### python

#### Join two RDDs on custom function - SPARK

```Is it possible to Join two RDDs in Spark on a custom function?
I have two big RDDs with a string as key. I want to join them not using the classic Join but a custom function like:
def my_func(a,b):
return Lev.distance(a,b) < 2
result_rdd = rdd1.join(rdd2, my_func)
If it's not possible, is there any alternative that will continue to use the benefits of spark clusters?
I wrote something like this but pyspark will not be able to distribuite the work on my small cluster.
def custom_join(rdd1, rdd2, my_func):
a = rdd1.sortByKey().collect()
b = rdd2.sortByKey().collect()
i = 0
j = 0
res = []
while i < len(a) and j < len(b):
if my_func(a[i][0],b[j][0]):
res += [((a[i][0],b[j][0]),(a[i][1],b[j][1]))]
i+=1
j+=1
elif a[i][0] < b[j][0]:
i+=1
else:
j+=1
return sc.parallelize(res)
Thanks in advance (and sorry for my english because I'm italian)
```
```You can use cartesian and then filter based on conditions.
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext
x = sc.parallelize([("a", 1), ("b", 4)])
y = sc.parallelize([("a", 2), ("b", 3)])
def customFunc(x):
# You may use any condition here
return x[0][0] ==x[1][0]
print(x.join(y).collect()) # normal join
# replicating join with cartesian
print(x.cartesian(y).filter(customFunc).flatMap(lambda x:x).groupByKey().mapValues(tuple).collect())
Output:
[('b', (4, 3)), ('a', (1, 2))]
[('a', (1, 2)), ('b', (4, 3))]```

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