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How Amazon, Netflix, Uber, and Etsy Broke Their Monoliths and Scaled to Unprecedented Heights with Microservices

Some of the most innovative and profitable microservices architecture examples among enterprise companies in the world — like Amazon, Netflix, Uber, and Etsy — attribute their IT initiatives’ enormous success in part to the adoption of microservices. Over time these enterprises dismantled their monolithic applications. They then refactored them into microservices-based architectures to quickly achieve scaling advantages, greater business agility, and unimaginable profits.

In this article, we’ll explore the microservices journeys of these wildly successful enterprises. But first, let’s look at the general circumstances that inspire enterprises to use microservices in the first place.

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Why Do Enterprises Adopt Microservices Architecture?

Some of the most innovative and profitable enterprises in the world – like Amazon, Netflix, Uber, and Etsy – attribute their IT initiatives’ enormous success in part to the adoption of microservices. Over time, these enterprises dismantled their monolithic applications and refactored them into microservices-based architectures. This helped to quickly achieve scaling advantages, greater business agility, and unimaginable profits.

In this article, we’ll explore the microservices examples of these wildly successful enterprises. But first, let’s look at the general circumstances that inspire enterprises to use microservices in the first place.

Why Do Enterprises Adopt Microservices?

Most enterprises start by designing their infrastructures as a single monolith or several tightly interdependent monolithic applications. The monolith carries out many functions. All of the programming for those functions resides in a cohesive piece of application code. 

Since the code for these functions is woven into one piece, it’s difficult to untangle. Changing or adding a single feature in a monolith can disrupt the code for the entire application. This makes any type of upgrade, even a very simple one, a time-consuming and expensive process. The more upgrades performed, the more complicated the programming becomes until upgrades and scaling are virtually impossible. 

Over time, companies cannot make further changes to their coding without starting over from scratch. The process rapidly becomes overwhelming and can result in enterprises remaining stuck in their older methods long after they should have moved on to more upgrades.

Building a Microservices Architecture

At this point, developers may choose to divide a monolith’s functionality into small, independently running microservices.  The microservices loosely connect via APIs to form a microservices-based application architecture. Microservices architecture offers greater agility and pluggability because enterprises can independently develop, deploy, and scale each microservice. They can do this without necessarily incurring service outages, negatively impacting other parts of the application, or needing to refactor other microservices. The process is simpler, and when one part of the application needs to be adjusted or upgraded, this may be done without affecting everything else. The entire system is made more manageable.

As you can see, there are many benefits to microservices. They are more likely to be used by enterprises that have outgrown the original monolith system, but many companies now design their microservices architecture from the beginning. They learn from the mistakes of others and have discovered that it is easier to start out with a plan for expansion.

Here are the steps to designing a microservices architecture:

1. Understand the monolith

Study the operation of the monolith and determine the component functions and services it performs. Since all the functions will be mixed together, this may pose a challenge. It is an important part of determining what is needed for the microservices, so it should be the first thing developers focus on. 

2. Develop the microservices

Develop each function of the application as an autonomous, independently running microservice. These usually run in a container on a cloud server. Each microservice answers to a single function — search, shipping, payment, accounting, payroll, etc. This allows for minor changes to be made without disrupting the other processes.

3.  Integrate the larger application

Loosely integrate the microservices via API gateways, so they work in concert to form the larger application. An iPaaS like DreamFactory can play an essential role in this step. Each microservice will work with the others to provide the necessary functions. Each section can be adjusted, adapted, or even removed without too much impact on the other parts of the application. 

4. Allocate system resources

Use container orchestration tools like Kubernetes to manage the allocation of system resources for each microservice. This step helps keep everything organized and ensures the entire system works as a whole.

*Read our complete guide to microservices for more detailed information on this application architecture. 

Examples of Microservices in Action

You may be wondering how this all works in practical applications. Let’s look at some examples of microservices in action. The enterprises below used microservices to resolve key scaling and server processing challenges.

1. Amazon

Amazon is known as an Internet retail giant, but it didn’t start that way. In the early 2000s, Amazon’s retail website behaved like a single monolithic application.

The tight connections between — and within — the multi-tiered services that comprised Amazon’s monolith meant that developers had to carefully untangle dependencies every time they wanted to upgrade or scale Amazon’s systems. It was a painstaking process that cost plenty of money and required time to adjust.

Previously, Amazon found that the monolith structure worked very well. However, the code base quickly expanded as more developers joined the team. There were too many updates and projects coming in, which had a negative impact on the software development and function. With such obvious drops in productivity, it was necessary to look at a better way of doing things. 

In 2001, development delays, coding challenges, and service interdependencies inhibited Amazon’s ability to meet the scaling requirements of its rapidly growing customer base. The company and its site were exploding, but there was no way to keep up with the growth.

Faced with the need to refactor its system from scratch, Amazon broke its monolithic applications into small, independently running, service-specific applications. The use of microservices immediately changed how the company worked. It was able to change individual features and resources, which made immediate, massive improvements to the site’s functionality.

Here’s how Amazon did it:

Developers analyzed the source code and pulled out units of code that served a single, functional purpose. As expected, this was a time-consuming process since all the code was mixed up together, and the various functions of the site were intertwined. However, the developers worked hard to sort it out and determine which sections could be turned into microservices.

Once they had the separate sections of code, the developers wrapped these units in a web service interface. For example, they developed a single service for the Buy button on a product page, a single service for the tax calculator function, and so on. Each function had its own section.

Amazon assigned ownership of each independent service to a team of developers, allowing them to view development bottlenecks more granularly. They could resolve challenges more efficiently since a small number of developers could direct all of their attention to a single service. That one service wouldn’t affect everything else, either, so it was easier to work on.

Amazon’s “service-oriented architecture” was largely the beginning of what we now call microservices. It led to Amazon developing a number of solutions to support microservices architectures — such as Amazon AWS and Apollo — which it currently sells to enterprises throughout the world. Without its transition to microservices, Amazon could not have grown to become the most valuable company in the world — valued by market cap at $1.6 trillion as of August 1, 2022.

This is a 2008 graphic of Amazon’s microservices infrastructure, aka the Death Star. You can see just how much more efficient it is.

2008 graphic of Amazon’s microservices infrastructure, a.k.a., the Death Star

*Image Source

2. Netflix

Amazon wasn’t the only company to pioneer the world of microservices. Netflix is another company that has found success through the use of microservices connected with APIs.

Similar to Amazon, this microservices example began its journey in 2008 before the term “microservices” had come into fashion. Netflix started its movie-streaming service in 2007. By 2008 it was suffering from service outages and scaling challenges; for three days, it was unable to ship DVDs to members.

At this point, the company was still dealing with physical DVDs, which put a damper on how well it could serve its customers. Streaming was still a dream, and online business, while thriving, was difficult. The monolith design was still not very functional beyond a certain point. Microservices architecture was a much better option, but it didn’t truly exist yet.

According to a Netflix vice president:

Our journey to the cloud at Netflix began in August of 2008, when we experienced a major database corruption and for three days could not ship DVDs to our members. That is when we realized that we had to move away from vertically scaled single points of failure, like relational databases in our datacenter, towards highly reliable, horizontally scalable, distributed systems in the cloud. We chose Amazon Web Services (AWS) as our cloud provider because it provided us with the greatest scale and the broadest set of services and features.” (source)

In 2009, Netflix began the gradual process of refactoring its monolithic architecture, service by service, into microservices. The first step was to migrate its non-customer-facing, movie-coding platform to run on Amazon AWS cloud servers as an independent microservice. Netflix spent the following two years converting its customer-facing systems to microservices, finalizing the process in 2012. 

The first step was to migrate its non-customer-facing, movie-coding platform to run on Amazon AWS cloud servers as an independent microservice. Netflix spent the following two years converting its customer-facing systems to microservices, finalizing the process in 2012. 

Here’s a diagram of Netflix’s gradual transition to microservices:

Diagram of Netflix’s gradual transition to microservices

*Image Source

Refactoring to microservices allowed Netflix to overcome its scaling challenges and service outages. By 2013, Netflix’s API gateway was handling two billion daily API edge requests, managed by over 500 cloud-hosted microservices. By 2017, its architecture consisted of over 700 loosely coupled microservices. Today, Netflix makes around $8 billion a year and streams approximately six billion hours of content weekly to more than 220 million subscribers in 190 countries, and it continues to grow.

Here’s a visual depiction of Netflix’s growth from 2007 to 2015:

Visual depiction of Netflix’s growth from 2007 to 2015

*Image Source

But that’s not all. Netflix received another benefit from microservices: cost reduction. According to the enterprise, its “cloud costs per streaming start ended up being a fraction of those in the data center, a welcome side benefit.” The company has also been responsible for VPN and proxy crackdowns around the world. It stopped users from viewing content through proxy servers, despite the fact the streaming service was available around the world. It is also responsible for changing the way people watch television shows. These days, you have to subscribe to a specific site to receive the shows you want, thanks to Netflix initiating the streaming wars. However, microservices architecture is behind it all.

This is Netflix Senior Engineer Dave Hahn proudly showing off the Netflix microservices architecture:

Netflix Senior Engineer Dave Hahn proudly showing off the Netflix microservices architecture

*Image Source

3. Uber

Despite being introduced to the world more recently than either of our previous examples, Uber also began with a monolith design.

This microservice example came not long after the launch of Uber, when the ride-sharing service encountered growth hurdles related to its monolithic application structure. The platform struggled to develop and launch new features efficiently, fix bugs, and integrate its rapidly growing global operations. Moreover, the complexity of Uber’s monolithic application architecture requires developers to have extensive experience working with the existing system just to make minor updates and changes to the system. 

Here’s how Uber’s monolithic structure worked at the time:

  • Passengers and drivers connected to Uber’s monolith through a REST API. 
  • There were three adapters – with embedded API for functions like billing, payment, and text messages.
  • There was a MySQL database. 
  • All features were contained in the monolith.

This design was clunky and difficult to make changes to. For the developers, the ride share’s popularity almost immediately caused problems. The company grew too fast to easily keep up with the app’s original design.

Here’s a diagram of Uber’s original monolith from Dzone:

Diagram of Uber’s original monolith from Dzone

*Image Source

To overcome the challenges of its existing application structure, Uber decided to break the monolith into cloud-based microservices. Subsequently, developers built individual microservices for functions like passenger management, trip management, and more. Similar to the Netflix example above, Uber connected its microservices via an API gateway. 

The changes were faster in this case, since it was done earlier in the business. There were fewer functions mixed into the monolith design, and this made it simpler to change things around when it was time to switch.

Here’s a diagram of Uber’s microservices architecture from Dzone:

Diagram of Uber’s microservices architecture from Dzone

*Image Source

Moving to this architectural style brought Uber myriad benefits. First, the developers were split into specific teams that had to focus on one service at a time. This ensured they became experts at their particular service. When there was an issue, they could fix it faster and without affecting the other service areas. The speed, quality, and manageability of new development immediately improved.

As Uber began to grow at an exponential speed, fast scaling was made easier. Teams could focus only on the services that needed to scale and leave the rest alone. Thanks to the microservices architecture, everything ran this way smoothly. Updating one service didn’t affect the others. The company also achieved more reliable fault tolerance.

However, there was a problem. Simply refactoring the monolith into microservices wasn’t the end of Uber’s journey. According to Uber’s site reliability engineer, Susan Fowler, the network of microservices needed a clear standardization strategy, or it would be in danger of “spiraling out of control.” 

Fowler said that Uber’s first approach to standardization was to create local standards for each microservice. This worked well, in the beginning, to help it get microservices off the ground, but Uber found that the individual microservices couldn’t always trust the availability of other microservices in the architecture due to differences in standards. If developers changed one microservice, they usually had to change the others to prevent service outages. This interfered with scalability because it was impossible to coordinate new standards for all the microservices after a change. 

In the end, Uber decided to develop global standards for all microservices. This once again changed everything for the company.

First, they analyzed the principles that resulted in availability — like fault tolerance, documentation, performance, reliability, stability, and scalability. Once they’d identified these, they began to establish quantifiable standards. These were measurable and designed to be followed. For example, the developers could look at business metrics, including webpage views and searches.

Finally, they converted the metrics into requests per second on a microservice. While it wasn’t a rapid change, it was a very necessary one. Uber appeared to be growing on the outside, but there was a real struggle on the inside to keep it in a state of growth without outages and service shortfalls.

According to Fowler, developing and implementing global standards for a microservices architecture like this is a long process; however, for Fowler, it was worth it — because implementing global standards was the final piece of the puzzle that solved Uber scaling difficulties. “It is something you can hand developers, saying, ‘I know you can build amazing services, here’s a system to help you build the best service possible.’ And developers see this and like it,” Fowler said.

Here’s a diagram of Uber’s microservices architecture from 2019:

Diagram of Uber’s microservices architecture from 2019

*Image Source

4. Etsy

Etsy’s transition to a microservices-based infrastructure came after the e-commerce platform started to experience performance issues caused by poor server processing time. The company’s development team set the goal of reducing processing to “1,000-millisecond time-to-glass” (i.e., the amount of time it takes for the screen to update on the user’s device). After that, Etsy decided that concurrent transactions were the only way to boost processing time to achieve this goal. However, the limitations of its PHP-based system made concurrent API calls virtually impossible.

Etsy was stuck in the sluggish world of sequential execution. Not only that, but developers needed to boost the platform’s extensibility for Etsy’s new mobile app features. To solve these challenges, the API team needed to design a new approach — one that kept the API both familiar and accessible for development teams. 

Guiding Inspiration

Taking cues from Netflix and other microservices adopters, Etsy implemented a two-layer API with meta-endpoints. Each of the meta-endpoints aggregated additional endpoints. At the risk of getting more technical, InfoQ notes that this strategy enabled “server-side composition of low-level, general-purpose resources into device- or view-specific resources,” which resulted in the following:

  • The full stack created a multi-level tree.
  • The customer-facing website and mobile app composed themselves into a custom view by consuming a layer of concurrent meta-endpoints. 
  • The concurrent meta-endpoints call the atomic component endpoints.
  • The non-meta-endpoints at the lowest level are the only ones that communicate with the database.

At this point, a lack of concurrency was still limiting Etsy’s processing speed. The meta-endpoints layer simplified and sped up the process of generating a bespoke version of the website and mobile app; however, sequential processing of multiple meta-endpoints still got in the way of meeting Etsy’s performance goals.

Eventually, the engineering team achieved API concurrency by using cURL for parallel HTTP calls. In addition to this, they also created a custom Etsy libcurl patch and developed monitoring tools. These show a request’s call hierarchy as it moves across the network. Further, Etsy also created various developer-friendly tools around the API to make things easier for developers and speed up the adoption of its two-layer API.

Etsy went live with the architectural style in 2016. Since then, the enterprise has benefited from a structure that supports continual innovation, concurrent processing, faster upgrades, and easy scaling. It stands as a successful microservices example. 

Here’s a slide depicting Etsy’s multi-level tree from a presentation by Etsy software engineer Stefanie Schirmer: 

A slide depicting Etsy’s multi-level tree from a presentation by Etsy software engineer Stefanie Schirmer

*Image Source

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DreamFactory: Automatic REST API Generation for Rapidly Connecting Your Microservices Architecture 

Reading the microservices examples above should help you understand the benefits, processes, and challenges of breaking a monolithic application to build a microservices architecture. However, one thing we haven’t addressed is the time and expense of developing custom APIs for connecting the individual microservices that comprise this architectural style. That’s where the DreamFactory iPaaS can help.

Moreover, the DreamFactory iPaaS offers a point-and-click, no-code interface that simplifies the process of developing and exposing APIs to integrate your microservices application architecture. Try DreamFactory for free and start building APIs for microservices today! 

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What Are Containerized Microservices, API Trends: Monolithic vs Microservices, ESB vs Microservices: Understanding Key Differences, Designing Web-Scale Workloads with Microservices,