How to Architect for Simplicity and Scale Without Microservices?
- September 19, 2023
- Posted by: Rutvik Bhatt
Creating simple systems that can scale up to the level of Google or Facebook involves careful architectural considerations. While microservices have become a popular approach, as a lot of tech folks rightly pointed out, they come with complexities. Lets understand what things we should be more careful when we architect for simplicity.
As technology continues to evolve, many companies have adopted microservices architecture to meet the demands of scalability, agility, and rapid development. Microservices promise modularity and flexibility, allowing organizations to scale components independently. However, the transition to microservices is not without its challenges, and some companies find themselves struggling to fully realize the benefits. In this article, we explore the current status of companies that have embraced microservices but are facing significant hurdles in various aspects of their architecture. These real-world examples shed light on the complexities and pitfalls that can arise, highlighting the need for careful consideration and continuous adaptation in the world of microservices.
Decomposition of Functionality
Instead of creating a microservice for every verb, a simpler system can start with a monolithic architecture and decompose functionality only when necessary. This means breaking the system into smaller components based on functional boundaries, not just verbs. As the system grows, you can selectively transition to microservices.
Uber initially embraced microservices extensively, which led to over 1,000 services. This caused complexities in managing the system. They later adopted a more balanced approach, moving back to a monolithic structure for certain parts of their application to simplify their architecture.
Monolithic to Modular Transition
Start with a monolithic architecture that is well-organized and modular. Use a clean separation of concerns and follow best practices like SOLID principles. This allows for easy componentization when the need arises, without the immediate overhead of microservices.
Data Consistency
Simple systems maintain strong data consistency through ACID transactions when needed. While distributed systems may use eventual consistency, complex distributed databases should be avoided unless absolutely necessary. Techniques like sharding can help scale traditional databases.
Twitter faced data consistency issues when transitioning to a microservices architecture. In some cases, inconsistencies in data led to problems such as tweets disappearing temporarily. Achieving strong data consistency across microservices can be challenging without the proper design.
State Management
Emphasize statelessness where possible. Keep state management at the edge, in client applications or use external services like Redis or Elasticsearch for specific needs. This reduces the complexity of handling state within the core system.
Airbnb encountered challenges with state management in their transition to microservices. Handling user sessions and maintaining state across multiple services became complex. They had to introduce additional technologies and tools to manage state effectively.
Observability and Monitoring
Implement thorough monitoring and observability early in the development process. Collect metrics, logs, and traces to gain insights into system behavior. Simple systems can focus on a limited set of components, making it easier to monitor and troubleshoot.
Netflix, a pioneer in microservices, realized the importance of observability early on. They invested heavily in tools like the Netflix Observability Pipeline (Eureka, Hystrix, etc.) to manage the complexities introduced by microservices. This illustrates how vital observability is in microservices architectures.
Fault Tolerance
Build fault tolerance into individual components and gradually expand it to the entire system. Use techniques like retries, circuit breakers, and timeouts to handle failures gracefully. A simpler system has fewer components to make fault-tolerant.
Amazon Web Services (AWS) experienced an outage in 2017 due to a fault in their S3 service. While not a direct microservices issue, it highlights the importance of fault tolerance and redundancy, which are crucial in microservices architectures.
High Availability
Achieve high availability through load balancing, redundancy, and failover strategies. Ensure that critical components are highly available while considering cost-effectiveness.
Docker, a company known for containerization technology, faced service disruptions due to issues in their microservices architecture. Ensuring high availability across numerous microservices can be challenging, and downtime in one service can affect the entire application.
Consensus and Distributed Systems
Avoid unnecessary complexity in distributed systems. Use distributed consensus mechanisms like Raft or Paxos sparingly. Simple systems can rely on single points of failure initially and then evolve to distribute critical components.
Twitter faced issues with their “FlockDB” distributed graph database, leading to data inconsistencies and scaling challenges. Distributed systems and consensus mechanisms, when not properly managed, can introduce complexity and potential problems.
Scaling Horizontally
Plan for horizontal scaling by deploying additional instances of components that are under heavy load. A simpler system is easier to scale horizontally because there are fewer moving parts to manage.
Etsy, an online marketplace, found it challenging to scale certain microservices due to uneven traffic patterns. While microservices can theoretically scale independently, achieving this in practice can be complex and resource-intensive.
Incremental Growth
Grow the system incrementally based on actual usage and performance bottlenecks. Invest in optimization and scaling efforts where they have the most impact rather than over-engineering from the start.
SoundCloud, a music streaming platform, initially embraced microservices but later faced financial difficulties partly due to the complexity introduced by microservices. They had to restructure their system and lay off employees to cut costs.
Resource Efficiency
Simple systems tend to be more resource-efficient. They can run on fewer servers, reducing infrastructure costs.
Medium, a content publishing platform, experienced increased infrastructure costs with the adoption of microservices. Maintaining multiple services consumed more resources than their earlier monolithic architecture.
Team Expertise
Consider the expertise of your development and operations teams. Scaling a system also means scaling your team’s ability to manage it. A simpler system is easier to understand and maintain.
Gilt Groupe, an online shopping platform, admitted to struggling with the expertise required to manage a complex microservices ecosystem. Smaller teams may find it challenging to maintain a plethora of microservices effectively.
In summary, simplicity in system design does not mean sacrificing scalability. It means making informed decisions based on actual needs, prioritizing simplicity in initial iterations, and evolving the architecture as the system grows. By carefully managing complexity and scaling incrementally, a simple system can indeed scale to handle the demands of large-scale applications like Google or Facebook.
In all these examples, while microservices offer benefits, they can introduce significant complexity if not managed carefully. Striking the right balance between simplicity and microservices is essential to ensure scalability and maintainability in real-world applications.
