README.md

Iterators Directory - The Iterator Design Pattern in Ride-Sharing Data Traversal

The Iterators directory in this project applies the Iterator Design Pattern to solve real-world data traversal problems within a ride-sharing service context. Here, three iterator classes—ProximityIterator, ExpandingProximityIterator, and SnakeOrderAcrossPoolsIterator—demonstrate how custom iteration strategies can streamline and enhance data handling in applications where access patterns need to be tailored. Each iterator class is designed to solve a specific problem in how a collection of Driver objects is accessed, providing both a robust illustration of design patterns and a practical approach to handling data dynamically.

These iterator implementations are fully coded to showcase different uses of iterators:

1. ProximityIterator: Filters drivers based on proximity to the client’s location.
2. ExpandingProximityIterator: Uses an increasing search range to prioritize closer drivers first.
3. SnakeOrderAcrossPoolsIterator: Interleaves multiple driver collections in a back-and-forth, or "snake-like," order.

By examining these implementations, you’ll gain insight into how to build modular, purpose-driven iterators that can adapt to complex real-world requirements.

Components of the Iterators Directory

Provided Interfaces and Classes

Before diving into the iterators themselves, three primary interfaces form the building blocks for the classes:

• Driver: Represents a ride-sharing driver. Each Driver is associated with a Vehicle.
• Vehicle: Represents the vehicle assigned to a driver and holds a Position.
• Position: Represents the GPS location of a vehicle as an (x, y) coordinate. This interface includes a getManhattanDistanceTo(Position p) method, which calculates the distance based on a grid system, mimicking urban navigation.

These interfaces allow the iterator classes to access driver information, vehicle locations, and distances, providing essential functionality for each iterator’s specific purpose.

Custom Iterators and Their Design Patterns

Each iterator implements Iterator<Driver> and is designed to handle data selection differently, showcasing unique applications of the iterator design pattern.

ProximityIterator: Filtering Drivers by Distance

The ProximityIterator class focuses on filtering drivers based on their distance from a client’s location. It iterates through a pool of drivers but only returns drivers within a specified proximityRange, measured in Manhattan distance. Drivers outside this range are ignored.

• Purpose: This iterator is ideal when a ride-sharing app needs to display only the closest drivers, ensuring that faraway drivers are excluded without changing the original driver order.
• Constructor Parameters:
  • driverPool: The available collection of Driver objects.
  • clientPosition: The client’s location, used as a reference for measuring proximity.
  • proximityRange: The maximum distance within which drivers are included in the iterator.

Design Insights and Mechanism:

1. Encapsulation of Filtering Logic: ProximityIterator encapsulates the logic for filtering within the iterator itself. Instead of creating a new filtered collection, it dynamically checks each driver’s distance as it iterates.
2. Deferred Retrieval: By maintaining a reference to the next eligible driver, the iterator retrieves drivers one by one based on the hasNext() and next() methods.
3. Efficient Data Selection: This iterator showcases how to apply a filtering pattern within an iterator, allowing selective access to data without modifying the original collection.

Usage Example: When iterating with ProximityIterator, the application can display nearby drivers based on distance to the client’s location, ignoring drivers outside the proximityRange.

ExpandingProximityIterator: Prioritized Driver Retrieval with Expanding Range

The ExpandingProximityIterator goes beyond simple proximity filtering by iterating through drivers within a progressively expanding distance range. Initially, it retrieves drivers within a 1-unit range. Once these drivers are exhausted, it increases the range by a configurable expansionStep, searching the pool again for drivers within this new range. This process continues, expanding the range each time until all drivers have been retrieved.

• Purpose: This iterator is designed for situations where closest drivers should be prioritized, but with the flexibility to search a wider area if no close drivers are found.
• Constructor Parameters:
  • driverPool: The collection of Driver objects to iterate through.
  • clientPosition: The client’s position, used as a central reference point.
  • expansionStep: The incremental amount by which to increase the search range each time through the pool.

Design Insights and Mechanism:

1. Layered Iteration: The iterator resets and searches through the driver pool at each range increment, allowing each range expansion to act like a layer, where drivers closer to the client are retrieved first.
2. Looped Search with Range Expansion: This iterator captures the expansion pattern, allowing the search criteria to expand dynamically as it iterates. By looping through the pool repeatedly, it ensures a comprehensive yet prioritized traversal.
3. End Condition Management: The iterator tracks when all drivers have been accessed, ensuring hasNext() returns false once no more drivers remain, regardless of range.

Usage Example: ExpandingProximityIterator could be used to find the closest drivers to a client and only broaden the search to farther drivers when no closer options are available.

SnakeOrderAcrossPoolsIterator: Interleaving Drivers from Multiple Pools in Snake Order

The SnakeOrderAcrossPoolsIterator iterates through multiple collections of drivers, interleaving them in a “snake-like” order—moving from the first to the last collection and back again. This back-and-forth sequence continues until all drivers across the collections have been retrieved.

• Purpose: This iterator is ideal for load balancing, ensuring fair selection across multiple driver pools or regions by accessing each pool one by one in a structured order.
• Constructor Parameters:
  • driverPools: A list of multiple collections (Iterable<Driver>) representing different driver pools.

Design Insights and Mechanism:

1. Multi-Pool Access: By interleaving drivers from multiple pools, this iterator balances access across all available collections, supporting situations where data from multiple sources must be combined.
2. Alternating Snake Pattern: The iterator leverages a snake order pattern, alternating between first-to-last and last-to-first sequences, ensuring equitable retrieval from each collection.
3. Dynamic Pool Management: As each pool is exhausted, the iterator dynamically skips over empty pools, maintaining its interleaved pattern until all pools are empty.

Usage Example: SnakeOrderAcrossPoolsIterator could be used in a ride-sharing system to balance driver selection across multiple service zones or regions, ensuring that drivers from all pools are fairly rotated.

Summary of the Iterators Directory

The Iterators directory provides a hands-on demonstration of the Iterator Design Pattern applied to different data traversal needs within a ride-sharing application. Each iterator class encapsulates a unique data-access strategy:

• ProximityIterator: Selects nearby drivers only, demonstrating filtering within an iterator.
• ExpandingProximityIterator: Retrieves drivers by expanding the search range incrementally, prioritizing closer drivers first.
• SnakeOrderAcrossPoolsIterator: Interleaves drivers across multiple collections in a balanced snake order, enabling equitable access across pools.

These classes exemplify how custom iterators can provide highly adaptable, purpose-driven solutions to data handling challenges, aligning with application-specific goals such as proximity filtering, dynamic prioritization, and multi-source interleaving. This directory illustrates the adaptability of the iterator design pattern, equipping you with tools to create tailored data access mechanisms that efficiently manage and prioritize information.