spark往hbase写数据

Question

val result: org.apache.spark.rdd.RDD[(String, Int)]
result.foreach(res =>
{ var put = new Put(java.util.UUID.randomUUID().toString.reverse.getBytes()) .add("lv6".getBytes(), res._1.toString.getBytes(), res._2.toString.getBytes) table.put(put) }
)
上面是程序，result里面是(key,value)的Array  保存到hbase报错各种没有序列化
Exception in thread "Thread-3" java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:186)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: org.apache.hadoop.hbase.client.HTablePool$PooledHTable
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1044)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1028)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1026)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1026)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:771)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$16$$anonfun$apply$1.apply$mcVI$sp(DAGScheduler.scala:901)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$16$$anonfun$apply$1.apply(DAGScheduler.scala:898)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$16$$anonfun$apply$1.apply(DAGScheduler.scala:898)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$16.apply(DAGScheduler.scala:898)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskCompletion$16.apply(DAGScheduler.scala:897)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:897)
at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1226)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)

Answer 1

我也是刚开始接触spark，楼主这个问题，我个人的一点建议，把一些类都实现serializable接口吧貌似你的spark是和hadoop整合的，不知道hadoop内部是不是有些没有序列化的

Answer 2

涉及到的类都需要序列化处理，还不行的话可以放弃foreach， 用别的方法替代http://blog.csdn.net/fighting_one_piece/article/details/38437647
希望对你有帮助

Answer 3

messages.map(new Function<Tuple2<String, String>, String>() {
@Override
public String call(Tuple2<String, String> tuple2) {
return tuple2._2();
}
}).foreach(new Function2<JavaRDD<String>, Time, Void>() { private HTableInterface table = null;

@Override
public Void call(JavaRDD<String> values, Time time)
throws Exception { values.foreach(new VoidFunction<String>() { @Override
public void call(String str) throws Exception {

HConnection connection = HConnectionManager.createConnection(HBaseConfiguration.create());
table = connection.getTable(tableName);
String[] strings = SPACE.split(str);
String tableName = strings[0];
String type = strings[1]; //if(null == table){
connection.getTable(tableName);
table.setAutoFlush(false);
//}

if (type.equals("DELETE")) {
Delete d = new Delete(strings[2].getBytes());
try {
table.delete(d);
} catch (IOException e) {
e.printStackTrace();
}
} else {
Put p = new Put(Bytes.toBytes(strings[2]));
for (int i = 3; i < strings.length; i++) {
String cstr = strings[i];
int index = cstr.indexOf("=");
if (index > 0) {
p.add(Bytes.toBytes(columnFamily), Bytes
.toBytes(cstr.substring(0, index)),
Bytes.toBytes(cstr
.substring(index + 1)));
} else {
p.add(Bytes.toBytes(columnFamily),
Bytes.toBytes(cstr),
Bytes.toBytes(""));
} }
table.put(p);
}
table.flushCommits();
}
}); return null;
}
});

Answer 4

试试这个object Blaher {
  def blah(row: Array[String]) { 
    val hConf = new HBaseConfiguration() 
    val hTable = new HTable(hConf, "table") 
    val thePut = new Put(Bytes.toBytes(row(0))) 
    thePut.add(Bytes.toBytes("cf"), Bytes.toBytes(row(0)), Bytes.toBytes(row(0))) 
    hTable.put(thePut) 
  } 
}object TheMain extends Serializable{
  def run() {
    val ssc = new StreamingContext(sc, Seconds(1)) 
    val lines = ssc.socketTextStream("localhost", 9977, StorageLevel.MEMORY_AND_DISK_SER) 
    val words = lines.map(_.split(",")) 
    val store = words.foreachRDD(rdd => rdd.foreach(Blaher.blah)) 
    ssc.start()
  } 
}TheMain.run()重新序列化一下就可以了

Answer 5

关键是你在foreach算子里访问外面的HTable对象了，而HTable对象是不可序列化的，所以要在foreach里面创建。但是创建HTable又要创建HConnection，而创建HConnection又是个重型操作，所以建议是foreachPartition的方式去做：tb.foreachPartition( it => {
      import org.apache.hadoop.conf.Configuration
      val hdfsConf = new Configuration(true)
      hdfsConf.set("hbase.master", "datanode01:60000")
      hdfsConf.set("hbase.zookeeper.property.clientport", "2181")
      hdfsConf.set("hbase.zookeeper.quorum", "namenode01:2181,datanode01:2181,datanode02:2181")      val hbaseConn = ConnectionFactory.createConnection(hdfsConf)
      val tableName = TableName.valueOf("dev","table_ttt")
      val table = hbaseConn1.getTable(tableName1)      for (t <- it) {
        val put = new Put(ByteArrayUtil.toByte(t._1))
        put.addColumn("tb".getBytes,"tb".getBytes(),ByteArrayUtil.toByte(t._2))
        table.put(put)
      }
      hbaseConn.close()
    })这样快得一比

Answer 6

加上这句话就好了
System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")