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) -- 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