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java 中Spark中將對象序列化存儲到hdfs

摘要: Spark應用中經常會遇到這樣一個需求: 需要將JAVA對象序列化并存儲到HDFS, 尤其是利用MLlib計算出來的一些模型, 存儲到hdfs以便模型可以反復利用. 下面的例子演示了Spark環境下從Hbase讀取數據, 生成一個word2vec模型, 存儲到hdfs.

廢話不多說, 直接貼代碼了. spark1.4 + hbase0.98

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									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)

									  //---------------------------------------重點看這里-------------------------------------------------------------

									  //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

									  //這里示例另外一個程序直接從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]

									  /*

									  * //你還可以將序列化文件從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

									  */

									  //--------------------------------------------------------------------------------------------------------------

									 }

									}