How to map over an Spark array without exploding it? - arrays

Question

MY case is that I have an array column that I'd like to filter. Consider the following:
+------------------------------------------------------+
| column|
+------------------------------------------------------+
|[prefix1-whatever, prefix2-whatever, prefix4-whatever]|
|[prefix1-whatever, prefix2-whatever, prefix3-whatever]|
|[prefix1-whatever, prefix2-whatever, prefix5-whatever]|
|[prefix1-whatever, prefix2-whatever, prefix3-whatever]|
+------------------------------------------------------+
I'd like to filter only columns containing prefix-4, prefix-5, prefix-6, prefix-7, [...]. So,using an "or" statement is not scalable here.
Of course, I can just:
val prefixesList = List("prefix-4", "prefix-5", "prefix-6", "prefix-7")
df
.withColumn("prefix", explode($"column"))
.withColumn("prefix", split($"prefix", "\\-").getItem(0))
.withColumn("filterColumn", $"prefix".inInCollection(prefixesList))
But that involves exploding, which I want to avoid. My plan right now is to define an array column from prefixesList, and then use array_intersect to filter it - for this to work, though, I have to get rid of the -whatever part (which is, obviously, different for each entry). Was this a Scala array, I could easily do a map over it. But, being it a Spark Array, I don't know if that is possible.
TL;DR I have a dataframe containing an array column. I'm trying to manipulate it and filter it without exploding (because, if I do explode, I'll have to manipulate it later to reverse the explode, and I'd like to avoid it).
Can I achieve that without exploding? If so, how?

Answer 1

Not sure if I understood your question correctly: you want to keep all lines that do not contain any of the prefixes in prefixesList?
If so, you can write your own filter function:
def filterPrefixes (row: Row) : Boolean = {
for( s <- row.getSeq[String](0)) {
for( p <- Seq("prefix4", "prefix5", "prefix6", "prefix7")) {
if( s.startsWith(p) ) {
return false
}
}
}
return true
}
and then use it as argument for the filter call:
df.filter(filterPrefixes _)
.show(false)
prints
+------------------------------------------------------+
|column |
+------------------------------------------------------+
|[prefix1-whatever, prefix2-whatever, prefix3-whatever]|
|[prefix1-whatever, prefix2-whatever, prefix3-whatever]|
+------------------------------------------------------+

Answer 2

It's relatively trivial to convert the Dataframe to a Dataset[Array[String]], and map over those arrays as whole elements. The basic idea is that you can iterate over your list of arrays easily, without having to flatten the entire dataset.
val df = Seq(Seq("prefix1-whatever", "prefix2-whatever", "prefix4-whatever"),
Seq("prefix1-whatever", "prefix2-whatever", "prefix3-whatever"),
Seq("prefix1-whatever", "prefix2-whatever", "prefix5-whatever"),
Seq("prefix1-whatever", "prefix2-whatever", "prefix3-whatever")
).toDF("column")
val pl = List("prefix4", "prefix5", "prefix6", "prefix7")
val df2 = df.as[Array[String]].map(a => {
a.flatMap(s => {
val start = s.split("-")(0)
if(pl.contains(start)) {
Some(s)
} else {
None
}
})
}).toDF("column")
df2.show(false)
The above code results in:
+------------------+
|column |
+------------------+
|[prefix4-whatever]|
|[] |
|[prefix5-whatever]|
|[] |
+------------------+
I'm not entirely sure how this would compare performance wise to actually flattening and recombining the data set. Doing this misses any catalyst optimizations, but avoids a lot of unnecessary shuffling of data.
P.S. I corrected for a minor issue in your prefix list, since "prefix-N" didn't match the data's pattern.

Answer 3

You can achieve it using SQL API. If you want to keep only rows that contain any of values prefix-4, prefix-5, prefix-6, prefix-7 you could use arrays_overlap function. Otherwise, if you want to keep rows that contain all of your values you could try array_intersect and then check if its size is equal to count of your values.
val df = Seq(
Seq("prefix1-a", "prefix2-b", "prefix3-c", "prefix4-d"),
Seq("prefix4-e", "prefix5-f", "prefix6-g", "prefix7-h", "prefix8-i"),
Seq("prefix6-a", "prefix7-b", "prefix8-c", "prefix9-d"),
Seq("prefix8-d", "prefix9-e", "prefix10-c", "prefix12-a")
).toDF("arr")
val schema = StructType(Seq(
StructField("arr", ArrayType.apply(StringType)),
StructField("arr2", ArrayType.apply(StringType))
))
val encoder = RowEncoder(schema)
val df2 = df.map(s =>
(s.getSeq[String](0).toArray, s.getSeq[String](0).map(s => s.substring(0, s.indexOf("-"))).toArray)
).map(s => RowFactory.create(s._1, s._2))(encoder)
val prefixesList = Array("prefix4", "prefix5", "prefix6", "prefix7")
val prefixesListSize = prefixesList.size
val prefixesListCol = lit(prefixesList)
df2.select('arr,'arr2,
arrays_overlap('arr2,prefixesListCol).as("OR"),
(size(array_intersect('arr2,prefixesListCol)) === prefixesListSize).as("AND")
).show(false)
it will give you:
+-------------------------------------------------------+---------------------------------------------+-----+-----+
|arr |arr2 |OR |AND |
+-------------------------------------------------------+---------------------------------------------+-----+-----+
|[prefix1-a, prefix2-b, prefix3-c, prefix4-d] |[prefix1, prefix2, prefix3, prefix4] |true |false|
|[prefix4-e, prefix5-f, prefix6-g, prefix7-h, prefix8-i]|[prefix4, prefix5, prefix6, prefix7, prefix8]|true |true |
|[prefix6-a, prefix7-b, prefix8-c, prefix9-d] |[prefix6, prefix7, prefix8, prefix9] |true |false|
|[prefix8-d, prefix9-e, prefix10-c, prefix12-a] |[prefix8, prefix9, prefix10, prefix12] |false|false|
+-------------------------------------------------------+---------------------------------------------+-----+-----+
so finally you can use:
df2.filter(size(array_intersect('arr2,prefixesListCol)) === prefixesListSize).show(false)
and you will get below result:
+-------------------------------------------------------+---------------------------------------------+
|arr |arr2 |
+-------------------------------------------------------+---------------------------------------------+
|[prefix4-e, prefix5-f, prefix6-g, prefix7-h, prefix8-i]|[prefix4, prefix5, prefix6, prefix7, prefix8]|
+-------------------------------------------------------+---------------------------------------------+