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Nicholas Pease
2025-12-18 23:06:51 +00:00
parent 68df013917
commit 47a7fed915
4 changed files with 176 additions and 325 deletions
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requirements.txt
-2
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@@ -1,2 +0,0 @@
ipykernel==6.4.1
pyspark==3.1.2
task1.py
+50 -214
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@@ -1,6 +1,7 @@
# HW3 - Task 1
# Nicholas Pease
# IMDB Data Loading into MongoDB
# I decided to try MongoD because structure of documents, which aligns closely with JSON format, where I store a majority of the data I work with in other projects.
# I stored the data in this assignment in a collection of movie documents, where each document contains information about a movie, its directors, and its cast.
import csv
import pandas as pd
@@ -10,7 +11,6 @@ import os
from typing import Dict, List, Optional
from collections import defaultdict
# Global data structures for processing
movies = {}
persons = {}
directors = {}
@@ -18,46 +18,25 @@ client = None
db = None
movies_collection = None
def connect(connection_string: str = "mongodb://localhost:27017/",
database_name: str = "imdb_database") -> bool:
def connect():
global client, db, movies_collection
try:
client = MongoClient(connection_string, serverSelectionTimeoutMS=5000)
client = MongoClient("mongodb://localhost:27017/", serverSelectionTimeoutMS=5000)
client.admin.command('ping')
db = client[database_name]
db = client["imdb_database"]
movies_collection = db.movies
print(f"Connected to MongoDB database: {database_name}")
return True
print(f"Connected to MongoDB database: imdb_database")
except ConnectionFailure as e:
print(f"Failed to connect to MongoDB: {e}")
return False
except Exception as e:
print(f"MongoDB connection error: {e}")
return False
def disconnect():
global client
if client:
client.close()
def load_movies_data(file_path: str, sample_size: Optional[int] = None) -> bool:
def load_movies_data(file_path):
global movies
try:
movies_loaded = 0
with open(file_path, 'r', encoding='latin-1') as file:
next(file)
next(file) # skip header
for line in file:
if sample_size and movies_loaded >= sample_size:
break
line = line.strip()
if not line:
continue
try:
parts = line.split(',')
movie_id = int(parts[0])
@@ -81,29 +60,11 @@ def load_movies_data(file_path: str, sample_size: Optional[int] = None) -> bool:
movies_loaded += 1
except (ValueError, IndexError):
continue
print(f"Loaded {len(movies)} movies")
return True
except Exception as e:
print(f"Error loading movies: {e}")
return False
def load_persons_data(file_path: str) -> bool:
def load_persons_data(file_path):
global persons
try:
persons_df = pd.read_csv(
file_path,
encoding='latin-1',
dtype={
'id': 'int32',
'fname': 'string',
'lname': 'string',
'gender': 'string'
}
)
persons_df = pd.read_csv(file_path,encoding='latin-1',dtype={'id': 'int32','fname': 'string','lname': 'string', 'gender': 'string'})
for _, row in persons_df.iterrows():
person_id = int(row['id'])
@@ -115,24 +76,10 @@ def load_persons_data(file_path: str) -> bool:
}
print(f"Loaded {len(persons)} persons")
return True
except Exception as e:
print(f"Error loading persons: {e}")
return False
def load_directors_data(file_path: str) -> bool:
def load_directors_data(file_path):
global directors
try:
directors_df = pd.read_csv(
file_path,
encoding="latin-1",
dtype={
'id': 'int32',
'fname': 'string',
'lname': 'string'
}
)
directors_df = pd.read_csv(file_path,encoding="latin-1",dtype={'id': 'int32','fname': 'string','lname': 'string'})
for _, row in directors_df.iterrows():
director_id = int(row['id'])
@@ -143,20 +90,10 @@ def load_directors_data(file_path: str) -> bool:
}
print(f"Loaded {len(directors)} directors")
return True
except Exception as e:
print(f"Error loading directors: {e}")
return False
def load_movie_directors_data(file_path: str) -> bool:
def load_movie_directors_data(file_path):
global movies, directors
try:
movie_directors_df = pd.read_csv(
file_path,
encoding='latin-1',
dtype={'did': 'int32', 'mid': 'int32'}
)
movie_directors_df = pd.read_csv(file_path,encoding='latin-1',dtype={'did': 'int32', 'mid': 'int32'})
linked_count = 0
for _, row in movie_directors_df.iterrows():
@@ -171,45 +108,28 @@ def load_movie_directors_data(file_path: str) -> bool:
movies[movie_id]['directors'].append(director_info)
linked_count += 1
return True
except Exception as e:
print(f"Error loading movie-director relationships: {e}")
return False
def load_cast_data(file_path: str, max_cast_per_movie: int = 25) -> bool:
def load_cast_data(file_path):
global movies, persons
try:
if not os.path.exists(file_path):
return True
movie_cast_count = defaultdict(int)
linked_count = 0
with open(file_path, 'r', encoding='latin-1') as file:
next(file)
for line_num, line in enumerate(file):
if line_num > 100000:
break
next(file) # skip header
for line in file:
line = line.strip()
if not line:
continue
try:
parts = line.split(',')
if len(parts) < 3:
continue
person_id = int(parts[0])
movie_id = int(parts[1])
role = ','.join(parts[2:]) if len(parts) > 2 else "[Unknown]"
if (movie_id in movies and person_id in persons and
movie_cast_count[movie_id] < max_cast_per_movie):
role = parts[2]
if (movie_id in movies and person_id in persons):
cast_info = {
'person_id': person_id,
'name': persons[person_id]['name'],
@@ -217,37 +137,16 @@ def load_cast_data(file_path: str, max_cast_per_movie: int = 25) -> bool:
'role': role
}
movies[movie_id]['cast'].append(cast_info)
movie_cast_count[movie_id] += 1
linked_count += 1
except (ValueError, KeyError):
continue
if linked_count > 0:
print(f"Loaded {linked_count} cast entries")
return True
except Exception as e:
return True
def create_indexes():
global movies_collection
try:
movies_collection.create_index("movie_name", unique=True, name="idx_movie_name")
movies_collection.create_index("year", name="idx_year")
movies_collection.create_index("rank", name="idx_rank")
movies_collection.create_index("directors.name", name="idx_director_name")
except Exception as e:
pass
def insert_movies_to_mongodb(batch_size: int = 2000) -> bool:
def insert_movies_to_mongodb():
global movies, movies_collection
try:
movies_collection.drop()
documents = []
inserted_count = 0
for movie_id, movie_data in movies.items():
document = {
'movie_id': movie_data['movie_id'],
@@ -257,78 +156,30 @@ def insert_movies_to_mongodb(batch_size: int = 2000) -> bool:
'directors': movie_data['directors'],
'cast': movie_data['cast']
}
documents.append(document)
if len(documents) >= batch_size:
movies_collection.insert_many(documents, ordered=False)
documents = []
movies_collection.insert_one(document)
inserted_count += 1
if documents:
movies_collection.insert_many(documents, ordered=False)
create_indexes()
# Create indexes
movies_collection.create_index("movie_name", name="idx_movie_name")
total_count = movies_collection.count_documents({})
print(f"Inserted {total_count} movies into MongoDB")
return True
except Exception as e:
print(f"Error inserting data into MongoDB: {e}")
return False
def query_movie_by_name(movie_name: str) -> Optional[Dict]:
global movies_collection
try:
result = movies_collection.find_one({"movie_name": movie_name})
return result
except Exception as e:
print(f"Error querying movie by name: {e}")
return None
def query_movies_by_year_range(start_year: int, end_year: int) -> List[Dict]:
global movies_collection
try:
query = {"year": {"$gte": start_year, "$lte": end_year}}
results = movies_collection.find(query).sort("year", 1)
return list(results)
except Exception as e:
print(f"Error querying movies by year range: {e}")
return []
print(f"Inserted {inserted_count} movies into MongoDB")
def main():
try:
print("IMDB DATA LOADING FOR MONGODB")
print("=" * 50)
# Load in the files
movie_file = os.path.join("IMDB", "IMDBMovie.txt")
person_file = os.path.join("IMDB", "IMDBPerson.txt")
director_file = os.path.join("IMDB", "IMDBDirectors.txt")
movie_director_file = os.path.join("IMDB", "IMDBMovie_Directors.txt")
cast_file = os.path.join("IMDB", "IMDBCast.txt")
data_directory = "IMDB"
sample_size = None
movie_file = os.path.join(data_directory, "IMDBMovie.txt")
person_file = os.path.join(data_directory, "IMDBPerson.txt")
director_file = os.path.join(data_directory, "IMDBDirectors.txt")
movie_director_file = os.path.join(data_directory, "IMDBMovie_Directors.txt")
cast_file = os.path.join(data_directory, "IMDBCast.txt")
if not load_movies_data(movie_file, sample_size=sample_size):
print("Failed to load movies")
return
if not load_persons_data(person_file):
print("Failed to load persons")
return
if not load_directors_data(director_file):
print("Failed to load directors")
return
if not load_movie_directors_data(movie_director_file):
print("Failed to load movie-director relationships")
return
if not load_cast_data(cast_file):
print("Failed to load cast data")
return
# Load the data into memory
load_movies_data(movie_file)
load_persons_data(person_file)
load_directors_data(director_file)
load_movie_directors_data(movie_director_file)
load_cast_data(cast_file)
total_movies = len(movies)
movies_with_directors = sum(1 for movie in movies.values() if movie['directors'])
@@ -338,31 +189,16 @@ def main():
print(f"Movies with directors: {movies_with_directors}")
print(f"Movies with cast: {movies_with_cast}")
if connect():
if insert_movies_to_mongodb(batch_size=1000):
print("Data inserted into MongoDB successfully")
# Insert the data into MongoDB
connect()
insert_movies_to_mongodb()
test_movie = query_movie_by_name("$ (1971)")
if test_movie:
print(f"\nFound movie: {test_movie['movie_name']} ({test_movie.get('year', 'N/A')})")
# b Query DB for "Shrek (2001)"
result = movies_collection.find_one({'movie_name': "Shrek (2001)"})
print('\nQuery Result for "Shrek (2001)":')
print(result)
total_in_db = movies_collection.count_documents({})
movies_with_directors_db = movies_collection.count_documents({"directors": {"$ne": []}})
global client
client.close()
print(f"Total movies in database: {total_in_db}")
print(f"Movies with directors: {movies_with_directors_db}")
else:
print("Failed to insert data into MongoDB")
else:
print("MongoDB not available")
except Exception as e:
print(f"Error in main execution: {e}")
finally:
disconnect()
if __name__ == "__main__":
main()
main()
task2.py
+7
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@@ -1,3 +1,10 @@
# HW3 - Task 2
# Nicholas Pease
# This loads a directed graph from an input file,
# computes the Page Rank for each vertex using Spark,
# and saves the results to an output file.
# This follows the same structure as the assignment description dictates.
from pyspark import SparkConf, SparkContext
def main():
task3.py
+11 -1
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@@ -1,3 +1,10 @@
# HW3 - Task 3
# Nicholas Pease
# This takes the output from Task 2 (Page Rank results),
# loads it into a Spark DataFrame, performs SQL queries to
# extract specific information, joins with another input file with labels,
# and saves the final results to a CSV file.
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
@@ -30,9 +37,12 @@ def main():
names_df = spark.createDataFrame(names_rdd, ["id", "name"])
names_df.createOrReplaceTempView("names")
# e. Join and print dataframes on id
# e. Join and print dataframes on id, save as CSV (t3-out.csv)
result = spark.sql("SELECT n.id, n.name, p.page_rank FROM names n JOIN page_rank p ON n.id = p.id")
print("Joined DataFrame (id, name, page_rank):")
result.show()
with open("t3-out.csv", "w") as f:
for row in result.collect():
f.write(f"{row['id']} {row['name']} {row['page_rank']}\n")
main()