I am having trouble using a UDF on a column of Vectors in PySpark which can be illustrated here:
from pyspark import SparkContext from pyspark.sql import Row from pyspark.sql.types import DoubleType from pyspark.sql.functions import udf from pyspark.mllib.linalg import Vectors FeatureRow = Row('id', 'features') data = sc.parallelize([(0, Vectors.dense([9.7, 1.0, -3.2])), (1, Vectors.dense([2.25, -11.1, 123.2])), (2, Vectors.dense([-7.2, 1.0, -3.2]))]) df = data.map(lambda r: FeatureRow(*r)).toDF() vector_udf = udf(lambda vector: sum(vector), DoubleType()) df.withColumn('feature_sums', vector_udf(df.features)).first()
This fails with the following stack trace:
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 31.0 failed 1 times, most recent failure: Lost task 5.0 in stage 31.0 (TID 95, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "/Users/colin/src/spark/python/lib/pyspark.zip/pyspark/worker.py", line 111, in main process() File "/Users/colin/src/spark/python/lib/pyspark.zip/pyspark/worker.py", line 106, in process serializer.dump_stream(func(split_index, iterator), outfile) x1 File "/Users/colin/src/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream vs = list(itertools.islice(iterator, batch)) File "/Users/colin/src/spark/python/pyspark/sql/functions.py", line 469, in <lambda> func = lambda _, it: map(lambda x: f(*x), it) File "/Users/colin/pokitdok/spark_mapper/spark_mapper/filters.py", line 143, in <lambda> TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'
Looking at what gets passed to the UDF, there seems to be something strange. The argument passed should be a Vector, but instead it gets passed a Python tuple like this:
(1, None, None, [9.7, 1.0, -3.2])
Is it not possible to use UDFs on DataFrame columns of Vectors?
So it was pointed out on the mailing list that this is a known issue. Going to accept the answer from @hyim since it does provider a temporary workaround for dense vectors.