from pyspark.sql import SparkSession, functions as F
from pyspark.sql.types import StructType, StructField, StringType, ArrayType


class ProfilePipeline:
    def __init__(
        self,
        name="Mariana Costa",
        role="data and more stuff",
        education="B.E. Production Engineering - UFRN (Brazil)",
        interests=("music", "HQ", "data literacy"),
        stack=("PySpark", "Databricks", "Terraform", "SQL", "Python"),
        app_name="mrncstt-profile",
    ):
        self.name = name
        self.role = role
        self.education = education
        self.interests = list(interests)
        self.stack = list(stack)
        self.spark = SparkSession.builder.appName(app_name).getOrCreate()

    def build_profile(self):
        schema = StructType([
            StructField("name", StringType()),
            StructField("role", StringType()),
            StructField("education", StringType()),
            StructField("interests", ArrayType(StringType())),
            StructField("stack", ArrayType(StringType())),
        ])

        data = [(self.name, self.role, self.education, self.interests, self.stack)]

        self.df_profile = (
            self.spark.createDataFrame(data, schema=schema)
            .withColumn("interests", F.array_join("interests", ", "))
            .withColumn("stack", F.array_join("stack", " | "))
            .withColumn("updated_at", F.current_date())
        )

    def stop(self):
        self.spark.stop()


def process_pipeline():
    pipeline = ProfilePipeline()
    pipeline.build_profile()
    display(pipeline.df_profile)
    pipeline.stop()


process_pipeline()