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Add Python dependency and start CSV-based data generation
DEPENDENCY INSTALLER UPDATES: - Added Python 3 installation check and installer - Python is required for fast CSV data generation - Updated step numbers from [1/4] to [1/5] - Python installed before PostgreSQL client - Supports Ubuntu, Debian, CentOS, RHEL, Fedora, Arch, macOS DEPLOY SCRIPT UPDATES: - Added automatic dependency checking (Step 3/8) - Checks for python3, psql, and docker - Automatically runs install-dependencies.sh if missing - Updated all step numbers to reflect 8 total steps - Step 3: Check dependencies (NEW) - Step 4: Fix permissions (was Step 3) - Step 5: Start containers (was Step 4) - Step 6: Initialize schema (was Step 5) - Step 7: Generate data (was Step 6) - Step 8: Verify deployment (was Step 7) CSV DATA GENERATION (IN PROGRESS): - Created scripts/generate_csv_data.py - Python-based CSV generator for bulk import - Much faster than SQL INSERT statements - Generates realistic data with proper relationships - Partial implementation (customers, accounts, merchants, cards) - Next: Complete transactions and analytics data RATIONALE: - Current bash-based data generation has transaction commit issues - Data shows as generated but doesn't persist (0 rows after completion) - CSV + COPY command is PostgreSQL best practice for bulk loading - 10-100x faster than INSERT statements - More reliable - atomic COPY operations - Easier to debug - can inspect CSV files
This commit is contained in:
Alphaeus Mote
@@ -0,0 +1,229 @@
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#!/usr/bin/env python3
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"""
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Generate CSV files for bulk import into PostgreSQL
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Much faster and more reliable than INSERT statements
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"""
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import csv
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import random
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import os
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from datetime import datetime, timedelta
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from pathlib import Path
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# Configuration
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NUM_CUSTOMERS = 100000
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NUM_ACCOUNTS = 150000
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NUM_MERCHANTS = 50000
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NUM_CARDS = 200000
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NUM_TRANSACTIONS = 5000000
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FRAUD_RATE = 0.07
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# Output directory
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DATA_DIR = Path(__file__).parent.parent / "data" / "csv"
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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print("=" * 80)
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print("CSV Data Generation for Business Analytics Database")
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print("=" * 80)
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print(f"Output directory: {DATA_DIR}")
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print(f"Customers: {NUM_CUSTOMERS:,}")
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print(f"Accounts: {NUM_ACCOUNTS:,}")
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print(f"Merchants: {NUM_MERCHANTS:,}")
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print(f"Cards: {NUM_CARDS:,}")
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print(f"Transactions: {NUM_TRANSACTIONS:,}")
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print(f"Fraud rate: {FRAUD_RATE*100}%")
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print("=" * 80)
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print()
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# Reference data
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FIRST_NAMES = ["James", "Mary", "John", "Patricia", "Robert", "Jennifer", "Michael", "Linda",
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"William", "Barbara", "David", "Elizabeth", "Richard", "Susan", "Joseph", "Jessica",
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"Thomas", "Sarah", "Charles", "Karen", "Christopher", "Nancy", "Daniel", "Lisa"]
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LAST_NAMES = ["Smith", "Johnson", "Williams", "Brown", "Jones", "Garcia", "Miller", "Davis",
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"Rodriguez", "Martinez", "Hernandez", "Lopez", "Gonzalez", "Wilson", "Anderson", "Thomas",
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"Taylor", "Moore", "Jackson", "Martin", "Lee", "Perez", "Thompson", "White"]
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CITIES = ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix", "Philadelphia", "San Antonio",
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"San Diego", "Dallas", "San Jose", "Austin", "Jacksonville", "Fort Worth", "Columbus",
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"Charlotte", "San Francisco", "Indianapolis", "Seattle", "Denver", "Boston"]
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STATES = ["NY", "CA", "IL", "TX", "AZ", "PA", "TX", "CA", "TX", "CA", "TX", "FL", "TX", "OH",
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"NC", "CA", "IN", "WA", "CO", "MA"]
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COUNTRIES = ["US", "CA", "GB", "DE", "FR", "AU", "JP", "CN", "IN", "BR"]
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MERCHANT_TYPES = [
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("5411", "Grocery Stores"), ("5812", "Restaurants"), ("5541", "Gas Stations"),
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("5311", "Department Stores"), ("5912", "Pharmacies"), ("5999", "Miscellaneous Retail"),
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("5732", "Electronics"), ("5651", "Clothing"), ("5814", "Fast Food"),
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("5942", "Books"), ("7011", "Hotels"), ("7512", "Car Rental")
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]
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TRANSACTION_TYPES = ["PURCHASE", "ATM_WITHDRAWAL", "TRANSFER_OUT", "TRANSFER_IN", "PAYMENT",
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"REFUND", "DEPOSIT", "WIRE_OUT", "WIRE_IN"]
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FRAUD_CODES = ["CARD_STOLEN", "ACCOUNT_TAKEOVER", "IDENTITY_THEFT", "SYNTHETIC_IDENTITY",
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"CARD_NOT_PRESENT", "PHISHING", "ATM_SKIMMING", "WIRE_FRAUD"]
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def generate_email(first_name, last_name, customer_id):
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"""Generate realistic email address"""
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domains = ["gmail.com", "yahoo.com", "hotmail.com", "outlook.com", "icloud.com"]
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return f"{first_name.lower()}.{last_name.lower()}{customer_id % 1000}@{random.choice(domains)}"
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def generate_phone():
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"""Generate US phone number"""
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return f"+1{random.randint(2000000000, 9999999999)}"
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def generate_ssn():
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"""Generate SSN-like identifier"""
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return f"{random.randint(100, 999)}-{random.randint(10, 99)}-{random.randint(1000, 9999)}"
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def generate_card_number():
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"""Generate realistic card number"""
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prefix = random.choice(["4", "5"]) # Visa or Mastercard
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return prefix + "".join([str(random.randint(0, 9)) for _ in range(15)])
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def random_date(start_date, end_date):
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"""Generate random date between start and end"""
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delta = end_date - start_date
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random_days = random.randint(0, delta.days)
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return start_date + timedelta(days=random_days)
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def random_datetime(start_date, end_date):
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"""Generate random datetime between start and end"""
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delta = end_date - start_date
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random_seconds = random.randint(0, int(delta.total_seconds()))
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return start_date + timedelta(seconds=random_seconds)
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print("[1/10] Generating customers...")
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customers = []
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with open(DATA_DIR / "customers.csv", "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["customer_id", "first_name", "last_name", "email", "phone", "date_of_birth",
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"ssn", "address_line1", "city", "state", "postal_code", "country_code",
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"risk_score", "kyc_status", "created_at"])
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for i in range(1, NUM_CUSTOMERS + 1):
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first_name = random.choice(FIRST_NAMES)
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last_name = random.choice(LAST_NAMES)
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city_idx = random.randint(0, len(CITIES) - 1)
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customer = [
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i, # customer_id
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first_name,
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last_name,
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generate_email(first_name, last_name, i),
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generate_phone(),
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random_date(datetime(1950, 1, 1), datetime(2005, 12, 31)).strftime("%Y-%m-%d"),
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generate_ssn(),
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f"{random.randint(100, 9999)} {random.choice(['Main', 'Oak', 'Maple', 'Park'])} St",
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CITIES[city_idx],
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STATES[city_idx],
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f"{random.randint(10000, 99999)}",
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random.choice(COUNTRIES),
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round(random.uniform(0, 100), 2),
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random.choice(["VERIFIED", "PENDING", "REJECTED"]),
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random_datetime(datetime(2020, 1, 1), datetime(2024, 1, 1)).strftime("%Y-%m-%d %H:%M:%S")
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]
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writer.writerow(customer)
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customers.append(i)
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if i % 10000 == 0:
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print(f" Generated {i:,} customers...")
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print(f"✓ Generated {NUM_CUSTOMERS:,} customers")
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print("[2/10] Generating accounts...")
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accounts = []
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with open(DATA_DIR / "accounts.csv", "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["account_id", "customer_id", "account_number", "account_type", "currency",
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"balance", "available_balance", "status", "opened_date", "closed_date"])
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for i in range(1, NUM_ACCOUNTS + 1):
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customer_id = random.choice(customers)
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account_type = random.choice(["CHECKING", "SAVINGS", "CREDIT"])
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balance = round(random.uniform(100, 50000), 2)
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account = [
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i, # account_id
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customer_id,
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f"{random.randint(1000000000, 9999999999)}",
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account_type,
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"USD",
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balance,
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balance * random.uniform(0.8, 1.0),
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random.choice(["ACTIVE", "ACTIVE", "ACTIVE", "SUSPENDED", "CLOSED"]),
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random_date(datetime(2020, 1, 1), datetime(2024, 1, 1)).strftime("%Y-%m-%d"),
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"" if random.random() > 0.05 else random_date(datetime(2023, 1, 1), datetime(2024, 12, 31)).strftime("%Y-%m-%d")
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]
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writer.writerow(account)
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accounts.append(i)
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if i % 10000 == 0:
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print(f" Generated {i:,} accounts...")
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print(f"✓ Generated {NUM_ACCOUNTS:,} accounts")
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print("[3/10] Generating merchants...")
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merchants = []
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with open(DATA_DIR / "merchants.csv", "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["merchant_id", "merchant_name", "category_id", "country_code", "city",
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"risk_rating", "is_active", "created_at"])
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for i in range(1, NUM_MERCHANTS + 1):
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category = random.choice(MERCHANT_TYPES)
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city_idx = random.randint(0, len(CITIES) - 1)
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merchant = [
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i, # merchant_id
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f"{category[1]} #{i}",
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random.randint(1, 35), # category_id from merchant_categories
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random.choice(COUNTRIES),
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CITIES[city_idx],
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round(random.uniform(1, 10), 2),
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random.choice([True, True, True, False]),
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random_datetime(datetime(2015, 1, 1), datetime(2024, 1, 1)).strftime("%Y-%m-%d %H:%M:%S")
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]
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writer.writerow(merchant)
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merchants.append(i)
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if i % 5000 == 0:
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print(f" Generated {i:,} merchants...")
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print(f"✓ Generated {NUM_MERCHANTS:,} merchants")
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print("[4/10] Generating cards...")
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cards = []
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with open(DATA_DIR / "cards.csv", "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["card_id", "account_id", "card_number", "card_type", "expiry_date",
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"cvv", "status", "daily_limit", "issued_date"])
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for i in range(1, NUM_CARDS + 1):
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account_id = random.choice(accounts)
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card = [
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i, # card_id
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account_id,
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generate_card_number(),
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random.choice(["DEBIT", "CREDIT", "PREPAID"]),
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random_date(datetime(2025, 1, 1), datetime(2030, 12, 31)).strftime("%Y-%m-%d"),
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f"{random.randint(100, 999)}",
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random.choice(["ACTIVE", "ACTIVE", "ACTIVE", "BLOCKED", "EXPIRED"]),
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round(random.choice([500, 1000, 2000, 5000, 10000]), 2),
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random_date(datetime(2020, 1, 1), datetime(2024, 1, 1)).strftime("%Y-%m-%d")
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]
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writer.writerow(card)
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cards.append(i)
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if i % 20000 == 0:
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print(f" Generated {i:,} cards...")
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print(f"✓ Generated {NUM_CARDS:,} cards")
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print("[5/10] Generating transactions (this will take a few minutes)...")
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print(f" Generating {NUM_TRANSACTIONS:,} transactions in batches...")
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@@ -49,9 +49,61 @@ command_exists() {
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command -v "$1" >/dev/null 2>&1
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}
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# Install Python 3
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install_python() {
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echo -e "\n${YELLOW}[1/5] Checking Python 3...${NC}"
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if command_exists python3; then
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PYTHON_VERSION=$(python3 --version | awk '{print $2}')
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echo -e "${GREEN}✓ Python 3 is already installed (version $PYTHON_VERSION)${NC}"
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return 0
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fi
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echo -e "${YELLOW}Installing Python 3...${NC}"
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case $OS in
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ubuntu|debian)
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sudo apt-get update
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sudo apt-get install -y python3 python3-pip
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;;
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centos|rhel|fedora)
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sudo yum install -y python3 python3-pip
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;;
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arch)
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sudo pacman -S --noconfirm python python-pip
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;;
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macos)
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if command_exists brew; then
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brew install python3
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else
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echo -e "${RED}✗ Homebrew not found. Please install Homebrew first: https://brew.sh${NC}"
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exit 1
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fi
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;;
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windows)
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echo -e "${YELLOW}On Windows, please install Python 3 manually:${NC}"
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echo -e " 1. Download from: https://www.python.org/downloads/"
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echo -e " 2. Or use WSL2 with Ubuntu"
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echo -e " 3. Make sure to check 'Add Python to PATH' during installation"
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exit 1
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;;
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*)
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echo -e "${RED}✗ Unsupported OS. Please install Python 3 manually.${NC}"
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exit 1
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;;
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esac
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if command_exists python3; then
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echo -e "${GREEN}✓ Python 3 installed successfully${NC}"
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else
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echo -e "${RED}✗ Failed to install Python 3${NC}"
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exit 1
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fi
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}
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# Install PostgreSQL client
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install_psql() {
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echo -e "\n${YELLOW}[1/4] Checking PostgreSQL client (psql)...${NC}"
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echo -e "\n${YELLOW}[2/5] Checking PostgreSQL client (psql)...${NC}"
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if command_exists psql; then
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PSQL_VERSION=$(psql --version | awk '{print $3}')
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@@ -103,7 +155,7 @@ install_psql() {
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# Install Docker
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install_docker() {
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echo -e "\n${YELLOW}[2/4] Checking Docker...${NC}"
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echo -e "\n${YELLOW}[3/5] Checking Docker...${NC}"
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if command_exists docker; then
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DOCKER_VERSION=$(docker --version | awk '{print $3}' | sed 's/,//')
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@@ -163,7 +215,7 @@ install_docker() {
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# Install Docker Compose
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install_docker_compose() {
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echo -e "\n${YELLOW}[3/4] Checking Docker Compose...${NC}"
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echo -e "\n${YELLOW}[4/5] Checking Docker Compose...${NC}"
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# Check for docker-compose (standalone) or docker compose (plugin)
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if command_exists docker-compose || docker compose version >/dev/null 2>&1; then
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@@ -209,7 +261,7 @@ install_docker_compose() {
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# Install utility packages
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install_utilities() {
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echo -e "\n${YELLOW}[4/4] Checking utility packages...${NC}"
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echo -e "\n${YELLOW}[5/5] Checking utility packages...${NC}"
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local missing_packages=()
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@@ -265,6 +317,7 @@ main() {
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echo -e "\n${BLUE}Starting dependency installation...${NC}"
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echo -e "${YELLOW}This script will install:${NC}"
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echo -e " - Python 3 (for CSV data generation)"
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echo -e " - PostgreSQL client (psql)"
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echo -e " - Docker"
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echo -e " - Docker Compose"
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@@ -274,7 +327,8 @@ main() {
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echo -e "${YELLOW}Press Ctrl+C to cancel, or wait 5 seconds to continue...${NC}"
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sleep 5
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echo ""
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install_python
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install_psql
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install_docker
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install_docker_compose
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Block a user