I have the following apache spark udf in scala:

```
val myFunc = udf {
  (userBias: Float, otherBiases: Map[Long, Float],
    userFactors: Seq[Float], context: Seq[String]) => 
    var result = Float.NaN

    if (userFactors != null) {
      var contexBias = 0f

      for (cc <- context) {
       contexBias += otherBiases(contextMapping(cc))
      }

      // definition of result
      // ...
    }
    result
}

```

Now I want to pass parameters to this function, however I always get the message Not Applicable due to the parameter `context`. I know that user defined functions take inputs by rows, and this function runs if I delete `context`... How to solve this issue? Why doesn't it read rows from `Array[Seq[String]]`, i.e. from `context`? Alternatively, it would be acceptable to pass`context` as `DataFrame` or something similar.

```
// context is Array[Seq[String]]
val a = sc.parallelize(Seq((1,2),(3,4))).toDF("a", "b")
val context = a.collect.map(_.toSeq.map(_.toString))

// userBias("bias"), otherBias("biases") and userFactors("features")
// have a type Column, while userBias... are DataFrames
myDataframe.select(dataset("*"),
                   myFunc(userBias("bias"),
                          otherBias("biases"),
                          userFactors("features"),
                          context)
                   .as($(newCol)))

```

**UPDATE:**

I tried the solution indicated in the answer of `zero323`, however still there is a small issue with `context: Array[Seq[String]]`. In particular the problem is with looping over this Array `for (cc <- context) { contexBias += otherBiases(contextMapping(cc)) }`. I should pass a String to `contextMapping`, not a `Seq[String]`:

```
  def myFunc(context: Array[Seq[String]]) = udf {
    (userBias: Float, otherBiases: Map[Long, Float],
     userFactors: Seq[Float]) =>
      var result = Float.NaN

      if (userFactors != null) {
        var contexBias = 0f
        for (cc <- context) {
          contexBias += otherBiases(contextMapping(cc))
        }

        // estimation of result

      }
      result
  }

```

Now I call it as follows:

```
myDataframe.select(dataset("*"),
                   myFunc(context)(userBias("bias"),
                                   otherBias("biases"),
                                   userFactors("features"))
           .as($(newCol)))

```