Erik Erlandson created SPARK-21277:
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Summary: Spark is invoking an incorrect serializer after UDAF
completion
Key: SPARK-21277
URL: https://issues.apache.org/jira/browse/SPARK-21277
Project: Spark
Issue Type: Bug
Components: Optimizer, SQL
Affects Versions: 2.1.0
Reporter: Erik Erlandson
I'm writing a UDAF that also requires some custom UDT implementations. The
UDAF (and UDT) logic appear to be executing properly up through the final UDAF
call to the {{evaluate}} method. However, after the evaluate method completes,
I am seeing the UDT {{deserialize}} method being called another time, however
this time it is being invoked on data that wasn't produced by my corresponding
{{serialize}} method, and it is crashing. The following REPL output shows the
execution and completion of {{evaluate}}, and then another call to
{{deserialize}} that sees some kind of {{UnsafeArrayData}} object that my
serialization doesn't produce, and so the method fails:
{code}entering evaluate
a=
[[0.5,10,2,org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@f1813f2c,org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@b3587fc7],[0.5,10,4,org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@d3065487,org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@f1a5ace9],[0.5,10,4,org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@d01fbbcf,org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@f1a5ace9]]
leaving evaluate
a= org.apache.spark.sql.catalyst.expressions.UnsafeArrayData@27d73513
java.lang.RuntimeException: Error while decoding:
java.lang.UnsupportedOperationException: Not supported on UnsafeArrayData.
createexternalrow(newInstance(class
org.apache.spark.isarnproject.sketches.udt.TDigestArrayUDT).deserialize,
StructField(tdigestmlvecudaf(features),TDigestArrayUDT,true))
{code}
To reproduce, check out the branch {{first-cut}} of {{isarn-sketches-spark}}:
https://github.com/erikerlandson/isarn-sketches-spark/tree/first-cut
Then invoke {{xsbt console}} to get a REPL with a spark session. In the REPL
execute:
{code}
Welcome to Scala 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_131).
Type in expressions for evaluation. Or try :help.
scala> val training = spark.createDataFrame(Seq((1.0, Vectors.dense(0.0, 1.1,
0.1)),(0.0, Vectors.dense(2.0, 1.0, -1.0)),(0.0, Vectors.dense(2.0, 1.3,
1.0)),(1.0, Vectors.dense(0.0, 1.2, -0.5)))).toDF("label", "features")
training: org.apache.spark.sql.DataFrame = [label: double, features: vector]
scala> val featTD = training.agg(TDigestMLVecUDAF(0.5,10)(training("features")))
featTD: org.apache.spark.sql.DataFrame = [tdigestmlvecudaf(features):
tdigestarray]
scala> featTD.first
{code}
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