Scala Rule Engine for Retail Order Discounts

Table of Contents

• Project Overview
• Features
• Technical Details
• Project Structure
• Setup and Running
• Discount Rules Summary
• Logging
• Project References
• Notes
• Author

Project Overview

This Scala project implements a rule engine that processes retail store orders to automatically calculate applicable discounts based on a set of business rules. It reads transaction data from a CSV file, applies qualifying rules to determine discounts, computes the final prices, and loads the processed data into a PostgreSQL database. The system also logs important events and errors in a dedicated log file.

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Features

• Qualifying rules based on:

  • Product expiry date (less than 30 days remaining)
  • Product category discounts (cheese, wine)
  • Special date discounts (orders on March 23rd)
  • Quantity-based discounts (bulk purchase)
  • Channel and payment method discounts (app sales, Visa payments)

• Discount calculation:

  • If multiple discounts apply, the two highest discounts are averaged.
  • Final price is calculated after applying the average discount.

• Data persistence:

  • Results are stored in PostgreSQL tables for further analysis or reporting.

• Logging:

  • Engine events, errors, and processing status are logged in logs/rule_engine.log with timestamps and log levels.

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Technical Details

• Functional Programming:
  Core logic is implemented using pure functions with no mutable variables or loops.
  All functions are deterministic and side-effect free.

• Scala Features Used:

  • Case classes for immutable data modeling.
  • Higher-order functions for rule definitions.
  • Using for safe resource management.
  • Strong typing with val only.

• Database:
  PostgreSQL connection is established using JDBC.
  Connection details can be found in the db object.

• Logging:
  Java’s built-in java.util.logging framework is configured to write logs to a file.

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Project Structure

src/
└── main/
├── scala/
│   └── engine/
│       ├── rules.scala       # Main rule engine application
│       ├── AppLogger.scala   # Logger configuration
│       └── db.scala          # Database connection utility
└── resources/
│      └── TRX1000.csv        # Input CSV file with order transactions
logs/
└── rule\_engine.log          # Log file generated during execution

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Setup and Running

1. Prerequisites:

   • Scala 2.13+ or Scala 3 installed.
   • SBT build tool installed.
   • PostgreSQL running locally with a database named scala.
   • PostgreSQL JDBC driver added as dependency.

2. Configure Database:

   • Update the connection details in db.scala if needed (URL, user, password).
   • Create the following tables in your database:

CREATE TABLE processed_orders_2 (
  product_name VARCHAR(100),
  total_before NUMERIC,
  discount NUMERIC,
  total_after NUMERIC
);

3. Input Data:

   • Place your input CSV file at src/main/resources/TRX1000.csv.

4. Build and Run:

   • Compile and run the project using SBT:

   sbt run

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Discount Rules Summary

Rule	Discount Calculation
Less than 30 days to product expiry	(30 - days_remaining) % discount (e.g., 29 days = 1%)
Cheese product	10% discount
Wine product	5% discount
Sold on March 23	50% discount
Quantity 6-9	5% discount
Quantity 10-14	7% discount
Quantity 15+	10% discount
Sold via App	Discount based on rounded quantity in multiples of 5
Payment via Visa	5% discount

If multiple discounts apply, the top 2 discounts are averaged for final discount.

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Logging

• Logs are saved to logs/rule_engine.log.

• Format: TIMESTAMP LOGLEVEL MESSAGE

• Example entries:

  2025-05-14 22:00:00 INFO Starting order processing pipeline...
  2025-05-14 22:01:00 SEVERE Error reading lines from CSV: File not found
  

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Project References

• Project requirements and video guide

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Notes

• The code is designed to be clean, readable, and purely functional.
• All side effects such as file IO, DB operations, and logging are wrapped appropriately.
• Error handling is included to log and throw exceptions on failures.

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👤 Author

Created by Ahmed Otifi
🔗 GitHub: https://github.com/otifi3