mirror of
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12
This commit is contained in:
@@ -1,366 +1,219 @@
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from __future__ import annotations
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from sparkstart import scon, spark
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from pyspark.sql import SparkSession
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import time
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import matplotlib.pyplot as plt
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import pandas as pd
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HDFSPATH_STATIONS = "hdfs://193.174.205.250:54310/home/heiserervalentin/"
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HDFSPATH_STOCKS = "hdfs://193.174.205.250:54310/stocks/"
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HDFSPATH = "hdfs://193.174.205.250:54310/"
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def init_view_stations(spark):
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"""Lädt die Stationsdaten für Aufgabe 11"""
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s_path = HDFSPATH_STATIONS + "german_stations.parquet"
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d_path = HDFSPATH_STATIONS + "german_stations_data.parquet"
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spark.read.parquet(s_path).createOrReplaceTempView("german_stations")
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spark.read.parquet(d_path).createOrReplaceTempView("german_stations_data")
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def read_parquet_tables(spark: SparkSession) -> None:
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stations_path = HDFSPATH + "home/heiserervalentin/german_stations.parquet"
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products_path = HDFSPATH + "home/heiserervalentin/german_stations_data.parquet"
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def init_view_stocks(spark):
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"""Lädt die Stocks-Daten für Aufgabe 12"""
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# Hinweis: Pfade anpassen, falls sie im Cluster anders liegen
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spark.read.parquet(HDFSPATH_STOCKS + "stocks.parquet").createOrReplaceTempView("stocks")
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spark.read.parquet(HDFSPATH_STOCKS + "portfolio.parquet").createOrReplaceTempView("portfolio")
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spark.read.parquet(stations_path).createOrReplaceTempView("german_stations")
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spark.read.parquet(products_path).createOrReplaceTempView("german_stations_data")
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# ---------------------------------------------------------
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# AUFGABE 11
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# ---------------------------------------------------------
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# --- Aufgabe A ---
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def task_11a_rollup(spark: SparkSession, station_name="Kempten"):
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print(f"\n--- Aufgabe 11a: Rollup & Plotting für {station_name} ---")
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start = time.time()
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# 1. Station ID finden
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sid_df = spark.sql(f"SELECT stationId FROM german_stations WHERE station_name LIKE '%{station_name}%'")
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try:
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sid = sid_df.collect()[0]['stationId']
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except IndexError:
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print(f"Station {station_name} nicht gefunden.")
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return
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# 2. Rollup Query vorbereiten
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q_prep = f"""
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SELECT
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YEAR(date) as yr,
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QUARTER(date) as qt,
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MONTH(date) as mo,
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DAY(date) as da,
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TT_TU
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FROM german_stations_data
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WHERE stationId = {sid} AND TT_TU IS NOT NULL AND TT_TU > -50
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def create_mma_rollup(spark: SparkSession, station_id: int):
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query = f"""
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WITH processed_data AS (
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SELECT
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TT_TU,
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hour AS messtunde,
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TO_DATE(SUBSTR(date, 1, 4), 'yyyy') AS jahr,
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TO_DATE(CONCAT(SUBSTR(date, 1, 4), '-', LPAD(CAST(QUARTER(TO_DATE(date, 'yyyyMMdd'))*3-2 AS STRING), 2, '0'), '-01')) AS quartal,
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TO_DATE(CONCAT(SUBSTR(date, 1, 4), '-', SUBSTR(date, 5, 2), '-01')) AS monat,
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TO_DATE(date, 'yyyyMMdd') AS tag
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FROM german_stations_data
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WHERE stationId = {station_id}
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AND TT_TU IS NOT NULL
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AND TT_TU <> -999
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)
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SELECT
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MIN(TT_TU) AS minTemperatur,
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MAX(TT_TU) AS maxTemperatur,
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AVG(TT_TU) AS avgTemperatur,
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jahr,
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quartal,
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monat,
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tag,
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messtunde
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FROM processed_data
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GROUP BY ROLLUP (jahr, quartal, monat, tag, messtunde)
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ORDER BY jahr, quartal, monat, tag, messtunde
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"""
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spark.sql(q_prep).createOrReplaceTempView("data_prep")
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q_rollup = """
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SELECT
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yr, qt, mo, da,
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MIN(TT_TU) as min_temp,
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MAX(TT_TU) as max_temp,
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AVG(TT_TU) as avg_temp,
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-- Datums-Konstruktion für Plots
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DATE(STRING(yr) || '-' || STRING(qt*3-2) || '-01') as qt_date,
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MAKE_DATE(yr, mo, 1) as mo_date,
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MAKE_DATE(yr, 1, 1) as yr_date,
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MAKE_DATE(yr, mo, da) as da_date
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FROM data_prep
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GROUP BY ROLLUP(yr, qt, mo, da)
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"""
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df_rollup = spark.sql(q_rollup)
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df_rollup.cache()
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df_rollup.createOrReplaceTempView("station_rollup")
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# Trigger Action for Cache & Time Measurement
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count = df_rollup.count()
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print(f"Rollup berechnet. Zeilen: {count}. Dauer: {time.time() - start:.2f}s")
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input(">> 11a: Check Spark UI (Stages/Storage) jetzt. Enter für Plots...")
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df = spark.sql(query)
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df.createOrReplaceTempView("mmacdcdata")
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df.cache()
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df.show(10)
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# Plot 1: Tageswerte (letzte 3 Jahre der Daten)
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q_days = """
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SELECT da_date as date, avg_temp
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FROM station_rollup
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WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NOT NULL AND da IS NOT NULL
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AND yr >= (SELECT MAX(yr) - 2 FROM station_rollup)
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ORDER BY date
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"""
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pdf_days = spark.sql(q_days).toPandas()
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def plot_date_values(spark: SparkSession, level: str):
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filters = {
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"days": "YEAR(jahr) > 2017 AND YEAR(jahr) < 2021 AND messtunde IS NULL AND tag IS NOT NULL",
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"months": "YEAR(jahr) > 1999 AND YEAR(jahr) < 2021 AND tag IS NULL AND monat IS NOT NULL",
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"quartals": "YEAR(jahr) > 1999 AND YEAR(jahr) < 2021 AND tag IS NULL AND monat IS NULL AND quartal IS NOT NULL",
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"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"
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}
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x_col = {"days": "tag", "months": "monat", "quartals": "quartal", "years": "jahr"}
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plt.figure(1, figsize=(10, 5))
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plt.plot(pdf_days['date'], pdf_days['avg_temp'], label='Daily Avg', linewidth=0.5)
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plt.title(f"{station_name}: Daily Average (Last 3 Years)")
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plt.xlabel('Date')
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plt.ylabel('Temp °C')
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plt.tight_layout()
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plt.show()
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pdf = spark.sql(f"SELECT * FROM mmacdcdata WHERE {filters[level]}").toPandas()
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if pdf.empty: return
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# Plot 2: Monatswerte (10-20 Jahre)
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q_months = """
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SELECT mo_date as date, avg_temp
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FROM station_rollup
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WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NOT NULL AND da IS NULL
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AND yr >= (SELECT MAX(yr) - 20 FROM station_rollup)
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ORDER BY date
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"""
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pdf_months = spark.sql(q_months).toPandas()
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plt.figure(2, figsize=(10, 5))
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plt.plot(pdf_months['date'], pdf_months['avg_temp'], color='green', label='Monthly Avg')
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plt.title(f"{station_name}: Monthly Average (Last 20 Years)")
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plt.xlabel('Date')
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plt.ylabel('Temp °C')
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plt.tight_layout()
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plt.show()
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# Plot 3: Quartalswerte
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q_quarters = """
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SELECT qt_date as date, avg_temp
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FROM station_rollup
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WHERE yr IS NOT NULL AND qt IS NOT NULL AND mo IS NULL AND da IS NULL
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AND yr >= (SELECT MAX(yr) - 20 FROM station_rollup)
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ORDER BY date
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"""
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pdf_quarters = spark.sql(q_quarters).toPandas()
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plt.figure(3, figsize=(10, 5))
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plt.plot(pdf_quarters['date'], pdf_quarters['avg_temp'], color='orange', marker='o', linestyle='-', label='Quarterly Avg')
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plt.title(f"{station_name}: Quarterly Average (Last 20 Years)")
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plt.show()
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# Plot 4: Jahreswerte
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q_years = """
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SELECT yr_date as date, min_temp, max_temp, avg_temp
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FROM station_rollup
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WHERE yr IS NOT NULL AND qt IS NULL AND mo IS NULL AND da IS NULL
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AND yr >= (SELECT MAX(yr) - 20 FROM station_rollup)
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ORDER BY date
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"""
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pdf_years = spark.sql(q_years).toPandas()
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plt.figure(4, figsize=(10, 5))
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plt.plot(pdf_years['date'], pdf_years['max_temp'], color='red', label='Max')
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plt.plot(pdf_years['date'], pdf_years['avg_temp'], color='black', label='Avg')
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plt.plot(pdf_years['date'], pdf_years['min_temp'], color='blue', label='Min')
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plt.title(f"{station_name}: Yearly Aggregates (Last 20 Years)")
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plt.figure(figsize=(10, 5))
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plt.plot(pdf[x_col[level]], pdf["maxTemperatur"], "red", label="Max")
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plt.plot(pdf[x_col[level]], pdf["avgTemperatur"], "green", label="Avg")
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plt.plot(pdf[x_col[level]], pdf["minTemperatur"], "blue", label="Min")
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plt.title(f"{level.capitalize()}")
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plt.legend()
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plt.grid(True)
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plt.show()
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# --- Aufgabe B ---
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def task_11b_rank(spark: SparkSession):
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print("\n--- Aufgabe 11b: TempMonat Ranking ---")
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start = time.time()
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q_tempmonat = """
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def create_tempmonat(spark: SparkSession):
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query = """
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WITH base_data AS (
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SELECT
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d.stationId,
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s.station_name,
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SUBSTR(CAST(d.date AS STRING), 1, 4) as year,
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SUBSTR(CAST(d.date AS STRING), 6, 2) as month,
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MIN(d.TT_TU) as min_t,
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MAX(d.TT_TU) as max_t,
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AVG(d.TT_TU) as avg_t
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gs.station_name AS stationsname,
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d.TT_TU,
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TO_DATE(SUBSTR(d.date, 1, 4), 'yyyy') AS jahr_val,
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TO_DATE(CONCAT(SUBSTR(d.date, 1, 4), '-', SUBSTR(d.date, 5, 2), '-01')) AS monat_val
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FROM german_stations_data d
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JOIN german_stations s ON d.stationId = s.stationId
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WHERE d.TT_TU IS NOT NULL AND d.TT_TU > -50
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GROUP BY d.stationId, s.station_name, year, month
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JOIN german_stations gs ON d.stationId = gs.stationId
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WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
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)
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SELECT
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stationId,
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stationsname,
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MIN(TT_TU) AS minTemperatur,
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MAX(TT_TU) AS maxTemperatur,
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AVG(TT_TU) AS avgTemperatur,
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jahr_val AS jahr,
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monat_val AS monat
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FROM base_data
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GROUP BY stationId, stationsname, jahr_val, monat_val
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"""
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df_tm = spark.sql(q_tempmonat)
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df_tm.createOrReplaceTempView("tempmonat")
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# 1. Ranking Partitioniert nach Monat im Jahr 2015
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print(" > Berechne Ranking für 2015 (partitioniert nach Monat)...")
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q_rank_2015 = """
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SELECT
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month, station_name, min_t,
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RANK() OVER (PARTITION BY month ORDER BY min_t ASC) as rank_min,
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RANK() OVER (PARTITION BY month ORDER BY max_t ASC) as rank_max,
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RANK() OVER (PARTITION BY month ORDER BY avg_t ASC) as rank_avg
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FROM tempmonat
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WHERE year = '2015'
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ORDER BY rank_min, month
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spark.sql(query).cache().createOrReplaceTempView("tempmonat")
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def rank_temperatures(spark: SparkSession, limit: int, year: int = None):
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where_clause = f"WHERE YEAR(jahr) = {year}" if year else ""
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query = f"""
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SELECT stationid, stationsname, monat, minTemperatur,
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RANK() OVER (ORDER BY minTemperatur ASC) AS rangMIN,
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maxTemperatur,
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RANK() OVER (ORDER BY maxTemperatur DESC) AS rangMAX,
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avgTemperatur,
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RANK() OVER (ORDER BY avgTemperatur DESC) AS rangAVG
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FROM tempmonat
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{where_clause}
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ORDER BY rangMIN
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"""
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spark.sql(q_rank_2015).show(10)
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spark.sql(query).show(limit, truncate=False)
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# 2. Globales Ranking (über alle Monate/Jahre hinweg)
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print(" > Berechne Ranking global (kälteste Monate aller Zeiten)...")
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q_rank_global = """
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# --- Aufgabe C ---
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def create_grouping_sets_view(spark: SparkSession):
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query = """
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WITH base_gs AS (
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SELECT
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year, month, station_name, min_t,
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RANK() OVER (ORDER BY min_t ASC) as rank_min,
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RANK() OVER (ORDER BY max_t ASC) as rank_max,
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RANK() OVER (ORDER BY avg_t ASC) as rank_avg
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FROM tempmonat
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ORDER BY rank_min
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"""
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spark.sql(q_rank_global).show(10)
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print(f"Dauer 11b: {time.time() - start:.2f}s")
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input(">> 11b: Check Spark UI (Jobs/Stages). Enter...")
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def task_11c_groupingsets(spark: SparkSession):
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print("\n--- Aufgabe 11c: Grouping Sets ---")
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start = time.time()
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q_prep = """
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SELECT
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CAST(SUBSTR(CAST(d.date AS STRING), 1, 4) AS INT) as year,
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CAST(SUBSTR(CAST(d.date AS STRING), 6, 2) AS INT) as month,
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s.station_name,
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s.bundesland,
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d.TT_TU
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d.stationId,
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-- TRIM entfernt Leerzeichen für saubere Tabellen
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TRIM(gs.bundesland) AS bundesland_clean,
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d.TT_TU,
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-- Extrahiere Jahr und Kalendermonat (1-12)
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YEAR(TO_DATE(d.date, 'yyyyMMdd')) AS jahr_val,
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MONTH(TO_DATE(d.date, 'yyyyMMdd')) AS monat_val
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FROM german_stations_data d
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JOIN german_stations s ON d.stationId = s.stationId
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WHERE d.TT_TU > -50
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JOIN german_stations gs ON d.stationId = gs.stationId
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WHERE d.TT_TU IS NOT NULL AND d.TT_TU <> -999
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)
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SELECT
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stationId,
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bundesland_clean AS bundesland,
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jahr_val AS jahr,
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monat_val AS monat,
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MIN(TT_TU) AS minTemperatur,
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MAX(TT_TU) AS maxTemperatur,
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AVG(TT_TU) AS avgTemperatur
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FROM base_gs
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GROUP BY GROUPING SETS (
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(bundesland_clean, jahr_val), -- 1. Jahr und Bundesland
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(stationId, jahr_val), -- 2. Jahr und Station
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(bundesland_clean, monat_val) -- 3. Monat und Bundesland
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)
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"""
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spark.sql(q_prep).createOrReplaceTempView("gs_base")
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df = spark.sql(query)
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df.cache()
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df.createOrReplaceTempView("tempmma_gs")
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q_sets = """
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def show_seperate_gs(spark: SparkSession, limit: int):
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# Filter: stationId muss NULL sein, monat muss NULL sein
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spark.sql("""
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SELECT
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year,
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month,
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||||
jahr,
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||||
bundesland,
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||||
station_name,
|
||||
MIN(TT_TU) as min_t, MAX(TT_TU) as max_t, AVG(TT_TU) as avg_t
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||||
FROM gs_base
|
||||
GROUP BY GROUPING SETS (
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||||
(year, bundesland),
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(year, station_name),
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(month, bundesland)
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||||
)
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||||
"""
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df_gs = spark.sql(q_sets)
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df_gs.cache()
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df_gs.createOrReplaceTempView("grouping_result")
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||||
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||||
# Action zum Cachen
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||||
df_gs.count()
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print(f"Grouping Sets berechnet. Dauer: {time.time() - start:.2f}s")
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minTemperatur,
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||||
maxTemperatur,
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||||
ROUND(avgTemperatur, 2) as avgTemperatur
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||||
FROM tempmma_gs
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WHERE bundesland IS NOT NULL
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||||
AND jahr IS NOT NULL
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||||
AND stationId IS NULL
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||||
AND monat IS NULL
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||||
ORDER BY jahr DESC, bundesland ASC
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||||
""").show(limit, truncate=False)
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||||
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print("Auswahl 1: Jahr & Bundesland")
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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)
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||||
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print("Auswahl 2: Jahr & Station")
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||||
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)
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print("Auswahl 3: Monat & Bundesland (Jahreszeitlicher Verlauf je Land)")
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||||
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)
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||||
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||||
input(">> 11c: Check Spark UI (Zugriffspläne/Storage). Enter...")
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||||
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||||
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||||
# ---------------------------------------------------------
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||||
# AUFGABE 12
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||||
# ---------------------------------------------------------
|
||||
|
||||
def task_12_stocks_analysis(spark: SparkSession):
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||||
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 = """
|
||||
# Filter: bundesland muss NULL sein, monat muss NULL sein
|
||||
spark.sql("""
|
||||
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")
|
||||
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)
|
||||
|
||||
# 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 = """
|
||||
# Filter: stationId muss NULL sein, jahr muss NULL sein
|
||||
spark.sql("""
|
||||
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")
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
# Aufgabe 12
|
||||
init_view_stocks(spark)
|
||||
task_12_stocks_analysis(spark)
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
main(scon, spark)
|
||||
Reference in New Issue
Block a user