System Design Simplified: A Beginner's Guide to Everything You Need to Know (Part 7)
Master the Basics of System Design with Clear Concepts, Practical Examples, and Essential Tips for Beginners.
The Era of Event-Driven Thinking
Hello, friends! How’s it going? Hope you are doing really well, especially with your job!
Today, we’re diving into a topic that, for some reason, has always fascinated me A LOT—event-driven architectures and consensus algorithms. I can’t quite put my finger on why (maybe I’m just a bit weird), but there’s something almost elegant (yeah, I really said that) about how distributed systems coordinate, react to events, and somehow, despite failures and chaos, need to reach an agreement, and they finally do. It’s like watching a well-rehearsed orchestra play a symphony—except the musicians are spread across the world, half of them drop their instruments mid-performance, and yet the melody somehow continues.
Event-driven architectures are essentially the backbone of modern, scalable systems. They allow services to communicate asynchronously, react to changes in real time, and remain decoupled enough to evolve independently. And then there’s consensus—the glue that holds distributed systems together. Without it, everything would fall apart. After all, what good is an event-driven system if its nodes can’t agree on what actually happened?
In this piece, we’re going really deep. We’ll explore things like:
How event-driven architectures work and why they matter
Event sourcing and why capturing state as a series of events is so powerful
The hidden benefits of decoupling and why it leads to more resilient systems
The unavoidable challenges of distributed systems—network failures, partitions, and the CAP theorem
Consensus algorithms like Paxos and Raft—the unsung heroes keeping distributed systems from descending into chaos
If you’ve ever wondered how massive, globally distributed systems stay consistent, handle failures gracefully, and still deliver near-instant responses, you’re in the right place. Let’s break it all down for you!
A Real Introduction
Software architecture has been undergoing a subtle but undeniably profound transformation over the past few years. And no, this isn’t just another dramatic take to grab your attention (hopefully this article will be useful enough for you to read it all)—I genuinely believe we’re now witnessing a shift that’s redefining how modern systems are designed, for better and (not) for worse.
Sure, monoliths are still around, and let’s be real and honest—they’re not going anywhere anytime soon. In many cases, they actually make a lot of sense, so it’s perfectly okay. Meanwhile, microservices have been the rock stars of software engineering conferences for years. You’ve probably seen it: groups of engineers at the bar, passionately debating service boundaries, API gateways, and whether they really needed that many microservices in the first place. That part of the hype cycle is (like every technological curve) going down to earth, and the trend is shifting towards some architectures that prioritize flexibility, scalability, and resilience.
So, what is the real game-changer nowadays? Without a doubt, we must admit it is Event-driven architecture (EDA).
It’s the shift that’s quietly reshaping how modern systems are built. Instead of services constantly making direct calls to each other, they communicate by emitting and reacting to events. This small but fundamental difference unlocks a ton of benefits—better scalability, fault tolerance, and the ability to build highly responsive, real-time systems. What used to be an esoteric pattern reserved for ultra-high-scale applications is now becoming a go-to strategy for designing distributed systems that can handle the unpredictable nature of modern workloads.
What used to be a niche pattern, mostly associated with hardcore distributed systems and high-performance messaging, has now become a mainstream architectural strategy. Why? Because EDA fundamentally changes how we think about building scalable, decoupled, and reactive systems. Instead of services calling each other directly in tightly coupled chains, they communicate by emitting and reacting to events. This means better fault tolerance, independent scaling, and the ability to handle real-time workloads with ease.
Of course, this isn’t some magic bullet—EDA introduces its own challenges (hello, eventual consistency and debugging nightmares that keep everyone awake at night). But there’s no denying that the way we design systems is evolving, and event-driven thinking is at the heart of that transformation. Distributed systems introduce challenges: maintaining consistency, handling failures, and ensuring consensus. And…. When we step into event sourcing—where state is derived from a log of immutable events—the complexity compounds A LOT. So, let’s unpack this labyrinth, diving deep into event-driven architecture, event sourcing, the benefits of decoupling, and the inherent challenges of distributed systems, including consensus algorithms like Paxos and Raft.
How Event-Driven Architectures Work and Why They Matter
The Shift Toward Event-Driven Thinking
Imagine you’re standing in a bustling city, maybe like London or New York, watching how everything moves around you. Things like cars obeying traffic signals, people waiting for pedestrian lights, buses following their routes, and so on. Now, think about how chaotic it would be if everyone had to call someone else before making a move—drivers would have to phone pedestrians to confirm they were crossing, buses would need permission from every passenger before stopping, and traffic lights would have to send requests to every car before changing. The whole system would grind to a halt or implode on itself due to the massive amount of information required in real-time to operate. We have basically two ways to solve this: empowering our network systems, increasing costs, network bugs/errors, and interferences, increased latency or simply changing the approach and doing something fundamentally different.
As you probably imagined, the first approach is exactly how traditional software architectures operate. In most everyday applications, components communicate via direct calls, expecting immediate responses. A user places an order, and the backend kicks off a chain reaction of tightly coupled service calls:
The order service checks stock.
It calls the payment service to process payment.
The payment service contacts a third-party gateway.
Upon success, the inventory service updates stock.
Finally, the shipping service prepares the order for dispatch.
Remember: Each step depends on the previous one completing successfully. If any service is slow or fails, the entire process is blocked, potentially causing cascading failures.Yeah, you got it right: scalability becomes a real nightmare—what if the payment service is handling a sudden surge while inventory remains steady? You’d have to scale everything together, wasting resources and probably a ton of time in solving related issues.
Now, let’s take a step back and rethink a bit about this. What if services didn’t have to wait for each other? What if they simply reacted to events as they occurred?
A New Paradigm: Events Over Commands
This is where Event-Driven Architecture (EDA) changes everything. Instead of direct synchronous calls, services communicate by publishing events and reacting just to events they care about.
Here’s how the same e-commerce workflow (previously discussed) would look in an event-driven world:
The Order Service emits an
OrderPlacedevent.The Payment Service listens for it and processes the payment asynchronously.
If successful, it emits a
PaymentProcessedevent.The Inventory Service picks up this event and decrements stock.
It then emits
StockUpdated, which the Shipping Service listens for to prepare the order.
At no point do these services directly call each other. They simply emit events and react to relevant ones. This decouples the system, making it more resilient, scalable, and flexible.
Why EDA Matters: The Key Advantages
EDA isn’t just about changing how systems communicate—it fundamentally alters how we design software to be more scalable, resilient, and adaptable. Here’s why it matters:
✅ Loose Coupling = More Flexibility
Traditional systems suffer from tight coupling—services rely on each other’s availability, structure, and response times. With EDA, services are allowed to communicate indirectly through events, meaning they don’t need to know about each other’s existence. This makes it easier to modify or replace individual components without disrupting the entire system.
✅ Independent Scalability
With synchronous architectures, scaling one service often means scaling others—even if they don’t need it. In EDA, each service scales independently based on the volume of events it processes. If the payment service is overwhelmed but inventory is fine, you can scale just the payment service without touching anything else.
✅ Fault Tolerance and Resilience
Failures in one part of the system don’t necessarily bring everything down. If a service crashes, events can persist in an event broker (Kafka, RabbitMQ, Pulsar) and are processed when the specific service recovers. This means your system can continue operating even when components fail.
✅ Real-Time Processing
Modern applications demand instant reactions—think of fraud detection, real-time analytics, and personalized recommendations. EDA enables reactive systems that process and respond to events as they happen, unlocking real-time capabilities that synchronous architectures struggle to match.
The Challenges: Why EDA Isn’t a Silver Bullet
EDA brings significant benefits, but it isn’t free of trade-offs. It introduces new challenges that require careful planning:
⚠ Eventual Consistency: The ACID vs. BASE Tradeoff
Traditional databases provide strong consistency (ACID transactions)—when you write data, it’s immediately available everywhere. But EDA is inherently asynchronous, which means data across services might not be consistent at all times. This is called eventual consistency—data will sync eventually, but there may be temporary inconsistencies.
For example, if a user checks their order status right after placing it, the inventory update might not have processed yet. You need strategies like saga patterns or idempotent consumers to manage these inconsistencies.
⚠ Debugging Complexity: Where Did My Event Go?
With a synchronous system, debugging is relatively straightforward—trace the API call stack, and you’ll find the issue. In EDA? Good luck ‘bout that. Events travel across multiple services, often asynchronously and at different speeds. Debugging requires distributed tracing, event logging, and proper observability tools like OpenTelemetry, Jaeger, or Zipkin.
⚠ Event Ordering & Duplication Handling
What if two services consume the same event at slightly different times, leading to incorrect sequencing? Or what if a service processes the same event twice due to a network failure? Without proper ordering guarantees and idempotency mechanisms, you risk incorrect updates, duplicate charges, or inconsistent data.
A Thought Experiment: The Parallel Between EDA and Human Societies
To be more clear, let’s take a step back from software and think about this in human terms.
Consider how communication works in a well-functioning society. Governments, businesses, and individuals don’t operate in a rigid, synchronous manner. A government doesn’t personally notify every citizen about new policies—it announces events (laws, regulations), and businesses and individuals react accordingly.
A tax reform is announced (event published).
Businesses adjust their pricing and payroll (event consumers).
Banks update interest rates based on the new policy (another consumer).
Each entity operates independently, reacting to events that matter to them. No central authority dictates the sequence of actions—it’s an organic, event-driven system.
Contrast this with a command-driven society, where every transaction requires immediate confirmation—chaos would ensue. The world operates in a naturally event-driven manner, so why shouldn’t our software?
When Should You Use EDA?
EDA isn’t one-size-fits-all, but it’s ideal for:
E-commerce platforms handling millions of orders.
IoT systems processing vast streams of sensor data.
Streaming services providing real-time recommendations.
Financial applications managing asynchronous transactions and fraud detection.
However, if you’re building a simple CRUD app with minimal scalability concerns, a traditional request-response model might be easier and more maintainable.
EDA requires a shift in mindset—designing for asynchronous workflows, embracing eventual consistency, and ensuring proper observability. Done right, it unlocks a level of scalability, resilience, and real-time processing that synchronous architectures struggle to achieve.
Event Sourcing and Why Capturing State as Events is So Powerful
Picture this: You're running an e-commerce site (maybe I should change my type of examples, but the E-commerce platform is simply so good!), and every time a user interacts with your platform—whether it’s adding an item to their cart, confirming an order, or making a payment—those actions create state changes. Normally, in traditional systems, you'd store only the current state: is the order confirmed? Is it shipped? That's fine, but here's the catch: you lose completely the story behind the state. What happened before the order was confirmed? Was there a problem with payment? How many times was the order updated? These are important details that give you more than just the "end result" of the process, which is still relevant of course, but not always crucial.
That’s where event sourcing comes in. Instead of saving just the final state of an object, you save every change that led to the current state. And each of these changes is an event—immutable and stored in a log. These events are not just snapshots of time; they are a historical record of how the system got from point A to point B.
Imagine this for a moment: Instead of a database row for an order that says “Order Shipped,” you store:
OrderCreated(orderId, userId, items, timestamp)
OrderConfirmed(orderId, paymentId, timestamp)
OrderShipped(orderId, trackingId, timestamp)
Each of these events represents a transitional change in the state of the order, but they also tell a story of what happened at each stage.
So Why Is Event Sourcing So Powerful?
At its core, event sourcing gives you the ability to audit your system completely. Imagine for a second all the possibilities! If there’s an issue down the road, you can go back and look at the entire history of events. If an order got shipped but the tracking number is wrong, you can trace back through all the events leading up to that, figure out where things went wrong, and fix the problem. It’s like having a debugger for your entire system, at the level of business logic.
But wait, doesn’t that sound like a lot of data? Yes, it can be. But here’s where it gets interesting. Not only does event sourcing give you full auditability, but it also lets you replay history. You can roll back to any state by replaying the events leading up to it, or even rebuild the entire system state just by processing the events from the very beginning. This capability is so useful in real-time debugging and tracking down bugs that would be hard to catch in a snapshot-based system.
Additionally, you get CQRS benefits. If you remember, CQRS (Command Query Responsibility Segregation), allows you to separate the way you handle commands (events that change state) from queries (the reads that tell you about the current state). By using event sourcing in this way, you can optimize both the writing of data and the reading of it, creating a system that can be both write-heavy and read-heavy, depending on your needs.
The Hidden Benefits of Decoupling and Why It Leads to More Resilient Systems
Now, let’s talk about decoupling. We already know that microservices are great for breaking down a big, monolithic application into smaller, more manageable pieces. But here’s the thing: decoupling in an event-driven world is a true game-changer. Why? Because when services are decoupled, they don’t directly communicate with each other. Instead, they communicate through events.
Let’s revisit the order system. In a traditional, tightly coupled system, your order service would need to directly talk to the inventory service before confirming an order. If inventory is down or slow, the entire order process could fail or be delayed. But in an event-driven architecture (EDA), the order service doesn’t need to care about whether the inventory system is available at the time of confirmation. It just publishes an event that the inventory service can consume when it’s ready.
Now think about the benefits of this setup:
Fault Isolation: A failure in the inventory system doesn't break the order service. It simply means the system hasn't processed the event yet, but other services are still running fine.
Scalability: If your order service is under heavy load, it can scale without worrying about the inventory service. In an event-driven setup, each service can scale independently, depending on its needs. So, if you’re handling a ton of orders but inventory updates are pretty light, you don’t need to scale both systems equally.
In a way, you're creating a system that's built to resist failure, rather than be brought down by it.
But…. Isn’t Decoupling Complicated?
Great question. Yes, it can be. Decoupling requires proper event design and an efficient way to handle asynchronous communication. One service sends events, and other services need to react to them. In the case of something like Kafka or RabbitMQ, the message broker acts as the middleman, as we alredy know. But here's the thing: once you've designed your events and infrastructure right, decoupling becomes a major advantage.
Asynchronous processing is another key element. In a synchronous system, each service needs to wait for a response from another before proceeding. This leads to delays and bottlenecks. But in an asynchronous event-driven system, things can happen in parallel. The order service doesn’t need to wait for a response from the inventory service. It just publishes an event and moves on to the next one, and inventory will handle it when it gets the chance. This massively improves throughput and reduces delays.
A Thought Experiment: Connecting Event Sourcing and Decoupling
At this point, I know that you're overwhelmed by this massive amount of information, and that's perfectly normal (would be weird not being so). In resolving this issue, I have prepared something for you to better understand how this stuff works in simpler terms.
Let’s put these concepts together in a more down to earth example. Imagine you're building a real-time analytics system for an e-commerce platform. You want to track not just when orders are placed, but the history of every interaction a customer has with your platform—from browsing products to adding items to their cart, and finally to checking out. You want a full picture of customer behavior, so you store everything as events: ProductViewed, ItemAddedToCart, OrderPlaced, and so on.
Now, suppose you want to analyze the data. You could query a traditional database and get the current snapshot of what’s happened, but that doesn’t tell you the whole story. Instead, with event sourcing, you have a complete history of every interaction, and you can replay those events to rebuild the customer's journey. Maybe you want to know what caused a sudden spike in abandoned carts. By replaying events for that specific time period, you can get a much deeper insight into the customer’s behavior.
But here's where decoupling comes into play. Your analytics service doesn’t need to talk to the order service or the inventory service. It simply listens for the relevant events and processes them as they come in. The order service doesn’t know or care if the analytics system is online. It just emits events, and any service that cares can consume them in its own time. The independence of services here is what makes this setup both scalable and resilient.
Why This Matters: A More Agile, Scalable, and Resilient System
By combining event sourcing with decoupling, you're not just creating a system that can respond in real time. You're also creating a system that's flexible and adaptable. You’re building resilience by ensuring that failure in one service doesn’t take down the whole system. You’re improving scalability because services can be scaled independently. And you’re unlocking a whole new level of auditability and traceability.
As you design these systems, ask yourself: how do you handle failures in your current setup? Are all your services tightly coupled? Can you separate commands from queries, and can you replay history to debug or analyze issues? If you start asking these questions and moving towards an event-driven approach, you’ll quickly see why event sourcing and decoupling are so powerful.
Challenges of Distributed Systems: Network Failures, Partitions, and the CAP Theorem
As we’ve discussed a lot during this massive series, distributed systems—systems where components are spread across multiple machines or geographical locations—introduce inherent complexities that centralize computing systems do not face AT ALL. As systems scale, the challenge of maintaining reliability, consistency, and availability becomes more pronounced, and in some real-world cases, it actually grows exponentially. A few of the most significant challenges are network failures, network partitions, and the trade-offs described by the CAP theorem.
Network Failures and Partitions
In distributed systems, communication between nodes is not guaranteed to be continuous or reliable. Network failures can occur due to a variety of reasons, such as latency, congestion, or hardware malfunctions.
When network failures result in nodes becoming unable to communicate with one another, network partitions occur. A network partition is a situation where a subset of nodes becomes isolated from the rest of the system. This isolation means that certain nodes cannot reach others, leading to potential inconsistencies and broken guarantees about system behavior.
Consider this simplified scenario where two partitions occur in a system of four nodes:
Partition 1: Nodes A and B can communicate with each other, but cannot reach Node C or D.
Partition 2: Nodes C and D can communicate, but cannot reach Nodes A or B.
This is an issue because each partition will believe it has the latest state, potentially leading to divergent data between the partitions. Handling this is one of the core concerns of distributed systems design.
The CAP Theorem: A Fundamental Trade-off
To refresh a bit our minds, we shall say that the CAP theorem, introduced by computer scientist Eric Brewer, states that a distributed system can only achieve two of the following three guarantees at any time:
Consistency: All nodes in the system have the same view of the data. This means that when data is read from the system, it reflects the most recent write.
Availability: Every request (read or write) receives a response. A system that is available will always provide an answer, even if some of the data might be outdated.
Partition Tolerance: The system continues to function despite network partitions. Partition tolerance is crucial because, in real-world networks, partitions are inevitable.
The CAP theorem impose that, in the presence of a partition, a system can either:
Prioritize Consistency (ensuring every read reflects the most recent write, even if some nodes are unavailable),
Or prioritize Availability (ensuring that every request gets a response, even if some data might not be consistent across nodes).
This leads to an obvious tough decision for architects: Do we prioritize consistency, ensuring data integrity but potentially making the system unavailable in the event of a network failure? Or do we prioritize availability, risking potential data divergence?
Consensus Algorithms: Paxos and Raft—The Unsung Heroes Keeping Distributed Systems from Descending into Chaos
The problem of network partitions has been a long-standing concern for system architects, dating back to the early days of distributed computing. As systems grew larger and more complex, the need to ensure consistency and availability in the face of network failures became increasingly critical. This concern led to a series of solutions and innovations over the years. In the late 20th century, during the 1980s and 1990s, several foundational consensus algorithms were developed to address these challenges.
Among these, Paxos (introduced in 1989 by Leslie Lamport) and Raft (introduced in 2013 by Diego Ongaro and John Ousterhout) have emerged as two of the most powerful and widely adopted algorithms for achieving consensus in distributed systems. Paxos, with its elegant but complex protocol, laid the groundwork for understanding how to reach agreement in an unreliable network. Raft, on the other hand, built upon these ideas but aimed to simplify the process, making it more practical and understandable for real-world applications.
Both algorithms provide critical mechanisms for ensuring that all nodes in a distributed system can agree on the same state, even when network partitions occur and some nodes are temporarily unavailable. These solutions remain fundamental to modern distributed systems, from large-scale databases to cloud services, helping architects navigate the challenges of building resilient systems in an unpredictable world.
Paxos: The Classical Approach
Paxos is one of the oldest and most well-known consensus algorithms, created by Leslie Lamport in 1989. It ensures that nodes in a distributed system agree on a single value, even when some nodes fail or there are network partitions. Paxos operates in several phases to reach consensus:
Propose Phase: A proposer sends a proposal to a majority of nodes (called acceptors) to request a proposal number and a corresponding value.
Promise Phase: Acceptors respond with a promise not to accept any proposal with a lower number than the one they’ve seen.
Accept Phase: Once the proposer receives a majority of promises, it can send the proposal to be accepted by the acceptors.
Here’s a basic example of a Paxos implementation in Python. It’s a simplified version to demonstrate the core idea:
import random
import time
# A simple Paxos node (Proposer/Acceptor)
class PaxosNode:
def __init__(self, id):
self.id = id
self.proposal_number = 0
self.accepted_value = None
self.promises = {}
def propose(self, value):
self.proposal_number = random.randint(1, 1000)
print(f"Node {self.id} proposes value: {value} with proposal number {self.proposal_number}")
return self.proposal_number, value
def receive_promise(self, proposal_number, value):
if proposal_number > self.proposal_number:
self.promises[proposal_number] = value
print(f"Node {self.id} promises to accept value: {value} with proposal number {proposal_number}")
def accept(self, proposal_number, value):
if proposal_number >= self.proposal_number:
self.accepted_value = value
print(f"Node {self.id} accepts value: {value} with proposal number {proposal_number}")
return True
return False
# Creating a simple Paxos network
node1 = PaxosNode(1)
node2 = PaxosNode(2)
node3 = PaxosNode(3)
# Proposing a value
proposal_number, value = node1.propose("value1")
# Receiving promises
node2.receive_promise(proposal_number, value)
node3.receive_promise(proposal_number, value)
# Accepting the proposal
node1.accept(proposal_number, value)
node2.accept(proposal_number, value)
node3.accept(proposal_number, value)
In this basic implementation, nodes propose values, promise to accept those values, and eventually reach consensus.
Raft: A Simpler, More Understandable Approach
While Paxos is theoretically sound, it’s often considered too complex for practical use. In response to this, Raft was introduced by Diego Ongaro and John Ousterhout in 2013 as a simpler alternative to Paxos. Raft simplifies the consensus process by breaking it down into three core components:
Leader Election: Raft uses a leader-follower model. The leader is responsible for log replication and ensuring consensus is maintained across the system.
Log Replication: The leader replicates log entries to the followers. Each log entry contains an operation (e.g., an update to the system state).
Safety: Raft ensures that all committed log entries are consistent and replicated across the majority of the nodes.
Here's a simplified Python implementation of Raft’s leader election logic:
import random
import time
class RaftNode:
def __init__(self, id):
self.id = id
self.state = "follower" # States: follower, leader, candidate
self.term = 0
self.votes = 0
def request_vote(self, term):
print(f"Node {self.id} requests vote for term {term}")
if self.state == "follower" and term > self.term:
self.state = "candidate"
self.term = term
self.votes = 1
return True
return False
def vote(self, term, node):
if self.state == "follower" and term >= self.term:
self.state = "follower"
self.term = term
print(f"Node {self.id} votes for Node {node.id}")
return True
return False
# Simulating a leader election
node1 = RaftNode(1)
node2 = RaftNode(2)
node3 = RaftNode(3)
# Initiating leader election
node1.request_vote(1)
node2.vote(1, node1)
node3.vote(1, node1)
# After the votes, Node 1 becomes the leader
print(f"Node {node1.id} is the leader for term {node1.term}")
In this simplified example, Raft nodes exchange votes to elect a leader. The leader then replicates logs across followers. Raft simplifies the consensus process and ensures safety and consistency across distributed systems, making it more practical than Paxos for many real-world use cases.
In the end….
Distributed systems, by nature, face challenges such as network failures, partitions, and the trade-offs outlined by the CAP theorem. Ensuring consistency, availability, and partition tolerance is a delicate balance, often requiring the use of consensus algorithms like Paxos and Raft.
Paxos, while foundational, can be complex to implement, whereas Raft provides a simpler, more understandable approach. Both algorithms aim to ensure that distributed systems can continue to function correctly, even when faced with failures or network partitions.
Understanding and implementing these algorithms is critical for building resilient, reliable distributed systems, and knowing when to prioritize consistency, availability, or partition tolerance will inform your architectural decisions.
Conclusion: Navigating the Event-Driven Distributed World
Event-driven architectures (EDA) and distributed systems offer immense benefits, such as scalability and flexibility, but they come with significant complexity. Event sourcing is a powerful pattern for modeling state by persisting events instead of the current state. However, it requires careful handling of consistency, storage, and schema evolution, especially as systems scale. Events need to be carefully managed to ensure that they’re processed reliably, and the system must accommodate future changes in data structure without losing backward compatibility.
Decoupled systems enable components to evolve and scale independently, providing resilience and flexibility. However, they introduce challenges in ensuring message delivery, maintaining reliability, and managing eventual consistency. The lack of tight coupling means that failure in one component can ripple across the system, making monitoring and debugging more difficult.
At the heart of distributed coordination are consensus algorithms like Paxos and Raft, which enable distributed systems to reach agreement even in the presence of network partitions or failures. Paxos is a proven but complex algorithm that guarantees consistency by having nodes propose values and vote on them. However, its intricacies make it harder to implement. Raft, on the other hand, simplifies consensus by introducing a leader-follower model, where a single leader node manages the log replication and coordinates the system, making it easier to understand and implement.
The real challenge in building modern distributed systems is not just writing code—it’s designing for failure, ensuring consistency, and optimizing for resilience. As architects, we need to balance the trade-offs between consistency, availability, and partition tolerance (the CAP theorem), all while maintaining system performance. This requires an engineering mindset that goes beyond theory, thinking through how the system will behave under stress, handle failures, and scale as needed.
As you venture into event-driven architectures, remember: events are the lifeblood of the system, ensuring actions are triggered and data flows correctly. But consensus is the backbone, ensuring that all components of your distributed system remain in agreement, even in an unreliable, partition-prone environment.