Spark实战之读写HBase

1 配置

1.1 开发环境：

• HBase：hbase-1.0.0-cdh5.4.5.tar.gz
• Hadoop：hadoop-2.6.0-cdh5.4.5.tar.gz
• ZooKeeper：zookeeper-3.4.5-cdh5.4.5.tar.gz
• Spark：spark-2.1.0-bin-hadoop2.6

1.2 Spark的配置

• Jar包：需要HBase的Jar如下（经过测试，正常运行，但是是否存在冗余的Jar并未证实，若发现多余的jar可自行进行删除）

• spark-env.sh
  添加以下配置：export SPARK_CLASSPATH=/home/hadoop/data/lib1/*
  注：如果使用spark-shell的yarn模式进行测试的话，那么最好每个NodeManager节点都有配置jars和hbase-site.xml
• spark-default.sh

spark.yarn.historyServer.address=slave11:18080
spark.history.ui.port=18080
spark.eventLog.enabled=true
spark.eventLog.dir=hdfs:///tmp/spark/events
spark.history.fs.logDirectory=hdfs:///tmp/spark/events
spark.driver.memory=1g
spark.serializer=org.apache.spark.serializer.KryoSerializer

1.3 数据

1）格式： barCode@item@value@standardValue@upperLimit@lowerLimit

01055HAXMTXG10100001@[email protected]@[email protected]@1.55
01055HAXMTXG10100001@[email protected]@[email protected]@0.8
01055HAXMTXG10100001@[email protected]@[email protected]@0.8
01055HAXMTXG10100001@[email protected]@[email protected]@0.8
01055HAXMTXG10100001@[email protected]@[email protected]@0.8
01055HAXMTXG10100001@[email protected]@[email protected]@1.65
01055HAXMTXG10100001@[email protected]@[email protected]@1.55

2 代码演示

2.1 准备动作

1）既然是与HBase相关，那么首先需要使用hbase shell来创建一个表

创建表格：create ‘data’,’v’，create ‘data1’,’v’

2）使用spark-shell进行操作，命令如下：

bin/spark-shell –master yarn –deploy-mode client –num-executors 5 –executor-memory 1g –executor-cores 2

3）import 各种类

import org.apache.spark._
import org.apache.spark.rdd.NewHadoopRDD
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat
import org.apache.hadoop.fs.Path
import org.apache.hadoop.hbase.client.Put
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapred.TableOutputFormat
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.HBaseAdmin
import org.apache.hadoop.hbase.client.HTable
import org.apache.hadoop.hbase.client.Scan
import org.apache.hadoop.hbase.client.Get
import org.apache.hadoop.hbase.protobuf.ProtobufUtil
import org.apache.hadoop.hbase.util.{Base64,Bytes}
import org.apache.hadoop.hbase.KeyValue
import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat
import org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles
import org.apache.hadoop.hbase.HColumnDescriptor
import org.apache.commons.codec.digest.DigestUtils

2.2 代码实战

创建conf和table

val conf= HBaseConfiguration.create()
conf.set(TableInputFormat.INPUT_TABLE,"data1")
val table = new HTable(conf,"data1")

2.2.1 数据写入

格式：

val put = new Put(Bytes.toBytes("rowKey"))
put.add("cf","q","value")

使用for来插入5条数据

for(i <- 1 to 5){ var put= new Put(Bytes.toBytes("row"+i));put.add(Bytes.toBytes("v"),Bytes.toBytes("value"),Bytes.toBytes("value"+i));table.put(put)}

到hbase shell中查看结果

2.2.2 数据读取

val hbaseRdd = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],classOf[org.apache.hadoop.hbase.client.Result])

1）take

hbaseRdd take 1

2）scan

var scan = new Scan();
scan.addFamily(Bytes.toBytes(“v”));
var proto = ProtobufUtil.toScan(scan)
var scanToString = Base64.encodeBytes(proto.toByteArray());
conf.set(TableInputFormat.SCAN,scanToString)

val datas = hbaseRdd.map( x=>x._2).map{result => (result.getRow,result.getValue(Bytes.toBytes("v"),Bytes.toBytes("value")))}.map(row => (new String(row._1),new String(row._2))).collect.foreach(r => (println(r._1+":"+r._2)))

2.3 批量插入

2.3.1 普通插入

1）代码

val rdd = sc.textFile("/data/produce/2015/2015-03-01.log")
val data = rdd.map(_.split("@")).map{x=>(x(0)+x(1),x(2))}
val result = data.foreachPartition{x => {val conf= HBaseConfiguration.create();conf.set(TableInputFormat.INPUT_TABLE,"data");conf.set("hbase.zookeeper.quorum","slave5,slave6,slave7");conf.set("hbase.zookeeper.property.clientPort","2181");conf.addResource("/home/hadoop/data/lib/hbase-site.xml");val table = new HTable(conf,"data");table.setAutoFlush(false,false);table.setWriteBufferSize(3*1024*1024); x.foreach{y => {
var put= new Put(Bytes.toBytes(y._1));put.add(Bytes.toBytes("v"),Bytes.toBytes("value"),Bytes.toBytes(y._2));table.put(put)};table.flushCommits}}}

2）执行时间如下：7.6 min

2.3.2 Bulkload

1) 代码：

val conf = HBaseConfiguration.create();
val tableName = "data1"
val table = new HTable(conf,tableName)
conf.set(TableOutputFormat.OUTPUT_TABLE,tableName)

lazy val job = Job.getInstance(conf)
job.setMapOutputKeyClass(classOf[ImmutableBytesWritable])
job.setMapOutputValueClass(classOf[KeyValue])
HFileOutputFormat.configureIncrementalLoad(job,table)

val rdd = sc.textFile("/data/produce/2015/2015-03-01.log").map(_.split("@")).map{x => (DigestUtils.md5Hex(x(0)+x(1)).substring(0,3)+x(0)+x(1),x(2))}.sortBy(x =>x._1).map{x=>{val kv:KeyValue = new KeyValue(Bytes.toBytes(x._1),Bytes.toBytes("v"),Bytes.toBytes("value"),Bytes.toBytes(x._2+""));(new ImmutableBytesWritable(kv.getKey),kv)}}

rdd.saveAsNewAPIHadoopFile("/tmp/data1",classOf[ImmutableBytesWritable],classOf[KeyValue],classOf[HFileOutputFormat],job.getConfiguration())
val bulkLoader = new LoadIncrementalHFiles(conf)
bulkLoader.doBulkLoad(new Path("/tmp/data1"),table)

2） 执行时间：7s

3）执行结果：
到hbase shell 中查看 list “data1”

通过对比我们可以发现bulkload批量导入所用时间远远少于普通导入，速度提升了60多倍，当然我没有使用更大的数据量测试，但是我相信导入速度的提升是非常显著的，强烈建议使用BulkLoad批量导入数据到HBase中。

关于Spark与Hbase之间操作就写到这里，如果有什么地方写得不对或者运行不了，欢迎指出，谢谢