One more thing -- just to set aside any question about my specific schema
or data, I used the sample schema and data record from Oracle's
documentation on Avro support. It's a pretty simple schema:
https://docs.oracle.com/cd/E26161_02/html/GettingStartedGuide/jsonbinding-overview.html
When I create a table with this schema and then try to query the
Avro-encoded record, I get the same type of error:
********************
org.apache.avro.AvroTypeException: Found avro.FullName, 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)
********************
To me, this "feels" like a bug -- I just can't identify if it's a Spark
issue or an Avro issue. Decoding the same files work fine with Hive, and I
imagine the same deserializer code is used there too.
Thoughts?
--
Chris Miller
On Thu, Mar 3, 2016 at 9:38 PM, Igor Berman <igor.ber...@gmail.com> wrote:
> your field name is
> *enum1_values*
>
> but you have data
> { "foo1": "test123", *"enum1"*: "BLUE" }
>
> i.e. since you defined enum and not union(null, enum)
> it tries to find value for enum1_values and doesn't find one...
>
> On 3 March 2016 at 11:30, Chris Miller <cmiller11...@gmail.com> wrote:
>
>> 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
>>>
>>
>>
>
