我对spark和hbase是新手。我正在处理hbase表的备份。这些备份在s3存储桶中。我使用newapihadoop文件通过spark（scala）读取它们，如下所示：

conf.set("io.serializations", "org.apache.hadoop.io.serializer.WritableSerialization,org.apache.hadoop.hbase.mapreduce.ResultSerialization")
val data = sc.newAPIHadoopFile(path,classOf[SequenceFileInputFormat[ImmutableBytesWritable, Result]], classOf[ImmutableBytesWritable], classOf[Result], conf)

这个表被称为emps。环境管理计划的模式是：

key: empid {COMPRESSION => 'gz' }
  family: data
    dob - date of birth of this employee.
    e_info - avro structure for storing emp info.
    e_dept- avro structure for storing info about dept.

  family: extra - Extra Metadata {NAME => 'extra', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'SNAPPY', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE => '0'}
    e_region - emp region
    e_status - some data about his achievements
    .
    .
    some more meta data

表中有些列包含简单的字符串数据，有些列包含avro结构。
我试图直接从s3中的hbase备份文件中读取这些数据。我不想在本地机器上重新创建这个hbase表，因为这个表非常非常大。
我就是这样想的：

data.keys.map{k=>(new String(k.get()))}.take(1)
res1: Array[String] = Array(111111111100011010102462)

data.values.map{ v =>{ for(cell <- v.rawCells()) yield{
                        val family = CellUtil.cloneFamily(cell);
                        val  column = CellUtil.cloneQualifier(cell);
                        val  value = CellUtil.cloneValue(cell);
                            new String(family) +"->"+ new String(column)+ "->"+ new String(value)
                         }
                      }  
}.take(1)
res2: Array[Array[String]] = Array(Array(info->dob->01/01/1996,  info->e_info->?ж�?�ո� ?�� ???̶�?�ո� ?�� ????, info->e_dept->?ж�??�ո� ?̶�??�ո� �ո� ??, extra->e_region-> CA, extra->e_status->, .....))

正如预期的那样，我可以正确地看到简单的字符串数据，但是avro数据是垃圾数据。
我尝试使用genericdatumreader阅读avro结构：

data.values.map{ v =>{ for(cell <- v.rawCells()) yield{
                        val family = new String(CellUtil.cloneFamily(cell));
                        val  column = new String(CellUtil.cloneQualifier(cell));
                        val  value = CellUtil.cloneValue(cell);
                        if(column=="e_info"){
                          var schema_obj =  new Schema.Parser
                          //schema_e_info contains the AVRO schema for e_info
                          var schema = schema_obj.parse(schema_e_info)
                          var READER2 = new GenericDatumReader[GenericRecord](schema)
                          var datum= READER2.read(null, DecoderFactory.defaultFactory.createBinaryDecoder(value,null))
                          var result=datum.get("type").toString()
                                family +"->"+column+ "->"+ new String(result) + "\n"
                            }
                        else
                           family +"->"+column+ "->"+ new String(value)+"\n"
                        }
                }        

}

但这给了我以下错误：

org.apache.spark.SparkException: Task not serializable
  at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
  at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
  at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
  at org.apache.spark.SparkContext.clean(SparkContext.scala:2101)
  at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:370)
  at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:369)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
  at org.apache.spark.rdd.RDD.map(RDD.scala:369)
  ... 74 elided
Caused by: java.io.NotSerializableException: org.apache.avro.Schema$RecordSchema
Serialization stack:
    - object not serializable (class: org.apache.avro.Schema$RecordSchema, value: .....

所以我想问：
有没有办法让不可序列化的类recordschema与map函数一起工作？
我的方法到这一点了吗？我很高兴知道处理这类数据的更好方法。
我读到在Dataframe中处理这个会容易得多。我试图将hadoop的rdd转换成一个Dataframe，但是我又一次盲目地在那里运行。