12

Aufgabe 10/Aufgabe10.py

@@ -5,7 +5,6 @@ import matplotlib.pyplot as plt
 
 HDFSPATH = "hdfs://193.174.205.250:54310/"
 
-
 def read_parquets(spark: SparkSession):
 	stations_path = HDFSPATH + "home/heiserervalentin/german_stations.parquet"
 	products_path = HDFSPATH + "home/heiserervalentin/german_stations_data.parquet"

Aufgabe 11/Aufgabe11.py

@@ -1,366 +1,187 @@
+from __future__ import annotations
 from sparkstart import scon, spark
 from pyspark.sql import SparkSession
-import time
 import matplotlib.pyplot as plt
-import pandas as pd
 
-HDFSPATH_STATIONS = "hdfs://193.174.205.250:54310/home/heiserervalentin/"
-HDFSPATH_STOCKS = "hdfs://193.174.205.250:54310/stocks/"
+HDFSPATH = "hdfs://193.174.205.250:54310/"
 
-def init_view_stations(spark):
-    """Lädt die Stationsdaten für Aufgabe 11"""
-    s_path = HDFSPATH_STATIONS + "german_stations.parquet"
-    d_path = HDFSPATH_STATIONS + "german_stations_data.parquet"
+def read_parquet_tables(spark: SparkSession) -> None:
+    # Use your specific paths from Aufgabe 9
+    stations_path = HDFSPATH + "home/heiserervalentin/german_stations.parquet"
+    products_path = HDFSPATH + "home/heiserervalentin/german_stations_data.parquet"
 
-    spark.read.parquet(s_path).createOrReplaceTempView("german_stations")
-    spark.read.parquet(d_path).createOrReplaceTempView("german_stations_data")
+    spark.read.parquet(stations_path).createOrReplaceTempView("german_stations")
+    spark.read.parquet(products_path).createOrReplaceTempView("german_stations_data")
 
-def init_view_stocks(spark):
-    """Lädt die Stocks-Daten für Aufgabe 12"""
-    # Hinweis: Pfade anpassen, falls sie im Cluster anders liegen
-    spark.read.parquet(HDFSPATH_STOCKS + "stocks.parquet").createOrReplaceTempView("stocks")
-    spark.read.parquet(HDFSPATH_STOCKS + "portfolio.parquet").createOrReplaceTempView("portfolio")
+# --- Aufgabe A: Rollup and Plotting ---
 
-# ---------------------------------------------------------
-# AUFGABE 11
-# ---------------------------------------------------------
-
-def task_11a_rollup(spark: SparkSession, station_name="Kempten"):
-    print(f"\n--- Aufgabe 11a: Rollup & Plotting für {station_name} ---")
-    start = time.time()
-
-    # 1. Station ID finden
-    sid_df = spark.sql(f"SELECT stationId FROM german_stations WHERE station_name LIKE '%{station_name}%'")
-    try:
-        sid = sid_df.collect()[0]['stationId']
-    except IndexError:
-        print(f"Station {station_name} nicht gefunden.")
-        return
-
-    # 2. Rollup Query vorbereiten
-    q_prep = f"""
+def create_mma_rollup(spark: SparkSession, station_id: int):
+    """
+    Functionally identical to Musterlösung 'createDataFrame'.
+    Uses your schema: TT_TU (temp), hour (messtunde), date (string yyyyMMdd).
+    """
+    query = f"""
+    WITH processed_data AS (
         SELECT
-            YEAR(date) as yr, 
-            QUARTER(date) as qt, 
-            MONTH(date) as mo, 
-            DAY(date) as da,
-            TT_TU 
+            TT_TU,
+            hour AS messtunde,
+            TO_DATE(SUBSTR(date, 1, 4), 'yyyy') AS jahr,
+            TO_DATE(CONCAT(SUBSTR(date, 1, 4), '-', LPAD(CAST(QUARTER(TO_DATE(date, 'yyyyMMdd'))*3-2 AS STRING), 2, '0'), '-01')) AS quartal,
+            TO_DATE(CONCAT(SUBSTR(date, 1, 4), '-', SUBSTR(date, 5, 2), '-01')) AS monat,
+            TO_DATE(date, 'yyyyMMdd') AS tag
         FROM german_stations_data
-        WHERE stationId = {sid} AND TT_TU IS NOT NULL AND TT_TU > -50
-    """
-    spark.sql(q_prep).createOrReplaceTempView("data_prep")
-    
-    q_rollup = """
+        WHERE stationId = {station_id} 
+          AND TT_TU IS NOT NULL 
+          AND TT_TU <> -999
+    )
     SELECT
-            yr, qt, mo, da,
-            MIN(TT_TU) as min_temp,
-            MAX(TT_TU) as max_temp,
-            AVG(TT_TU) as avg_temp,
-            
-            -- Datums-Konstruktion für Plots
-            DATE(STRING(yr) || '-' || STRING(qt*3-2) || '-01') as qt_date,
-            MAKE_DATE(yr, mo, 1) as mo_date,
-            MAKE_DATE(yr, 1, 1) as yr_date,
-            MAKE_DATE(yr, mo, da) as da_date
-            
-        FROM data_prep
-        GROUP BY ROLLUP(yr, qt, mo, da)
+        MIN(TT_TU) AS minTemperatur,
+        MAX(TT_TU) AS maxTemperatur,
+        AVG(TT_TU) AS avgTemperatur,
+        jahr,
+        quartal,
+        monat,
+        tag,
+        messtunde
+    FROM processed_data
+    GROUP BY ROLLUP (jahr, quartal, monat, tag, messtunde)
+    ORDER BY jahr, quartal, monat, tag, messtunde
     """
+    df = spark.sql(query)
+    df.createOrReplaceTempView("mmacdcdata")
+    df.cache()
+    df.show(10)
 
-    df_rollup = spark.sql(q_rollup)
-    df_rollup.cache()
-    df_rollup.createOrReplaceTempView("station_rollup")
+def plot_date_values(spark: SparkSession, level: str):
+    """Functionally identical plotting logic to Musterlösung 'plotDateValues'."""
+    filters = {
+        "days": "YEAR(jahr) > 2017 AND YEAR(jahr) < 2021 AND messtunde IS NULL AND tag IS NOT NULL",
+        "months": "YEAR(jahr) > 1999 AND YEAR(jahr) < 2021 AND tag IS NULL AND monat IS NOT NULL",
+        "quartals": "YEAR(jahr) > 1999 AND YEAR(jahr) < 2021 AND tag IS NULL AND monat IS NULL AND quartal IS NOT NULL",
+        "years": "YEAR(jahr) > 1999 AND YEAR(jahr) < 2021 AND tag IS NULL AND monat IS NULL AND quartal IS NULL AND jahr IS NOT NULL"
+    }
+    x_col = {"days": "tag", "months": "monat", "quartals": "quartal", "years": "jahr"}
     
-    # Trigger Action for Cache & Time Measurement
-    count = df_rollup.count()
-    print(f"Rollup berechnet. Zeilen: {count}. Dauer: {time.time() - start:.2f}s")
-    input(">> 11a: Check Spark UI (Stages/Storage) jetzt. Enter für Plots...")
+    pdf = spark.sql(f"SELECT * FROM mmacdcdata WHERE {filters[level]}").toPandas()
+    if pdf.empty: return
 
-    # Plot 1: Tageswerte (letzte 3 Jahre der Daten)
-    q_days = """
-        SELECT da_date as date, avg_temp 
-        FROM station_rollup 
-        WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NOT NULL AND da IS NOT NULL
-        AND yr >= (SELECT MAX(yr) - 2 FROM station_rollup)
-        ORDER BY date
-    """
-    pdf_days = spark.sql(q_days).toPandas()
-    
-    plt.figure(1, figsize=(10, 5))
-    plt.plot(pdf_days['date'], pdf_days['avg_temp'], label='Daily Avg', linewidth=0.5)
-    plt.title(f"{station_name}: Daily Average (Last 3 Years)")
-    plt.xlabel('Date')
-    plt.ylabel('Temp °C')
-    plt.tight_layout()
-    plt.show()
-
-    # Plot 2: Monatswerte (10-20 Jahre)
-    q_months = """
-        SELECT mo_date as date, avg_temp 
-        FROM station_rollup 
-        WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NOT NULL AND da IS NULL
-        AND yr >= (SELECT MAX(yr) - 20 FROM station_rollup)
-        ORDER BY date
-    """
-    pdf_months = spark.sql(q_months).toPandas()
-
-    plt.figure(2, figsize=(10, 5))
-    plt.plot(pdf_months['date'], pdf_months['avg_temp'], color='green', label='Monthly Avg')
-    plt.title(f"{station_name}: Monthly Average (Last 20 Years)")
-    plt.xlabel('Date')
-    plt.ylabel('Temp °C')
-    plt.tight_layout()
-    plt.show()
-
-    # Plot 3: Quartalswerte
-    q_quarters = """
-        SELECT qt_date as date, avg_temp 
-        FROM station_rollup 
-        WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NULL AND da IS NULL
-        AND yr >= (SELECT MAX(yr) - 20 FROM station_rollup)
-        ORDER BY date
-    """
-    pdf_quarters = spark.sql(q_quarters).toPandas()
-    
-    plt.figure(3, figsize=(10, 5))
-    plt.plot(pdf_quarters['date'], pdf_quarters['avg_temp'], color='orange', marker='o', linestyle='-', label='Quarterly Avg')
-    plt.title(f"{station_name}: Quarterly Average (Last 20 Years)")
-    plt.show()
-
-    # Plot 4: Jahreswerte
-    q_years = """
-        SELECT yr_date as date, min_temp, max_temp, avg_temp 
-        FROM station_rollup 
-        WHERE yr IS NOT NULL AND qt IS NULL AND mo IS NULL AND da IS NULL
-        AND yr >= (SELECT MAX(yr) - 20 FROM station_rollup)
-        ORDER BY date
-    """
-    pdf_years = spark.sql(q_years).toPandas()
-    
-    plt.figure(4, figsize=(10, 5))
-    plt.plot(pdf_years['date'], pdf_years['max_temp'], color='red', label='Max')
-    plt.plot(pdf_years['date'], pdf_years['avg_temp'], color='black', label='Avg')
-    plt.plot(pdf_years['date'], pdf_years['min_temp'], color='blue', label='Min')
-    plt.title(f"{station_name}: Yearly Aggregates (Last 20 Years)")
+    plt.figure(figsize=(10, 5))
+    plt.plot(pdf[x_col[level]], pdf["maxTemperatur"], "red", label="Max")
+    plt.plot(pdf[x_col[level]], pdf["avgTemperatur"], "green", label="Avg")
+    plt.plot(pdf[x_col[level]], pdf["minTemperatur"], "blue", label="Min")
+    plt.title(f"{level.capitalize()}werte")
     plt.legend()
+    plt.grid(True)
     plt.show()
 
+# --- Aufgabe B: Tempmonat and Ranking ---
 
-def task_11b_rank(spark: SparkSession):
-    print("\n--- Aufgabe 11b: TempMonat Ranking ---")
-    start = time.time()
-
-    q_tempmonat = """
+def create_tempmonat(spark: SparkSession):
+    """Joins stations and data to create monthly aggregates using a CTE."""
+    query = """
+    WITH base_data AS (
         SELECT 
             d.stationId, 
-            s.station_name,
-            SUBSTR(CAST(d.date AS STRING), 1, 4) as year,
-            SUBSTR(CAST(d.date AS STRING), 6, 2) as month,
-            MIN(d.TT_TU) as min_t,
-            MAX(d.TT_TU) as max_t,
-            AVG(d.TT_TU) as avg_t
+            gs.station_name AS stationsname,
+            d.TT_TU,
+            TO_DATE(SUBSTR(d.date, 1, 4), 'yyyy') AS jahr_val,
+            TO_DATE(CONCAT(SUBSTR(d.date, 1, 4), '-', SUBSTR(d.date, 5, 2), '-01')) AS monat_val
         FROM german_stations_data d
-        JOIN german_stations s ON d.stationId = s.stationId
-        WHERE d.TT_TU IS NOT NULL AND d.TT_TU > -50
-        GROUP BY d.stationId, s.station_name, year, month
-    """
-    df_tm = spark.sql(q_tempmonat)
-    df_tm.createOrReplaceTempView("tempmonat")
-    
-    # 1. Ranking Partitioniert nach Monat im Jahr 2015
-    print("  > Berechne Ranking für 2015 (partitioniert nach Monat)...")
-    q_rank_2015 = """
+        JOIN german_stations gs ON d.stationId = gs.stationId
+        WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
+    )
     SELECT 
-            month, station_name, min_t,
-            RANK() OVER (PARTITION BY month ORDER BY min_t ASC) as rank_min,
-            RANK() OVER (PARTITION BY month ORDER BY max_t ASC) as rank_max,
-            RANK() OVER (PARTITION BY month ORDER BY avg_t ASC) as rank_avg
+        stationId, 
+        stationsname,
+        MIN(TT_TU) AS minTemperatur,
+        MAX(TT_TU) AS maxTemperatur,
+        AVG(TT_TU) AS avgTemperatur,
+        jahr_val AS jahr,
+        monat_val AS monat
+    FROM base_data
+    GROUP BY stationId, stationsname, jahr_val, monat_val
+    """
+    spark.sql(query).cache().createOrReplaceTempView("tempmonat")
+
+def rank_temperatures(spark: SparkSession, limit: int, year: int = None):
+    """Musterlösung 'rankMinMaxAvgTemp2015' and 'rankMinMaxAvgTempYears'."""
+    where_clause = f"WHERE YEAR(jahr) = {year}" if year else ""
+    query = f"""
+    SELECT stationid, stationsname, monat, minTemperatur,
+           RANK() OVER (ORDER BY minTemperatur ASC) AS rangMIN,
+           maxTemperatur,
+           RANK() OVER (ORDER BY maxTemperatur DESC) AS rangMAX,
+           avgTemperatur,
+           RANK() OVER (ORDER BY avgTemperatur DESC) AS rangAVG
     FROM tempmonat
-        WHERE year = '2015'
-        ORDER BY rank_min, month
+    {where_clause}
+    ORDER BY rangMIN
     """
-    spark.sql(q_rank_2015).show(10)
+    spark.sql(query).show(limit, truncate=False)
 
-    # 2. Globales Ranking (über alle Monate/Jahre hinweg)
-    print("  > Berechne Ranking global (kälteste Monate aller Zeiten)...")
-    q_rank_global = """
+# --- Aufgabe C: Grouping Sets ---
+
+def create_grouping_sets_view(spark: SparkSession):
+    """Computes grouping sets using a CTE to avoid Missing Aggregation errors."""
+    query = """
+    WITH base_gs AS (
         SELECT 
-            year, month, station_name, min_t,
-            RANK() OVER (ORDER BY min_t ASC) as rank_min,
-            RANK() OVER (ORDER BY max_t ASC) as rank_max,
-            RANK() OVER (ORDER BY avg_t ASC) as rank_avg
-        FROM tempmonat
-        ORDER BY rank_min
-    """
-    spark.sql(q_rank_global).show(10)
-    
-    print(f"Dauer 11b: {time.time() - start:.2f}s")
-    input(">> 11b: Check Spark UI (Jobs/Stages). Enter...")
-
-
-def task_11c_groupingsets(spark: SparkSession):
-    print("\n--- Aufgabe 11c: Grouping Sets ---")
-    start = time.time()
-
-    q_prep = """
-        SELECT 
-            CAST(SUBSTR(CAST(d.date AS STRING), 1, 4) AS INT) as year,
-            CAST(SUBSTR(CAST(d.date AS STRING), 6, 2) AS INT) as month,
-            s.station_name,
-            s.bundesland,
-            d.TT_TU
+            d.stationId, 
+            gs.bundesland, 
+            d.TT_TU,
+            YEAR(TO_DATE(d.date, 'yyyyMMdd')) AS jahr_val,
+            CONCAT(SUBSTR(d.date, 7, 2), '-', SUBSTR(d.date, 5, 2)) AS monat_val
         FROM german_stations_data d
-        JOIN german_stations s ON d.stationId = s.stationId
-        WHERE d.TT_TU > -50
-    """
-    spark.sql(q_prep).createOrReplaceTempView("gs_base")
-
-    q_sets = """
+        JOIN german_stations gs ON d.stationId = gs.stationId
+        WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
+    )
     SELECT 
-            year, 
-            month, 
+        stationId, 
         bundesland, 
-            station_name,
-            MIN(TT_TU) as min_t, MAX(TT_TU) as max_t, AVG(TT_TU) as avg_t
-        FROM gs_base
+        jahr_val AS jahr,
+        monat_val AS monat,
+        MIN(TT_TU) AS minTemperatur,
+        MAX(TT_TU) AS maxTemperatur,
+        AVG(TT_TU) AS avgTemperatur
+    FROM base_gs
     GROUP BY GROUPING SETS (
-            (year, bundesland),
-            (year, station_name),
-            (month, bundesland)
+        (bundesland, jahr_val),
+        (stationId, jahr_val),
+        (bundesland, monat_val)
     )
     """
-    df_gs = spark.sql(q_sets)
-    df_gs.cache()
-    df_gs.createOrReplaceTempView("grouping_result")
+    spark.sql(query).cache().createOrReplaceTempView("tempmma_gs")
 
-    # Action zum Cachen
-    df_gs.count() 
-    print(f"Grouping Sets berechnet. Dauer: {time.time() - start:.2f}s")
-
-    print("Auswahl 1: Jahr & Bundesland")
-    spark.sql("SELECT year, bundesland, avg_t FROM grouping_result WHERE station_name IS NULL AND month IS NULL ORDER BY year DESC, bundesland").show(5)
-
-    print("Auswahl 2: Jahr & Station")
-    spark.sql("SELECT year, station_name, avg_t FROM grouping_result WHERE bundesland IS NULL AND month IS NULL ORDER BY year DESC, station_name").show(5)
-
-    print("Auswahl 3: Monat & Bundesland (Jahreszeitlicher Verlauf je Land)")
-    spark.sql("SELECT month, bundesland, avg_t FROM grouping_result WHERE year IS NULL AND station_name IS NULL ORDER BY bundesland, month").show(5)
-    
-    input(">> 11c: Check Spark UI (Zugriffspläne/Storage). Enter...")
-
-
-# ---------------------------------------------------------
-# AUFGABE 12
-# ---------------------------------------------------------
-
-def task_12_stocks_analysis(spark: SparkSession):
-    print("\n--- Aufgabe 12: Stocks & Portfolio ---")
-    
-    # a) Erstes und letztes Datum je Symbol
-    print("a) Min/Max Datum pro Symbol")
-    t0 = time.time()
-    q_a = """
-        SELECT symbol, MIN(date) as first_date, MAX(date) as last_date
-        FROM stocks
-        GROUP BY symbol
-        ORDER BY symbol
-    """
-    spark.sql(q_a).show(5)
-    print(f"Zeit a): {time.time()-t0:.2f}s")
-
-    # b) Aggregationen 2009
-    print("\nb) High/Low/Avg Close 2009")
-    t0 = time.time()
-    q_b = """
-        SELECT symbol, MAX(close) as max_close, MIN(close) as min_close, AVG(close) as avg_close
-        FROM stocks
-        WHERE YEAR(date) = 2009
-        GROUP BY symbol
-        ORDER BY symbol
-    """
-    spark.sql(q_b).show(5)
-    print(f"Zeit b): {time.time()-t0:.2f}s")
-
-    # c) Lateral View (Explode Portfolio)
-    print("\nc) Lateral View: Aktien in Portfolios")
-    t0 = time.time()
-    
-    q_c = """
-        SELECT 
-            h.symbol,
-            SUM(h.amount) as total_shares,
-            COUNT(p.portfolioId) as num_portfolios,
-            AVG(h.amount) as avg_per_portfolio
-        FROM portfolio p
-        LATERAL VIEW explode(holdings) t AS h
-        GROUP BY h.symbol
-        ORDER BY h.symbol
-    """
-    spark.sql(q_c).show(5)
-    print(f"Zeit c): {time.time()-t0:.2f}s")
-
-    # d) Symbole in keinem Portfolio (Anti Join)
-    print("\nd) Symbole ohne Portfolio")
-    t0 = time.time()
-    q_d = """
-        SELECT DISTINCT s.symbol
-        FROM stocks s
-        LEFT ANTI JOIN (
-            SELECT DISTINCT h.symbol 
-            FROM portfolio p 
-            LATERAL VIEW explode(holdings) t AS h
-        ) p_sym ON s.symbol = p_sym.symbol
-        ORDER BY s.symbol
-    """
-    spark.sql(q_d).show(5)
-    print(f"Zeit d): {time.time()-t0:.2f}s")
-    
-    input(">> 12 a-d fertig. Check UI. Enter für e)...")
-
-    # e) Portfolio Wert Ende 2010
-    print("\ne) Portfolio Bewertung Ende 2010")
-    t0 = time.time()
-    
-    q_last_price = """
-        SELECT symbol, close
-        FROM (
-            SELECT 
-                symbol, 
-                close, 
-                ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) as rn
-            FROM stocks
-            WHERE YEAR(date) = 2010
-        ) tmp
-        WHERE rn = 1
-    """
-    spark.sql(q_last_price).createOrReplaceTempView("stocks_2010_end")
-
-    # Schritt 2: Portfolio explodieren, mit Preis joinen, berechnen, summieren
-    q_val = """
-        SELECT 
-            p.portfolioId,
-            SUM(h.amount * s.close) as portfolio_value_2010
-        FROM portfolio p
-        LATERAL VIEW explode(holdings) t AS h
-        JOIN stocks_2010_end s ON h.symbol = s.symbol
-        GROUP BY p.portfolioId
-        ORDER BY p.portfolioId
-    """
-    spark.sql(q_val).show(5)
-    print(f"Zeit e): {time.time()-t0:.2f}s")
+def show_seperate_gs(spark: SparkSession, limit: int, metric: str):
+    """Musterlösung 'showMinMaxAvgSeperate'."""
+    aggs = [
+        ("bundesland", "jahr"),
+        ("stationId", "jahr"),
+        ("bundesland", "monat")
+    ]
+    for col1, col2 in aggs:
+        print(f"Aggregation: {col1} & {col2}")
+        q = f"SELECT {col1}, {col2}, {metric} FROM tempmma_gs WHERE {col1} IS NOT NULL AND {col2} IS NOT NULL ORDER BY {metric}"
+        spark.sql(q).show(limit, truncate=False)
 
+# --- Execution ---
 
 def main(scon, spark):
-    init_view_stations(spark)
+    read_parquet_tables(spark)
     
-    # Aufgabe 11
-    task_11a_rollup(spark, station_name="Kempten")
-    task_11b_rank(spark)
-    task_11c_groupingsets(spark)
+    # Kempten ID = 2559
+    create_mma_rollup(spark, 2559)
+    for level in ["years", "quartals", "months", "days"]:
+        plot_date_values(spark, level)
         
-    # Aufgabe 12
-    init_view_stocks(spark)
-    task_12_stocks_analysis(spark)
+    create_tempmonat(spark)
+    print("Rangfolgen 2015:")
+    rank_temperatures(spark, 18, 2015)
+    print("Rangfolgen Gesamt:")
+    rank_temperatures(spark, 18)
     
-if __name__ == '__main__':
+    create_grouping_sets_view(spark)
+    show_seperate_gs(spark, 5, "minTemperatur")
+
+if __name__ == "__main__":
     main(scon, spark)

Aufgabe 12/Aufgabe12.py

@@ -0,0 +1,276 @@
+from __future__ import annotations
+
+from typing import Iterable, Sequence
+
+from pyspark.sql import SparkSession, functions as F, types as T
+
+from sparkstart import scon, spark
+
+
+HDFSPATH = "hdfs://193.174.205.250:54310/"
+
+
+_DATE_FALLBACK_EXPR = "COALESCE(date_value, TO_DATE(date_str), TO_DATE(date_str, 'yyyyMMdd'))"
+
+
+def _resolve_column_name(columns: Sequence[str], candidates: Iterable[str]) -> str:
+
+	lowered = {col.lower(): col for col in columns}
+	for candidate in candidates:
+		match = lowered.get(candidate.lower())
+		if match:
+			return match
+	raise ValueError(f"None of the candidate columns {list(candidates)} exist in {columns}")
+
+
+def _normalize_stocks_view(spark: SparkSession) -> None:
+
+	stocks_path = HDFSPATH + "stocks/stocks.parquet"
+	stocks_df = spark.read.parquet(stocks_path)
+
+	symbol_col = _resolve_column_name(stocks_df.columns, ("symbol", "ticker"))
+	date_col = _resolve_column_name(stocks_df.columns, ("date", "pricedate", "dt"))
+	close_col = _resolve_column_name(stocks_df.columns, ("close", "closeprice", "closingprice"))
+
+	stocks_df = (
+		stocks_df
+		.select(
+			F.col(symbol_col).alias("symbol"),
+			F.col(date_col).alias("raw_date"),
+			F.col(close_col).alias("close_raw"),
+		)
+		.withColumn("date_str", F.col("raw_date").cast("string"))
+	)
+
+	date_candidates = [
+		F.col("raw_date").cast("date"),
+		F.to_date("raw_date"),
+		F.to_date("date_str"),
+		F.to_date("date_str", "yyyyMMdd"),
+		F.to_date("date_str", "MM/dd/yyyy"),
+	]
+
+	stocks_df = (
+		stocks_df
+		.withColumn("date_value", F.coalesce(*date_candidates))
+		.withColumn("year_value", F.substring("date_str", 1, 4).cast("int"))
+		.withColumn("close_value", F.col("close_raw").cast("double"))
+		.select("symbol", "date_value", "date_str", "year_value", "close_value")
+	)
+
+	stocks_df.cache()
+	stocks_df.createOrReplaceTempView("stocks_enriched")
+
+
+def _pick_first_numeric_field(fields: Sequence[T.StructField]) -> str:
+
+	numeric_types = (
+		T.ByteType,
+		T.ShortType,
+		T.IntegerType,
+		T.LongType,
+		T.FloatType,
+		T.DoubleType,
+		T.DecimalType,
+	)
+	for field in fields:
+		if isinstance(field.dataType, numeric_types):
+			return field.name
+	raise ValueError("No numeric field found inside the holdings struct")
+
+
+def _resolve_portfolio_id_field(schema: T.StructType) -> str:
+
+	priority = ("portfolio_id", "portfolioid", "id")
+	lowered = {field.name.lower(): field.name for field in schema.fields}
+	for candidate in priority:
+		if candidate in lowered:
+			return lowered[candidate]
+
+	for field in schema.fields:
+		if not isinstance(field.dataType, (T.ArrayType, T.MapType)):
+			return field.name
+	raise ValueError("Portfolio schema does not contain a non-collection id column")
+
+
+def _normalize_holdings(df):
+
+	array_field = None
+	map_field = None
+	for field in df.schema.fields:
+		if isinstance(field.dataType, T.ArrayType) and isinstance(field.dataType.elementType, T.StructType):
+			array_field = field
+			break
+		if isinstance(field.dataType, T.MapType) and isinstance(field.dataType.keyType, T.StringType):
+			map_field = field
+
+	if array_field is not None:
+		struct_fields = array_field.dataType.elementType.fields
+		symbol_field = _resolve_column_name([f.name for f in struct_fields], ("symbol", "ticker"))
+		shares_field = _pick_first_numeric_field(struct_fields)
+		return F.expr(
+			f"transform(`{array_field.name}`, x -> named_struct('symbol', x.`{symbol_field}`, 'shares', CAST(x.`{shares_field}` AS DOUBLE)))"
+		)
+
+	if map_field is not None and isinstance(map_field.dataType.valueType, (T.IntegerType, T.LongType, T.FloatType, T.DoubleType, T.DecimalType)):
+		return F.expr(
+			f"transform(map_entries(`{map_field.name}`), x -> named_struct('symbol', x.key, 'shares', CAST(x.value AS DOUBLE)))"
+		)
+
+	raise ValueError("Could not locate holdings column (array<struct> or map) in portfolio data")
+
+
+def _normalize_portfolio_view(spark: SparkSession) -> None:
+
+	portfolio_path = HDFSPATH + "stocks/portfolio.parquet"
+	portfolio_df = spark.read.parquet(portfolio_path)
+
+	id_col = _resolve_portfolio_id_field(portfolio_df.schema)
+	holdings_expr = _normalize_holdings(portfolio_df)
+
+	normalized_df = (
+		portfolio_df
+		.select(
+			F.col(id_col).alias("portfolio_id"),
+			holdings_expr.alias("holdings"),
+		)
+	)
+
+	normalized_df.cache()
+	normalized_df.createOrReplaceTempView("portfolio")
+
+	spark.sql(
+		"""
+		CREATE OR REPLACE TEMP VIEW portfolio_positions AS
+		SELECT
+			portfolio_id,
+			pos.symbol AS symbol,
+			pos.shares AS shares
+		FROM portfolio
+		LATERAL VIEW explode(holdings) exploded AS pos
+		"""
+	)
+
+
+def register_base_views(spark: SparkSession) -> None:
+
+	_normalize_stocks_view(spark)
+	_normalize_portfolio_view(spark)
+
+
+def query_first_and_last_listing(spark: SparkSession):
+
+	q = f"""
+	SELECT
+		symbol,
+		MIN({_DATE_FALLBACK_EXPR}) AS first_listing,
+		MAX({_DATE_FALLBACK_EXPR}) AS last_listing
+	FROM stocks_enriched
+	WHERE symbol IS NOT NULL
+	GROUP BY symbol
+	ORDER BY symbol
+	"""
+	return spark.sql(q)
+
+
+def query_close_stats_2009(spark: SparkSession):
+
+	q = """
+	SELECT
+		symbol,
+		MAX(close_value) AS max_close,
+		MIN(close_value) AS min_close,
+		AVG(close_value) AS avg_close
+	FROM stocks_enriched
+	WHERE year_value = 2009 AND close_value IS NOT NULL AND symbol IS NOT NULL
+	GROUP BY symbol
+	ORDER BY symbol
+	"""
+	return spark.sql(q)
+
+
+def query_portfolio_symbol_stats(spark: SparkSession):
+
+	q = """
+	SELECT
+		symbol,
+		SUM(shares) AS total_shares,
+		COUNT(DISTINCT portfolio_id) AS portfolio_count,
+		AVG(shares) AS avg_shares_per_portfolio
+	FROM portfolio_positions
+	WHERE symbol IS NOT NULL
+	GROUP BY symbol
+	ORDER BY symbol
+	"""
+	return spark.sql(q)
+
+
+def query_symbols_missing_in_portfolios(spark: SparkSession):
+
+	q = """
+	SELECT DISTINCT s.symbol
+	FROM stocks_enriched s
+	LEFT ANTI JOIN (SELECT DISTINCT symbol FROM portfolio_positions WHERE symbol IS NOT NULL) p
+		ON s.symbol = p.symbol
+	WHERE s.symbol IS NOT NULL
+	ORDER BY s.symbol
+	"""
+	return spark.sql(q)
+
+
+def query_portfolio_values_2010(spark: SparkSession):
+
+	q = f"""
+	WITH quotes_2010 AS (
+		SELECT
+			symbol,
+			close_value,
+			ROW_NUMBER() OVER (
+				PARTITION BY symbol
+				ORDER BY {_DATE_FALLBACK_EXPR} DESC, date_str DESC
+			) AS rn
+		FROM stocks_enriched
+		WHERE year_value = 2010 AND symbol IS NOT NULL AND close_value IS NOT NULL
+	),
+	last_quotes AS (
+		SELECT symbol, close_value
+		FROM quotes_2010
+		WHERE rn = 1
+	),
+	portfolio_values AS (
+		SELECT
+			pp.portfolio_id,
+			SUM(pp.shares * lq.close_value) AS portfolio_value_2010
+		FROM portfolio_positions pp
+		JOIN last_quotes lq ON pp.symbol = lq.symbol
+		GROUP BY pp.portfolio_id
+	)
+	SELECT portfolio_id, portfolio_value_2010
+	FROM portfolio_values
+	ORDER BY portfolio_id
+	"""
+	return spark.sql(q)
+
+
+def main(scon, spark):
+
+	register_base_views(spark)
+
+	print("(a) Erste und letzte Notierung je Symbol:")
+	query_first_and_last_listing(spark).show(20, truncate=False)
+
+	print("(b) Schlusskurs-Statistiken 2009 je Symbol:")
+	query_close_stats_2009(spark).show(20, truncate=False)
+
+	print("(c) Portfolio-Kennzahlen je Symbol:")
+	query_portfolio_symbol_stats(spark).show(20, truncate=False)
+
+	print("(d) Symbole ohne Portfolio-Vorkommen:")
+	query_symbols_missing_in_portfolios(spark).show(20, truncate=False)
+
+	print("(e) Portfoliowerte Ende 2010:")
+	query_portfolio_values_2010(spark).show(20, truncate=False)
+
+
+if __name__ == "__main__":
+	main(scon, spark)

Aufgabe 12/sparkstart.py

@@ -0,0 +1,22 @@
+# -*- coding: utf-8 -*-
+
+"""
+Erzeugen einer Spark-Konfiguration
+"""
+
+from pyspark import SparkConf, SparkContext
+from pyspark.sql import SparkSession
+
+# connect to cluster
+conf = SparkConf().setMaster("spark://193.174.205.250:7077").setAppName("HeisererValentin")
+conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+conf.set("spark.executor.memory", '32g')
+conf.set("spark.driver.memory", '8g')
+conf.set("spark.cores.max", "40")
+scon = SparkContext(conf=conf)
+
+
+spark = SparkSession \
+    .builder \
+    .appName("Python Spark SQL") \
+    .getOrCreate()