Chris Miller created SPARK-13709: ------------------------------------ Summary: Spark unable to decode Avro when partitioned Key: SPARK-13709 URL: https://issues.apache.org/jira/browse/SPARK-13709 Project: Spark Issue Type: Bug Affects Versions: 1.6.0 Reporter: Chris Miller
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} _Note:_ 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