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   "source": [
    "# Transformations for all schemas with a given column using DataSetImplements\n",
    "\n",
    "Let's illustrate this with an example! First, we'll define some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "91c48423",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession.Builder().config(\"spark.ui.showConsoleProgress\", \"false\").getOrCreate()\n",
    "spark.sparkContext.setLogLevel(\"ERROR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99c453be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import LongType, StringType\n",
    "from typedspark import (\n",
    "    Schema,\n",
    "    Column,\n",
    "    create_empty_dataset,\n",
    ")\n",
    "\n",
    "\n",
    "class Person(Schema):\n",
    "    name: Column[StringType]\n",
    "    age: Column[LongType]\n",
    "    job: Column[StringType]\n",
    "\n",
    "\n",
    "class Pet(Schema):\n",
    "    name: Column[StringType]\n",
    "    age: Column[LongType]\n",
    "    type: Column[StringType]\n",
    "\n",
    "\n",
    "class Fruit(Schema):\n",
    "    type: Column[StringType]\n",
    "\n",
    "\n",
    "person = create_empty_dataset(spark, Person)\n",
    "pet = create_empty_dataset(spark, Pet)\n",
    "fruit = create_empty_dataset(spark, Fruit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca634c83",
   "metadata": {},
   "source": [
    "Now, suppose we want to define a function `birthday()` that works on all schemas that contain the column `age`. With `DataSet`, we'd have to specifically indicate which schemas contain the `age` column. We could do this with for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c948c8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import TypeVar, Union\n",
    "\n",
    "from typedspark import DataSet, transform_to_schema\n",
    "\n",
    "T = TypeVar(\"T\", bound=Union[Person, Pet])\n",
    "\n",
    "\n",
    "def birthday(df: DataSet[T]) -> DataSet[T]:\n",
    "    return transform_to_schema(\n",
    "        df,\n",
    "        df.typedspark_schema,\n",
    "        {Person.age: Person.age + 1},\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9784804d",
   "metadata": {},
   "source": [
    "This can get tedious if the list of schemas with the column `age` changes, for example because new schemas are added, or because the `age` column is removed from a schema! It's also not great that we're using `Person.age` here to define the `age` column...\n",
    "\n",
    "Fortunately, we can do better! Consider the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2436106",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Protocol\n",
    "\n",
    "from typedspark import DataSetImplements\n",
    "\n",
    "\n",
    "class Age(Schema, Protocol):\n",
    "    age: Column[LongType]\n",
    "\n",
    "\n",
    "T = TypeVar(\"T\", bound=Schema)\n",
    "\n",
    "\n",
    "def birthday(df: DataSetImplements[Age, T]) -> DataSet[T]:\n",
    "    return transform_to_schema(\n",
    "        df,\n",
    "        df.typedspark_schema,\n",
    "        {Age.age: Age.age + 1},\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2088742c",
   "metadata": {},
   "source": [
    "Here, we define `Age` to be both a `Schema` and a `Protocol` ([PEP-0544](https://peps.python.org/pep-0544/)). \n",
    "\n",
    "We then define `birthday()` to:\n",
    "\n",
    "1. Take as an input `DataSetImplements[Age, T]`: a `DataSet` that implements the protocol `Age` as `T`. \n",
    "2. Return a `DataSet[T]`: a `DataSet` of the same type as the one that was provided.\n",
    "\n",
    "Let's see this in action!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5658210f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns a DataSet[Person]\n",
    "happy_person = birthday(person)\n",
    "\n",
    "# returns a DataSet[Pet]\n",
    "happy_pet = birthday(pet)\n",
    "\n",
    "try:\n",
    "    # Raises a linting error:\n",
    "    # Argument of type \"DataSet[Fruit]\" cannot be assigned to\n",
    "    # parameter \"df\" of type \"DataSetImplements[Age, T@birthday]\"\n",
    "    birthday(fruit)\n",
    "except Exception as e:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bfb99ed",
   "metadata": {},
   "source": []
  }
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