Space-Based Architecture Pattern – What and Why

Before we delve into the technicalities of the “why” of Space-Based Architecture patterns, let’s start by defining a few concepts. First we’ll look at architectural patterns, and then discuss the Space Based Architecture pattern and some specific use cases.

Architecture Patterns

Architectural Patterns are the fundamental structural organization for software systems. They provide a set of predefined subsystems and their responsibilities that include rules and guidelines for organizing the relationships between them. Architectural patterns define the high-level strategies that concern large-scale components, the mechanisms of a system and its global properties. Some of the most commonly used architectural patterns include:

• Layered Architecture Pattern: used to structure programs that can be decomposed into groups of subtasks; each layer of abstraction provides services to the layer above 
• Space-Based Architecture (SBA) Pattern: described below
• Client-server pattern: consists of a server and multiple clients that request services from the server, the server offers relevant services to those clients
• Microservices Architecture Pattern: full-stack, highly scalable, this pattern structures the application as a set of independently deployable, loosely coupled, components (services)  
• Event-Driven Architecture (EDA) Pattern: built to record, transmit, and process events through a decoupled architecture 
• Service-Oriented Architecture (SOA) Pattern: with this pattern, components provide services to other components typically over a network, via a communications protocol

Comparison of Architecture Patterns

Each architecture pattern has its own unique set of characteristics that make it suitable for specific use cases and projects. For example, applications that require high scalability and flexibility would benefit from the microservices architecture pattern, whereas applications that require a clear separation of concerns and modularity are better off using the layered architecture pattern. When determining which architecture pattern to implement, here are some of the considerations: 

• The ease of testing and maintaining the code and separating between tasks
• Whether the data flow is synchronous or asynchronous 
• Whether the workloads and resources should be centralized 
• The number of developers who will be building the system, and the timeline 
• How well the pattern performs under heavy load or high traffic
• How well the pattern scales and how well it handles sudden peaks 
• How well the pattern supports modular design and separation of concerns
• Can the system be easily deployed and managed 

Best Architecture Patterns for Scalability

When designing and building software systems, scalability is usually a critical concern. Some of the architecture patterns that are best suited for scalability include:

• Microservices Architecture Pattern: well-suited for applications that require high scalability and agility, since it allows for the creation of small, independent services that can be scaled horizontally, often compromising performance for better decoupling
• Event-Driven Architecture Pattern: for applications that require high scalability and responsiveness, event-driven architecture enables asynchronous communication between components, perfect for extreme use cases such as IoT, when data ingestion and processing causes a trickle effect between services and applications 
• Space-Based Architecture Pattern: for applications that require high scalability and fault-tolerance, this pattern allows for the efficient management and processing of large amounts of data across a distributed network of nodes; it encloses the value proposition of the Microservices Architecture Pattern without compromising on overall performance 

The Space-Based Architecture Pattern

This pattern is often used for applications that require ultra-fast access to data, such as real-time data processing, just-in-time analytics, and leveraging production machine learning models.

Definition of the Space-Based Architecture Pattern

The Space Based Architecture pattern is an architectural pattern that uses a distributed in-memory store to manage and process large amounts of data across a distributed network of nodes. It splits both the processing and the storage across multiple servers. The Space Based Architecture pattern is designed to provide high scalability, fault-tolerance, and low-latency data access by storing data in memory across multiple nodes. It offers fast access and reduces the need for expensive disk I/O, criteria that is often overlooked or even suggested to promote further decoupling, when evaluating the Microservices Pattern.

Source: Oreilly

Advantages and Characteristics of a Space-Based Pattern

The Space Based Pattern provides a number of advantages over other architectural patterns, including:

• In-Memory Data Storage: data is stored in memory, partitioned across multiple nodes
• Low Latency Data Access: designed for quick and efficient data access from any node in the network, mainly due to colocated code running in the same memory space (heap) as the data
• High Scalability: the distributed network of nodes provides for efficient processing of large amounts of data, allowing for the efficient management and processing of large amounts of data across a distributed network of nodes, as well as supports both horizontal and vertical scalability, automatically or on demand
• Efficient Data Management: the distributed nature of this pattern enables efficient management of data by partitioning and replicating it across multiple nodes, ensuring  high availability and fault-tolerance
• Fault-tolerance: the distributed nature of this architecture ensures that data is available even in the event of a node failure 
• Cost-effective: this pattern can be a cost-effective solution for managing and processing large amounts of data, by reducing the need for expensive disk I/O

Real-Life Examples that Implement Space-Based Patterns

The Space-Based Pattern is a powerful and flexible architectural pattern that can be used to build real-time, scalable, and fault-tolerant applications in a variety of industries. Each of the following industries manages, processes and analyzes vast amounts of data, which in some cases is mission-critical: 

• Healthcare Applications: data related to patient health, medical records, and research – in specific cases, this data can be life-saving, and compliance is a major concern 
• Transportation: data related to safety, logistics, scheduling, and other operational concerns
• Retail: centralizing and processing data from different sources including inventory, brick-and-mortar shops, payments systems, customer data
• Financial Trading Systems: unite data from multiple systems and process transactional data in real-time; these operations are highly regulated  
• Ecommerce Platforms: data related to customer behavior, inventory, and transactions, enabling personalized customer service and promotions and more accurate pricing 
• Social Media Platforms: data related to user behavior, content, and engagement, often connected to targeted advertising 

Space-Based Data Hub

Overview 

A Space-Based Data Hub is a type of architectural pattern, or a platform, that allows for the efficient management and processing of large amounts of data across a distributed network of nodes.  Since data is stored in memory, across multiple nodes, it offers fast access and reduces the need for expensive disk I/O. For applications that require high scalability, fault tolerance, and low latency, such as real-time data processing, analytics, and ML modeling, the Space Based Data Hub is a particularly powerful platform. 

This pattern is based on the concept of a distributed data fabric, where the data is partitioned and replicated across the nodes, ensuring high availability and fault-tolerance. Implementing distributed processing with a partitioned space, increases the overall RAM capacity of a single space by engaging the RAM of multiple nodes. In addition, it enables parallel processing of queries by distributing the workload across multiple machines. 

Space-Based Data Hubs provide insights and value to businesses in real-time. In our data-first world, these Hubs have become increasingly important, as more and more organizations rely on data to drive their business decisions. No longer are monthly or weekly reports acceptable, businesses need their data accurate – now. The systems that supply this data must offer reliable, secure data processing and analysis, in near real-time. This is particularly important in mission-critical industries such as finance, healthcare, and ecommerce, where real-time data processing can make a significant difference in the success of the health of the business – or the patient. Space-Based Data Hubs also enable the creation of real-time applications that can react quickly to changing data. 

Source: Oreilly

Space-Based Data Hubs Use Cases 

The Space-Based Data Hub can be implemented for one or more of the following primary usages. 

Accelerating API Serving

The Space-Based Data Hub can be used as an API serving layer for data access services deployed on the Hub. The Hub’s RESTful API enables the data services to be deployed as microservices and accessed via the Hub. This can simplify the application architecture by providing a central point of access for data services and allows for low-latency efficient data access.

The Space Based Architecture Pattern enables efficient caching of data, which can improve application performance and reduce the load on backend services. For applications that require real-time data access, such as financial trading systems or real-time monitoring applications this is quite beneficial. 

Data Pipelines

A data pipeline is a set of processes that move data from one system to another, typically for data integration or replication. The Space-Based Pattern can be used to implement efficient and fault-tolerant data pipelines by leveraging the distributed cache and in-memory data storage provided by the pattern. Organizations can ensure that their data is efficiently and reliably replicated across different systems, allowing for seamless data integration and analytics. This can be especially useful for applications that require real-time access to consolidated data from multiple sources, such as retail, insuretech, manufacturing, educational institutions and many others. 

Data Processing

The in-memory data storage provided by the Space-Based Architecture Pattern allows for fast and efficient processing of massive amounts of data in real time. Therefore, applications that require real-time processing, such as financial trading systems or real-time fraud detection often implement a space-based data hub.

Source: Oreilly

Data Storage

The distributed cache used by the Space Based Pattern allows for the efficient management and storage of large amounts of data across a distributed network of nodes. This makes it ideal for applications that require high availability and fault tolerance, such as ecommerce platforms or healthcare applications.

Data is stored in memory; however, data tiering is also a technique that is often leveraged to enable storage of vast amounts of data without scaling horizontally. 

Conclusion

The Space Based Data Hub offers a distributed data processing and storage system that

provides a flexible and scalable architecture, making it a wise choice for applications that require real-time processing and data access. Its fast and efficient data caching and in-memory storage are highly flexible, encapsulating  a wide variety of capabilities such as data pipelines, analytics, data processing, storage, and API serving acceleration. 

By leveraging the distributed cache and in-memory data storage provided by the pattern, organizations can ensure fast and efficient data processing and storage, enabling real-time data access and analysis. Since this architecture pattern is highly fault-tolerant, it ensures that the system remains operational, even in the event of node or instance failures or even in a region or data-center failures.

Organizations can use the Space-Based Data Hub Architecture to ensure that their data processing and storage needs are met in a fast, efficient, and reliable manner.