Hi @singhanuj2803,

It is correct that Spark SQL does not natively support recursive Common Table Expressions (CTEs). However, there are some workarounds and alternative methods you can use to achieve similar results.

1. Using DataFrame API with Loops: You can use the DataFrame API in combination with loops in Scala or Python to simulate recursive queries. This involves iteratively applying transformations until a condition is met.
2. Using Temporary Tables: Another approach is to use temporary tables to store intermediate results and repeatedly update these tables until the desired result is achieved.
3. User-Defined Functions (UDFs): You can implement the recursive logic within a UDF in Scala or Python. This allows you to encapsulate the recursive logic and apply it to your DataFrame.
4. Workaround Example: Here is a simplified example of how you might implement a recursive query using a loop in PySpark:

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, expr

spark = SparkSession.builder.appName("RecursiveCTE").getOrCreate()

# Initial DataFrame

df = spark.createDataFrame([(1,)], ["id"])

# Recursive logic

max_iterations = 10

for _ in range(max_iterations):

     df = df.union(df.select(expr("id + 1"))).distinct()

df.show()

Please refer to: https://sqlandhadoop.com/how-to-implement-recursive-queries-in-spark/