Spark中將對象序列化存儲到hdfs

廢話很少說, 直接貼代碼了. spark1.4 + hbase0.98

import org.apache.spark.storage.StorageLevel
import scala.collection.JavaConverters._
import java.io.File
import java.io.FileInputStream
import java.io.FileOutputStream
import java.io.ObjectInputStream
import java.io.ObjectOutputStream
import java.net.URI
import java.util.Date
import org.ansj.library.UserDefineLibrary
import org.ansj.splitWord.analysis.NlpAnalysis
import org.ansj.splitWord.analysis.ToAnalysis
import org.apache.hadoop.fs.FSDataInputStream
import org.apache.hadoop.fs.FSDataOutputStream
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.FileUtil
import org.apache.hadoop.fs.Path
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor, TableName}
import org.apache.hadoop.hbase.filter.FilterList
import org.apache.hadoop.hbase.filter.PageFilter
import org.apache.hadoop.hbase.filter.RegexStringComparator
import org.apache.hadoop.hbase.filter.SingleColumnValueFilter
import org.apache.hadoop.hbase.filter.CompareFilter.CompareOp
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.protobuf.ProtobufUtil
import org.apache.hadoop.hbase.util.{Base64, Bytes}
import com.feheadline.fespark.db.Neo4jManager
import com.feheadline.fespark.util.Env
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd._
import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
import scala.math.log
import scala.io.Source

object Word2VecDemo {

  def convertScanToString(scan: Scan) = {
    val proto = ProtobufUtil.toScan(scan)
    Base64.encodeBytes(proto.toByteArray)
  }

  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("Word2Vec Demo")
    sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
    sparkConf.set("spark.kryoserializer.buffer", "256m")
    sparkConf.set("spark.kryoserializer.buffer.max","2046m")
    sparkConf.set("spark.akka.frameSize", "500")
    sparkConf.set("spark.rpc.askTimeout", "30")
    

    val sc = new SparkContext(sparkConf)
    val hbaseConf = HBaseConfiguration.create()
    hbaseConf.set("hbase.zookeeper.quorum", "myzookeeper")

    hbaseConf.set(TableInputFormat.INPUT_TABLE, "crawled")

    val scan = new Scan()
    val filterList:FilterList = new FilterList(FilterList.Operator.MUST_PASS_ALL)
    
    val comp:RegexStringComparator = new RegexStringComparator(""".{1500,}""")
    
    val articleFilter:SingleColumnValueFilter = new SingleColumnValueFilter(
    "data".getBytes,
    "article".getBytes,
    CompareOp.EQUAL,
    comp
    )
    
    filterList.addFilter(articleFilter)
    filterList.addFilter(new PageFilter(100))
    
    scan.setFilter(filterList)
    scan.setCaching(50)
    scan.setCacheBlocks(false)
    hbaseConf.set(TableInputFormat.SCAN,convertScanToString(scan))

    val crawledRDD = sc.newAPIHadoopRDD(
      hbaseConf,
      classOf[TableInputFormat],
      classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
      classOf[org.apache.hadoop.hbase.client.Result]
    )
 
    val articlesRDD = crawledRDD.filter{
      case (_,result) => {
          val content = Bytes.toString(result.getValue("data".getBytes,"article".getBytes))
          content != null
      }
    }

    val wordsInDoc = articlesRDD.map{
      case (_,result) => {
          val content = Bytes.toString(result.getValue("data".getBytes,"article".getBytes))
          if(content!=null)ToAnalysis.parse(content).asScala.map(_.getName).toSeq
          else Seq("")
      }
    }
    
    val fitleredWordsInDoc = wordsInDoc.filter(_.nonEmpty)
    
    val word2vec = new Word2Vec()
    val model = word2vec.fit(fitleredWordsInDoc)
   
    //---------------------------------------重點看這裏-------------------------------------------------------------
    //將上面的模型存儲到hdfs
    val hadoopConf = sc.hadoopConfiguration
    hadoopConf.set("fs.defaultFS", "hdfs://myhadoop:9000/")
    val fileSystem = FileSystem.get(hadoopConf)
    val path = new Path("/user/hadoop/data/mllib/word2vec-object")
    val oos = new ObjectOutputStream(new FSDataOutputStream(fileSystem.create(path)))
    oos.writeObject(model)
    oos.close
    
    //這裏示例另一個程序直接從hdfs讀取序列化對象使用模型
    val ois = new ObjectInputStream(new FSDataInputStream(fileSystem.open(path)))
    val sample_model = ois.readObject.asInstanceOf[Word2VecModel]
   
    /*
    * //你還能夠將序列化文件從hdfs放到本地, scala程序使用模型
    * import java.io._
    * import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
    * val ois = new ObjectInputStream(new FileInputStream("/home/cherokee/tmp/word2vec-object"))
    * val sample_model = ois.readObject.asInstanceOf[Word2VecModel]
    * ois.close
    */
    //--------------------------------------------------------------------------------------------------------------
  }
}