I have created a bunch of protobuf based parquet files that I want to
read/inspect using Spark SQL. However, I am running into exceptions and not
able to proceed much further:
This succeeds successfully (probably because there is no action yet). I can
also printSchema() and count() without any issues:
scala> val df = sqlContext.load(“my_root_dir/201504101000", "parquet")
scala> df.select(df("summary")).first
15/04/18 17:03:03 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 5.0
(TID 27, xxx.yyy.com): parquet.io.ParquetDecodingException: Can not read value
at 0 in block -1 in file
hdfs://xxx.yyy.com:8020/my_root_dir/201504101000/00000.parquet
at
parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:213)
at
parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:204)
at
org.apache.spark.rdd.NewHadoopRDD$$anon$1.hasNext(NewHadoopRDD.scala:143)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
at scala.collection.Iterator$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$$anonfun$2.apply(SparkPlan.scala:122)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:122)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1498)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1498)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
at org.apache.spark.scheduler.Task.run(Task.scala:64)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: parquet.io.ParquetDecodingException: The requested schema is not
compatible with the file schema. incompatible types: optional group …
I could convert my protos into json and then back to parquet, but that seems
wasteful !
Also, I will be happy to contribute and make protobuf work with Spark SQL if I
can get some guidance/help/pointers. Help appreciated.
-Abhishek-
