Ideal software product engineering services pave a more flexible architectural approach to building applications. Although microservices are fast, reliable, secure, and loosely coupled, they require optimal planning to integrate with other technologies effectively.
There are hundreds of successful examples of microservice architecture and thousands of failed ones, too. Businesses, especially startups, can learn a lot from such use cases while setting up their scalability benchmarking tools and services for the upcoming product.
Let's get to know about microservice architecture in more detail.
It's a design approach, to be honest, widely adopted by software product engineering companies globally. A system that is a collection of mutually integrated, loosely coupled, small, and independent services, or nodes. Every service can perform its functions through pre-defined and robust APIs.
The pre-existence of microservice architecture and monoliths was quite popular among the initial stage software brands. However as the product growth rate started increasing, it usually became difficult to scale a monolith structure. With microservices, this problem doesn't exist because it's not the entire system that needs to scale every single time!
Connect with our industry experts now to know every detail about implementing scalability in microservice architecture. We help businesses worldwide to upgrade their legacy systems and experience a full-scale digital transformation every now and then!
Microservices are modular, have easier scalability attributes, and support flexible system development and maintenance. Its main characteristics also include
Decentralized architecture approach
Failure resistance
Self-sufficient with improved continuous delivery
Easier to scale, upgrade, and develop
Scalability in microservice architecture foresees a fantastic opportunity to build loosely coupled component-based systems with separate databases that can be built by utilizing different tech stacks. It provides an extended hand to the developers to flexibly plan software product development.
With time and development, there came many scalability challenges in IoT applications, or say scalability challenges in e-commerce platforms, for example. But a continuous transition into microservice architecture was adopted by 81% of companies of small, medium, and enterprise levels.
The latest report predicts the microservice architecture to reach a mark of USD 13.1 B by 2033. This transition will pacify with a growth rate of 12.7% (CAGR) in between 2025-2033.
The popularity of microservice architecture speaks for itself, as the majority of the big tech companies already adopted this structure and left the monolith far away in their initial branding tenure. The reasons are obvious, but the hook click is because microservice architecture solves too many business problems to be neglected.
The foremost challenge solved by microservice architecture is scalability issues. With this approach, businesses can rapidly scale their system by defining only service-specific servers on an independent basis. Unlike monolith architecture, the whole system does not need to scale up completely.
Developing a system becomes complex and slow in large-scale monolith architecture. In microservices, teams can work independently by developing and releasing new features without handling cross-team concerns. This practice promotes fast business growth and a flexible approach to developing the end product.
Adopting a microservices architecture means benefiting from utilizing a vast technology stack as well! Developers can work with a wider range of advanced technologies within a system. They can choose and pick the best option as per their project requirements, comfort, and market demand.
With an increasing number of components, complexity in monolithic architecture arises, plus an additional burden is its anti-fault tolerance nature. Microservices do not depend on a single architecture to make the whole system a success. Hence, its fault tolerance is maximum, and system complexity is vice versa.
Before you decide to adopt a holistic scaling approach, let's know more about the top use cases businesses have experienced so far
A UK-based digital bank, Monzo, suffered the microservices overload by initially adopting a high-end distributive microservice architecture. Too many microservices made things run out of control when they grew too fast.
❌ Monzo struggled to scale due to adopting too many microservices initially without proper planning.
❌ Over 1500 microservices made identifying, pinpointing bugs and tracking system failures a nightmare.
❌ Managing microservices at scale became complex due to operational overheads.
❌ Monzo didn't maintain the balance and over-fragmented its services and landed in chaos.
❌ Overcomplicating architecture = Microservices Sprawl Problem
❌ Too many small services led to excessive inter-service calls, causing a monitoring nightmare.
❌ Monzo's idea of more services = better scalability didn't work out.
A popular music streaming platform, SoundCloud, transitioned itself into microservices, only to get it backfired for doing it too quickly. Its poor inter-service communication brought system chattiness & latency issues for its users. Eventually, SoundCloud ran into many communication bottlenecks in the name of scaling its music streaming platform.
❌ The team was unable to focus on developing new features due to managing microservice complexities.
❌ Unreliable inter-service communication due to chatty microservices
❌ The system became latent and underwent downtime frequently, promoting high maintenance expenses.
❌ Poor integration caused even a minor update to be coordinated across too many loosely connected services.
❌ Too many cross-service calls led to an excessive increase in service dependencies.
❌ A single API request required multiple service communications, increasing the system response time and making it slower.
❌ System dependencies underwent cascading failures eventually.
The "Fail Whale" years (pre-2013) saw Twitter's large-scale struggle with its microservices due to poor planning and insufficient database scalability and management.
Though, it was fixed later by implementing database sharding, read replicas, and event-driven messaging. But Twitter learned its lesson to scale databases, too, alongside microservices.
❌ Twitter's single database became a big-time bottleneck, as all services relied on one massive database.
❌ Database underwent high read/write latency due to an inability to handle high volumes of tweets and retweets.
❌ Only sticking to fixing application logic didn’t solve the database issues.
❌ Scaling at the App Level Instead of focusing on the data layer.
❌ Lack of technical readiness led to disastrous system performance issues, ignoring infrastructure upgrades.
❌ Poor business decisions and not implementing advanced scalability benchmarking tools and services adversely affected product market-fit.
Scalability challenges in e-commerce platforms like Home Depot occurred because of continued ignorance regarding system observability and monitoring. Though Home Depot attempted well into microservice transitioning, it quickly ran into blind monitoring spots due to insufficient readiness.
It needed to work on scalability benchmarking tools and services and invest more in advanced options to track system performance and potential errors.
❌ Anti-centralized logging and scalability metrics led to tracing loopholes across services.
❌ Lack of a distributed tracing system made debugging a difficult task to accomplish.
❌ The system underwent a high failure rate due to unstable server compatibility, impacting others.
This America-based e-commerce platform got thumped with massive data breach issues back in 2013, affecting its 40M+ customers worldwide. It happened because Target switched to microservice architecture without planning to back it up. The system experienced huge API vulnerabilities, leading to permanent security failure for the platform.
Target didn't opt to take up the security of its microservice architecture, landing in a nightmare later. Following the incident, Target opted for advanced security and authentication metrics such as OAuth and API Key-Based Authentication, firewalls, rate limits, and data encryption techniques.
❌ Target didn't utilize API gateways and data authentication to prevent attacks.
❌ Target didn't even have strict access control and data encryption, leading to weak authentication & authorization.
❌ It made the system vulnerable with no proper rate limiting, threat detection, and poor API gateway configuration.
Now let's move to the successful examples of microservice architecture adoptions, where the brands adopted the technology with a better, smarter move
If anyone ever debates the success of scalability in microservice architecture, Netflix's name will rise to the top. The long but adventurous journey undertaken by Netflix to become the world's leading streaming entertainment service was not easy. The brand took almost a decade to transition itself fully from monolithic to microservice architecture.
Eventually, Netflix turned into a more flexible and adaptable system, offering resilient services that handle workloads efficiently. The brand utilized the decomposition of its applications into hundreds of microservices, operating independently, and became an excellent example of microservice support for a customer-focused online service.
With its successful scalability metrics to monitor applications and cost-efficient techniques, Netflix became the gold standard of scalable microservices.
✅ Fully adopted microservices & content delivery networks (CDNs).
✅ Adopted AWS for elastic scaling by moving on from on-premise data centers.
✅ Utilizing M and AI-driven load balancing techniques to predict traffic spikes.
✅ Chaos Engineering and Auto-Scaling to intentionally break services.
✅ Embraced cloud scalability to handle millions of users.
Previously, Amazon was developed as a monolith, with a primary focus on establishing an online bookstore. However with increased popularity and demand, it transitioned into microservices and diversified its product range and services. This magnificent shift allowed Amazon to successfully and efficiently scale, deploy, and modify its system much faster.
The typical journey of most companies from being monolithic to microservices brings a ton of benefits, cost-effectiveness, and system resiliency. However, companies can start initially as a monolith (like Amazon did). But later on, with an increasing user base, they need to undergo a transition (like Amazon did!). Amazon is smart enough to make an early decision to scale itself into microservices, and hence it was able to do it quicker than Netflix.
Amazon is the ultimate example of a successful transition and scaling with microservices.
✅ Internal system scalability with Amazon Web Services (AWS)
✅ Robust auto-scaling system architecture, which scales up and down as per demand.
✅ Amazon optimized its supply chain scalability, global warehousing, and logistics.
✅ Implemented asynchronous messaging for decoupled communication.
✅ 99.99% multi-region microservices uptime.
✅ Clear service boundaries with data-driven and real-time auto-scaling features.
When Uber's user base increased, its monolithic architecture became incapable of settling down the required functionalities to connect drivers, passengers, and process transitions. Finally, Uber broke itself into several microservice architectures but initially failed with too much captivity and unexpected scaling challenges.
Uber experienced the mess of not pre-planning its microservice transitions. With the abrupt dilution into 2000+ microservices, it became problematic for its team to figure out service dependency. However, Uber was quick to handle this logistical nightmare of poorly defined boundaries and adopted some market-hit microservice techniques later.
✅ Uber grouped relevant services into a well-defined Domain-Driven Design (DDD) to reduce dependencies.
✅ Built a service discovery/observability platform to track system dependencies.
✅ Implemented active, real-time automation and monitoring techniques to reduce manual labor.
✅ To enhance system resilience, they adopted a multi-cloud service strategy.
✅ Utilized efficient API gateways and rate limits to reduce unnecessary calls and cascading failures.
✅ Ensured fast-operating geo-distributed databases for real-time trip matching and pricing, globally.
✅ Utilized Kafka and real-time analytics-based event-driven system architecture to manage ride demands.
Microservice architecture does not only require scaling but also more than just breaking its monolith system. If Spotify had stopped its scaling just by splitting its coding structure, then maybe today it wouldn't have gotten to the place where it stands now!
Spotify is successfully scaling music streaming efficiently, with a well-planned, utilizable, and domain-driven design. Spotify heads already knew that if not done right, microservices become MORE complex than monoliths. Therefore, they implemented a strategy that combines microservices, caching, and AI to achieve seamless scaling, once it for all!
Without breaking the system, Spotify implemented rapid feature updates via a robust microservice structure.
✅ Efficient inter-service communication via event-driven messaging, search, recommendations, etc., via an independent microservice architecture.
✅ Independent deployment of service pipelines without affecting other microservices.
✅ Well-structured service boundaries prevent bottlenecks and overutilization of services.
✅ Reduced latency database queries with intelligent load balancing techniques and caching frequently played songs.
✅ Microservices are grouped according to domain-oriented business logic.
✅ AI-powered, highly scalable databases that predict traffic spikes and perform API call optimization.
Contact our team of experts, who will guide you from scratch to create a scalable system architecture that works wonders for your business! Our secure, fast, and cutting-edge software product engineering services leave no stone unturned!
Microservices need an approach and strategy; only trends and hype aren't going to help keep up with the product's success. Microservices are a better, much better alternative for monolithics, but they aren't the magic wand either!
Businesses need to make agile scalability metrics to monitor applications for their performance, work processing, and task execution. If the scalability measure is enabled and implemented correctly, your system will surely experience seamless scalability with faster development cycles. But, to achieve this, businesses must understand and accept that scaling microservices is a multifaceted challenge. It will require careful consideration, willpower, expertise, and adaptability at every development stage.
Once the best practices of a scalable system architecture are achieved, it's a much easier road ahead for everyone!
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