-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathquery2b.py
More file actions
75 lines (60 loc) · 3.28 KB
/
Copy pathquery2b.py
File metadata and controls
75 lines (60 loc) · 3.28 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, when, round, rank, trim, upper
from pyspark.sql.window import Window
import time
# Δημιουργία SparkSession
spark = SparkSession.builder.appName("CrimeAnalysis").getOrCreate()
# Καταγραφή χρόνου έναρξης για CSV
start_time_csv = time.time()
# Φόρτωση δεδομένων από CSV
crime_data_csv = spark.read.csv("data/CrimeData/Crime_Data_from_2010_to_2019_20241101.csv", header=True, inferSchema=True)
crime_data_csv = crime_data_csv.union(spark.read.csv("data/CrimeData/Crime_Data_from_2020_to_Present_20241101.csv", header=True, inferSchema=True))
# Επιλογή των απαραίτητων στηλών
crime_data_csv = crime_data_csv.select(
col("DATE OCC").alias("date"),
col("AREA NAME").alias("precinct"),
col("Status").alias("status")
)
# Καθαρισμός τιμών στη στήλη Status
crime_data_csv = crime_data_csv.withColumn("status", trim(upper(col("status"))))
# Εξαγωγή του έτους από την ημερομηνία
crime_data_csv = crime_data_csv.withColumn("year", col("date").substr(7, 4).cast("int"))
# Καταγραφή χρόνου λήξης για CSV
end_time_csv = time.time()
print(f"Execution Time (CSV Loading): {end_time_csv - start_time_csv:.2f} seconds")
# Αποθήκευση των δεδομένων σε Parquet
crime_data_csv.write.parquet("data/output/crime_data.parquet", mode="overwrite")
# Καταγραφή χρόνου έναρξης για Parquet
start_time_parquet = time.time()
# Φόρτωση των δεδομένων από Parquet
crime_data_parquet = spark.read.parquet("data/output/crime_data.parquet")
# Καταγραφή χρόνου λήξης για Parquet
end_time_parquet = time.time()
print(f"Execution Time (Parquet Loading): {end_time_parquet - start_time_parquet:.2f} seconds")
# Υλοποίηση με SQL API
crime_data_parquet.createOrReplaceTempView("crime_data")
query = """
WITH ranked_crimes AS (
SELECT
year,
precinct,
ROUND((SUM(CASE WHEN status IN ('AA', 'JA') THEN 1 ELSE 0 END) * 100.0) / COUNT(status), 5) AS closed_case_rate,
RANK() OVER (PARTITION BY year ORDER BY (SUM(CASE WHEN status IN ('AA', 'JA') THEN 1 ELSE 0 END) * 100.0) / COUNT(status) DESC) AS ranking
FROM crime_data
GROUP BY year, precinct
)
SELECT * FROM ranked_crimes WHERE ranking <= 3 ORDER BY year, ranking
"""
# Καταγραφή χρόνου έναρξης για SQL Query σε Parquet
start_time_query_parquet = time.time()
sql_result_parquet = spark.sql(query)
sql_result_parquet.show(truncate=False, n=sql_result_parquet.count())
end_time_query_parquet = time.time()
print(f"Execution Time (Parquet Query): {end_time_query_parquet - start_time_query_parquet:.2f} seconds")
# Καταγραφή χρόνου έναρξης για SQL Query σε CSV
crime_data_csv.createOrReplaceTempView("crime_data")
start_time_query_csv = time.time()
sql_result_csv = spark.sql(query)
sql_result_csv.show(truncate=False, n=sql_result_csv.count())
end_time_query_csv = time.time()
print(f"Execution Time (CSV Query): {end_time_query_csv - start_time_query_csv:.2f} seconds")