Hi Reader,

In 2023, consumers spent $5.6 billion online on Thanksgiving Day, a jump of 5.5% year over year (source: Adobe Analytics).

Every day of the year is our online thanks- giving (and receiving) day where – we, the consumers – search and transact relentlessly on online – from eCommerce to travel, from grocery to food.

And scaling consumer focused systems to meet increasing demands is a key challenge of online players.

Over the years, architects and engineers have devised various strategies to achieve scalability, often focusing on stateless systems.

However, as applications become more sophisticated, the need for scaling stateful components, such as databases, emerges as a critical consideration.

In this episode of Monday Muse, let us have a deep dive into the significance of scaling stateful components, and the challenges and design patterns involved in scaling such systems.

Let's get started.

🏛️ The History of Scaling - Stateless Systems

The evolution of scaling strategies can be traced back to the early days of computing when systems were monolithic and vertical scaling was the norm.

As demands grew, this approach became unsustainable, prompting the separation of application into horizontal layers and the adoption of horizontal scaling.

The intent was this:

The emergence of cloud computing and microservices architecture brought about the era of stateless systems, governed by 12-Factor App principles, cloud native pronciples.

Stateless systems are easier to scale horizontally as they do not rely on preserving session state between requests.

This approach simplified deployment and maintenance, making it ideal for modern, distributed applications.

🗽 Stateful Components

While stateless systems offer numerous benefits, many applications inherently require stateful components to manage persistent data.

Databases, for instance, are quintessential stateful components that store critical information such as user accounts, transactions, and preferences.

Integration platforms that hold messages or data for seconds, minutes, days also hold state.

Without making the databases and messaging platforms (stateful) scalable, applications would struggle to maintain coherence and integrity across sessions.

Challenges in Scaling Stateful Systems

Scaling stateful systems presents a number of challenges, primarily due to the complexities involved in managing persistent data while ensuring high availability, consistency, and performance.

Let’s have a quick look at some of the key challenges faced in scaling stateful systems:

🔴 #1 - Consensus Mechanism or Algorithm:

Maintaining consistency and coherence across multiple nodes in a distributed system requires a robust consensus mechanism or algorithm.

Consensus algorithms like Raft or Paxos facilitate agreement among nodes on the state of shared data and the order of operations.

However, implementing and managing these algorithms introduces overhead and complexity, especially as the system scales.

Ensuring fault tolerance and handling network partitions further complicates the consensus process, making it a significant challenge in scaling stateful systems.

🔴 #2 - Data Migration Lag:

Scaling stateful systems often involves adding or removing nodes to accommodate changes in demand or to maintain performance.

However, migrating data between nodes introduces latency and can lead to data migration lag.

During this transition period, where data is being replicated or moved, there’s a risk of inconsistency or divergence between replicas, potentially impacting the overall system’s integrity and availability.

Minimizing data migration lag while ensuring data consistency is crucial for seamless scaling of stateful systems.

🔴 #3 - Demand Variability (Fast vs. Slow):

Stateful systems must contend with variability in demand, which can fluctuate rapidly or gradually over time.

Sudden spikes in traffic, such as during peak usage periods or due to unexpected events, pose challenges in scaling resources dynamically to meet demand without sacrificing performance or incurring excessive costs.

Conversely, slow demand growth may lead to underutilization of resources, resulting in inefficiencies and increased operational overhead.

Balancing resource allocation and capacity planning to accommodate both fast and slow demand variability is essential for effectively scaling stateful systems.

🕹️Design Patterns Used in Scaling Stateful Systems

Designing scalable stateful systems requires careful consideration of various factors such as data consistency, fault tolerance, and performance optimization.

To address these challenges, there are some useful design patterns tailored specifically for scaling stateful components such as databases. Let’s explore some of these design patterns:

✅ #1 - Writer as Leader:

In distributed systems, ensuring consistency of data across multiple replicas is crucial.

The Writer as Leader pattern designates one replica as the primary writer, responsible for handling write operations.

This approach simplifies coordination and ensures that updates are applied in a consistent order across all replicas, thereby maintaining data integrity.

✅ #2 - Read Replica:

Creating copies of data across multiple nodes to ensure fault tolerance and high availability. Master-slave replication and multi-master replication are common approaches.

To alleviate the read workload on the primary database, the Read Replica pattern involves creating one or more secondary replicas dedicated to handling read queries.

These replicas are asynchronously updated from the primary database, allowing them to serve read requests without impacting the performance of the primary writer.

This pattern not only improves read scalability but also enhances fault tolerance by providing additional points of access to the data.

✅ #3 - Triggering of Autoscaling:

Autoscaling enables the system to dynamically adjust its resources based on fluctuating demand.

In the context of stateful systems, triggering autoscaling involves monitoring key metrics such as CPU usage, memory consumption, and database connections.

When these metrics surpass predefined thresholds, autoscaling mechanisms can automatically provision additional resources such as database instances or storage capacity to handle the increased workload.

By scaling resources in real-time, this pattern ensures optimal performance and cost efficiency.

✅ #4 - Sharding:

Distributing data across multiple database instances based on a predefined criteria, such as customer ID or geographic location, to distribute the workload and improve performance.

✅ #5 - Election of a Leader:

In distributed systems with multiple nodes, the Election of a Leader pattern designates a primary node responsible for coordinating operations and maintaining system consistency.

This leader node is elected dynamically through distributed consensus algorithms such as Raft or Paxos.

If the current leader fails or becomes unavailable, a new leader is elected to take its place.

This pattern ensures fault tolerance and resilience against node failures while enabling efficient coordination and data consistency across the system.

✅ #6 - Load Manager:

The Load Manager pattern involves distributing incoming requests or tasks evenly across multiple nodes to prevent overloading any single component.

In the context of stateful systems, load management is particularly important to ensure that write operations are evenly distributed among replicas to prevent hotspots and bottlenecks.

Load managers utilize techniques such as round-robin routing, consistent hashing, or weighted load balancing to distribute traffic effectively and optimize resource utilization.

By leveraging design patterns we can architect robust and resilient stateful components of ecosystem capable of handling the demands of modern applications.

🏁 In Conclusion

Scaling stateful systems in production is an interesting aspect (where we can show off some of our understanding of the nuances of stateful architecture 😊) and it needs careful consideration of architectural design, data management strategies, and performance optimization techniques.

While the need of scalability remains consistent across stateless and stateful systems, the latter introduces unique complexities related to data consistency, availability, and performance.

By making use of the appropriate design patterns and scaling strategies, organizations can effectively manage the demands of modern, data-intensive applications, ensuring seamless performance and reliability for end-users.

The intent is to be aware of the nuances/challenges, look out for right design patterns as required.

That’s all for this week. Hope, this has been useful.

Till next week!

Please feel free to write to me if you have any views w.r.t. any points above or about any specific topic that you want me to write upon in the coming weeks!