12

Aufgabe 11/Aufgabe11.py

@@ -1,219 +1,276 @@
-from __future__ import annotations
 from sparkstart import scon, spark
 from pyspark.sql import SparkSession
 import matplotlib.pyplot as plt
+import pandas as pd
 
 HDFSPATH = "hdfs://193.174.205.250:54310/"
+HDFSPATH_STOCKS = "hdfs://193.174.205.250:54310/stocks/"
 
-def read_parquet_tables(spark: SparkSession) -> None:
-    stations_path = HDFSPATH + "home/heiserervalentin/german_stations.parquet"
-    products_path = HDFSPATH + "home/heiserervalentin/german_stations_data.parquet"
+def read_parquets(spark: SparkSession):
+	stations_path = HDFSPATH + "home/heiserervalentin/german_stations.parquet"
+	products_path = HDFSPATH + "home/heiserervalentin/german_stations_data.parquet"
 
-    spark.read.parquet(stations_path).createOrReplaceTempView("german_stations")
-    spark.read.parquet(products_path).createOrReplaceTempView("german_stations_data")
+	stations_df = spark.read.parquet(stations_path)
+	stations_df.createOrReplaceTempView("german_stations")
 
-# --- Aufgabe A ---
+	products_df = spark.read.parquet(products_path)
+	products_df.createOrReplaceTempView("german_stations_data")
 
-def create_mma_rollup(spark: SparkSession, station_id: int):
-    query = f"""
-    WITH processed_data AS (
+	stations_df.cache()
+	products_df.cache()
+
+def task_11a_rollup(spark: SparkSession, station_name="Kempten"):
+    print(f"\n--- Aufgabe 11a: Rollup & Plotting für {station_name} ---")
+    start_time = time.time()
+
+    # 1. Station ID finden
+    # Case-insensitive search
+    sid_df = spark.sql(f"SELECT stationId FROM german_stations WHERE lower(station_name) LIKE '%{station_name.lower()}%'")
+    try:
+        sid = sid_df.collect()[0]['stationId']
+        print(f"Station found: {station_name} -> ID {sid}")
+    except IndexError:
+        print(f"Station {station_name} nicht gefunden.")
+        return
+
+    # 2. Rollup Query vorbereiten
+    # FIX: Parse string date 'YYYYMMDD' to real DATE object first
+    q_prep = f"""
         SELECT 
-            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
+            YEAR(TO_DATE(date, 'yyyyMMdd')) as yr, 
+            QUARTER(TO_DATE(date, 'yyyyMMdd')) as qt, 
+            MONTH(TO_DATE(date, 'yyyyMMdd')) as mo, 
+            DAY(TO_DATE(date, 'yyyyMMdd')) as da,
+            TT_TU 
         FROM german_stations_data 
-        WHERE stationId = {station_id} 
+        WHERE stationId = {sid} 
           AND TT_TU IS NOT NULL 
-          AND TT_TU <> -999
-    )
-    SELECT
-        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
+          AND TT_TU > -50 
+          AND TT_TU < 60
     """
-    df = spark.sql(query)
-    df.createOrReplaceTempView("mmacdcdata")
-    df.cache()
-    df.show(10)
+    spark.sql(q_prep).createOrReplaceTempView("data_prep")
     
-def plot_date_values(spark: SparkSession, level: str):
-    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"}
+    # 3. Rollup Execution
+    # Note: We use string construction for quarters/months to ensure we get a valid date string for plotting
+    q_rollup = """
+        SELECT 
+            yr, qt, mo, da,
+            MIN(TT_TU) as min_temp,
+            MAX(TT_TU) as max_temp,
+            AVG(TT_TU) as avg_temp,
             
-    pdf = spark.sql(f"SELECT * FROM mmacdcdata WHERE {filters[level]}").toPandas()
-    if pdf.empty: return
+            -- Construct dates for plotting (handling the NULLs from ROLLUP)
+            -- For Quarter: Use 1st month of quarter
+            DATE(concat_ws('-', yr, cast(qt*3-2 as int), '01')) as qt_date,
+            -- For Month: Use 1st day of month
+            MAKE_DATE(yr, mo, 1) as mo_date,
+            -- For Year: Use Jan 1st
+            MAKE_DATE(yr, 1, 1) as yr_date,
+            -- For Day: Use actual date
+            MAKE_DATE(yr, mo, da) as da_date
             
-    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()}")
-    plt.legend()
-    plt.grid(True)
-    plt.show()
+        FROM data_prep
+        GROUP BY ROLLUP(yr, qt, mo, da)
+    """
     
-# --- Aufgabe B ---
+    df_rollup = spark.sql(q_rollup)
+    df_rollup.cache()
+    df_rollup.createOrReplaceTempView("station_rollup")
     
-def create_tempmonat(spark: SparkSession):
-    query = """
-    WITH base_data AS (
+    # Trigger Action
+    count = df_rollup.count()
+    print(f"Rollup berechnet. Zeilen: {count}. Dauer: {time.time() - start_time:.2f}s")
+
+    # --- PLOTTING ---
+
+    # Plot 1: Tageswerte (letzte 3 Jahre)
+    # Filter: All levels must be present (not null)
+    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 WHERE yr IS NOT NULL)
+        ORDER BY date
+    """
+    pdf_days = spark.sql(q_days).toPandas()
+    
+    if pdf_days.empty:
+        print("Warnung: Keine Daten für Tages-Plot gefunden.")
+    else:
+        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)
+    # Filter: Day is NULL (aggregation level), but Month is NOT NULL
+    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 WHERE yr IS NOT NULL)
+        ORDER BY date
+    """
+    pdf_months = spark.sql(q_months).toPandas()
+
+    if not pdf_months.empty:
+        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
+    # Filter: Month is NULL, Quarter is NOT NULL
+    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 WHERE yr IS NOT NULL)
+        ORDER BY date
+    """
+    pdf_quarters = spark.sql(q_quarters).toPandas()
+    
+    if not pdf_quarters.empty:
+        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.tight_layout()
+        plt.show()
+
+    # Plot 4: Jahreswerte
+    # Filter: Quarter is NULL, Year is NOT NULL
+    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 WHERE yr IS NOT NULL)
+        ORDER BY date
+    """
+    pdf_years = spark.sql(q_years).toPandas()
+    
+    if not pdf_years.empty:
+        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.legend()
+        plt.tight_layout()
+        plt.show()
+
+
+def task_11b_rank(spark: SparkSession):
+    print("\n--- Aufgabe 11b: TempMonat Ranking ---")
+    
+
+    q_tempmonat = """
         SELECT 
             d.stationId, 
-            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
+            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
         FROM german_stations_data d
-        JOIN german_stations gs ON d.stationId = gs.stationId
-        WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
-    )
-    SELECT 
-        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
+        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
     """
-    spark.sql(query).cache().createOrReplaceTempView("tempmonat")
+    df_tm = spark.sql(q_tempmonat)
+    df_tm.createOrReplaceTempView("tempmonat")
     
-def rank_temperatures(spark: SparkSession, limit: int, year: int = None):
-    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_clause}
-    ORDER BY rangMIN
-    """
-    spark.sql(query).show(limit, truncate=False)
-
-# --- Aufgabe C ---
-
-def create_grouping_sets_view(spark: SparkSession):
-    query = """
-    WITH base_gs AS (
+    # 1. Ranking Partitioniert nach Monat im Jahr 2015
+    print("  > Berechne Ranking für 2015 (partitioniert nach Monat)...")
+    q_rank_2015 = """
         SELECT 
-            d.stationId, 
-            -- TRIM entfernt Leerzeichen für saubere Tabellen
-            TRIM(gs.bundesland) AS bundesland_clean, 
-            d.TT_TU,
-            -- Extrahiere Jahr und Kalendermonat (1-12)
-            YEAR(TO_DATE(d.date, 'yyyyMMdd')) AS jahr_val,
-            MONTH(TO_DATE(d.date, 'yyyyMMdd')) AS monat_val
+            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
+        FROM tempmonat
+        WHERE year = '2015'
+        ORDER BY rank_min, month
+    """
+    spark.sql(q_rank_2015).show(10)
+
+    # 2. Globales Ranking (über alle Monate/Jahre hinweg)
+    print("  > Berechne Ranking global (kälteste Monate aller Zeiten)...")
+    q_rank_global = """
+        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("11b: Fertig.")
+
+
+def task_11c_groupingsets(spark: SparkSession):
+    print("\n--- Aufgabe 11c: Grouping Sets ---")
+    
+
+    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
         FROM german_stations_data d
-        JOIN german_stations gs ON d.stationId = gs.stationId
-        WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
-    )
-    SELECT 
-        stationId, 
-        bundesland_clean AS bundesland, 
-        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 (
-        (bundesland_clean, jahr_val),  -- 1. Jahr und Bundesland
-        (stationId, jahr_val),        -- 2. Jahr und Station
-        (bundesland_clean, monat_val) -- 3. Monat und Bundesland
-    )
+        JOIN german_stations s ON d.stationId = s.stationId
+        WHERE d.TT_TU > -50
     """
-    df = spark.sql(query)
+    spark.sql(q_prep).createOrReplaceTempView("gs_base")
 
-    df.cache()
-    
-    df.createOrReplaceTempView("tempmma_gs")
-
-
-def show_seperate_gs(spark: SparkSession, limit: int):
-    
-    # Filter: stationId muss NULL sein, monat muss NULL sein
-    spark.sql("""
+    q_sets = """
         SELECT 
-            jahr, 
+            year, 
+            month, 
             bundesland, 
-            minTemperatur, 
-            maxTemperatur, 
-            ROUND(avgTemperatur, 2) as avgTemperatur
-        FROM tempmma_gs 
-        WHERE bundesland IS NOT NULL 
-          AND jahr IS NOT NULL 
-          AND stationId IS NULL 
-          AND monat IS NULL
-        ORDER BY jahr DESC, bundesland ASC
-    """).show(limit, truncate=False)
+            station_name,
+            MIN(TT_TU) as min_t, MAX(TT_TU) as max_t, AVG(TT_TU) as avg_t
+        FROM gs_base
+        GROUP BY GROUPING SETS (
+            (year, bundesland),
+            (year, station_name),
+            (month, bundesland)
+        )
+    """
+    df_gs = spark.sql(q_sets)
+    df_gs.cache()
+    df_gs.createOrReplaceTempView("grouping_result")
     
-    # Filter: bundesland muss NULL sein, monat muss NULL sein
-    spark.sql("""
-        SELECT 
-            jahr, 
-            stationId, 
-            minTemperatur, 
-            maxTemperatur, 
-            ROUND(avgTemperatur, 2) as avgTemperatur 
-        FROM tempmma_gs 
-        WHERE stationId IS NOT NULL 
-          AND jahr IS NOT NULL 
-          AND bundesland IS NULL
-        ORDER BY jahr DESC, stationId ASC
-    """).show(limit, truncate=False)
+    # Action zum Cachen
+    df_gs.count()
+    print("Grouping Sets berechnet.")
 
-    # Filter: stationId muss NULL sein, jahr muss NULL sein
-    spark.sql("""
-        SELECT 
-            monat, 
-            bundesland, 
-            minTemperatur, 
-            maxTemperatur, 
-            ROUND(avgTemperatur, 2) as avgTemperatur
-        FROM tempmma_gs 
-        WHERE bundesland IS NOT NULL 
-          AND monat IS NOT NULL 
-          AND jahr IS NULL
-        ORDER BY monat ASC, bundesland ASC
-    """).show(limit, truncate=False)
+    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)
 
 def main(scon, spark):
-    read_parquet_tables(spark)
+    read_parquets(spark)
     
-    # Kempten ID = 2559
-    create_mma_rollup(spark, 2559)
-    for level in ["years", "quartals", "months", "days"]:
-        plot_date_values(spark, level)
+    # Aufgabe 11
+    task_11a_rollup(spark, station_name="Kempten")
+    task_11b_rank(spark)
+    task_11c_groupingsets(spark)
 
-    create_tempmonat(spark)
-    print("Rangfolgen 2015:")
-    rank_temperatures(spark, 18, 2015)
-    print("Rangfolgen Gesamt:")
-    rank_temperatures(spark, 18)
-    
-    create_grouping_sets_view(spark)
-    show_seperate_gs(spark, 10)
-
-if __name__ == "__main__":
+if __name__ == '__main__':
     main(scon, spark)

Aufgabe 12/Aufgabe12.py

@@ -1,190 +1,123 @@
-from sparkstart import spark
 from sparkstart import scon, spark
 from pyspark.sql import SparkSession
+import time
+import matplotlib.pyplot as plt
+import pandas as pd
 
-HDFSPATH = "hdfs://193.174.205.250:54310/"
-SOURCEPATH = HDFSPATH + "stocks/"
+HDFSPATH_STATIONS = "hdfs://193.174.205.250:54310/home/heiserervalentin/"
+HDFSPATH_STOCKS = "hdfs://193.174.205.250:54310/stocks/"
 
-def read_data(spark: SparkSession) -> None:
-    """
-    Loads the existing Parquet files from HDFS into Spark Views.
-    """
-    print(f"--- Loading Views from {SOURCEPATH} ---")
-    try:
-        # Load Stocks
-        spark.read.parquet(SOURCEPATH + "stocks.parquet").createOrReplaceTempView("stocks")
-        print("-> View 'stocks' loaded.")
+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")
 
-        # Load Portfolio
-        spark.read.parquet(SOURCEPATH + "portfolio.parquet").createOrReplaceTempView("portfolio")
-        print("-> View 'portfolio' loaded.")
+# ---------------------------------------------------------
+# AUFGABE 12
+# ---------------------------------------------------------
 
-    except Exception as e:
-        print(f"CRITICAL ERROR: Could not load data. {e}")
-        print("Please check if the path exists in HDFS.")
+def task_12_stocks_analysis(spark: SparkSession):
+    print("\n--- Aufgabe 12: Stocks & Portfolio ---")
     
-# --- Aufgabe A ---
-def first_last_quotation(spark: SparkSession, num: int = 10) -> None:
-    print("\n--- Aufgabe A: First/Last Quotation ---")
-    query = """
-    SELECT symbol,
-           MIN(dt) AS altNotierung,
-           MAX(dt) AS neuNotierung
-    FROM stocks
-    GROUP BY symbol
-    ORDER BY symbol
-    """
-       
-    df_quotation = spark.sql(query)
-    df_quotation.show(num, truncate=False)
-    
-    df_quotation.write.mode('overwrite').parquet(HDFSPATH + "home/heiserervalentin/nyse1.parquet")
-    print("-> Imported nyse1")
-
-
-# --- Aufgabe B ---
-def min_max_avg_close(spark: SparkSession, num: int = 10) -> None:
-    print("\n--- Aufgabe B: Min/Max/Avg Close 2009 ---")
-    query = """
-    SELECT symbol,
-           MIN(close) AS minClose,
-           MAX(close) AS maxClose,
-           AVG(close) AS avgClose
-    FROM stocks
-    WHERE YEAR(dt) = 2009
-    GROUP BY symbol
-    ORDER BY symbol
-    """
-             
-    df_close = spark.sql(query)
-    df_close.show(num, truncate=False)
-    
-    df_close.write.mode('overwrite').parquet(HDFSPATH + "home/heiserervalentin/nyse2.parquet")
-    print("-> Imported nyse2")
-
-
-# --- Aufgabe C ---
-def sum_count_avg_portfolios(spark: SparkSession, num: int = 10) -> None:
-    print("\n--- Aufgabe C: Portfolio Aggregations ---")
-    # 1. Explode
-    query_explode = """
-    SELECT pid, Attr
-    FROM portfolio
-    LATERAL VIEW EXPLODE(bonds) AS Attr
-    """
-    df_temp = spark.sql(query_explode)
-    df_temp.createOrReplaceTempView("temp")
-    
-    # 2. Aggregate
-    query_agg = """
-    SELECT Attr.symbol AS symbol,
-           COUNT(pid) AS anzpid,
-           SUM(Attr.num) AS anzAktien,
-           AVG(Attr.num) AS avgAnzAktien
-    FROM temp
-    GROUP BY symbol
-    ORDER BY symbol
-    """
-             
-    df_sum_sel_cnt_avg = spark.sql(query_agg)
-    df_sum_sel_cnt_avg.show(num, truncate=False)
-    
-    df_sum_sel_cnt_avg.write.mode('overwrite').parquet(HDFSPATH + "home/heiserervalentin/nyse3.parquet")
-    print("-> Imported nyse3")
-
-
-# --- Aufgabe D ---
-def symbols_not_in_portfolio(spark: SparkSession, num: int = 10) -> None:
-    print("\n--- Aufgabe D: Symbols not in Portfolio ---")
-    query_explode = """
-    SELECT Attr
-    FROM portfolio
-    LATERAL VIEW EXPLODE(bonds) AS Attr
-    """
-    
-    df_temp = spark.sql(query_explode)
-    df_temp.createOrReplaceTempView("tempport")
-    
-    query_distinct = """
-    SELECT DISTINCT s.symbol 
-    FROM stocks s 
-    LEFT OUTER JOIN tempport p ON s.symbol = p.Attr.symbol 
-    WHERE p.Attr.symbol IS NULL 
-    ORDER BY s.symbol
-    """
-             
-    df_symbols = spark.sql(query_distinct)
-    df_symbols.show(num, truncate=False)    
-    
-    df_symbols.write.mode('overwrite').parquet(HDFSPATH + "home/heiserervalentin/nyse4.parquet")
-    print("-> Imported nyse4")
-
-
-# --- Aufgabe E ---
-def value_portfolio_2010(spark: SparkSession, num: int = 10) -> None:
-    print("\n--- Aufgabe E: Portfolio Value 2010 ---")
-    
-    # 1. Portfolio explodieren
-    query_portfolio = """
-    SELECT pid, Attr.symbol AS symbol, Attr.num AS anzAktien
-    FROM portfolio
-    LATERAL VIEW EXPLODE(bonds) AS Attr
-    ORDER BY pid
-    """
-    df_lview = spark.sql(query_portfolio)
-    df_lview.createOrReplaceTempView("tempportfolio")
-    # df_lview.show(num, truncate=False) # Optional zur Kontrolle
-    
-    # 2. Stocks filtern (Neuester Kurs in 2010)
-    query_stocks = """
-    SELECT s.symbol, s.dt, s.close
-    FROM stocks s
-    INNER JOIN (
-        SELECT symbol, MAX(dt) AS datum 
+    # 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
-    ) AS grpStocks 
-    ON s.symbol = grpStocks.symbol AND s.dt = grpStocks.datum
-    WHERE YEAR(dt) = 2010
-    ORDER BY datum
+        ORDER BY symbol
     """
-    df_2010 = spark.sql(query_stocks)
-    df_2010.createOrReplaceTempView("tempstocks") 
-    # df_2010.show(num, truncate=False) # Optional zur Kontrolle
+    spark.sql(q_a).show(5)
+    print(f"Zeit a): {time.time()-t0:.2f}s")
 
-    # 3. Wert berechnen (Join)
-    query_value = """
-    SELECT p.*, s.close * p.anzAktien AS wert 
-    FROM tempportfolio p, tempstocks s
-    WHERE s.symbol = p.symbol
-    ORDER BY p.pid
+    # 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
     """
-    df_value = spark.sql(query_value)
-    df_value.createOrReplaceTempView("tempvalue")
-    # df_value.show(num, truncate=False) # Optional zur Kontrolle
+    spark.sql(q_b).show(5)
+    print(f"Zeit b): {time.time()-t0:.2f}s")
 
-    # 4. Gesamtwert aggregieren
-    query_sum = """
-    SELECT pid, SUM(wert) AS gesamtwert 
-    FROM tempvalue 
-    GROUP BY pid 
-    ORDER BY pid
+    # 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
     """
-    df_sum = spark.sql(query_sum)
-    df_sum.show(num, truncate=False)      
+    spark.sql(q_c).show(5)
+    print(f"Zeit c): {time.time()-t0:.2f}s")
 
-    df_sum.write.mode('overwrite').parquet(HDFSPATH + "home/heiserervalentin/nyse5.parquet")
-    print("-> Imported nyse5")
+    # 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 main(scon, spark):
-	read_data(spark)
+    # Aufgabe 12
+    init_view_stocks(spark)
+    task_12_stocks_analysis(spark)
 
-	first_last_quotation(spark, 10)
-	min_max_avg_close(spark, 10)
-	sum_count_avg_portfolios(spark, 5)
-	symbols_not_in_portfolio(spark, 5)
-	value_portfolio_2010(spark, 10)
-
-if __name__ == "__main__":
+if __name__ == '__main__':
     main(scon, spark)