使用spark将RDD保存到hbase时，输出目录未设置异常

Question

我有一个工作要从hbase检索数据，使用spark作为rdd并执行过滤，然后将其作为样本数据保存回基础，如下所示：

object FilterData {
  def main(args: Array[String]) {
    filterData()
  }

  def filterData() = {
    val sparkConf = new SparkConf().setAppName("filterData").setMaster("spark://spark:7077")
    val sc = new SparkContext(sparkConf)
    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.quorum", "172.16.1.10,172.16.1.11,172.16.1.12")
    conf.setInt("timeout", 120000)
    conf.set(TableInputFormat.INPUT_TABLE, "dataset")
    val hbaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result])
    val filtered = hbaseRDD.filter{
        case tuple => {
         val result = tuple._2
         val hostId = new String(result.getValue("user", "id"))
         hostId == "12345" // <-- only retrieve the row when user:id is 12345
        }
    }
    val jobConfig: JobConf = new JobConf(conf, this.getClass)
    jobConfig.setOutputFormat(classOf[TableOutputFormat])
    jobConfig.set(TableOutputFormat.OUTPUT_TABLE,  "sample_data")
    filtered.saveAsNewAPIHadoopDataset(jobConfig)
  }
}

我已经使用hbase shell在hbase中创建了一个表，并指定了列Family

但是当运行此代码时，它会给出以下异常：

Exception in thread "main" org.apache.hadoop.mapred.InvalidJobConfException: Output directory not set.
    at org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:138)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1088)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1.apply(PairRDDFunctions.scala:1074)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1.apply(PairRDDFunctions.scala:1074)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.PairRDDFunctions.saveAsNewAPIHadoopDataset(PairRDDFunctions.scala:1074)
    at com.xinmeiti.spark.weibo.FilterData$.filterData(FilterData.scala:45)
    at com.xinmeiti.spark.weibo.FilterData$.main(FilterData.scala:20)
    at com.xinmeiti.spark.weibo.FilterData.main(FilterData.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

什么地方出了错？

Answer 1

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.client.Result
import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat

object FilterData {
  def main(args: Array[String]) {
    filterData()
  }

  def filterData() = {
    val sparkConf = new SparkConf().setAppName("filterData").setMaster("spark://spark:7077")
    val sc = new SparkContext(sparkConf)
    val conf = HBaseConfiguration.create()
    conf.set("hbase.zookeeper.quorum", "172.16.1.10,172.16.1.11,172.16.1.12")
    conf.setInt("timeout", 120000)
    conf.set(TableInputFormat.INPUT_TABLE, "dataset")
    val hbaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result])
    val filtered = hbaseRDD.filter {
      case tuple => {
        val result = tuple._2
        val hostId = new String(result.getValue("user".getBytes, "id".getBytes))
        hostId == "12345" // <-- only retrieve the row when user:id is 12345
      }
    }
    val jobConfig: Job = Job.getInstance(conf)
    jobConfig.setOutputFormatClass(classOf[TableOutputFormat[ImmutableBytesWritable]])
    jobConfig.getConfiguration().set(TableOutputFormat.OUTPUT_TABLE, "sample_data")
    filtered.saveAsNewAPIHadoopDataset(jobConfig.getConfiguration())
  }
}