[ https://issues.apache.org/jira/browse/SPARK-13709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yin Huai resolved SPARK-13709. ------------------------------ Resolution: Fixed Fix Version/s: 2.0.0 Issue resolved by pull request 13865 [https://github.com/apache/spark/pull/13865] > Spark unable to decode Avro when partitioned > -------------------------------------------- > > Key: SPARK-13709 > URL: https://issues.apache.org/jira/browse/SPARK-13709 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.6.0 > Reporter: Chris Miller > Assignee: Cheng Lian > Fix For: 2.0.0 > > > There is a problem decoding Avro data with SparkSQL when partitioned. The > schema and encoded data are valid -- I'm able to decode the data with the > avro-tools CLI utility. I'm also able to decode the data with non-partitioned > SparkSQL tables, Hive, other tools as well... except partitioned SparkSQL > schemas. > For a simple example, I took the example schema and data found in the Oracle > documentation here: > *Schema* > {code:javascript} > { > "type": "record", > "name": "MemberInfo", > "namespace": "avro", > "fields": [ > {"name": "name", "type": { > "type": "record", > "name": "FullName", > "fields": [ > {"name": "first", "type": "string"}, > {"name": "last", "type": "string"} > ] > }}, > {"name": "age", "type": "int"}, > {"name": "address", "type": { > "type": "record", > "name": "Address", > "fields": [ > {"name": "street", "type": "string"}, > {"name": "city", "type": "string"}, > {"name": "state", "type": "string"}, > {"name": "zip", "type": "int"} > ] > }} > ] > } > {code} > *Data* > {code:javascript} > { > "name": { > "first": "Percival", > "last": "Lowell" > }, > "age": 156, > "address": { > "street": "Mars Hill Rd", > "city": "Flagstaff", > "state": "AZ", > "zip": 86001 > } > } > {code} > *Create* (no partitions - works) > If I create with no partitions, I'm able to query the data just fine. > {code:sql} > CREATE EXTERNAL TABLE IF NOT EXISTS foo > 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 '/path/to/data/dir' > TBLPROPERTIES ('avro.schema.url'='/path/to/schema.avsc'); > {code} > *Create* (partitions -- does NOT work) > If I create with no partitions, and then manually add a partition, all of my > queries return an error. (I need to manually add partitions because I cannot > control the structure of the data directories, so dynamic partitioning is not > an option.) > {code:sql} > CREATE EXTERNAL TABLE IF NOT EXISTS foo > 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'='/path/to/schema.avsc'); > ALTER TABLE foo ADD PARTITION (ds='1') LOCATION '/path/to/data/dir'; > {code} > The error: > {code} > spark-sql> SELECT * FROM foo WHERE ds = '1'; > Driver stacktrace: > at > org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) > at > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) > at > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) > at scala.Option.foreach(Option.scala:236) > at > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) > at > org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) > at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) > at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858) > at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929) > at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) > at org.apache.spark.rdd.RDD.collect(RDD.scala:926) > at > org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:166) > at > org.apache.spark.sql.execution.SparkPlan.executeCollectPublic(SparkPlan.scala:174) > at > org.apache.spark.sql.hive.HiveContext$QueryExecution.stringResult(HiveContext.scala:635) > at > org.apache.spark.sql.hive.thriftserver.SparkSQLDriver.run(SparkSQLDriver.scala:64) > at > org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processCmd(SparkSQLCLIDriver.scala:308) > at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:376) > at > org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver$.main(SparkSQLCLIDriver.scala:226) > at > org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.main(SparkSQLCLIDriver.scala) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:483) > at > org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731) > at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181) > at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206) > at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121) > at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) > Caused by: org.apache.avro.AvroTypeException: Found avro.FullName, expecting > union > at org.apache.avro.io.ResolvingDecoder.doAction(ResolvingDecoder.java:292) > at org.apache.avro.io.parsing.Parser.advance(Parser.java:88) > at > org.apache.avro.io.ResolvingDecoder.readIndex(ResolvingDecoder.java:267) > at > org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:155) > at > org.apache.avro.generic.GenericDatumReader.readField(GenericDatumReader.java:193) > at > org.apache.avro.generic.GenericDatumReader.readRecord(GenericDatumReader.java:183) > at > org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:151) > at > org.apache.avro.generic.GenericDatumReader.read(GenericDatumReader.java:142) > at > org.apache.hadoop.hive.serde2.avro.AvroDeserializer$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$11.next(Iterator.scala:328) > at scala.collection.Iterator$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.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927) > at > org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927) > 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:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > {code} > *Additional Info* > For what it's worth, I found an issue (DRILL-957) reported in Apache Drill > and related fix that look very simliar to this. I'll look that to this issue. > Originally [posted > here|http://stackoverflow.com/questions/35826850/spark-unable-to-decode-avro-when-partitioned] > on StackOverflow as a question, but I felt strongly that this is indeed a > bug so I created this issue. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org