State vs Event-Based Data Model
State vs Event-Based Data Model
In today’s fast-evolving tech landscape, businesses need to evaluate the role and function of their databases and how data is stored. Traditional databases are designed to maintain only the current state of an application or system. In contrast, EventStoreDB leverages an event-based data model where the history of changes is immutably stored as events, which can be replayed to produce the current state. Understanding the differences and nuances between these data models can help organizations make more informed decisions on how to manage its data.
This article examines the nuances between these models, the challenges they address, and how EventStoreDB provides an alternate approach to data management and system architecture.
Two Perspectives: Current State and Historical Events
Business objects in a system can be viewed as current state or historical events. The current state, or state-based data model, represents an object’s condition at a particular time—such as the balance or withdrawal limit of a digital wallet. In contrast, an event-based data model reflects the actions that have shaped the current state, like deposits, withdrawals, and limit updates.
Example
Leveraging a state-based data model, an individual viewing the table below understands the current state of a digital wallet. The wallet holder is John Doe. On September 24, 2024, the current balance is US$200 and the withdrawal limit is US$300.
| Date | September 24, 2024 |
|---|---|
| Wallet Holder | John Doe |
| Balance | $200 |
| Currency | USD |
| Withdraw Limit | $300 |
Under an event-based data model, an individual can sequentially replay the historical events that compose the current state.
- Registered John Doe to the USD digital wallet with a $100 withdrawal limit (July 1, 2024)
- Deposited $100 to the digital wallet (July 3, 2024)
- Withdrew $100 from the digital wallet (July 22, 2024)
- Deposited $500 to the digital wallet (August 13, 2024)
- Increased withdrawal limit of digital wallet to $300 (September 2, 2024)
- Withdrew $300 from digital wallet (September 12, 2024)
State-based and event-based models each offer unique insights. A state-based digital wallet provides quick and direct information, offering a clear view of "what" exists now. In contrast, the event-based model provide a complete record of historical insights and the "why" behind the digital wallet's current state, which can be derived by replaying the actions that led to the present (or any point in time).
To fully manage and understand any business object, both perspectives—"what" and "why"—are necessary.
Modern Challenges of the State-Based Data Model
Summary of modern challenges of the state-based data model:
| Challenge Area | Description of Challenge |
|---|---|
| Data Analytics | Absence of historical context: - Hinders advanced analytics and machine learning - Restricts understanding of user behavior over time - Limits root cause analysis and data lineage tracking |
| Data Integration | Delays between data collection and processing: - Prevent real-time insights - Lead to inefficiencies and inconsistencies Batching large datasets: - Slows down system synchronization - Can be resource intensive and introduces delays |
| Application Development | Tightly coupled systems: - Reduce scalability - Makes systems harder to evolve - Makes debugging difficult |
Data Analytics
Traditional RDBMS only store current states and lack the historical context crucial for advanced analytics and predictive modeling using machine learning and AI. For instance, if an e-commerce company only stores the latest status of customer purchases, it becomes difficult to analyze why certain products were returned or why specific customers churned. This absence of historical data and the "why" behind actions and behaviors makes it harder to analyze root causes and trace data lineage.
Data Integration
Batch processing, commonly used to handle large datasets, introduces delays between data collection and processing, making it harder to generate real-time insights or take swift action. For example, a logistics company may batch-process shipment data overnight, delaying the detection of errors in real-time package tracking. This lag can lead to inefficiencies and difficulties in synchronizing distributed systems, which can increase resource usage and result in inconsistent data.
Application Development
Application development also faces challenges with systems that depend on state-based data models. Due to the centralized, transactional nature of RDBMS, these systems frequently lead to tightly coupled architectures. For example, in a healthcare application, updates to patient records might require changes to multiple interconnected internal and vendor services, making the system harder to scale and maintain as new features are added or the system grows more complex.
EventStoreDB and Event-Based Data Model
EventStoreDB, an event-native database, effectively solves many of the challenges posed by traditional state-based models by focusing on events as the core unit of data.
EventStoreDB preserves the entire history of business processes by storing, processing, and delivering data as events, making it ideal for event-based data models. Unlike state-based data models, where updates overwrite previous data, EventStoreDB retains every event in its original form.
While historical events can be replayed to reconstruct the current state, the inverse is impossible with state-based systems. Once reduced to a single state, the detailed history of the events that shape the current state is irretrievably lost. The event-based model, being more raw and granular than the state-based model, offers broad and in-depth historical insights. At the same time, it can be transformed into the current state for quick and efficient analysis.
Info
Event-based models provide the best of both worlds, offering the flexibility to present data in both state and event-based data models.
EventStoreDB as a Solution to State-Based Challenges
Summary of EventStoreDB Solution with Event-Based Data Model:
| Challenge Area | EventStoreDB Solution with Event-Based Data Model | Example |
|---|---|---|
| Data Analytics | - Provides granular insights into behaviors, actions, and system changes over time. - Allows for time travel and temporal analysis. - Enables tracking of system evolution and predicting future outcomes. | E-commerce: Track every step of a customer’s journey, from browsing to purchasing, to analyze buying patterns. |
| Data Integration | - Processes events incrementally, reducing computational overhead. - Handles real-time updates efficiently. - Ensures fast synchronization with other systems. | Logistics: Process each package scan in real-time, ensuring timely updates without lengthy batch processing delays. |
| Application Development | - Decouples the current state from business logic and actions. - Simplifies scaling and maintenance. - Facilitates adding new functionality incrementally without affecting other system parts. | Healthcare: Easily update patient records across internal and external systems without tightly coupling services, improving scalability. |
Data Analytics
For data analytics, EventStoreDB’s event-based model offers new possibilities by providing raw, granular insights into behaviors, actions, and system changes over time—far beyond the properties and attributes available in state-based models. For example, in an e-commerce system, analysts can track every step of a customer’s journey, from browsing products to final purchase, allowing for detailed behavioral analysis.
The historical context it preserves allows for time travel and temporal analysis, enabling analysts to track a system’s evolution, better understand past behaviors, reconstruct current states, and even predict future outcomes using machine learning models. For instance, understanding user purchase patterns over time could help predict future buying trends.
System Integration
For system integration, EventStoreDB offers significant advantages for real-time applications by processing events incrementally. This reduces computational overhead by handling individual changes (events) instead of processing entire datasets as in batch processing.
For example, in a logistics system, only specific updates—such as a package being scanned at a checkpoint—are processed in real-time rather than batch-processing millions of scans simultaneously. This triggers quick updates and immediate action to ensure fast, seamless synchronization that keeps systems up to date without delays.
Application Development
For application development, EventStoreDB’s event-based model effectively decouples the current state (e.g., a table) from the business logic and actions that manipulate it.
With fewer dependencies, decoupled systems can scale horizontally more efficiently, allowing new functionality to be added incrementally without affecting other parts of the system. This leads to cleaner, more modular codebases that are less prone to breaking when changes are introduced, reducing the likelihood of bugs and issues.
Comparison of State-Based and Event-Based Data Models
Comparison table that contrasts the state-based and event-based data models:
| Aspect | State-Based Data Model | Event-Based Data Model |
|---|---|---|
| Primary Focus | Stores the current state of an object or entity. | Records and stores every event or change that occurs to an object or entity. |
| Data Representation | Focuses on "what" the state is at any given time. | Focuses on "why" and "how" of the current state by tracking the sequence of changes over time as events. |
| Historical Data | Only the latest state is stored; previous states are overwritten. | Retains the full history of events, allowing reconstruction of any past state. |
| Storage Requirements | Efficient in terms of storage as only the most recent state is saved. | Generally requires more storage since every event is retained. |
| Transparency & Traceability | Lacks historical context, making it difficult to audit changes or trace past states. | Provides complete transparency, enabling detailed traceability and auditing. |
| Analytics & Machine Learning | Limited, as only the current state is available for analysis, lacking the historical context. | Ideal for advanced analytics and machine learning, as every event can be analyzed in detail. |
| Real-Time Processing | Often relies on batch processing, leading to delays in real-time insights. | Supports real-time processing by handling and processing events as they occur. |
| Scalability | Tends to tightly couple systems, leading to challenges in scaling and adding new features. | Loosely coupled, making systems easier to scale and extend without affecting other parts. |
| System Integration | Typically relies on centralized, tightly coupled architectures. | Encourages decoupled, event-driven architectures, improving modularity and flexibility. |
Summary
- Data can be viewed from two perspectives: the current state, which presents the “what,“ and historical events, which present the "why" and "how"
- Traditional state-based models are prevalent in relational database management systems (RDBMS) and store only the most recent data.
- Despite technological advancements, state-based systems struggle with modern challenges like machine learning, real-time analytics, and scalability.
- EventStoreDB is a practical database that leverages an event-based storage model. Storing all historical events preserves detailed insights and enables replayability for reconstructing current (and past) states.
- Event-based models can generate the current state via replay of historical events. The inverse is not possible since state-based models can not be reduced to events that were not stored.
- This event-based model offers transparency, real-time processing, and loosely coupled systems while addressing key challenges in data analytics, system integration, and application development.