Streamline Your Data Processing: Unlocking the Power of PySpark Auto-Generated Code

DataFrame 1:

1.California,39.5,163, Sacramento, Los Angeles
2.Texas,29,268, Austin, Houston
3.New York,19.5,54, Albany, New York City
4.Florida,21.5,65, Tallahassee, Jacksonville
5.Illinois,12.8,57, Springfield, Chicago
6.Pennsylvania,12.8,46, Harrisburg, Philadelphia
7.Ohio,11.8,44, Columbus, Columbus
8.Georgia,10.8,59, Atlanta, Atlanta
9.Michigan,10,97, Lansing, Detroit
10.North Carolina,10.5,53, Raleigh, Charlotte

DataFrame 2 :

1.State,
2.Population (millions)
3.Area (sq. miles)
4.Capital
5.Largest City

from github import Github
from tempfile import NamedTemporaryFile
from pyspark.sql.types import StructType

def generate_spark_join(dataframe1_name, dataframe2_name, dataframe1_file_path, dataframe2_file_path, join_column):
    schema1 = StructType() \
        .add("col1", "string") \
        .add("col2", "string") \
        .add("col3", "string") \
        .add("col4", "string") \
        .add("col5", "string")

    schema2 = StructType() \
        .add("col1", "string") \
        .add("col2", "string")

    spark_code = f"""
from pyspark.sql import SparkSession

# Create Spark session
spark = SparkSession.builder \
    .appName("SparkJoinTransformation") \
    .getOrCreate()

# Read text files and create DataFrames
{dataframe1_name} = spark.read.option("header", "true").csv("{dataframe1_file_path}",schema={schema1})
{dataframe2_name} = spark.read.option("header", "true").csv("{dataframe2_file_path}",schema={schema2})

# Perform join transformation
{dataframe1_name}_joined = {dataframe1_name}.join({dataframe2_name}, on='{join_column}', how='inner')

# Show the resulting DataFrame
{dataframe1_name}_joined.show()

"""
    return spark_code

def create_github_release(repo_owner, repo_name, access_token, tag_name, release_name, release_body, asset_file_path):
    g = Github(access_token)
    repo = g.get_repo(f"{repo_owner}/{repo_name}")
    release = repo.create_git_release(tag=tag_name, name=release_name, message=release_body)
    release.upload_asset(asset_file_path)

def main():
    print("Welcome to the Spark Join Transformation Generator!")

    # Read text files and create DataFrames
    dataframe1_file_path = input("Enter the path to the first text file: ")
    dataframe1_name = input("Enter the name for the first DataFrame: ")

    dataframe2_file_path = input("Enter the path to the second text file: ")
    dataframe2_name = input("Enter the name for the second DataFrame: ")

    # Prompt user for join details
    join_column = input("Enter the column to perform the join on: ")
    output_dataframe_name = input("Enter the name for the output DataFrame: ")

    # Generate Spark code for join transformation
    spark_join_code = generate_spark_join(dataframe1_name, dataframe2_name, dataframe1_file_path, dataframe2_file_path, join_column)
    print("\nGenerated Spark Code:")
    print(spark_join_code)

    # Write Spark code to a temporary text file
    with NamedTemporaryFile(mode='w', delete=False) as temp_file:
        temp_file.write(spark_join_code)
        asset_file_path = temp_file.name

    # Deploy the generated code to GitHub as an asset file
    deploy_to_github = input("Do you want to deploy the generated code to GitHub? (yes/no): ")
    if deploy_to_github.lower() == "yes":
        repo_owner = input("Enter the owner of the GitHub repository: ")
        repo_name = input("Enter the name of the GitHub repository: ")
        access_token = input("Enter your GitHub personal access token: ")
        tag_name = input("Enter the tag name for the release: ")
        release_name = input("Enter the name for the release: ")
        release_body = input("Enter the body for the release: ")

        create_github_release(repo_owner, repo_name, access_token, tag_name, release_name, release_body, asset_file_path)
        print("Release created successfully on GitHub!")
    else:
        print("Deployment to GitHub skipped.")

if __name__ == "__main__":
    main()

The above Code are published on github.

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