Issue with UDF on a column of Vectors in PySpark DataFrame
Question: 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?

**EDIT**

So it was pointed out on the mailing list that this is a [known
issue](https://issues.apache.org/jira/browse/SPARK-7902). Going to accept the
answer from @hyim since it does provider a temporary workaround for dense
vectors.


Answer: In spark-sql, vectors are treated (type, size, indices, value) tuple.

You can use udf on vectors with pyspark. Just modify some code to work with
values in vector type.

    
    
    vector_udf = udf(lambda vector: sum(vector[3]), DoubleType())
    
    df.withColumn('feature_sums', vector_udf(df.features)).first()
    

<https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala>

