I've been digging into this a little deeper. Here's what I've found:
test1.avsc:
********************
{
"namespace": "com.cmiller",
"name": "test1",
"type": "record",
"fields": [
{ "name":"foo1", "type":"string" }
]
}
********************
test2.avsc:
********************
{
"namespace": "com.cmiller",
"name": "test1",
"type": "record",
"fields": [
{ "name":"foo1", "type":"string" },
{ "name":"enum1", "type": { "type":"enum", "name":"enum1_values",
"symbols":["BLUE","RED", "GREEN"]} }
]
}
********************
test1.json (encoded and saved to test/test1.avro):
********************
{ "foo1": "test123" }
********************
test2.json (encoded and saved to test/test1.avro):
********************
{ "foo1": "test123", "enum1": "BLUE" }
********************
Here is how I create the tables and add the data:
********************
CREATE TABLE test1
PARTITIONED BY (ds 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'
TBLPROPERTIES ('avro.schema.url'='hdfs:///path/to/schemas/test1.avsc');
ALTER TABLE test1 ADD PARTITION (ds='1') LOCATION
's3://spark-data/dev/test1';
CREATE TABLE test2
PARTITIONED BY (ds 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'
TBLPROPERTIES ('avro.schema.url'='hdfs:///path/to/schemas/test2.avsc');
ALTER TABLE test2 ADD PARTITION (ds='1') LOCATION
's3://spark-data/dev/test2';
********************
And here's what I get:
********************
SELECT * FROM test1;
-- works fine, shows data
SELECT * FROM test2;
org.apache.avro.AvroTypeException: Found com.cmiller.enum1_values,
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$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at
scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
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)
********************
In addition to the above, I also tried putting the test Avro files on HDFS
instead of S3 -- the error is the same. I also tried querying from Scala
instead of using Zeppelin, and I get the same error.
Where should I begin with troubleshooting this problem? This same query
runs fine on Hive. Based on the error, it appears to be something in the
deserializer though... but if it were a bug in the Avro deserializer, why
does it only appear with Spark? I imagine Hive queries would be using the
same deserializer, no?
Thanks!
--
Chris Miller
On Thu, Mar 3, 2016 at 4:33 AM, Chris Miller <cmiller11...@gmail.com> wrote:
> Hi,
>
> I have a strange issue occurring when I use manual partitions.
>
> If I create a table as follows, I am able to query the data with no
> problem:
>
> ********
> CREATE TABLE test1
> 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-bucket/prod/logs/avro/2016/03/02/'
> TBLPROPERTIES ('avro.schema.url'='hdfs:///data/schemas/schema.avsc');
> ********
>
> If I create the table like this, however, and then add a partition with a
> LOCATION specified, I am unable to query:
>
> ********
> CREATE TABLE test2
> PARTITIONED BY (ds 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'
> TBLPROPERTIES ('avro.schema.url'='hdfs:///data/schemas/schema.avsc');
>
> ALTER TABLE test7 ADD PARTITION (ds='1') LOCATION
> 's3://analytics-bucket/prod/logs/avro/2016/03/02/';
> ********
>
> This is what happens
>
> ********
> SELECT * FROM test2 LIMIT 1;
>
> org.apache.avro.AvroTypeException: Found ActionEnum, 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 scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at
> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
> at
> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
> at scala.collection.TraversableOnce$class.to
> (TraversableOnce.scala:273)
> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
> at
> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at
> org.apache.spark.executor.Executor$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)
> ********
>
> The data is exactly the same, and I can still go back and query the test1
> table without issue. I don't have control over the directory structure, so
> I need to add the partitions manually so that I can specify a location.
>
> For what it's worth, "ActionEnum" is the first field in my schema. This
> same table and query structure works fine with Hive. When I try to run this
> with SparkSQL, however, I get the above error.
>
> Anyone have any idea what the problem is here? Thanks!
>
> --
> Chris Miller
>
