Using Data Structures in Distributed Cache with NCache

High performance, scalability, and real-time responsiveness are requirements for modern applications. In a distributed environment, traditional data structures frequently become a bottleneck, even though they work well for standalone applications. They only run in one application process, which restricts concurrency and makes it difficult to share data between servers.

As such, distributed data structures provide an effective means of managing and modifying data across numerous processes, application instances, and servers. These structures guarantee data availability and consistency while enabling parallel computing.

Why Use Data Structures in Distributed Caching?

Data structures play an important role in application development by organizing, storing, and retrieving data efficiently. They greatly improve speed by reducing database dependency, facilitating quicker data retrieval, and guaranteeing data integrity over multiple nodes when integrated with a distributed caching solution like NCache.

How Are Data Structures Different from Caching Regular Objects?

Caching regular objects involves storing serialized data as key-value pairs, which must be fetched, deserialized, modified, and then stored back into the cache. This approach can lead to inefficiencies in distributed environments, especially when multiple instances need to modify or read the same object concurrently.

In contrast, distributed data structures in NCache offer built-in operations that allow direct interaction with data while maintaining consistency across servers. Instead of retrieving and modifying entire objects manually, developers can perform operations such as adding to a list, dequeuing from a queue, or incrementing a counter in place. This ensures lower latency, better concurrency control, and reduced network overhead compared to handling raw objects in a distributed cache.

Comparison of Data Structures vs. Caching Single Objects

The table below explains the different data structures:

Data Structure	Purpose	Behavior	Example Use Cases
List	Ordered collection allowing efficient additions/removals	Allows duplicate values, maintains order	Shopping carts, Leaderboards
Queue	First-In-First-Out (FIFO) data handling	Ensures sequential processing	Message queues, Task scheduling
Dictionary	Key-value mapping for fast lookups	Efficient retrieval and updates	Session storage, Configuration management
Set	An unordered collection ensures uniqueness	Prevents duplicate entries	Unique user tracking, IP address storage
Counter	Atomic increment/decrement operations	Thread-safe and concurrent	Page view tracking, Stock management

These distributed data structures provide more efficient and structured ways to handle data as compared to caching single objects, which typically requires explicit fetching, modification, and reinsertion.

For instance, in an e-commerce platform, using a distributed list ensures that every server has an updated version of a user’s shopping cart. Similarly, a distributed queue can manage asynchronous tasks without relying on periodic database polling.

How NCache Provides Data Structures

NCache, an in-memory distributed caching solution, extends conventional data structures to operate across multiple servers, enabling real-time data synchronization between applications, processes, and server instances. It supports dynamic scalability, high availability, and automatic data replication to prevent data loss.

NCache provides several distributed data structures, each designed to address different application needs:

Distributed List

A distributed list functions as a synchronized collection of items that supports efficient addition, retrieval, and removal operations. It ensures data consistency across all nodes in a distributed environment.

Use Cases

Shopping Carts: Ensures that every user’s cart remains updated across multiple servers.
Leaderboards: Stores real-time game scores, ensuring consistency across different gaming sessions.

Example Code (Using .NET 8)

// Create and initialize a distributed list

IDistributedList<Product> productList = cache.DataTypeManager.GetList<Product>("ProductList");

productList.Add(new Product { Name = "Laptop", Category = "Electronics", UnitPrice = 999.99 });

productList.Add(new Product { Name = "Phone", Category = "Electronics", UnitPrice = 499.99 });

// Retrieve and iterate over items in the list

foreach (var product in productList)

{

Console.WriteLine($"Product: {product.Name}, Price: {product.UnitPrice}");

}

Distributed Queue

A FIFO (First-In, First-Out) queue is used for handling event-driven operations and task scheduling efficiently.

Use Cases

Message Processing: Distributes messages across multiple servers for reliable processing.
Task Scheduling: Manages job queues in microservices architectures.

Example Code

// Create and initialize a distributed queue

IDistributedQueue<TaskItem> taskQueue = cache.DataTypeManager.CreateQueue<TaskItem>("TaskQueue");

taskQueue.Enqueue(new TaskItem { Id = 1, Description = "Process Order" });

taskQueue.Enqueue(new TaskItem { Id = 2, Description = "Send Email Notification" });

// Dequeue an item for processing

TaskItem nextTask = taskQueue.Dequeue();

Console.WriteLine($"Processing Task: {nextTask.Description}");

Distributed HashSet

A set is a unique collection of unordered items that prevent duplication, commonly used for tracking distinct records.

Use Cases

IP Address Tracking: Keeps a unique record of logged-in users.
User Interest Groups: Stores unique customer preferences for product recommendations.

Example Code

// Create and populate a HashSet

IDistributedHashSet<int> userSet = cache.DataTypeManager.CreateHashSet<int>("UserSet");

userSet.Add(1001);

userSet.Add(1002);

userSet.Add(1003);

// Check if an item exists

bool exists = userSet.Contains(1001);

Console.WriteLine($"User exists: {exists}");

Distributed Dictionary

A dictionary is a key-value data structure that allows efficient lookups, making it ideal for high-performance data retrieval.

Use Cases

Session Management: Caches user session data across distributed environments.
Configuration Storage: Stores application-wide settings and key-value configurations.

Example Code

// Create and populate a distributed dictionary

IDistributedDictionary<string, UserSession> sessionCache = cache.DataTypeManager.CreateDictionary<string, UserSession>("UserSessionCache");

sessionCache["Session1"] = new UserSession { UserId = "User123", IsActive = true };

sessionCache["Session2"] = new UserSession { UserId = "User456", IsActive = false };

// Retrieve session details

UserSession session = sessionCache["Session1"];

Console.WriteLine($"User ID: {session.UserId}, Active: {session.IsActive}");

Distributed Counter

Counters provide a way to implement distributed counters that can be incremented or decremented atomically, and used in scenarios like real-time analytics and counting events.

Use Cases

Page-view count: Easily track web page views per hour or day, depending on your needs.
Tweets Analysis: Keeps track of the number of likes and dislikes, comments, etc., enabling you to handle high-frequency updates efficiently

// Create and use a distributed counter

ICounter pageViewCounter = cache.DataTypeManager.CreateCounter("PageViewCounter", 0);

long currentCount = pageViewCounter.Increment();

Console.WriteLine($"Page Views: {currentCount}");

Conclusion

By leveraging distributed lists, queues, dictionaries, hash sets, and counters, developers can efficiently manage and process high volumes of data without overloading databases. With NCache and .NET 8, your applications can scale seamlessly, handle concurrent workloads, and deliver a smooth user experience.