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Posted to user@spark.apache.org by Yana Kadiyska <ya...@gmail.com> on 2014/12/10 21:46:01 UTC
Trouble with cache() and parquet
Hi folks, wondering if anyone has thoughts. Trying to create something akin
to a materialized view (sqlContext is a HiveContext connected to external
metastore):
val last2HourRdd = sqlContext.sql(s"select * from mytable")
//last2HourRdd.first prints out a org.apache.spark.sql.Row = [...] with
valid data
last2HourRdd.cache()
//last2HourRdd.first now fails in an executor with the following:
In the driver:
14/12/10 20:24:01 INFO TaskSetManager: Starting task 0.1 in stage 25.0
(TID 35, iphere, NODE_LOCAL, 2170 bytes)
14/12/10 20:24:01 INFO TaskSetManager: Lost task 0.1 in stage 25.0
(TID 35) on executor iphere: java.lang.ClassCastException (null)
[duplicate 1]
And in executor:
14/12/10 19:56:57 ERROR Executor: Exception in task 0.1 in stage 20.0 (TID 27)
java.lang.ClassCastException: java.lang.String cannot be cast to
java.lang.Integer
at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
at org.apache.spark.sql.catalyst.expressions.MutableInt.update(SpecificMutableRow.scala:73)
at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.update(SpecificMutableRow.scala:231)
at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.setString(SpecificMutableRow.scala:236)
at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:328)
at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:310)
at org.apache.spark.sql.columnar.compression.RunLengthEncoding$Decoder.next(compressionSchemes.scala:168)
at org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor$class.extractSingle(CompressibleColumnAccessor.scala:37)
at org.apache.spark.sql.columnar.NativeColumnAccessor.extractSingle(ColumnAccessor.scala:64)
at org.apache.spark.sql.columnar.BasicColumnAccessor.extractTo(ColumnAccessor.scala:54)
at org.apache.spark.sql.columnar.NativeColumnAccessor.org$apache$spark$sql$columnar$NullableColumnAccessor$$super$extractTo(ColumnAccessor.scala:64)
at org.apache.spark.sql.columnar.NullableColumnAccessor$class.extractTo(NullableColumnAccessor.scala:52)
at org.apache.spark.sql.columnar.NativeColumnAccessor.extractTo(ColumnAccessor.scala:64)
at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:279)
at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:275)
at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
at scala.collection.Iterator$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.Limit$$anonfun$4.apply(basicOperators.scala:141)
at org.apache.spark.sql.execution.Limit$$anonfun$4.apply(basicOperators.scala:141)
at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
Any thoughts on this? Not sure if using the external metastore for the
inital pull is a problem or if I'm just hitting a bug.
Re: Trouble with cache() and parquet
Posted by Michael Armbrust <mi...@databricks.com>.
For many operations, Spark SQL will just pass the data through without
looking at it. Caching, in contrast, has to process the data so that we
can build up compressed column buffers. So the schema is mismatched in
both cases, but only the caching case shows it.
Based on the exception, it looks more like there is a type mismatch (the
metastore is reporting an Integer, but the parquet data is actually
producing a String).
On Thu, Dec 11, 2014 at 6:38 AM, Yana Kadiyska <ya...@gmail.com>
wrote:
>
> I see -- they are the same in design but the difference comes from
> partitioned Hive tables: when the RDD is generated by querying an external
> Hive metastore, the partition is appended as part of the row, and shows up
> as part of the schema. Can you shed some light on why this is a problem:
>
> last2HourRdd.first <-- works ok
> last2HourRdd.cache()
>
> last2HourRdd.first <-- does not work
>
>
>
> The first call shows K+1 columns (and so does print schema, where K
> columns are from the backing parquet files and the K+1st is the partition
> inlined. My impression is that the second call to .first would just force
> the cache() call and dump out that RDD to disk (with all of it's K+1
> columns and store the schema info, again with K+1 columns), and then just
> return a single entry. I am not sure why the fact that Hive metastore
> exposes an extra column over the raw parquet file is a problem since it
> does so both on the schema and in the
> data: last2HourRdd.schema.fields.length reports K+1, and so does
> last2HourRdd.first.length.
>
> I also tried
> calling sqlContext.applySchema(last2HourRdd,parquetFile.schema) before
> caching but it does not fix the issue. The only workaround I've come up
> with so far is to replace select * with a select <list_of_columns>. But I'd
> love to understand a little better why the cache call trips this scenario
>
>
>
> On Wed, Dec 10, 2014 at 3:50 PM, Michael Armbrust <mi...@databricks.com>
> wrote:
>
>> Have you checked to make sure the schema in the metastore matches the
>> schema in the parquet file? One way to test would be to just use
>> sqlContext.parquetFile(...) which infers the schema from the file instead
>> of using the metastore.
>>
>> On Wed, Dec 10, 2014 at 12:46 PM, Yana Kadiyska <ya...@gmail.com>
>> wrote:
>>
>>>
>>> Hi folks, wondering if anyone has thoughts. Trying to create something
>>> akin to a materialized view (sqlContext is a HiveContext connected to
>>> external metastore):
>>>
>>>
>>> val last2HourRdd = sqlContext.sql(s"select * from mytable")
>>> //last2HourRdd.first prints out a org.apache.spark.sql.Row = [...] with
>>> valid data
>>>
>>> last2HourRdd.cache()
>>> //last2HourRdd.first now fails in an executor with the following:
>>>
>>> In the driver:
>>>
>>> 14/12/10 20:24:01 INFO TaskSetManager: Starting task 0.1 in stage 25.0 (TID 35, iphere, NODE_LOCAL, 2170 bytes)
>>> 14/12/10 20:24:01 INFO TaskSetManager: Lost task 0.1 in stage 25.0 (TID 35) on executor iphere: java.lang.ClassCastException (null) [duplicate 1]
>>>
>>>
>>>
>>>
>>> And in executor:
>>>
>>> 14/12/10 19:56:57 ERROR Executor: Exception in task 0.1 in stage 20.0 (TID 27)
>>> java.lang.ClassCastException: java.lang.String cannot be cast to java.lang.Integer
>>> at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
>>> at org.apache.spark.sql.catalyst.expressions.MutableInt.update(SpecificMutableRow.scala:73)
>>> at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.update(SpecificMutableRow.scala:231)
>>> at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.setString(SpecificMutableRow.scala:236)
>>> at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:328)
>>> at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:310)
>>> at org.apache.spark.sql.columnar.compression.RunLengthEncoding$Decoder.next(compressionSchemes.scala:168)
>>> at org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor$class.extractSingle(CompressibleColumnAccessor.scala:37)
>>> at org.apache.spark.sql.columnar.NativeColumnAccessor.extractSingle(ColumnAccessor.scala:64)
>>> at org.apache.spark.sql.columnar.BasicColumnAccessor.extractTo(ColumnAccessor.scala:54)
>>> at org.apache.spark.sql.columnar.NativeColumnAccessor.org$apache$spark$sql$columnar$NullableColumnAccessor$$super$extractTo(ColumnAccessor.scala:64)
>>> at org.apache.spark.sql.columnar.NullableColumnAccessor$class.extractTo(NullableColumnAccessor.scala:52)
>>> at org.apache.spark.sql.columnar.NativeColumnAccessor.extractTo(ColumnAccessor.scala:64)
>>> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:279)
>>> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:275)
>>> at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>> at scala.collection.Iterator$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.Limit$$anonfun$4.apply(basicOperators.scala:141)
>>> at org.apache.spark.sql.execution.Limit$$anonfun$4.apply(basicOperators.scala:141)
>>> at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
>>> at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
>>> at org.apache.spark.scheduler.Task.run(Task.scala:56)
>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
>>> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>>> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>>
>>>
>>> Any thoughts on this? Not sure if using the external metastore for the
>>> inital pull is a problem or if I'm just hitting a bug.
>>>
>>>
>>>
>>
>
Re: Trouble with cache() and parquet
Posted by Yana Kadiyska <ya...@gmail.com>.
I see -- they are the same in design but the difference comes from
partitioned Hive tables: when the RDD is generated by querying an external
Hive metastore, the partition is appended as part of the row, and shows up
as part of the schema. Can you shed some light on why this is a problem:
last2HourRdd.first <-- works ok
last2HourRdd.cache()
last2HourRdd.first <-- does not work
The first call shows K+1 columns (and so does print schema, where K columns
are from the backing parquet files and the K+1st is the partition inlined.
My impression is that the second call to .first would just force the
cache() call and dump out that RDD to disk (with all of it's K+1 columns
and store the schema info, again with K+1 columns), and then just return a
single entry. I am not sure why the fact that Hive metastore exposes an
extra column over the raw parquet file is a problem since it does so both
on the schema and in the data: last2HourRdd.schema.fields.length reports
K+1, and so does last2HourRdd.first.length.
I also tried
calling sqlContext.applySchema(last2HourRdd,parquetFile.schema) before
caching but it does not fix the issue. The only workaround I've come up
with so far is to replace select * with a select <list_of_columns>. But I'd
love to understand a little better why the cache call trips this scenario
On Wed, Dec 10, 2014 at 3:50 PM, Michael Armbrust <mi...@databricks.com>
wrote:
> Have you checked to make sure the schema in the metastore matches the
> schema in the parquet file? One way to test would be to just use
> sqlContext.parquetFile(...) which infers the schema from the file instead
> of using the metastore.
>
> On Wed, Dec 10, 2014 at 12:46 PM, Yana Kadiyska <ya...@gmail.com>
> wrote:
>
>>
>> Hi folks, wondering if anyone has thoughts. Trying to create something
>> akin to a materialized view (sqlContext is a HiveContext connected to
>> external metastore):
>>
>>
>> val last2HourRdd = sqlContext.sql(s"select * from mytable")
>> //last2HourRdd.first prints out a org.apache.spark.sql.Row = [...] with
>> valid data
>>
>> last2HourRdd.cache()
>> //last2HourRdd.first now fails in an executor with the following:
>>
>> In the driver:
>>
>> 14/12/10 20:24:01 INFO TaskSetManager: Starting task 0.1 in stage 25.0 (TID 35, iphere, NODE_LOCAL, 2170 bytes)
>> 14/12/10 20:24:01 INFO TaskSetManager: Lost task 0.1 in stage 25.0 (TID 35) on executor iphere: java.lang.ClassCastException (null) [duplicate 1]
>>
>>
>>
>>
>> And in executor:
>>
>> 14/12/10 19:56:57 ERROR Executor: Exception in task 0.1 in stage 20.0 (TID 27)
>> java.lang.ClassCastException: java.lang.String cannot be cast to java.lang.Integer
>> at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
>> at org.apache.spark.sql.catalyst.expressions.MutableInt.update(SpecificMutableRow.scala:73)
>> at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.update(SpecificMutableRow.scala:231)
>> at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.setString(SpecificMutableRow.scala:236)
>> at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:328)
>> at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:310)
>> at org.apache.spark.sql.columnar.compression.RunLengthEncoding$Decoder.next(compressionSchemes.scala:168)
>> at org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor$class.extractSingle(CompressibleColumnAccessor.scala:37)
>> at org.apache.spark.sql.columnar.NativeColumnAccessor.extractSingle(ColumnAccessor.scala:64)
>> at org.apache.spark.sql.columnar.BasicColumnAccessor.extractTo(ColumnAccessor.scala:54)
>> at org.apache.spark.sql.columnar.NativeColumnAccessor.org$apache$spark$sql$columnar$NullableColumnAccessor$$super$extractTo(ColumnAccessor.scala:64)
>> at org.apache.spark.sql.columnar.NullableColumnAccessor$class.extractTo(NullableColumnAccessor.scala:52)
>> at org.apache.spark.sql.columnar.NativeColumnAccessor.extractTo(ColumnAccessor.scala:64)
>> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:279)
>> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:275)
>> at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>> at scala.collection.Iterator$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.Limit$$anonfun$4.apply(basicOperators.scala:141)
>> at org.apache.spark.sql.execution.Limit$$anonfun$4.apply(basicOperators.scala:141)
>> at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
>> at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
>> at org.apache.spark.scheduler.Task.run(Task.scala:56)
>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
>> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>
>>
>> Any thoughts on this? Not sure if using the external metastore for the
>> inital pull is a problem or if I'm just hitting a bug.
>>
>>
>>
>
Re: Trouble with cache() and parquet
Posted by Michael Armbrust <mi...@databricks.com>.
Have you checked to make sure the schema in the metastore matches the
schema in the parquet file? One way to test would be to just use
sqlContext.parquetFile(...) which infers the schema from the file instead
of using the metastore.
On Wed, Dec 10, 2014 at 12:46 PM, Yana Kadiyska <ya...@gmail.com>
wrote:
>
> Hi folks, wondering if anyone has thoughts. Trying to create something
> akin to a materialized view (sqlContext is a HiveContext connected to
> external metastore):
>
>
> val last2HourRdd = sqlContext.sql(s"select * from mytable")
> //last2HourRdd.first prints out a org.apache.spark.sql.Row = [...] with
> valid data
>
> last2HourRdd.cache()
> //last2HourRdd.first now fails in an executor with the following:
>
> In the driver:
>
> 14/12/10 20:24:01 INFO TaskSetManager: Starting task 0.1 in stage 25.0 (TID 35, iphere, NODE_LOCAL, 2170 bytes)
> 14/12/10 20:24:01 INFO TaskSetManager: Lost task 0.1 in stage 25.0 (TID 35) on executor iphere: java.lang.ClassCastException (null) [duplicate 1]
>
>
>
>
> And in executor:
>
> 14/12/10 19:56:57 ERROR Executor: Exception in task 0.1 in stage 20.0 (TID 27)
> java.lang.ClassCastException: java.lang.String cannot be cast to java.lang.Integer
> at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
> at org.apache.spark.sql.catalyst.expressions.MutableInt.update(SpecificMutableRow.scala:73)
> at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.update(SpecificMutableRow.scala:231)
> at org.apache.spark.sql.catalyst.expressions.SpecificMutableRow.setString(SpecificMutableRow.scala:236)
> at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:328)
> at org.apache.spark.sql.columnar.STRING$.setField(ColumnType.scala:310)
> at org.apache.spark.sql.columnar.compression.RunLengthEncoding$Decoder.next(compressionSchemes.scala:168)
> at org.apache.spark.sql.columnar.compression.CompressibleColumnAccessor$class.extractSingle(CompressibleColumnAccessor.scala:37)
> at org.apache.spark.sql.columnar.NativeColumnAccessor.extractSingle(ColumnAccessor.scala:64)
> at org.apache.spark.sql.columnar.BasicColumnAccessor.extractTo(ColumnAccessor.scala:54)
> at org.apache.spark.sql.columnar.NativeColumnAccessor.org$apache$spark$sql$columnar$NullableColumnAccessor$$super$extractTo(ColumnAccessor.scala:64)
> at org.apache.spark.sql.columnar.NullableColumnAccessor$class.extractTo(NullableColumnAccessor.scala:52)
> at org.apache.spark.sql.columnar.NativeColumnAccessor.extractTo(ColumnAccessor.scala:64)
> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:279)
> at org.apache.spark.sql.columnar.InMemoryColumnarTableScan$$anonfun$9$$anonfun$14$$anon$2.next(InMemoryColumnarTableScan.scala:275)
> at scala.collection.Iterator$$anon$13.next(Iterator.scala:372)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
> at scala.collection.Iterator$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.Limit$$anonfun$4.apply(basicOperators.scala:141)
> at org.apache.spark.sql.execution.Limit$$anonfun$4.apply(basicOperators.scala:141)
> at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
> at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
> at org.apache.spark.scheduler.Task.run(Task.scala:56)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>
>
> Any thoughts on this? Not sure if using the external metastore for the
> inital pull is a problem or if I'm just hitting a bug.
>
>
>