Abstract
Microservices architecture and API design are foundational to building scalable, flexible, and maintainable software systems in today’s fast-paced technological landscape. By deconstructing complex applications into small, independent services, microservices offer enhanced agility and scalability. APIs serve as the communication bridges between these microservices, ensuring seamless interactions and data exchange. According to a 2020 report by the O’Reilly Media, 67% of organizations are adopting microservices to streamline application development, reduce deployment times, and improve overall operational efficiency.
Effective microservices design begins with clearly defining bounded contexts using Domain-Driven Design (DDD), ensuring each service focuses on a specific business function and avoids overlapping responsibilities. In addition, 76% of companies favor independent deployment for each microservice, allowing teams to update, scale, and troubleshoot services without affecting others. Data isolation, with each service having its own database, is key to maintaining loose coupling and preventing cascading failures. By following error handling patterns like circuit breakers and retries, teams can ensure resilient systems that remain operational during partial failures.
For APIs, following REST or GraphQL best practices enhances system flexibility. REST is perfect for simplicity and broad compatibility, while GraphQL excels in dynamic data queries for complex use cases. API versioning ensures backward compatibility, while adopting tools like Swagger or OpenAPI facilitates effective documentation, reducing friction for developers. With security at the forefront, enforcing HTTPS, authentication mechanisms, and input validation prevents breaches and vulnerabilities. Additionally, optimizing performance with strategies like caching, pagination, and gRPC guarantees faster, more efficient services.
Transcript
This transcript was autogenerated. To make changes, submit a PR.
We are diving into something that's shaping the way modern systems are
built, microservices and API design.
These aren't just technical decisions.
They're how high performing teams move fast, stay flexible, and build systems
that scale without slowing down.
Let's get into it.
So imagine being able to ship new features weekly without downtime.
Imagine your team deploying changes independently without MER complex.
And imagine an architecture that adapts to traffic spikes instead
of crashing under pressure.
That's what microservices and smart API design unlock systems
that move at the speed of your business, not the other way around.
S users expect fast, seamless, and reliable experiences every time.
If your system is slow or fragile, they'll move on in seconds.
Microservices break down big, bloated code bases into smaller agile
units that evolve independently.
APIs connects those units into something greater than the sum of the parts.
Together.
They give you a foundation that's not just scalable, but adaptable,
silent, and ready for what's next.
So now let's understand microservice architecture.
So let's start with the basics.
Like why are microservices such a game changer?
Independent services, microservices, break down your application into
small, independent units, each focused on doing one thing really well.
And at a time.
These services are autonomous, and because of that, they're easier
to build, test, deploy, and scale.
What's powerful here is how this changes the way teams operate.
Instead of coordinating across a massive code base, each team owns
their own service from start to finish.
That means faster edition, making quicker releases and fewer bottlenecks.
Technology diversity.
So now imagine you are building a real time recommendation
engine for an e-commerce site.
You might need lightning fast response times, so you maybe you build that
service and rest or go for performance.
On the other hand, your audit history service where speed isn't as critical
could be built in no JS or Python, where development is faster and more flexible.
Team autonomy.
Each service can use the tools that are best suited for its specific purpose.
And the beauty of it is these choices don't interfere with
other parts of your system.
You also get pin points, scalability.
If one service is under pressure, you don't need to scale your entire
system, just scale that one service.
It's precise, efficient, and cost effective, and
deployment becomes a breeze.
You're no longer tied to a Big Bang release where everything ships at once.
Services go live when they're ready, which means less risk and faster attrition.
Microservices shift our mindset.
We stop thinking in terms of projects and start thinking in terms of products.
Teams don't just deliver code, they own outcomes, and that's a huge step
forward in building better software.
Okay, now let's understand what domain driven des design means and build
what the business peaks already.
So we have decided to build microservices.
The next big question is how do we decide where one service
ends and another service begins?
So this is where domain driven design comes in.
It's like giving your architecture a map of the business.
start by identifying bonded context.
These are self-contained zones of logic where everything makes sense in that word.
for example, what the word order means in a sales context might be
very different from what it means in a shipping context, right?
each bonnet context gets its own vocabulary and rules Try
to establish a unique language.
that means everyone.
Developers, product managers, stakeholders, uses the same terminology.
So instead of one person saying basket and another saying, checkout
payload, everyone just says, cut that alignment avoids misunderstandings
and speeds up collaboration.
And the next step is to model domain entities.
This approach also keeps your services from overlapping or becoming plurry.
You won't end up with two services doing this kind of same thing,
but with slightly different names.
And the next step is to define service boundaries.
So why does this matter?
you are no longer building from where requirements, right?
You're directly coding the business logic into your services.
It's cleaner, more accurate, and when business needs change,
your systems adapt faster.
Moving on to the implementation strategies.
So let's talk about turning your microservices design
into something real and fast.
Independent deployment.
So the key here is independent delivery.
Each service should have its own CI CID pipeline.
That means when one team finishes a feature, they don't have to wait for
another team or coordinate a big release.
They can test it, ship it, and monitor it all on their own timeline.
And the next one is data isolation.
A key microservices principle is that each service should own its own database
or data store with separate data stores.
Services stay independent, making it easier to evolve and deploy
them without breaking others.
And communication patterns we have to choose wisely here.
use rest or GRPC when the response is immediate and necessary.
But for everything else, lean on to graph well, Kafka Rabbit mq.
This allows your services to talk without being tightly bound to each other.
If one is slower down, the rest of your system doesn't have to wait.
On the deployment side, embrace containerization.
So Docker makes your service portable.
Kubernetes, add orchestration, scaling, and self-healing.
It's like giving your architecture superpowers.
And here's a big one.
Instrument, everything locks, metrics, traces.
You want to know what exactly going on in your services at all times.
Observability isn't optional when you have got dozens or hundreds of moving parts.
Next, let's look at the resilience patterns.
Designing for chaos.
I would call it as, let's get real for a second.
Things will go wrong.
Services will fail.
Network will time out.
Dependencies will become unreliable.
The question isn't if something will break.
It's how your system responds when it does.
That's where resilience patterns comes in.
So these are your safety nets.
Let's look at what circuit breaker pattern is.
It's like the fuse box in your house.
When a service starts failing repeatedly, the circuit breaker trips and
temporarily stops making calls straight.
Instead of hammering a broken service and causing a bigger failure, the system
backs off and checks in periodically to see if the service is healthy again.
This is how you avoid cascading outages.
And the next one is bulkhead pattern.
So this is named after ship design.
Ships have compartments to contain flooding if one area is breached, right?
So the same idea A applies here.
If one part of your system is under stress, you isolate its
resources, CPU, memory and threats.
So it can't bring down everything else with it.
And the next one is retry pattern.
And sometimes failures are just bad luck.
a blip in the network.
a temporary database hiccup in these cases automatically retrying the
request with some delay, can resolve the issue without any user impact.
And the last one would be fallback pattern.
So these are degraded, but functional responses that kick in when the
real service isn't available.
Maybe it's K data or a simplified calculation.
Or even a friendly message saying, Hey, we are having some issues.
We'll be right back.
Fallback keeps the user experience smooth even during failures now.
Now let's try to see the a PS styles for the real world.
So let's zoom in on how these services actually talk to each
other and to the outside world.
So let's look at rest.
Of course it's been around for a while and for good reason.
REST is simple, widely understood and works great for credit operations.
It leverages standard HGDP works, get post, put, delete, and it's
super compatible with browsers, mobile apps, zoom, name it.
But rest isn't always the best fit, especially when clients need
precision in what they ask for, and that's where GraphQL shines.
So with GraphQL, the client defines exactly what data it wants and
gets just that no more, no less.
It solves the over fetching and under fetching problem,
especially in complex UIs or mobile apps where bandwidth matters.
On the other hand, GRPC uses HTTP two and protocol purpose to enabling
lightning fast communication between internal service.
It supports bidirectional streaming and for strict contracts, making it perfect
for service to service communication in large scale high throughput systems.
So how do you choose between these things?
you can use rest when you need simplicity, broad compatibility
and stateless communication.
and you can use GraphQL when your clients need flexibility and you
want to avoid loaded payrolls.
And we can use GRPC when unit need, draw speed, low latency and
strict type enforcement, especially for internal microservices.
And here's the twist.
You don't have to pick one.
Many modern systems.
Mix and match rest for public APIs, graph for frontend agility and GRPC
behind the scenes for speed Now.
let's, look at the API versioning strategies, meaning
like no broken clients.
so you have built a beautiful API.
It's like clients are using it, business is blooming.
Now comes the hard part.
How do you change it without breaking everything?
That's where a PA versioning comes in.
It's not just about like supporting the old while rolling out the
new, it's about doing it with clarity, stability, and trust.
Let's walk through the major approaches.
First up, URA path versioning.
This one's simple and visible.
You version right in the URL like slash a p slash we even slash products.
It's easy to understand and out, but the downside is clutter.
You'll end up with multiple endpoints for the same resource.
Then there's query parameter versioning.
You keep the base URA clean and pass the version.
products version is equals to two.
So this means, this, of course this approach is like neat and flexible and
it works well when you want to switch versions without, like rewriting paths,
like what we have seen previously.
You are a path versioning.
And the next one is header versioning.
So you keep your URA completely version agnostic and specify
the version in a h TT P header.
accept version or V one, V two, et cetera.
It's super clean for developers, but it does add a little complexity to
testing and debugging since versioning is now hidden in the headers.
And finally, the most elegant but complex content, complex
One is the content negotiation.
So here your client, ask for a specific version using the accept
header with the custom media type.
This one's in.
Incredibly powerful.
And aligns well with HTT P standards, but it also requires
more sophisticated client handling.
So which one's best?
That depends on your use case.
The visibility and ease of use.
Meta most go with URA or query para if your consumers are
more advanced or internal.
And content negotiation offer cleaner, long-term scalability.
But here is a golden rule.
Version, early version often, and never rate your clients without warning.
Versioning isn't a workaround, it's a commitment to backwards
compatibility and smooth evolution.
Moving on to the next slide, so here comes the documentation part for the APIs.
So let's be honest.
no one allows writing documentation, but when you do it right.
It becomes your secret weapon.
So start with the foundation.
pick open API or swagger.
These are mission readable specs that define your API contract and they unlock
a whole ecosystem of auto-generated documentation, testing tools, mock
server, and even a stick case.
It's like writing your API once and getting five things for free.
Next, don't just describe parameters.
Show real examples.
Give developer sample request and responses in multiple language
bonus points for including edge cases and common mistakes.
The faster someone can copy, paste and see success, the better their experiences.
Error documentation is also huge.
Don't make people guess why something failed.
Explain each other clearly.
Include the status quo, a meaningful message, and this is
key what they can do to fix it.
And don't forget, change laws.
Versioning isn't enough if no one knows what changed.
Keep a clear chronological list of updates, deprecation, and new
features, and let's share your system.
Let's discuss about API security.
So your API is the front door of your system, and if it's not
logged down properly, you're basically inviting bad actors in.
So let's start with basics.
Transport security.
Every API call should be encrypted, no exceptions.
That means H-T-T-P-S with TLS 1.2 or higher.
Plus things like HSTS and.
Secure cookie attributes.
It's a bare minimum for predicting data in motion and guarding
against man in the middle attacks.
Next we move into the authentication and authorization part.
So in this context, don't try to reinvent the wheel here.
Use proven standards like O2 oh and open.
Id connect for secure delegate delegated access.
Once a user or system is authenticated, use JSON web tokens or JWT to
pass identity claims securely.
Just make sure to validate those tokens with signatures and expiration checks.
And let's not forget rule based access control.
not every user should be able to access every endpoint, right?
Define rules, assign permissions, and enforce those tools by the API gateway.
that's where security scales.
Another must have is input, validation, validate all incoming data at the edge
types, formats, blends and ranges.
Sanitization prevents injection attacks and malformed payloads, and
all kinds of unwanted surprises.
And this isn't just for user input, it applies to service calls.
Also finally implement rate limiting and throttling.
You don't want a misbehaving client or worse, a partner hammering your APIs
until your infrastructure crashes, right?
I have seen a lot of examples like that previously.
So try to set some sort of like thresholds based on user rules, ips,
or authentication scopes, and always return those friendly rate limit headers
so clients know when to back off.
And next slide.
Tuning for performance.
alright.
And now we have got our microservices built, our API secured, and now let's
try to make them fast, because let's face it, performance isn't just a
technical metric anymore, right?
It's a user experience metric and users don't wait.
So one of the, strategy that we can use to improve performance is caching, of course.
So we use HTDP.
caching headers like EAG and Cache control to prevent unnecessary
request for even more speed.
Offload frequently requested data to something like red.
Caching isn't just about speed, it's about offloading pressure from your
core services, ation and filtering.
So never return thousands of reports at once.
no one wants that.
And your API can't handle it.
So instead, use cursor based ation per performance and stability.
So let's use this filter down to what they just need.
It's faster for them and cheaper for you.
and also always zz your responses.
It's a tiny change that can save huge bandwidth and speed up
mobile experiences dramatically.
Connection, pooling.
So every time you open a new database or HT DP connection, there's overhead,
right posts let you reuse connections, cutting latency, and improving throughput.
Your services stay fast, even under note.
And the last one is asynchronous processing.
So not everything needs to happen in request response cycle, right?
Push long running or resource heavy task into background jobs.
Use cues.
Let your API written quickly and handle heavy lifting behind the scenes.
So what are we getting from it?
how can we like make use of microservices and what are the next step?
so we have covered a lot, right?
And let's try to bring it all together with some key takeaways
and roadmap for what to do next.
First, microservices are your agility engine.
So instead of one big team stepping in, one team, stepping onto each other
toes, you have got small focus teams shipping independently and confidently.
So now what can you do with all this?
You can start small.
Pick one service that's a good candidate to be carved out.
Design it intentionally around the business domain and build
it with autonomy in mind.
And then standardize your APIs.
define your naming, versioning and error handling strategy.
It's one of those things that's easy to overlook, but critical when you start to
scale and automate everything you can.
Testing, deployment.
Monitoring automation isn't just about spirit.
It's about consistency and peace of mind, of course, and then prioritize
security and observability from day one.
you don't want to boil those on later.
You want them to beg to your architecture and finally stay.
You don't need to have it all figured out.
Just take the next step.
Every cycle gets you closer to your system.
That's not just working, but working with you.
Because at the end of the day, this isn't just a tech strategy.
It's a mindset shift.
One that empowers your teams to build faster and smarter.
Thanks for being here.
Let's go build what's next.