topic Re: Transpile a SQL Script into PySpark DataFrame API equivalent code in Data Engineering

Transpile a SQL Script into PySpark DataFrame API equivalent code

thecodecache — Wed, 08 May 2024 07:09:52 GMT

Input SQL Script (assume any dialect) :

SELECT b.se10,
       b.se3,
       b.se_aggrtr_indctr,
       b.key_swipe_ind
FROM
  (SELECT se10,
          se3,
          se_aggrtr_indctr,
          ROW_NUMBER() OVER (PARTITION BY SE10
                             ORDER BY se_aggrtr_indctr DESC) AS rn,
          key_swipe_ind
   FROM fraud_details_data_whole
   GROUP BY se10,
            se3,
            se_aggrtr_indctr ,
            key_swipe_ind) b
WHERE b.rn<2

Output PySpark Code using DataFrame API :

from pyspark.sql import SparkSession
from pyspark.sql.window import Window
from pyspark.sql.functions import row_number

# Create a SparkSession
spark = SparkSession.builder \
    .appName("TranspileSQLExample") \
    .getOrCreate()

# Sample data (replace with your actual DataFrame)
data = [
    ("se10_value1", "se3_value1", "aggrtr_value1", "swipe_value1"),
    ("se10_value1", "se3_value2", "aggrtr_value2", "swipe_value2"),
    ("se10_value2", "se3_value3", "aggrtr_value3", "swipe_value3"),
    ("se10_value2", "se3_value4", "aggrtr_value4", "swipe_value4"),
    ("se10_value3", "se3_value5", "aggrtr_value5", "swipe_value5"),
    ("se10_value3", "se3_value6", "aggrtr_value6", "swipe_value6")
]

# Create DataFrame
fraud_details_data_whole = spark.createDataFrame(data, ["se10", "se3", "se_aggrtr_indctr", "key_swipe_ind"])

# Define Window specification for row_number() function
windowSpec = Window.partitionBy("se10").orderBy(fraud_details_data_whole["se_aggrtr_indctr"].desc())

# Add row number column
fraud_details_data_whole = fraud_details_data_whole.withColumn("rn", row_number().over(windowSpec))

# Filter rows where rn < 2
result_df = fraud_details_data_whole.filter("rn < 2")

# Select required columns
result_df = result_df.select("se10", "se3", "se_aggrtr_indctr", "key_swipe_ind")

# Show the result DataFrame
result_df.show()

Is there any way to translate the above Sql Query into its equivalent PySpark DataFrame API code? The result must be equal when we execute Sql Script and its transpiled PySpark code separately.

Kindly suggest

https://stackoverflow.com/q/78446543/6842300

Re: Transpile a SQL Script into PySpark DataFrame API equivalent code

thecodecache — Tue, 21 May 2024 12:25:56 GMT

Hi @Retired_mod, Thanks for your response.
I'm looking for a utility or an automated way of translating any generic SQL into PySpark DataFrame code.

So, the translate function should look like below:

def translate(input_sql):
# translate/convert it into pyspark dataframe code
return pyspark_code

Please suggest

Re: Transpile a SQL Script into PySpark DataFrame API equivalent code

MathieuDB — Fri, 17 Jan 2025 18:17:33 GMT

Hello @thecodecache ,

Have a look the SQLGlot project: https://github.com/tobymao/sqlglot?tab=readme-ov-file#faq

It can easily transpile SQL to Spark SQL, like that:

import sqlglot from pyspark.sql import SparkSession # Initialize Spark session spark = SparkSession.builder.appName("SQLGlot Example").getOrCreate() # Original SQL query sql_query = """ SELECT bar.a, b + 1 AS b FROM bar JOIN baz ON bar.a = baz.a WHERE bar.a > 1 """ # Convert SQL to Spark SQL dialect spark_sql = sqlglot.transpile(sql_query, read="generic", write="spark")[0] # Create a DataFrame from the Spark SQL query df = spark.sql(spark_sql) # Show the resulting DataFrame df.show()