Hi, I have a small Hadoop cluster w/ Hive, SparkSQL, etc. setup on Amazon EMR. I have avro files stored on S3 that I want to be able to access from SparkSQL. I have confirmed the files are valid and I am able to decode them using avro-tools.
Here's the full schema for reference: https://gist.github.com/ee99ee/8692a538b1a51f2dca5e In SparkSQL, I created a table for these files as follows: CREATE EXTERNAL TABLE IF NOT EXISTS impressions PARTITIONED BY (year STRING, month STRING, day STRING) ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe' STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat' LOCATION 's3://analytics/prod/backend/avro/' TBLPROPERTIES ('avro.schema.url'='hdfs:///data/schemas/impressionSchema.avsc') Then I add a partition: ALTER TABLE impressions ADD PARTITION (year='2016', month='02', day='26') LOCATION 's3://analytics/prod/backend/avro/2016/02/26' Looking at the table structure, everything seems to match the schema correctly. If I run any query, I get an error: SELECT COUNT(*) FROM impressions org.apache.avro.AvroTypeException: Found MetaSchema, expecting union at org.apache.avro.io.ResolvingDecoder.doAction(ResolvingDecoder.java:292) at org.apache.avro.io.parsing.Parser.advance(Parser.java:88) at org.apache.avro.io.ResolvingDecoder.readIndex(ResolvingDecoder.java:267) at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:155) at org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:193) at org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:183) at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:151) at org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:142) at org.apache.hadoop.hive.serde2.avro.AvroDeserializer$SchemaReEncoder.reencode(AvroDeserializer.java:111) at org.apache.hadoop.hive.serde2.avro.AvroDeserializer.deserialize(AvroDeserializer.java:175) at org.apache.hadoop.hive.serde2.avro.AvroSerDe.deserialize(AvroSerDe.java:201) at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:409) at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:408) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:505) at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.<init>(TungstenAggregationIterator.scala:686) at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:95) at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:89) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) I know the Avro files are valid because I can pull them down off S3, decode them, and see all the data I expect. I'm pretty sure Hive is reading my files because "MetaSchema" is the first field in the schema. Anyone have any idea what is going on here or how I can further debug? -- Chris Miller
