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Credit – DesignGurus.io
Hello folks, Scaling a Microservices architecture is not as easy as adding more services, it require good understanding of many System design components and Distributed System best practices to scale a Microservice architecture.
While its one thing to create a Microservices which is working, its totally different thing to create a Microservices which is working with millions of user sending billions of events every day.
Microservices architecture has gained significant popularity in recent years due to its ability to create modular, independent, and highly scalable software systems.
However, as an application’s user base grows and traffic increases, effectively scaling microservices becomes a critical challenge.
In the past, I have talked about common system design questions like API Gateway vs Load Balancer and Horizontal vs Vertical Scaling, Forward proxy vs reverse proxy as well common System Design problems and in this article I am going to share 3 strategies to scale your microservides which are also important for technical interviews.
I will and also give you an example of Scaling Microservices to millions while maintaining 99% uptime from none other than Uber.com, one of the most popular taxi haling app and also pioneer or Microservices architecture along with Netflix.
After going through this article, you will learn strategies for handling increased traffic and demand in Microservices-based applications, focusing on load balancing, auto-scaling, and performance optimization.
We will also explore a real-world example to illustrate these concepts in action.
By the way, if you are preparing for System design interviews and want to learn System Design in depth then you can also checkout sites like ByteByteGo, Design Guru, Exponent, Educative, Codemia.io, and Udemy which have many great System design courses and a System design interview template like this which you can use to answer any System Design question.
If you need more choices, you can also see this list of best System Design courses, books, and websites
So, what are we waiting for, let’s jump right into it
Microservices Architecture Scalling Challenges
Microservices architecture breaks down a monolithic application into smaller, interconnected services that communicate with each other over the network.
This design allows for flexibility, better resource utilization, and independent development and deployment of services. However, as user traffic grows, individual services might experience bottlenecks and performance issues.
Scaling Microservices presents several challenges that organizations need to address to ensure their systems can handle increased traffic and demand effectively. Some of the main challenges include:
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Service Dependencies
Microservices often rely on other services to perform specific functions. As traffic increases, the inter-dependencies between services can lead to bottlenecks and performance issues, affecting the overall system’s scalability. -
Data Management
Coordinating and managing data across distributed microservices can become complex, especially when dealing with large datasets. Ensuring data consistency, availability, and integrity across services can be a challenge. -
Network Communication Overhead
Microservices communicate over a network, and as the number of services grows, network communication can become a significant overhead. This can lead to latency and reduced response times, impacting user experience. -
Operational Complexity
Operating and monitoring a large number of microservices requires robust infrastructure, deployment pipelines, and monitoring tools. As the system scales, managing these aspects becomes increasingly complex. -
Dynamic Scalability
While auto-scaling is a key strategy, dynamically adding or removing instances based on demand can lead to resource contention, especially if not implemented properly. -
Consistency and Transactions
Maintaining transactional consistency across distributed services can be challenging. Ensuring that updates to multiple services are either fully completed or fully rolled back in case of failures is complex. -
State Management
Managing the state of microservices, especially in scenarios where state is necessary, can be difficult. Handling failover and replication of stateful services adds complexity to the scaling process. -
Security and Authorization
AEnsuring security across microservices while handling increased traffic requires robust authentication and authorization mechanisms. As the number of requests grows, managing security becomes more critical.
Addressing these challenges requires careful planning, architectural considerations, and the adoption of best practices in microservices development, deployment, and operations.
3 Strategies to Scale your Microservices to Millions of users keeping 99% Efficiency
Successful scaling involves a combination of technical expertise, monitoring tools, and continuous optimization efforts.
But, don’t worry we are going to see best practices you can follow to scale your Microservices to millions of users keeping 99% efficiency.
1. Load Balancing
Load balancing is a critical strategy for distributing incoming network traffic across multiple instances of a service to ensure even resource utilization and prevent overloading any single instance.
There are various load balancing algorithms, each with its strengths and use cases:
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Round Robin: Requests are distributed evenly across instances in a circular order.
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Least Connections: Traffic is routed to the server with the fewest active connections.
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Weighted Round Robin: Servers are assigned different weights, reflecting their capacity.
You can also use Patterns like API Gateway as a load balancer in a microservices architecture to distribute incoming traffic across multiple instances of your microservices.
An API Gateway acts as a single entry point for clients to access various microservices, providing benefits such as load balancing, routing, security, and more.
I also talked about in depth on my earlier post how API Gateway works, you can see that as well
2. Auto-Scaling: Adapting to Demand
This is another strategy you can use to scale your Microservices to millions. Auto-scaling allows Microservices to dynamically adjust their resources based on traffic fluctuations.
Instead of manually provisioning and de-provisioning instances, an auto-scaling system continuously monitors the load and adds or removes instances as needed.
This ensures optimal performance and cost efficiency.
Key metrics for triggering auto-scaling include CPU utilization, memory consumption, and response time. All major Cloud platforms like AWS, Google Cloud, and Azure provide auto-scaling services that can be integrated with microservices deployments.
Here is an example of setting up Auto Scaling in AWS Cloud:
3. Performance Optimization: Ensuring Efficient Execution
This is probably the oldest strategy to scale your application and also maintain its responsiveness.
Performance optimization involves fine-tuning microservices to maximize efficiency and minimize response times.
Techniques include:
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Caching: Storing frequently accessed data in memory to reduce database queries.
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Asynchronous Processing: Moving resource-intensive tasks to background workers or queues.
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Database Sharding: Distributing data across multiple database instances to improve read and write performance.
Here is another great example of Caching in Serverless architecture by AWS
3. Real-World Example: Uber’s Journey to Scalability
There is no better way to understand anything then seeing an example.
Let’s take a look at a real-world example to illustrate these scaling strategies in action: Uber, the ride-hailing giant.
Uber’s Microservices architecture has enabled it to operate seamlessly in numerous cities worldwide, serving millions of users daily.
As Uber’s popularity soared, effectively scaling its services became crucial.
Btw, this is another thing I do and suggest fellow developer to read engineering blog from tech giants like Uber, Netflix etc to learn how they are solving challenges. This will improve your knowledge and understanding .
Here is how Uber’s Microservice architecture looks like:
You can see that they are using API Gateway, Cache Proxy and many more things which you can learn to scale your Microservices to millions.
3.1. Load Balancing in Uber
To manage high traffic, Uber employs a robust load balancing mechanism. When a user opens the Uber app, their request is directed to a nearby data center.
Within the data center, load balancers distribute the request to the least busy microservice instance using the Least Connections algorithm.
This ensures even distribution of user requests, preventing any single microservice instance from becoming overwhelmed.
You can read more about Uber’s real time load balancing here.
3.2 . Auto-Scaling in Uber to Meet Demand
During peak hours or special events, Uber experiences a surge in ride requests. To handle these spikes, Uber utilizes auto-scaling extensively.
For instance, the “Ride Request” Microservice, responsible for matching riders and drivers, dynamically scales its instances based on metrics like the number of incoming requests and average response time.
Uber’s auto-scaling system monitors these metrics and automatically adjusts the number of instances to ensure low response times and quick ride matching.
When the traffic subsides, unnecessary instances are scaled down, optimizing resource usage and cost.
3.3. Performance Optimization for Seamless Experience
Uber’s app provides real-time location tracking, which demands high-performance microservices. To optimize performance, Uber uses caching extensively.
Driver and rider data, as well as ride status updates, are cached in memory, reducing the need for frequent database queries.
Furthermore, Uber employs asynchronous processing for tasks like calculating ride fares and sending notifications.
By moving these tasks to background workers and queues, microservices can quickly process ride data without affecting the user experience.
12 Best System Design Interview Resources
And, here are curated list of best system design books, online courses, and practice websites which you can check to better prepare for System design interviews. Most of these courses also answer questions I have shared here.
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DesignGuru’s Grokking System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.
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Codemia.io: This is another great platform to practice System design problems for interviews. It has more than 120+ System design problems, many of which are free and it also has a proper structure to solve them.
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ByteByteGo: A live book and course by Alex Xu for System design interview preparation. It contains all the content of System Design Interview book volumes 1 and 2 and will be updated with volume 3 which is coming soon.
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Exponent: A specialized site for interview prep especially for FAANG companies like Amazon and Google, They also have a great system design course and many other materials that can help you crack FAAN interviews
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“System Design Interview” by Alex Xu: This book provides an in-depth exploration of system design concepts, strategies, and interview preparation tips.
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“Designing Data-Intensive Applications” by Martin Kleppmann: A comprehensive guide that covers the principles and practices for designing scalable and reliable systems.
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LeetCode System Design Tag: LeetCode is a popular platform for technical interview preparation. The System Design tag on LeetCode includes a variety of questions to practice.
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“System Design Primer” on GitHub: A curated list of resources, including articles, books, and videos, to help you prepare for system design interviews.
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Educative’s System Design Course: An interactive learning platform with hands-on exercises and real-world scenarios to strengthen your system design skills.
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High Scalability Blog: A blog that features articles and case studies on the architecture of high-traffic websites and scalable systems.
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YouTube Channels: Check out channels like “Gaurav Sen” and “Tech Dummies” for insightful videos on system design concepts and interview preparation.
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InterviewReddy.io: This site has been created by Gaurav Sen, an ex-Google engineer, and popular YouTuber and creator of the System Design simplified course. If you are aiming for a FAANG interview, you can also check this website.
image_credit — ByteByteGo
You should also remember to combine theoretical knowledge with practical application by working on real-world projects and participating in mock interviews. Continuous practice and learning will give you confidence for system design interviews.
Conclusion
That’s all about the essential things you can do to scale your Microservices to millions of users. While this may seems simple when you actually do it you will realize that it require careful planning and execution.
As user demands continue to increase, scaling microservices effectively is a critical aspect of modern application development.
Load balancing, auto-scaling, and performance optimization are essential strategies to ensure optimal performance, reliability, and cost-efficiency in microservices-based applications.
Real-world examples like Uber demonstrate the practical implementation of these strategies, showcasing their effectiveness in handling high traffic and demand.
By leveraging load balancing algorithms, auto-scaling mechanisms, and performance optimization techniques, you can create resilient and responsive microservices ecosystems that provide a seamless user experience even during peak usage periods.
In conclusion, mastering the art of scaling microservices is essential for businesses aiming to deliver high-quality digital services in the face of ever-growing user expectations and traffic volumes.
Also, here is a nice system design interview cheatsheet from tryExponent.com for quick revision
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