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 +1,219 @@
+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 = "hdfs://193.174.205.250:54310/"
-HDFSPATH_STOCKS = "hdfs://193.174.205.250:54310/stocks/"
 
-def init_view_stations(spark):
+def read_parquet_tables(spark: SparkSession) -> None:
-    """Lädt die Stationsdaten für Aufgabe 11"""
+    stations_path = HDFSPATH + "home/heiserervalentin/german_stations.parquet"
-    s_path = HDFSPATH_STATIONS + "german_stations.parquet"
+    products_path = HDFSPATH + "home/heiserervalentin/german_stations_data.parquet"
-    d_path = HDFSPATH_STATIONS + "german_stations_data.parquet"
 
-    spark.read.parquet(s_path).createOrReplaceTempView("german_stations")
+    spark.read.parquet(stations_path).createOrReplaceTempView("german_stations")
-    spark.read.parquet(d_path).createOrReplaceTempView("german_stations_data")
+    spark.read.parquet(products_path).createOrReplaceTempView("german_stations_data")
 
-def init_view_stocks(spark):
+# --- Aufgabe A ---
-    """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")
 
-# ---------------------------------------------------------
+def create_mma_rollup(spark: SparkSession, station_id: int):
-# AUFGABE 11
+    query = f"""
-# ---------------------------------------------------------
+    WITH processed_data AS (
-
-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"""
         SELECT
-            YEAR(date) as yr, 
+            TT_TU,
-            QUARTER(date) as qt, 
+            hour AS messtunde,
-            MONTH(date) as mo, 
+            TO_DATE(SUBSTR(date, 1, 4), 'yyyy') AS jahr,
-            DAY(date) as da,
+            TO_DATE(CONCAT(SUBSTR(date, 1, 4), '-', LPAD(CAST(QUARTER(TO_DATE(date, 'yyyyMMdd'))*3-2 AS STRING), 2, '0'), '-01')) AS quartal,
-            TT_TU 
+            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
+        WHERE stationId = {station_id} 
-    """
+          AND TT_TU IS NOT NULL 
-    spark.sql(q_prep).createOrReplaceTempView("data_prep")
+          AND TT_TU <> -999
-    
+    )
-    q_rollup = """
     SELECT
-            yr, qt, mo, da,
+        MIN(TT_TU) AS minTemperatur,
-            MIN(TT_TU) as min_temp,
+        MAX(TT_TU) AS maxTemperatur,
-            MAX(TT_TU) as max_temp,
+        AVG(TT_TU) AS avgTemperatur,
-            AVG(TT_TU) as avg_temp,
+        jahr,
-            
+        quartal,
-            -- Datums-Konstruktion für Plots
+        monat,
-            DATE(STRING(yr) || '-' || STRING(qt*3-2) || '-01') as qt_date,
+        tag,
-            MAKE_DATE(yr, mo, 1) as mo_date,
+        messtunde
-            MAKE_DATE(yr, 1, 1) as yr_date,
+    FROM processed_data
-            MAKE_DATE(yr, mo, da) as da_date
+    GROUP BY ROLLUP (jahr, quartal, monat, tag, messtunde)
-            
+    ORDER BY jahr, quartal, monat, tag, messtunde
-        FROM data_prep
-        GROUP BY ROLLUP(yr, qt, mo, da)
     """
+    df = spark.sql(query)
+    df.createOrReplaceTempView("mmacdcdata")
+    df.cache()
+    df.show(10)
 
-    df_rollup = spark.sql(q_rollup)
+def plot_date_values(spark: SparkSession, level: str):
-    df_rollup.cache()
+    filters = {
-    df_rollup.createOrReplaceTempView("station_rollup")
+        "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
+    pdf = spark.sql(f"SELECT * FROM mmacdcdata WHERE {filters[level]}").toPandas()
-    count = df_rollup.count()
+    if pdf.empty: return
-    print(f"Rollup berechnet. Zeilen: {count}. Dauer: {time.time() - start:.2f}s")
-    input(">> 11a: Check Spark UI (Stages/Storage) jetzt. Enter für Plots...")
 
-    # Plot 1: Tageswerte (letzte 3 Jahre der Daten)
+    plt.figure(figsize=(10, 5))
-    q_days = """
+    plt.plot(pdf[x_col[level]], pdf["maxTemperatur"], "red", label="Max")
-        SELECT da_date as date, avg_temp 
+    plt.plot(pdf[x_col[level]], pdf["avgTemperatur"], "green", label="Avg")
-        FROM station_rollup 
+    plt.plot(pdf[x_col[level]], pdf["minTemperatur"], "blue", label="Min")
-        WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NOT NULL AND da IS NOT NULL
+    plt.title(f"{level.capitalize()}")
-        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.legend()
+    plt.grid(True)
     plt.show()
 
+# --- Aufgabe B ---
 
-def task_11b_rank(spark: SparkSession):
+def create_tempmonat(spark: SparkSession):
-    print("\n--- Aufgabe 11b: TempMonat Ranking ---")
+    query = """
-    start = time.time()
+    WITH base_data AS (
-
-    q_tempmonat = """
         SELECT 
             d.stationId, 
-            s.station_name,
+            gs.station_name AS stationsname,
-            SUBSTR(CAST(d.date AS STRING), 1, 4) as year,
+            d.TT_TU,
-            SUBSTR(CAST(d.date AS STRING), 6, 2) as month,
+            TO_DATE(SUBSTR(d.date, 1, 4), 'yyyy') AS jahr_val,
-            MIN(d.TT_TU) as min_t,
+            TO_DATE(CONCAT(SUBSTR(d.date, 1, 4), '-', SUBSTR(d.date, 5, 2), '-01')) AS monat_val
-            MAX(d.TT_TU) as max_t,
-            AVG(d.TT_TU) as avg_t
         FROM german_stations_data d
-        JOIN german_stations s ON d.stationId = s.stationId
+        JOIN german_stations gs ON d.stationId = gs.stationId
-        WHERE d.TT_TU IS NOT NULL AND d.TT_TU > -50
+        WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
-        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 = """
     SELECT 
-            month, station_name, min_t,
+        stationId, 
-            RANK() OVER (PARTITION BY month ORDER BY min_t ASC) as rank_min,
+        stationsname,
-            RANK() OVER (PARTITION BY month ORDER BY max_t ASC) as rank_max,
+        MIN(TT_TU) AS minTemperatur,
-            RANK() OVER (PARTITION BY month ORDER BY avg_t ASC) as rank_avg
+        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):
+    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'
+    {where_clause}
-        ORDER BY rank_min, month
+    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)
+# --- Aufgabe C ---
-    print("  > Berechne Ranking global (kälteste Monate aller Zeiten)...")
+
-    q_rank_global = """
+def create_grouping_sets_view(spark: SparkSession):
+    query = """
+    WITH base_gs AS (
         SELECT 
-            year, month, station_name, min_t,
+            d.stationId, 
-            RANK() OVER (ORDER BY min_t ASC) as rank_min,
+            -- TRIM entfernt Leerzeichen für saubere Tabellen
-            RANK() OVER (ORDER BY max_t ASC) as rank_max,
+            TRIM(gs.bundesland) AS bundesland_clean, 
-            RANK() OVER (ORDER BY avg_t ASC) as rank_avg
+            d.TT_TU,
-        FROM tempmonat
+            -- Extrahiere Jahr und Kalendermonat (1-12)
-        ORDER BY rank_min
+            YEAR(TO_DATE(d.date, 'yyyyMMdd')) AS jahr_val,
-    """
+            MONTH(TO_DATE(d.date, 'yyyyMMdd')) AS monat_val
-    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
         FROM german_stations_data d
-        JOIN german_stations s ON d.stationId = s.stationId
+        JOIN german_stations gs ON d.stationId = gs.stationId
-        WHERE d.TT_TU > -50
+        WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
-    """
+    )
-    spark.sql(q_prep).createOrReplaceTempView("gs_base")
-
-    q_sets = """
     SELECT 
-            year, 
+        stationId, 
-            month, 
+        bundesland_clean AS bundesland, 
-            bundesland, 
+        jahr_val AS jahr,
-            station_name,
+        monat_val AS monat,
-            MIN(TT_TU) as min_t, MAX(TT_TU) as max_t, AVG(TT_TU) as avg_t
+        MIN(TT_TU) AS minTemperatur,
-        FROM gs_base
+        MAX(TT_TU) AS maxTemperatur,
+        AVG(TT_TU) AS avgTemperatur
+    FROM base_gs
     GROUP BY GROUPING SETS (
-            (year, bundesland),
+        (bundesland_clean, jahr_val),  -- 1. Jahr und Bundesland
-            (year, station_name),
+        (stationId, jahr_val),        -- 2. Jahr und Station
-            (month, bundesland)
+        (bundesland_clean, monat_val) -- 3. Monat und Bundesland
     )
     """
-    df_gs = spark.sql(q_sets)
+    df = spark.sql(query)
-    df_gs.cache()
-    df_gs.createOrReplaceTempView("grouping_result")
     
-    # Action zum Cachen
+    df.cache()
-    df_gs.count() 
-    print(f"Grouping Sets berechnet. Dauer: {time.time() - start:.2f}s")
     
-    print("Auswahl 1: Jahr & Bundesland")
+    df.createOrReplaceTempView("tempmma_gs")
-    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...")
 
 
-# ---------------------------------------------------------
+def show_seperate_gs(spark: SparkSession, limit: int):
-# AUFGABE 12
-# ---------------------------------------------------------
     
-def task_12_stocks_analysis(spark: SparkSession):
+    # Filter: stationId muss NULL sein, monat muss NULL sein
-    print("\n--- Aufgabe 12: Stocks & Portfolio ---")
+    spark.sql("""
-    
-    # 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,
+            jahr, 
-            SUM(h.amount) as total_shares,
+            bundesland, 
-            COUNT(p.portfolioId) as num_portfolios,
+            minTemperatur, 
-            AVG(h.amount) as avg_per_portfolio
+            maxTemperatur, 
-        FROM portfolio p
+            ROUND(avgTemperatur, 2) as avgTemperatur
-        LATERAL VIEW explode(holdings) t AS h
+        FROM tempmma_gs 
-        GROUP BY h.symbol
+        WHERE bundesland IS NOT NULL 
-        ORDER BY h.symbol
+          AND jahr IS NOT NULL 
-    """
+          AND stationId IS NULL 
-    spark.sql(q_c).show(5)
+          AND monat IS NULL
-    print(f"Zeit c): {time.time()-t0:.2f}s")
+        ORDER BY jahr DESC, bundesland ASC
+    """).show(limit, truncate=False)
 
-    # d) Symbole in keinem Portfolio (Anti Join)
+    # Filter: bundesland muss NULL sein, monat muss NULL sein
-    print("\nd) Symbole ohne Portfolio")
+    spark.sql("""
-    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, 
+            jahr, 
-                close, 
+            stationId, 
-                ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) as rn
+            minTemperatur, 
-            FROM stocks
+            maxTemperatur, 
-            WHERE YEAR(date) = 2010
+            ROUND(avgTemperatur, 2) as avgTemperatur 
-        ) tmp
+        FROM tempmma_gs 
-        WHERE rn = 1
+        WHERE stationId IS NOT NULL 
-    """
+          AND jahr IS NOT NULL 
-    spark.sql(q_last_price).createOrReplaceTempView("stocks_2010_end")
+          AND bundesland IS NULL
+        ORDER BY jahr DESC, stationId ASC
+    """).show(limit, truncate=False)
 
-    # Schritt 2: Portfolio explodieren, mit Preis joinen, berechnen, summieren
+    # Filter: stationId muss NULL sein, jahr muss NULL sein
-    q_val = """
+    spark.sql("""
         SELECT 
-            p.portfolioId,
+            monat, 
-            SUM(h.amount * s.close) as portfolio_value_2010
+            bundesland, 
-        FROM portfolio p
+            minTemperatur, 
-        LATERAL VIEW explode(holdings) t AS h
+            maxTemperatur, 
-        JOIN stocks_2010_end s ON h.symbol = s.symbol
+            ROUND(avgTemperatur, 2) as avgTemperatur
-        GROUP BY p.portfolioId
+        FROM tempmma_gs 
-        ORDER BY p.portfolioId
+        WHERE bundesland IS NOT NULL 
-    """
+          AND monat IS NOT NULL 
-    spark.sql(q_val).show(5)
+          AND jahr IS NULL
-    print(f"Zeit e): {time.time()-t0:.2f}s")
+        ORDER BY monat ASC, bundesland ASC
-
+    """).show(limit, truncate=False)
 
 def main(scon, spark):
-    init_view_stations(spark)
+    read_parquet_tables(spark)
     
-    # Aufgabe 11
+    # Kempten ID = 2559
-    task_11a_rollup(spark, station_name="Kempten")
+    create_mma_rollup(spark, 2559)
-    task_11b_rank(spark)
+    for level in ["years", "quartals", "months", "days"]:
-    task_11c_groupingsets(spark)
+        plot_date_values(spark, level)
         
-    # Aufgabe 12
+    create_tempmonat(spark)
-    init_view_stocks(spark)
+    print("Rangfolgen 2015:")
-    task_12_stocks_analysis(spark)
+    rank_temperatures(spark, 18, 2015)
+    print("Rangfolgen Gesamt:")
+    rank_temperatures(spark, 18)
     
-if __name__ == '__main__':
+    create_grouping_sets_view(spark)
+    show_seperate_gs(spark, 10)
+
+if __name__ == "__main__":
     main(scon, spark)

Aufgabe 12/Aufgabe12.py

@@ -0,0 +1,190 @@
+from sparkstart import spark
+from sparkstart import scon, spark
+from pyspark.sql import SparkSession
+
+HDFSPATH = "hdfs://193.174.205.250:54310/"
+SOURCEPATH = HDFSPATH + "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.")
+        
+        # Load Portfolio
+        spark.read.parquet(SOURCEPATH + "portfolio.parquet").createOrReplaceTempView("portfolio")
+        print("-> View 'portfolio' loaded.")
+        
+    except Exception as e:
+        print(f"CRITICAL ERROR: Could not load data. {e}")
+        print("Please check if the path exists in HDFS.")
+
+# --- 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 
+        FROM stocks 
+        GROUP BY symbol
+    ) AS grpStocks 
+    ON s.symbol = grpStocks.symbol AND s.dt = grpStocks.datum
+    WHERE YEAR(dt) = 2010
+    ORDER BY datum
+    """
+    df_2010 = spark.sql(query_stocks)
+    df_2010.createOrReplaceTempView("tempstocks") 
+    # df_2010.show(num, truncate=False) # Optional zur Kontrolle
+    
+    # 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
+    """
+    df_value = spark.sql(query_value)
+    df_value.createOrReplaceTempView("tempvalue")
+    # df_value.show(num, truncate=False) # Optional zur Kontrolle
+    
+    # 4. Gesamtwert aggregieren
+    query_sum = """
+    SELECT pid, SUM(wert) AS gesamtwert 
+    FROM tempvalue 
+    GROUP BY pid 
+    ORDER BY pid
+    """
+    df_sum = spark.sql(query_sum)
+    df_sum.show(num, truncate=False)      
+  
+    df_sum.write.mode('overwrite').parquet(HDFSPATH + "home/heiserervalentin/nyse5.parquet")
+    print("-> Imported nyse5")
+
+
+def main(scon, spark):
+	read_data(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__":
+    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()