[jira] [Created] (SPARK-8128) Dataframe Fails to Recognize Column in Schema

["Brad Willard (JIRA)" <ji...@apache.org>]

Brad Willard created SPARK-8128:
-----------------------------------

             Summary: Dataframe Fails to Recognize Column in Schema
                 Key: SPARK-8128
                 URL: https://issues.apache.org/jira/browse/SPARK-8128
             Project: Spark
          Issue Type: Bug
          Components: PySpark, Spark Core
    Affects Versions: 1.3.1
            Reporter: Brad Willard


I'm loading a folder of parquet files with about 600 parquet files and loading it into one dataframe so schema merging is involved. There is some bug with the schema merging that you print the schema and it shows and attributes. However when you run a query and filter on that attribute is errors saying it's not in the schema.

I think this bug could be related to an attribute name being reused in a nested object.  "mediaProcessingState" appears twice in the schema and is the problem.

sdf = sql_context.parquet('/parquet/big_data_folder')
sdf.printSchema()
root
 |-- _id: string (nullable = true)
 |-- addedOn: string (nullable = true)
 |-- attachment: string (nullable = true)
 .......
|-- items: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- _id: string (nullable = true)
 |    |    |-- addedOn: string (nullable = true)
 |    |    |-- authorId: string (nullable = true)
 |    |    |-- mediaProcessingState: long (nullable = true)
 |-- mediaProcessingState: long (nullable = true)
 |-- title: string (nullable = true)
 |-- key: string (nullable = true)

sdf.filter(sdf.mediaProcessingState == 3).count()

causes this exception

Py4JJavaError: An error occurred while calling o67.count.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1106 in stage 4.0 failed 30 times, most recent failure: Lost task 1106.29 in stage 4.0 (TID 70565, XXXXXXXXXXXXXXX): java.lang.IllegalArgumentException: Column [mediaProcessingState] was not found in schema!
    at parquet.Preconditions.checkArgument(Preconditions.java:47)
    at parquet.filter2.predicate.SchemaCompatibilityValidator.getColumnDescriptor(SchemaCompatibilityValidator.java:172)
    at parquet.filter2.predicate.SchemaCompatibilityValidator.validateColumn(SchemaCompatibilityValidator.java:160)
    at parquet.filter2.predicate.SchemaCompatibilityValidator.validateColumnFilterPredicate(SchemaCompatibilityValidator.java:142)
    at parquet.filter2.predicate.SchemaCompatibilityValidator.visit(SchemaCompatibilityValidator.java:76)
    at parquet.filter2.predicate.SchemaCompatibilityValidator.visit(SchemaCompatibilityValidator.java:41)
    at parquet.filter2.predicate.Operators$Eq.accept(Operators.java:162)
    at parquet.filter2.predicate.SchemaCompatibilityValidator.validate(SchemaCompatibilityValidator.java:46)
    at parquet.filter2.compat.RowGroupFilter.visit(RowGroupFilter.java:41)
    at parquet.filter2.compat.RowGroupFilter.visit(RowGroupFilter.java:22)
    at parquet.filter2.compat.FilterCompat$FilterPredicateCompat.accept(FilterCompat.java:108)
    at parquet.filter2.compat.RowGroupFilter.filterRowGroups(RowGroupFilter.java:28)
    at parquet.hadoop.ParquetRecordReader.initializeInternalReader(ParquetRecordReader.java:158)
    at parquet.hadoop.ParquetRecordReader.initialize(ParquetRecordReader.java:138)
    at org.apache.spark.rdd.NewHadoopRDD$$anon$1.<init>(NewHadoopRDD.scala:133)
    at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:104)
    at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:66)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:64)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
    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)



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