Hi all,
I can read in Avro files to Spark with HadoopRDD and submit the schema in
the jobConf, but with the guidance I've seen so far, I'm left with a avro
GenericRecord of Java objects without type. How do I actually use the
schema to have the types inferred?
Example:
scala> AvroJob.setInputSchema(jobConf,schema);
scala> val rdd =
sc.hadoopRDD(jobConf,classOf[org.apache.avro.mapred.AvroInputFormat[Generic
Record]],classOf[org.apache.avro.mapred.AvroWrapper[GenericRecord]],classOf
[org.apache.hadoop.io.NullWritable],10)
14/07/29 09:27:49 INFO storage.MemoryStore: ensureFreeSpace(134254) called
with curMem=0, maxMem=308713881
14/07/29 09:27:49 INFO storage.MemoryStore: Block broadcast_0 stored as
values to memory (estimated size 131.1 KB, free 294.3 MB)
rdd:
org.apache.spark.rdd.RDD[(org.apache.avro.mapred.AvroWrapper[org.apache.avr
o.generic.GenericRecord], org.apache.hadoop.io.NullWritable)] =
HadoopRDD[0] at hadoopRDD at <console>:50
scala> rdd.first._1.datum.get("amt")
14/07/29 09:31:34 INFO spark.SparkContext: Starting job: first at
<console>:53
14/07/29 09:31:34 INFO scheduler.DAGScheduler: Got job 3 (first at
<console>:53) with 1 output partitions (allowLocal=true)
14/07/29 09:31:34 INFO scheduler.DAGScheduler: Final stage: Stage 3(first
at <console>:53)
14/07/29 09:31:34 INFO scheduler.DAGScheduler: Parents of final stage:
List()
14/07/29 09:31:34 INFO scheduler.DAGScheduler: Missing parents: List()
14/07/29 09:31:34 INFO scheduler.DAGScheduler: Computing the requested
partition locally
14/07/29 09:31:34 INFO rdd.HadoopRDD: Input split:
hdfs://nameservice1:8020/user/nylab/prod/persistent_tables/creditsetl_ref_e
txns/201201/part-00000.avro:0+34279385
14/07/29 09:31:34 INFO spark.SparkContext: Job finished: first at
<console>:53, took 0.061220615 s
res11: Object = 24.0
Thanks!
Ben
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