I am running on master, pulled yesterday I believe but saw the same issue with 1.2.0
On Thu, Nov 20, 2014 at 1:37 PM, Michael Armbrust <mich...@databricks.com> wrote: > Which version are you running on again? > > On Thu, Nov 20, 2014 at 8:17 AM, Sadhan Sood <sadhan.s...@gmail.com> > wrote: > >> Also attaching the parquet file if anyone wants to take a further look. >> >> On Thu, Nov 20, 2014 at 8:54 AM, Sadhan Sood <sadhan.s...@gmail.com> >> wrote: >> >>> So, I am seeing this issue with spark sql throwing an exception when >>> trying to read selective columns from a thrift parquet file and also when >>> caching them: >>> On some further digging, I was able to narrow it down to at-least one >>> particular column type: map<string, set<string>> to be causing this issue. >>> To reproduce this I created a test thrift file with a very basic schema and >>> stored some sample data in a parquet file: >>> >>> Test.thrift >>> =========== >>> typedef binary SomeId >>> >>> enum SomeExclusionCause { >>> WHITELIST = 1, >>> HAS_PURCHASE = 2, >>> } >>> >>> struct SampleThriftObject { >>> 10: string col_a; >>> 20: string col_b; >>> 30: string col_c; >>> 40: optional map<SomeExclusionCause, set<SomeId>> col_d; >>> } >>> ============= >>> >>> And loading the data in spark through schemaRDD: >>> >>> import org.apache.spark.sql.SchemaRDD >>> val sqlContext = new org.apache.spark.sql.SQLContext(sc); >>> val parquetFile = "/path/to/generated/parquet/file" >>> val parquetFileRDD = sqlContext.parquetFile(parquetFile) >>> parquetFileRDD.printSchema >>> root >>> |-- col_a: string (nullable = true) >>> |-- col_b: string (nullable = true) >>> |-- col_c: string (nullable = true) >>> |-- col_d: map (nullable = true) >>> | |-- key: string >>> | |-- value: array (valueContainsNull = true) >>> | | |-- element: string (containsNull = false) >>> >>> parquetFileRDD.registerTempTable("test") >>> sqlContext.cacheTable("test") >>> sqlContext.sql("select col_a from test").collect() <-- see the exception >>> stack here >>> >>> org.apache.spark.SparkException: Job aborted due to stage failure: Task >>> 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage >>> 0.0 (TID 0, localhost): parquet.io.ParquetDecodingException: Can not read >>> value at 0 in block -1 in file file:/tmp/xyz/part-r-00000.parquet >>> at >>> parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:213) >>> at >>> parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:204) >>> at >>> org.apache.spark.rdd.NewHadoopRDD$$anon$1.hasNext(NewHadoopRDD.scala:145) >>> at >>> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>> at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:388) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>> at scala.collection.Iterator$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$$anonfun$16.apply(RDD.scala:780) >>> at org.apache.spark.rdd.RDD$$anonfun$16.apply(RDD.scala:780) >>> at >>> org.apache.spark.SparkContext$$anonfun$runJob$3.apply(SparkContext.scala:1223) >>> at >>> org.apache.spark.SparkContext$$anonfun$runJob$3.apply(SparkContext.scala:1223) >>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) >>> at org.apache.spark.scheduler.Task.run(Task.scala:56) >>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:195) >>> 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: java.lang.ArrayIndexOutOfBoundsException: -1 >>> at java.util.ArrayList.elementData(ArrayList.java:418) >>> at java.util.ArrayList.get(ArrayList.java:431) >>> at parquet.io.GroupColumnIO.getLast(GroupColumnIO.java:95) >>> at parquet.io.GroupColumnIO.getLast(GroupColumnIO.java:95) >>> at parquet.io.PrimitiveColumnIO.getLast(PrimitiveColumnIO.java:80) >>> at parquet.io.PrimitiveColumnIO.isLast(PrimitiveColumnIO.java:74) >>> at >>> parquet.io.RecordReaderImplementation.<init>(RecordReaderImplementation.java:282) >>> at parquet.io.MessageColumnIO$1.visit(MessageColumnIO.java:131) >>> at parquet.io.MessageColumnIO$1.visit(MessageColumnIO.java:96) >>> at >>> parquet.filter2.compat.FilterCompat$NoOpFilter.accept(FilterCompat.java:136) >>> at parquet.io.MessageColumnIO.getRecordReader(MessageColumnIO.java:96) >>> at >>> parquet.hadoop.InternalParquetRecordReader.checkRead(InternalParquetRecordReader.java:126) >>> at >>> parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:193) >>> ... 27 more >>> >>> If you take out the col_d from the thrift file, the problem goes away. >>> The problem also shows up when trying to read the particular column without >>> caching the table first. The same file can be dumped/read using >>> parquet-tools just fine. Here is the file dump using parquet-tools: >>> >>> row group 0 >>> -------------------------------------------------------------------------------- >>> col_a: BINARY UNCOMPRESSED DO:0 FPO:4 SZ:89/89/1.00 VC:9 ENC >>> [more]... >>> col_b: BINARY UNCOMPRESSED DO:0 FPO:93 SZ:89/89/1.00 VC:9 EN >>> [more]... >>> col_c: BINARY UNCOMPRESSED DO:0 FPO:182 SZ:89/89/1.00 VC:9 E >>> [more]... >>> col_d: >>> .map: >>> ..key: BINARY UNCOMPRESSED DO:0 FPO:271 SZ:29/29/1.00 VC:9 E >>> [more]... >>> ..value: >>> ...value_tuple: BINARY UNCOMPRESSED DO:0 FPO:300 SZ:29/29/1.00 VC:9 E >>> [more]... >>> >>> col_a TV=9 RL=0 DL=1 >>> >>> ---------------------------------------------------------------------------- >>> page 0: DLE:RLE RLE:BIT_PACKED VLE:PLAIN SZ:60 VC:9 >>> >>> col_b TV=9 RL=0 DL=1 >>> >>> ---------------------------------------------------------------------------- >>> page 0: DLE:RLE RLE:BIT_PACKED VLE:PLAIN SZ:60 VC:9 >>> >>> col_c TV=9 RL=0 DL=1 >>> >>> ---------------------------------------------------------------------------- >>> page 0: DLE:RLE RLE:BIT_PACKED VLE:PLAIN SZ:60 VC:9 >>> >>> col_d.map.key TV=9 RL=1 DL=2 >>> >>> ---------------------------------------------------------------------------- >>> page 0: DLE:RLE RLE:RLE VLE:PLAIN SZ:12 VC:9 >>> >>> col_d.map.value.value_tuple TV=9 RL=2 DL=4 >>> >>> ---------------------------------------------------------------------------- >>> page 0: DLE:RLE RLE:RLE VLE:PLAIN SZ:12 VC:9 >>> >>> BINARY col_a >>> -------------------------------------------------------------------------------- >>> *** row group 1 of 1, values 1 to 9 *** >>> value 1: R:1 D:1 V:a1 >>> value 2: R:1 D:1 V:a2 >>> value 3: R:1 D:1 V:a3 >>> value 4: R:1 D:1 V:a4 >>> value 5: R:1 D:1 V:a5 >>> value 6: R:1 D:1 V:a6 >>> value 7: R:1 D:1 V:a7 >>> value 8: R:1 D:1 V:a8 >>> value 9: R:1 D:1 V:a9 >>> >>> BINARY col_b >>> -------------------------------------------------------------------------------- >>> *** row group 1 of 1, values 1 to 9 *** >>> value 1: R:1 D:1 V:b1 >>> value 2: R:1 D:1 V:b2 >>> value 3: R:1 D:1 V:b3 >>> value 4: R:1 D:1 V:b4 >>> value 5: R:1 D:1 V:b5 >>> value 6: R:1 D:1 V:b6 >>> value 7: R:1 D:1 V:b7 >>> value 8: R:1 D:1 V:b8 >>> value 9: R:1 D:1 V:b9 >>> >>> BINARY col_c >>> -------------------------------------------------------------------------------- >>> *** row group 1 of 1, values 1 to 9 *** >>> value 1: R:1 D:1 V:c1 >>> value 2: R:1 D:1 V:c2 >>> value 3: R:1 D:1 V:c3 >>> value 4: R:1 D:1 V:c4 >>> value 5: R:1 D:1 V:c5 >>> value 6: R:1 D:1 V:c6 >>> value 7: R:1 D:1 V:c7 >>> value 8: R:1 D:1 V:c8 >>> value 9: R:1 D:1 V:c9 >>> >>> BINARY col_d.map.key >>> -------------------------------------------------------------------------------- >>> *** row group 1 of 1, values 1 to 9 *** >>> value 1: R:0 D:0 V:<null> >>> value 2: R:0 D:0 V:<null> >>> value 3: R:0 D:0 V:<null> >>> value 4: R:0 D:0 V:<null> >>> value 5: R:0 D:0 V:<null> >>> value 6: R:0 D:0 V:<null> >>> value 7: R:0 D:0 V:<null> >>> value 8: R:0 D:0 V:<null> >>> value 9: R:0 D:0 V:<null> >>> >>> BINARY col_d.map.value.value_tuple >>> -------------------------------------------------------------------------------- >>> *** row group 1 of 1, values 1 to 9 *** >>> value 1: R:0 D:0 V:<null> >>> value 2: R:0 D:0 V:<null> >>> value 3: R:0 D:0 V:<null> >>> value 4: R:0 D:0 V:<null> >>> value 5: R:0 D:0 V:<null> >>> value 6: R:0 D:0 V:<null> >>> value 7: R:0 D:0 V:<null> >>> value 8: R:0 D:0 V:<null> >>> value 9: R:0 D:0 V:<null> >>> >>> >>> I am happy to provide more information but any help is appreciated. >>> >>> >>> On Sun, Nov 16, 2014 at 7:40 PM, Sadhan Sood <sadhan.s...@gmail.com> >>> wrote: >>> >>>> Hi Cheng, >>>> >>>> I tried reading the parquet file(on which we were getting the >>>> exception) through parquet-tools and it is able to dump the file and I can >>>> read the metadata, etc. I also loaded the file through hive table and can >>>> run a table scan query on it as well. Let me know if I can do more to help >>>> resolve the problem, I'll run it through a debugger and see if I can get >>>> more information on it in the meantime. >>>> >>>> Thanks, >>>> Sadhan >>>> >>>> On Sun, Nov 16, 2014 at 4:35 AM, Cheng Lian <lian.cs....@gmail.com> >>>> wrote: >>>> >>>>> (Forgot to cc user mail list) >>>>> >>>>> >>>>> On 11/16/14 4:59 PM, Cheng Lian wrote: >>>>> >>>>> Hey Sadhan, >>>>> >>>>> Thanks for the additional information, this is helpful. Seems that >>>>> some Parquet internal contract was broken, but I'm not sure whether it's >>>>> caused by Spark SQL or Parquet, or even maybe the Parquet file itself was >>>>> damaged somehow. I'm investigating this. In the meanwhile, would you mind >>>>> to help to narrow down the problem by trying to scan exactly the same >>>>> Parquet file with some other systems (e.g. Hive or Impala)? If other >>>>> systems work, then there must be something wrong with Spark SQL. >>>>> >>>>> Cheng >>>>> >>>>> On Sun, Nov 16, 2014 at 1:19 PM, Sadhan Sood <sadhan.s...@gmail.com> >>>>> wrote: >>>>> >>>>>> Hi Cheng, >>>>>> >>>>>> Thanks for your response. Here is the stack trace from yarn logs: >>>>>> >>>>>> Caused by: java.lang.ArrayIndexOutOfBoundsException: -1 >>>>>> at java.util.ArrayList.elementData(ArrayList.java:418) >>>>>> at java.util.ArrayList.get(ArrayList.java:431) >>>>>> at parquet.io.GroupColumnIO.getLast(GroupColumnIO.java:95) >>>>>> at parquet.io.GroupColumnIO.getLast(GroupColumnIO.java:95) >>>>>> at >>>>>> parquet.io.PrimitiveColumnIO.getLast(PrimitiveColumnIO.java:80) >>>>>> at parquet.io.PrimitiveColumnIO.isLast(PrimitiveColumnIO.java:74) >>>>>> at >>>>>> parquet.io.RecordReaderImplementation.<init>(RecordReaderImplementation.java:282) >>>>>> at parquet.io.MessageColumnIO$1.visit(MessageColumnIO.java:131) >>>>>> at parquet.io.MessageColumnIO$1.visit(MessageColumnIO.java:96) >>>>>> at >>>>>> parquet.filter2.compat.FilterCompat$NoOpFilter.accept(FilterCompat.java:136) >>>>>> at >>>>>> parquet.io.MessageColumnIO.getRecordReader(MessageColumnIO.java:96) >>>>>> at >>>>>> parquet.hadoop.InternalParquetRecordReader.checkRead(InternalParquetRecordReader.java:126) >>>>>> at >>>>>> parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:193) >>>>>> ... 26 more >>>>>> >>>>>> >>>>>> On Sat, Nov 15, 2014 at 9:28 AM, Cheng Lian <lian.cs....@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Hi Sadhan, >>>>>>> >>>>>>> Could you please provide the stack trace of the >>>>>>> ArrayIndexOutOfBoundsException (if any)? The reason why the first >>>>>>> query succeeds is that Spark SQL doesn’t bother reading all data from >>>>>>> the >>>>>>> table to give COUNT(*). In the second case, however, the whole >>>>>>> table is asked to be cached lazily via the cacheTable call, thus >>>>>>> it’s scanned to build the in-memory columnar cache. Then thing went >>>>>>> wrong >>>>>>> while scanning this LZO compressed Parquet file. But unfortunately the >>>>>>> stack trace at hand doesn’t indicate the root cause. >>>>>>> >>>>>>> Cheng >>>>>>> >>>>>>> On 11/15/14 5:28 AM, Sadhan Sood wrote: >>>>>>> >>>>>>> While testing SparkSQL on a bunch of parquet files (basically used >>>>>>> to be a partition for one of our hive tables), I encountered this error: >>>>>>> >>>>>>> import org.apache.spark.sql.SchemaRDD >>>>>>> import org.apache.hadoop.fs.FileSystem; >>>>>>> import org.apache.hadoop.conf.Configuration; >>>>>>> import org.apache.hadoop.fs.Path; >>>>>>> >>>>>>> val sqlContext = new org.apache.spark.sql.SQLContext(sc) >>>>>>> >>>>>>> val parquetFileRDD = sqlContext.parquetFile(parquetFile) >>>>>>> parquetFileRDD.registerTempTable("xyz_20141109") >>>>>>> sqlContext.sql("SELECT count(*) FROM xyz_20141109").collect() <-- >>>>>>> works fine >>>>>>> sqlContext.cacheTable("xyz_20141109") >>>>>>> sqlContext.sql("SELECT count(*) FROM xyz_20141109").collect() <-- >>>>>>> fails with an exception >>>>>>> >>>>>>> parquet.io.ParquetDecodingException: Can not read value at 0 in >>>>>>> block -1 in file >>>>>>> hdfs://xxxxxxxx::9000/event_logs/xyz/20141109/part-00009359b87ae-a949-3ded-ac3e-3a6bda3a4f3a-r-00009.lzo.parquet >>>>>>> >>>>>>> at >>>>>>> parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:213) >>>>>>> >>>>>>> at >>>>>>> parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:204) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.NewHadoopRDD$anon$1.hasNext(NewHadoopRDD.scala:145) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39) >>>>>>> >>>>>>> at >>>>>>> scala.collection.Iterator$anon$11.hasNext(Iterator.scala:327) >>>>>>> >>>>>>> at >>>>>>> scala.collection.Iterator$anon$14.hasNext(Iterator.scala:388) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.sql.columnar.InMemoryRelation$anonfun$3$anon$1.hasNext(InMemoryColumnarTableScan.scala:136) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:248) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:163) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) >>>>>>> >>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:228) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263) >>>>>>> >>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263) >>>>>>> >>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263) >>>>>>> >>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:230) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) >>>>>>> >>>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:56) >>>>>>> >>>>>>> at >>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:195) >>>>>>> >>>>>>> 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: java.lang.ArrayIndexOutOfBoundsException >>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>> >> >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >> For additional commands, e-mail: user-h...@spark.apache.org >> > >