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

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