apache.hadoop.hbase.client.HBaseAdmin@1260466



接下来用haoop api来创建一个RDD




scala> val hrdd = sc.newAPIHadoopRDD(configuration, classOf[TableInputFormat],

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

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

2014-07-01 00:51:06,683 WARN  [main] util.SizeEstimator (Logging.scala:logWarning(70)) - Failed to check whether UseCompressedOops is set; assuming yes

2014-07-01 00:51:06,936 INFO  [main] storage.MemoryStore (Logging.scala:logInfo(58)) - ensureFreeSpace(85877) called with curMem=0, maxMem=308910489

2014-07-01 00:51:06,946 INFO  [main] storage.MemoryStore (Logging.scala:logInfo(58)) - Block broadcast_0 stored as values to memory (estimated size 83.9 KB, free 294.5 MB)

hrdd: org.apache.spark.rdd.RDD[(org.apache.hadoop.hbase.io.ImmutableBytesWritable, org.apache.hadoop.hbase.client.Result)] = NewHadoopRDD[0] at newAPIHadoopRDD at <console>:22





版本号一：（最新的版本号下列code可能不work，请看版本号二）


读取记录：




这里我们take 1 条数据，能够看到格式是依照我们设定的HadoopRDD。key是一个不变的ImmutableBytesWritable，value是Hbase的Result




scala> hrdd take 1

2014-07-01 00:51:50,371 INFO  [main] spark.SparkContext (Logging.scala:logInfo(58)) - Starting job: take at <console>:25

2014-07-01 00:51:50,423 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Got job 0 (take at <console>:25) with 1 output partitions (allowLocal=true)

2014-07-01 00:51:50,425 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Final stage: Stage 0(take at <console>:25)

2014-07-01 00:51:50,426 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Parents of final stage: List()

2014-07-01 00:51:50,477 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Missing parents: List()

2014-07-01 00:51:50,478 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Computing the requested partition locally

2014-07-01 00:51:50,509 INFO  [Local computation of job 0] rdd.NewHadoopRDD (Logging.scala:logInfo(58)) - Input split: localhost:,

2014-07-01 00:51:50,894 INFO  [main] spark.SparkContext (Logging.scala:logInfo(58)) - Job finished: take at <console>:25, took 0.522612687 s

res5: Array[(org.apache.hadoop.hbase.io.ImmutableBytesWritable, org.apache.hadoop.hbase.client.Result)] = Array((4a 69 6d,keyvalues={Jim/course:art/1404142440676/Put/vlen=2/mvcc=0, Jim/course:math/1404142434405/Put/vlen=2/mvcc=0, Jim/grade:/1404142422653/Put/vlen=1/mvcc=0}))



找到Result对象




scala> val res = hrdd.take(1)

2014-07-01 01:09:13,486 INFO  [main] spark.SparkContext (Logging.scala:logInfo(58)) - Starting job: take at <console>:24

2014-07-01 01:09:13,487 INFO  [spark-akka.actor.default-dispatcher-15] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Got job 4 (take at <console>:24) with 1 output partitions (allowLocal=true)

2014-07-01 01:09:13,487 INFO  [spark-akka.actor.default-dispatcher-15] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Final stage: Stage 4(take at <console>:24)

2014-07-01 01:09:13,487 INFO  [spark-akka.actor.default-dispatcher-15] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Parents of final stage: List()

2014-07-01 01:09:13,488 INFO  [spark-akka.actor.default-dispatcher-15] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Missing parents: List()

2014-07-01 01:09:13,488 INFO  [spark-akka.actor.default-dispatcher-15] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Computing the requested partition locally

2014-07-01 01:09:13,488 INFO  [Local computation of job 4] rdd.NewHadoopRDD (Logging.scala:logInfo(58)) - Input split: localhost:,

2014-07-01 01:09:13,504 INFO  [main] spark.SparkContext (Logging.scala:logInfo(58)) - Job finished: take at <console>:24, took 0.018069267 s

res: Array[(org.apache.hadoop.hbase.io.ImmutableBytesWritable, org.apache.hadoop.hbase.client.Result)] = Array((4a 69 6d,keyvalues={Jim/course:art/1404142440676/Put/vlen=2/mvcc=0, Jim/course:math/1404142434405/Put/vlen=2/mvcc=0, Jim/grade:/1404142422653/Put/vlen=1/mvcc=0}))

scala> res(0)

res33: (org.apache.hadoop.hbase.io.ImmutableBytesWritable, org.apache.hadoop.hbase.client.Result) = (4a 69 6d,keyvalues={Jim/course:art/1404142440676/Put/vlen=2/mvcc=0, Jim/course:math/1404142434405/Put/vlen=2/mvcc=0, Jim/grade:/1404142422653/Put/vlen=1/mvcc=0})

scala> res(0)._2

res34: org.apache.hadoop.hbase.client.Result = keyvalues={Jim/course:art/1404142440676/Put/vlen=2/mvcc=0, Jim/course:math/1404142434405/Put/vlen=2/mvcc=0, Jim/grade:/1404142422653/Put/vlen=1/mvcc=0}

scala> val rs = res(0)._2

rs: org.apache.hadoop.hbase.client.Result = keyvalues={Jim/course:art/1404142440676/Put/vlen=2/mvcc=0, Jim/course:math/1404142434405/Put/vlen=2/mvcc=0, Jim/grade:/1404142422653/Put/vlen=1/mvcc=0}

scala> rs.

asInstanceOf             cellScanner              containsColumn           containsEmptyColumn      containsNonEmptyColumn   copyFrom

getColumn                getColumnCells           getColumnLatest          getColumnLatestCell      getExists                getFamilyMap

getMap                   getNoVersionMap          getRow                   getValue                 getValueAsByteBuffer     isEmpty

isInstanceOf             list                     listCells                loadValue                raw                      rawCells

setExists                size                     toString                 value





遍历这条记录，取出每一个cell的值：

scala> val kv_array = rs.raw

warning: there were 1 deprecation warning(s); re-run with -deprecation for details

kv_array: Array[org.apache.hadoop.hbase.KeyValue] = Array(Jim/course:art/1404142440676/Put/vlen=2/mvcc=0, Jim/course:math/1404142434405/Put/vlen=2/mvcc=0, Jim/grade:/1404142422653/Put/vlen=1/mvcc=0)




遍历记录


scala> for(keyvalue <- kv) println("rowkey:"+ new String(keyvalue.getRow)+ " cf:"+new String(keyvalue.getFamily()) + " column:" + new String(keyvalue.getQualifier) + " " + "value:"+new String(keyvalue.getValue()))

warning: there were 4 deprecation warning(s); re-run with -deprecation for details

rowkey:Jim cf:course column:art value:67

rowkey:Jim cf:course column:math value:77

rowkey:Jim cf:grade column: value:3



查询记录个数






scala> hrdd.count

2014-07-01 01:26:03,133 INFO  [main] spark.SparkContext (Logging.scala:logInfo(58)) - Starting job: count at <console>:25

2014-07-01 01:26:03,134 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Got job 5 (count at <console>:25) with 1 output partitions (allowLocal=false)

2014-07-01 01:26:03,134 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Final stage: Stage 5(count at <console>:25)

2014-07-01 01:26:03,134 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Parents of final stage: List()

2014-07-01 01:26:03,135 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Missing parents: List()

2014-07-01 01:26:03,166 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Submitting Stage 5 (NewHadoopRDD[0] at newAPIHadoopRDD at <console>:22), which has no missing parents

2014-07-01 01:26:03,397 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Submitting 1 missing tasks from Stage 5 (NewHadoopRDD[0] at newAPIHadoopRDD at <console>:22)

2014-07-01 01:26:03,401 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.TaskSchedulerImpl (Logging.scala:logInfo(58)) - Adding task set 5.0 with 1 tasks

2014-07-01 01:26:03,427 INFO  [spark-akka.actor.default-dispatcher-16] scheduler.FairSchedulableBuilder (Logging.scala:logInfo(58)) - Added task set TaskSet_5 tasks to pool default

2014-07-01 01:26:03,439 INFO  [spark-akka.actor.default-dispatcher-5] scheduler.TaskSetManager (Logging.scala:logInfo(58)) - Starting task 5.0:0 as TID 0 on executor 0: 192.168.2.105 (PROCESS_LOCAL)

2014-07-01 01:26:03,469 INFO  [spark-akka.actor.default-dispatcher-5] scheduler.TaskSetManager (Logging.scala:logInfo(58)) - Serialized task 5.0:0 as 1305 bytes in 7 ms

2014-07-01 01:26:11,015 INFO  [Result resolver thread-0] scheduler.TaskSetManager (Logging.scala:logInfo(58)) - Finished TID 0 in 7568 ms on 192.168.2.105 (progress: 1/1)

2014-07-01 01:26:11,017 INFO  [Result resolver thread-0] scheduler.TaskSchedulerImpl (Logging.scala:logInfo(58)) - Removed TaskSet 5.0, whose tasks have all completed, from pool default

2014-07-01 01:26:11,036 INFO  [spark-akka.actor.default-dispatcher-4] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Completed ResultTask(5, 0)

2014-07-01 01:26:11,057 INFO  [spark-akka.actor.default-dispatcher-4] scheduler.DAGScheduler (Logging.scala:logInfo(58)) - Stage 5 (count at <console>:25) finished in 7.605 s

2014-07-01 01:26:11,067 INFO  [main] spark.SparkContext (Logging.scala:logInfo(58)) - Job finished: count at <console>:25, took 7.933270634 s

res71: Long = 3





版本号二、




hrdd.map(tuple => tuple._2).map(result => (result.getRow, result.getColumn("course".getBytes(), "art".getBytes()))).map(row => {

(

  row._1.map(_.toChar).mkString,

  row._2.asScala.reduceLeft {

    (a, b) => if (a.getTimestamp > b.getTimestamp) a else b

  }.getValue.map(_.toChar).mkString

)

}).take(10)




这样就能得到row key 和相应 column family的值了。






四、总结




Spark操作Hbase事实上和java client操作Hbas大体流程是一致的，都是客户端去连接HMaster，终于利用java api来操作hbase。




仅仅只是Spark提供了一种与RDD结合的概念，而且利用scala的语法简洁性。提高了编程效率。






——EOF——




原创文章，转载请注明来自：http://blog.csdn.net/oopsoom/article/details/36071323


												


											
Spark操作hbase的更多相关文章
	

    
						
  1. spark 操作hbase
		
    HBase经过七年发展,终于在今年2月底,发布了 1.0.0 版本.这个版本提供了一些让人激动的功能,并且,在不牺牲稳定性的前提下,引入了新的API.虽然 1.0.0 兼容旧版本的 API,不过还是应 ...
    
		
    2. 
								
  2. Spark操作HBase问题：java.io.IOException: Non-increasing Bloom keys
		
    1 问题描述 在使用Spark BulkLoad数据到HBase时遇到以下问题: 17/05/19 14:47:26 WARN scheduler.TaskSetManager: Lost task  ...
    
		
    3. 
						
  3. Spark操作HBase报:org.apache.hadoop.hbase.client.RetriesExhaustedWithDetailsException异常解决方案
		
    一.异常信息 19/03/21 15:01:52 WARN scheduler.TaskSetManager: Lost task 4.0 in stage 21.0 (TID 14640, hnte ...
    
		
    4. 
						
  4. spark 对hbase 操作
		
    本文将分两部分介绍,第一部分讲解使用 HBase 新版 API 进行 CRUD 基本操作:第二部分讲解如何将 Spark 内的 RDDs 写入 HBase 的表中,反之,HBase 中的表又是如何以  ...
    
		
    5. 
						
  5. 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<Inte ...
    
		
    6. 
						
  6. spark(2.1.0) 操作hbase(1.0.2)
		
    一.写操作 1.spark中引入外部jar包 1)创建/usr/software/spark_jars目录,把hbase里的lib里的以下七个jar放入/usr/software/spark_jars ...
    
		
    7. 
						
  7. Spark-读写HBase，SparkStreaming操作，Spark的HBase相关操作
		
    Spark-读写HBase,SparkStreaming操作,Spark的HBase相关操作 1.sparkstreaming实时写入Hbase(saveAsNewAPIHadoopDataset方法 ...
    
		
    8. 
						
  8. Spark 下操作 HBase（1.0.0 新 API）
		
    hbase1.0.0版本提供了一些让人激动的功能,并且,在不牺牲稳定性的前提下,引入了新的API.虽然 1.0.0 兼容旧版本的 API,不过还是应该尽早地来熟悉下新版API.并且了解下如何与当下正红 ...
    
		
    9. 
						
  9. PySpark操作HBase时设置scan参数
		
    在用PySpark操作HBase时默认是scan操作,通常情况下我们希望加上rowkey指定范围,即只获取一部分数据参加运算.翻遍了spark的python相关文档,搜遍了google和stackov ...
    
		
    10. 
		

	


随机推荐
	

    
									
  1. rsync 只是测试，请看下一篇
			
    实现从客户服务器去同步资源服务器 1.解压 # tar -xzpvf rsync-2.5.6.tar.gz   编译安装 # cd rsync-2.5.6/  # ./configure --pref ...
    
			
    2. 
						
  2. mac在变化mysql－rootpassword－各种解决问题的能力
			
    官方数据:http://dev.mysql.com/doc/refman/5.0/en/resetting-permissions.html#resetting-permissions-unix 另值 ...
    
			
    3. 
						
  3. django1.7 配置demo教程（环境搭建）
			
    近期又用到django做个简单项目,1年多没用过了有些手生,按理说没啥问题吧 以下是一个简单的环境搭建demo过程: 前提条件:准备了python2.7已经安装 1.搭建django环境下载 http ...
    
			
    4. 
						
  4. Oracle基于学习3--Oracle创建用户和授权
			
    Oracleserver端的操作,如以下一般: 1)       安装Oracleserver软件 2)       创建数据库(安装时自己主动创建) 3)       配置监听(安装时自己主动配置) ...
    
			
    5. 
						
  5. freemarker错误七
			
    1.错误叙述性说明 五月 30, 2014 11:33:57 下午 freemarker.log.JDK14LoggerFactory$JDK14Logger error 严重: Template p ...
    
			
    6. 
						
  6. android性能测试内存泄漏
			
    1.什么是内存泄漏?     适用于该系统的内存使用内存泄漏,未回复(释放),该内存可以没有事业,也不能被其他人使用使用自己. 2.出有什么差别?    内存泄漏是分配出去的内存无法回收.    内存 ...
    
			
    7. 
						
  7. HDFS建筑与shell操作
			
    一个.hadoop1.1.0演示 hadoop它适合于大容量数据存储和分布式计算平台 hadoop核心由hdfs和mapreduce组成 hdfs这是一个主从结构,仅有一个.是namenode:从节点 ...
    
			
    8. 
						
  8. 【Android基础】eclipse常用快捷键
			
    Alt+/ 内容辅助键--列出相关内容 Ctrl+shift+o 导入所需的所有类包 Ctrl+1 对现有错误的n中解决方案 Crtl+d 删除所在行或者全部 CTRL+alt+上下键 上下复制 Cr ...
    
			
    9. 
						
  9. hadoop得知；block数据块；mapreduce实现样例；UnsupportedClassVersionError变态；该项目的源代码相关联
			
    对于开源的东西.特别是刚出来不久.我认为最好的学习方法是能够看到源代码,doc,样品测试 为了方便查看源代码,导入与项目相关的源代码 watermark/2/text/aHR0cDovL2Jsb2cu ...
    
			
    10. 
						
  10. 大教堂与集市(The Cathedral and the Bazaar)读书笔记
			
    大教堂与集市The Cathedral and the Bazaar,一本不像计算机方面的计算机书籍 命令式管理,适合和奴隶共事目标共识型管理,适合和自由人共事 心性气层 只要眼多,bug好找 黑客开 ...
    
			
    11.