Check all the elements of an array present in another array

Question

I have a df1 Spark dataframe

id     transactions
1      [1, 2, 3, 5]
2      [1, 2, 3, 6]
3      [1, 2, 9, 8]
4      [1, 2, 5, 6]

root
 |-- id: int (nullable = true)
 |-- transactions: array (nullable = false)
     |-- element: int(containsNull = true)
 None

I have a df2 Spark dataframe

items   cost
  [1]    1.0
  [2]    1.0
 [2, 1]  2.0
 [6, 1]  2.0

root
 |-- items: array (nullable = false)
    |-- element: int (containsNull = true)
 |-- cost: int (nullable = true)
 None

I want to check whether all the array elements from items column are in transactions column.

The first row ([1, 2, 3, 5]) contains [1],[2],[2, 1] from items column. Hence I need to sum up their corresponding costs: 1.0 + 1.0 + 2.0 = 4.0

The output I want is

id     transactions    score
1      [1, 2, 3, 5]   4.0
2      [1, 2, 3, 6]   6.0
3      [1, 2, 9, 8]   4.0
4      [1, 2, 5, 6]   6.0

I tried using a loop with collect()/toLocalIterator but it does not seem to be efficient. I will have large data.

I think creating an UDF like this will solve it. But it throws an error.

from pyspark.sql.functions import udf
def containsAll(x, y):
  result = all(elem in x for elem in y)

  if result:
    print("Yes, transactions contains all items")    
  else :
    print("No")

contains_udf = udf(containsAll)
dataFrame.withColumn("result", contains_udf(df2.items, df1.transactions)).show()

Is there any other way around?

Answer 1

A valid udf before 2.4 (note that it hast to return something

from pyspark.sql.functions import udf

@udf("boolean")
def contains_all(x, y):
    if x is not None and y is not None:
        return set(y).issubset(set(x))

In 2.4 or later no udf is required:

from pyspark.sql.functions import array_intersect, size

def contains_all(x, y):
    return size(array_intersect(x, y)) == size(y)

Usage:

from pyspark.sql.functions import col, sum as sum_, when

df1 = spark.createDataFrame(
   [(1, [1, 2, 3, 5]), (2, [1, 2, 3, 6]), (3, [1, 2, 9, 8]), (4, [1, 2, 5, 6])],
   ("id", "transactions")
)

df2 = spark.createDataFrame(
    [([1], 1.0), ([2], 1.0), ([2, 1], 2.0), ([6, 1], 2.0)],
    ("items", "cost")
)


(df1
    .crossJoin(df2).groupBy("id", "transactions")
    .agg(sum_(when(
        contains_all("transactions", "items"), col("cost")
    )).alias("score"))
    .show())

The result:

+---+------------+-----+                                                        
| id|transactions|score|
+---+------------+-----+
|  1|[1, 2, 3, 5]|  4.0|
|  4|[1, 2, 5, 6]|  6.0|
|  2|[1, 2, 3, 6]|  6.0|
|  3|[1, 2, 9, 8]|  4.0|
+---+------------+-----+

If df2 is small it could preferred to use it as a local variable:

items = sc.broadcast([
    (set(items), cost) for items, cost in df2.select("items", "cost").collect()
])

def score(y):
    @udf("double")
    def _(x):
        if x is not None:
            transactions = set(x)
            return sum(
                cost for items, cost in y.value 
                if items.issubset(transactions))
    return _


df1.withColumn("score", score(items)("transactions")).show()

+---+------------+-----+
| id|transactions|score|
+---+------------+-----+
|  1|[1, 2, 3, 5]|  4.0|
|  2|[1, 2, 3, 6]|  6.0|
|  3|[1, 2, 9, 8]|  4.0|
|  4|[1, 2, 5, 6]|  6.0|
+---+------------+-----+

Finally it is possible to explode and join

from pyspark.sql.functions import explode

costs = (df1
    # Explode transactiosn
    .select("id", explode("transactions").alias("item"))
    .join(
        df2 
            # Add id so we can later use it to identify source
            .withColumn("_id", monotonically_increasing_id().alias("_id"))
             # Explode items
            .select(
                "_id", explode("items").alias("item"), 
                # We'll need size of the original items later
                size("items").alias("size"), "cost"), 
         ["item"])
     # Count matches in groups id, items
     .groupBy("_id", "id", "size", "cost")
     .count()
     # Compute cost
     .groupBy("id")
     .agg(sum_(when(col("size") == col("count"), col("cost"))).alias("score")))

costs.show()

+---+-----+                                                                      
| id|score|
+---+-----+
|  1|  4.0|
|  3|  4.0|
|  2|  6.0|
|  4|  6.0|
+---+-----+

and then join the result back with original df1,

df1.join(costs, ["id"])

but that's much less straightforward solution, and requires multiple shuffles. It might be still preferable over Cartesian product (crossJoin), but it will depend on the actual data.

Answer 2

Spark 3.0+ has one more option using forall

F.expr("forall(look_for, x -> array_contains(look_in, x))")

^{Syntax alternative for Spark 3.1+ - F.forall('look_for', lambda x: F.array_contains('look_in', x))}

---

Comparing this with the option (array_intersect from Spark 2.4)

F.size(F.array_intersect('look_for', 'look_in')) == F.size('look_for')

They differ in dealing with duplicates and null values.

from pyspark.sql import functions as F
df = spark.createDataFrame(
    [(['a', 'b', 'c'], ['a']),
     (['a', 'b', 'c'], ['d']),
     (['a', 'b', 'c'], ['a', 'b']),
     (['a', 'b', 'c'], ['c', 'd']),
     (['a', 'b', 'c'], ['a', 'b', 'c']),
     (['a', 'b', 'c'], ['a', None]),
     (['a', 'b',None], ['a', None]),
     (['a', 'b',None], ['a']),
     (['a', 'b',None], [None]),
     (['a', 'b', 'c'], None),
     (None, ['a']),
     (None, None),
     (['a', 'b', 'c'], ['a', 'a']),
     (['a', 'a', 'a'], ['a']),
     (['a', 'a', 'a'], ['a', 'a', 'a']),
     (['a', 'a', 'a'], ['a', 'a',None]),
     (['a', 'a',None], ['a', 'a', 'a']),
     (['a', 'a',None], ['a', 'a',None])],
    ['look_in', 'look_for'])
df = df.withColumn('spark_3_0', F.expr("forall(look_for, x -> array_contains(look_in, x))"))
df = df.withColumn('spark_2_4', F.size(F.array_intersect('look_for', 'look_in')) == F.size('look_for'))

Removing nulls from inside arrays may be useful in some cases, it's easiest done using array_compact from Spark 3.4+.