读取保存在HBase列中的AVRO结构

Question

I am new to Spark and HBase. I am working with the backups of a HBase table. These backups are in a S3 bucket. I am reading them via spark(scala) using newAPIHadoopFile like this:

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)

The table in question is called Emps . The schema of Emps is :

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

The table has some columns that have simple string data in them, and some columns that has AVRO stuctures in them.

I am trying to read this data directly from the HBase backup files in the S3. I do not want to re-create this HBase table in my local machine as the table is very, very large.

This is how I am trying to read this:

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->, .....))

As expected I can see the simple string data correctly, but the AVRO data is garbage.

I tried reading the AVRO structures using GenericDatumReader :

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"
                        }
                }        

}

But this is giving me the following error:

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: .....

So I want to ask:

1. Is there any way to make the non-serializable class RecordSchema work with the map function?
2. Is my approach right upto this point? I would be glad to know about better approaches to handle this kind of data.
3. I read that handling this in a Dataframe would be a lot easier. I tried to convert the Hadoop RDD so formed into a Dataframe, but again I am running blindly there.

Answer 1

As the exception says - the schema is non-serializable. Can you initialize it inside the mapper function? So that it doesn't need to get shipped from the driver to the executors.

Alternatively, you can also create a scala singleton object that contains the schema. You get one scala singleton initialized on each executor, so when you access any member from the singleton, it doesn't need to be serialized & sent across the network. This avoids the unnecessary overhead of re-creating the schema for each and every row in the data.

Just for the purpose of checking that you can read the data fine - you can also convert it to a byte array on the executors, collect it on the driver and do the deserialization (parsing the AVRO data) in the driver code. But this obviously won't scale, it's just to make sure that your data looks good and to avoid spark-related complications while you're writing your prototype code to extract the data.