#distributed system

Ever had your hand yank away from something hot before you even realized it burned you?

Yeah. That wasn’t your brain. That was your spinal cord making a snap decision without bothering to loop in headquarters. Welcome to your nervous system—a beautifully chaotic, ultra-efficient, slightly rebellious network that behaves a lot like a distributed system.

In other words, your body is basically running like the internet.

Let me explain.

🤯 Wait, What’s a Distributed System? Think of Netflix. You hit play, and a bunch of servers all over the world coordinate to deliver your episode with (hopefully) zero buffering. There's no single "main" server doing all the work. It's a network of systems talking to each other.

Now imagine your body doing the same thing. You are the network.

🧠 The Brain Is NOT the Boss (All the Time) We love the idea that the brain is this genius CEO issuing orders and micromanaging everything. But honestly? It’s more like upper management—important, but often unaware of what's happening on the ground.

Your nervous system delegates. Heavily.

Burn your finger? Your spinal cord pulls your hand back before your brain even gets the memo.

Need to digest lunch? Your gut's nervous system—yes, it has its own—takes the lead.

Trying to walk and talk at the same time? Your motor circuits handle the walking while your brain gabs away.

It's like your body is a startup that figured out how to scale early—with local decision-making baked in.

🔁 Reflexes Are Basically Edge Computing In tech, we talk about edge computing—processing data close to where it’s generated instead of sending everything back to the cloud (or main server). It’s faster and more efficient.

You do the same thing. Your spinal cord handles urgent stuff locally, without checking in with the brain. It’s your body’s way of saying, “Look, we don’t have time for a meeting about this. Just act.”

Honestly? Genius.

🧬 Redundancy = Survival Distributed systems are built with backup nodes in case one fails. Your body’s got this too.

Stroke victims sometimes retrain other parts of their brain to take over lost functions.

The "second brain" in your gut can keep your digestive system moving even if your brain is knocked out.

Redundancy isn’t just a design feature—it’s survival logic.

⏳ Nothing Is Synchronized (and That’s Okay) In tech, distributed systems don’t need everything to be perfectly timed. The same goes for your body. Signals move at different speeds:

Super-fast reflexes.

Slow, simmering hormone signals.

Emotional gut feelings that hit before your conscious brain even knows what's up.

Everything’s asynchronous—but it all works. (Well, usually.)

🎯 Local Smarts, Global Magic Your brain isn’t giving you step-by-step instructions to walk. Your spinal cord and cerebellum handle the mechanics. Your eyes keep track of the path. Your brain just says, “Let’s go over there.”

All these subsystems are smart on their own. But together? They make you you.

It’s a miracle of coordination without central control—basically the same principle behind swarming bees, flocking birds, or how Waze reroutes you mid-drive.

TL;DR: You’re Not a Machine—You’re a Network You're not a brain driving a meat suit. You're a self-coordinating ecosystem of intelligent agents doing their best to keep you upright, fed, balanced, focused, calm (sometimes), and alive.

Distributed System :

A distributed system is a computing environment in which various components are spread across multiple computers (or other computing devices) on a network. These devices split up the work, coordinating their efforts to complete the job more efficiently than if a single device had been responsible for the task.

Distributed systems are an important development for IT and computer science as an increasing number of related jobs are so massive and complex that it would be impossible for a single computer to handle them alone. But distributed computing also offers additional advantages over traditional computing environments. Distributed systems reduce the risks involved with having a single point of failure, bolstering reliability and fault tolerance. Modern distributed systems are generally designed to be scalable in near real-time; also, you can spin up additional computing resources on the fly, increasing performance and further reducing time to completion.

Read More

Historically, distributed computing was expensive, complex to configure and difficult to manage. But thanks to software as a service (SaaS) platforms that offer expanded functionality, distributed computing has become more streamlined and affordable for businesses large and small. As a result, all types of computing jobs — from database management to video games — use distributed computing. In fact, many types of software, such as cryptocurrency systems, scientific simulations, blockchain technologies and AI platforms, wouldn’t be possible at all without these platforms.

In this article, we’ll explore the operation of such systems, the challenges and risks of these platforms, and the myriad benefits of distributed computing.

How does a distributed system work?

Distributed systems have evolved over time, but today’s most common implementations are largely designed to operate via the internet and, more specifically, the cloud. A distributed system begins with a task, such as rendering a video to create a finished product ready for release. The web application, or distributed applications, managing this task — like a video editor on a client computer — splits the job into pieces. In this simple example, the algorithm that gives one frame of the video to each of a dozen different computers (or nodes) to complete the rendering. Once the frame is complete, the managing application gives the node a new frame to work on. This process continues until the video is finished and all the pieces are put back together. A system like this doesn’t have to stop at just 12 nodes — the job may be distributed among hundreds or even thousands of nodes, turning a task that might have taken days for a single computer to complete into one that is finished in a matter of minutes.

There are many models and architectures of distributed systems in use today. Client-server systems, the most traditional and simple type of distributed system, involve a multitude of networked computers that interact with a central server for data storage, processing or other common goal. Cell phone networks are an advanced type of distributed system that share workloads among handsets, switching systems and internet-based devices. Peer-to-peer networks, in which workloads are distributed among hundreds or thousands of computers all running the same software, are another example of a distributed system architecture.

Read More

The most common forms of distributed systems in the enterprise today are those that operate over the web, handing off workloads to dozens of cloud-based virtual server instances that are created as needed, then terminated when the task is complete.

characteristics of a distributed system:

Distributed systems are commonly defined by the following key characteristics and features:

· Scalability: The ability to grow as the size of the workload increases is an essential feature of distributed systems, accomplished by adding additional processing units or nodes to the network as needed.

· Concurrency: Distributed system components run simultaneously. They’re also characterized by the lack of a “global clock,” when tasks occur out of sequence and at different rates.

· Availability/fault tolerance: If one node fails, the remaining nodes can continue to operate without disrupting the overall computation.

· Transparency: An external programmer or end user sees a distributed system as a single computational unit rather than as its underlying parts, allowing users to interact with a single logical device rather than being concerned with the system’s architecture.

· Heterogeneity: In most distributed systems, the nodes and components are often asynchronous, with different hardware, middleware, software and operating systems. This allows the distributed systems to be extended with the addition of new components.

· Replication: Distributed systems enable shared information and messaging, ensuring consistency between redundant resources, such as software or hardware components, improving fault tolerance, reliability and accessibility.

distributed tracing :

Distributed tracing, sometimes called distributed request tracing, is a method for monitoring applications — typically those built on a microservices architecture — which are commonly deployed on distributed systems. Distributed tracing is essentially a form of distributed computing in that it’s commonly used to monitor the operations of applications running on distributed systems.

In software development and operations, tracing is used to follow the course of a transaction as it travels through an application — an online credit card transaction as it winds its way from a customer’s initial purchase to the verification and approval process to the completion of the transaction, for example. A tracing system monitors this process step by step, helping a developer to uncover bugs, bottlenecks, latency or other problems with the application.

Distributed tracing is necessary because of the considerable complexity of modern software architectures. A distributed tracing system is designed to operate on a distributed services infrastructure, where it can track multiple applications and processes simultaneously across numerous concurrent nodes and computing environments. Without distributed tracing, an application built on a microservices architecture and running on a system as large and complex as a globally distributed system environment would be impossible to monitor effectively.

What are patterns in a distributed system?

A software design pattern is a programming language defined as an ideal solution to a contextualized programming problem. Patterns are reusable solutions to common problems that represent the best practices available at the time, and while they don’t provide finished code, they provide replication capabilities and offer guidance on how to solve a certain issue or implement a needed feature.

When thinking about the challenges of a distributed computing platform, the trick is to break it down into a series of interconnected patterns; simplifying the system into smaller, more manageable and more easily understood components helps abstract a complicated architecture. Patterns are commonly used to describe distributed systems, such as command and query responsibility segregation (CQRS) and two-phase commit (2PC). Different combinations of patterns are used to design distributed systems, and each approach has unique benefits and drawbacks.

Benefits, Challenges an Risks of Distributed Systems

Benefits of distributed systems:

Read More

Distributed systems offer a number of advantages over monolithic, or single, systems, including:

· Greater flexibility: It is easier to add computing power as the need for services grows. In most cases today, you can add servers to a distributed system on the fly.

· Reliability: A well-designed distributed system can withstand failures in one or more of its nodes without severely impacting performance. In a monolithic system, the entire application goes down if the server goes down.

· Enhanced speed: Heavy traffic can bog down single servers when traffic gets heavy, impacting performance for everyone. The scalability of distributed databases and other distributed systems makes them easier to maintain and also sustain high-performance levels.

· Geo-distribution: Distributed content delivery is both intuitive for any internet user, and vital for global organizations.

Challenges of distributed systems:

Distributed systems are considerably more complex than monolithic computing environments, and raise a number of challenges around design, operations and maintenance. These include:

· Increased opportunities for failure: The more systems added to a computing environment, the more opportunity there is for failure. If a system is not carefully designed and a single node crashes, the entire system can go down. While distributed systems are designed to be fault tolerant, that fault tolerance isn’t automatic or foolproof.

· Synchronization process challenges: Distributed systems work without a global clock, requiring careful programming to ensure that processes are properly synchronized to avoid transmission delays that result in errors and data corruption. In a complex system — such as a multiplayer video game — synchronization can be challenging, especially on a public network that carries data traffic.

· Imperfect scalability: Doubling the number of nodes in a distributed system doesn’t necessarily double performance. Architecting an effective distributed system that maximizes scalability is a complex undertaking that needs to take into account load balancing, bandwidth management and other issues.

· More complex security: Managing a large number of nodes in a heterogeneous or globally distributed environment creates numerous security challenges. A single weak link in a file system or larger distributed system network can expose the entire system to attack.

· Increased complexity: Distributed systems are more complex to design, manage and understand than traditional computing environments.

Risks of distributed systems:

The challenges of distributed systems as outlined above create a number of correlating risks. These include:

· Security: Distributed systems are as vulnerable to attack as any other system, but their distributed nature creates a much larger attack surface that exposes organizations to threats.

· Risk of network failure: Distributed systems are beholden to public networks in order to transmit and receive data. If one segment of the internet becomes unavailable or overloaded, distributed system performance may decline.

· Governance and control issues: Distributed systems lack the governability of monolithic, single-server-based systems, creating auditing and adherence issues around global privacy laws such as GDPR. Globally distributed environments can impose barriers to providing certain levels of assurance and impair visibility into where data resides.

· Cost control: Unlike centralized systems, the scalability of distributed systems allows administrators to easily add additional capacity as needed, which can also increase costs. Pricing for cloud-based distributed computing systems are based on usage (such as the number of memory resources and CPU power consumed over time). If demand suddenly spikes, organizations can face a massive bill.

How do you apply access control in distributed systems?

Administrators use a variety of approaches to manage access control in distributed computing environments, ranging from traditional access control lists (ACLs) to role-based access control (RBAC). One of the most promising access control mechanisms for distributed systems is attribute-based access control (ABAC), which controls access to objects and processes using rules that include information about the user, the action requested and the environment of that request. Administrators can also refine these types of roles to restrict access to certain times of day or certain locations.

Distributed Systems Use Cases

How are distributed systems used?

Distributed systems are used when a workload is too great for a single computer or device to handle. They’re also helpful in situations when the workload is subject to change, such as e-commerce traffic on Cyber Monday. Today, virtually every internet-connected web application that exists is built on top of some form of distributed system.

Some of the most common examples of distributed systems:

· Telecommunications networks (including cellular networks and the fabric of the internet)

Read More

· Graphical and video-rendering systems

· Scientific computing, such as protein folding and genetic research

· Airline and hotel reservation systems

· Multiuser video conferencing systems

· Cryptocurrency processing systems (e.g. Bitcoin)

· Peer-to-peer file-sharing systems (e.g. BitTorrent)

· Distributed community compute systems (e.g. Folding@Home)

· Multiplayer video games

· Global, distributed retailers and supply chain management (e.g. Amazon)

What are different types of distributed deployments?

Distributed deployments can range from tiny, single department deployments on local area networks to large-scale, global deployments. In addition to their size and overall complexity, organizations can consider deployments based on the size and capacity of their computer network, the amount of data they’ll consume, how frequently they run processes, whether they’ll be scheduled or ad hoc, the number of users accessing the system, capacity of their data center and the necessary data fidelity and availability requirements.

Based on these considerations, distributed deployments are categorized as departmental, small enterprise, medium enterprise or large enterprise. While there are no official taxonomies delineating what separates a medium enterprise from a large enterprise, these categories represent a starting point for planning the needed resources to implement a distributed computing system. Distributed systems can also evolve over time, transitioning from departmental to small enterprise as the enterprise grows and expands.

Why do we need distributed systems now?

Modern computing wouldn’t be possible without distributed systems. They’re essential to the operations of wireless networks, cloud computing services and the internet. If distributed systems didn’t exist, neither would any of these technologies.

But do we still need distributed systems for enterprise-level jobs that don’t have the complexity of an entire telecommunications network? In most cases, the answer is yes. Distributed systems provide scalability and improved performance in ways that monolithic systems can’t, and because they can draw on the capabilities of other computing devices and processes, distributed systems can offer features that would be difficult or impossible to develop on a single system.

Distributed System :

A distributed system is a computing environment in which various components are spread across multiple computers (or other computing devices) on a network. These devices split up the work, coordinating their efforts to complete the job more efficiently than if a single device had been responsible for the task.

Distributed systems are an important development for IT and computer science as an increasing number of related jobs are so massive and complex that it would be impossible for a single computer to handle them alone. But distributed computing also offers additional advantages over traditional computing environments. Distributed systems reduce the risks involved with having a single point of failure, bolstering reliability and fault tolerance. Modern distributed systems are generally designed to be scalable in near real-time; also, you can spin up additional computing resources on the fly, increasing performance and further reducing time to completion.

Read More

Historically, distributed computing was expensive, complex to configure and difficult to manage. But thanks to software as a service (SaaS) platforms that offer expanded functionality, distributed computing has become more streamlined and affordable for businesses large and small. As a result, all types of computing jobs — from database management to video games — use distributed computing. In fact, many types of software, such as cryptocurrency systems, scientific simulations, blockchain technologies and AI platforms, wouldn’t be possible at all without these platforms.

In this article, we’ll explore the operation of such systems, the challenges and risks of these platforms, and the myriad benefits of distributed computing.

 How does a distributed system work?

 Distributed systems have evolved over time, but today’s most common implementations are largely designed to operate via the internet and, more specifically, the cloud. A distributed system begins with a task, such as rendering a video to create a finished product ready for release. The web application, or distributed applications, managing this task — like a video editor on a client computer — splits the job into pieces. In this simple example, the algorithm that gives one frame of the video to each of a dozen different computers (or nodes) to complete the rendering. Once the frame is complete, the managing application gives the node a new frame to work on. This process continues until the video is finished and all the pieces are put back together. A system like this doesn’t have to stop at just 12 nodes — the job may be distributed among hundreds or even thousands of nodes, turning a task that might have taken days for a single computer to complete into one that is finished in a matter of minutes.

There are many models and architectures of distributed systems in use today. Client-server systems, the most traditional and simple type of distributed system, involve a multitude of networked computers that interact with a central server for data storage, processing or other common goal. Cell phone networks are an advanced type of distributed system that share workloads among handsets, switching systems and internet-based devices. Peer-to-peer networks, in which workloads are distributed among hundreds or thousands of computers all running the same software, are another example of a distributed system architecture. 

Read More

The most common forms of distributed systems in the enterprise today are those that operate over the web, handing off workloads to dozens of cloud-based virtual server instances that are created as needed, then terminated when the task is complete.

characteristics of a distributed system:

 Distributed systems are commonly defined by the following key characteristics and features:

·         Scalability: The ability to grow as the size of the workload increases is an essential feature of distributed systems, accomplished by adding additional processing units or nodes to the network as needed.

·         Concurrency: Distributed system components run simultaneously. They’re also characterized by the lack of a “global clock,” when tasks occur out of sequence and at different rates.

·         Availability/fault tolerance: If one node fails, the remaining nodes can continue to operate without disrupting the overall computation.

·         Transparency: An external programmer or end user sees a distributed system as a single computational unit rather than as its underlying parts, allowing users to interact with a single logical device rather than being concerned with the system’s architecture.

·         Heterogeneity: In most distributed systems, the nodes and components are often asynchronous, with different hardware, middleware, software and operating systems. This allows the distributed systems to be extended with the addition of new components.

·         Replication: Distributed systems enable shared information and messaging, ensuring consistency between redundant resources, such as software or hardware components, improving fault tolerance, reliability and accessibility.

distributed tracing :

 Distributed tracing, sometimes called distributed request tracing, is a method for monitoring applications — typically those built on a microservices architecture — which are commonly deployed on distributed systems. Distributed tracing is essentially a form of distributed computing in that it’s commonly used to monitor the operations of applications running on distributed systems.

In software development and operations, tracing is used to follow the course of a transaction as it travels through an application — an online credit card transaction as it winds its way from a customer’s initial purchase to the verification and approval process to the completion of the transaction, for example. A tracing system monitors this process step by step, helping a developer to uncover bugs, bottlenecks, latency or other problems with the application.

Distributed tracing is necessary because of the considerable complexity of modern software architectures. A distributed tracing system is designed to operate on a distributed services infrastructure, where it can track multiple applications and processes simultaneously across numerous concurrent nodes and computing environments. Without distributed tracing, an application built on a microservices architecture and running on a system as large and complex as a globally distributed system environment would be impossible to monitor effectively.

What are patterns in a distributed system?

 A software design pattern is a programming language defined as an ideal solution to a contextualized programming problem. Patterns are reusable solutions to common problems that represent the best practices available at the time, and while they don’t provide finished code, they provide replication capabilities and offer guidance on how to solve a certain issue or implement a needed feature.

When thinking about the challenges of a distributed computing platform, the trick is to break it down into a series of interconnected patterns; simplifying the system into smaller, more manageable and more easily understood components helps abstract a complicated architecture. Patterns are commonly used to describe distributed systems, such as command and query responsibility segregation (CQRS) and two-phase commit (2PC). Different combinations of patterns are used to design distributed systems, and each approach has unique benefits and drawbacks.

Benefits, Challenges an Risks of Distributed Systems

Benefits of distributed systems:

 Read More

Distributed systems offer a number of advantages over monolithic, or single, systems, including:

·         Greater flexibility: It is easier to add computing power as the need for services grows. In most cases today, you can add servers to a distributed system on the fly.

·         Reliability: A well-designed distributed system can withstand failures in one or more of its nodes without severely impacting performance. In a monolithic system, the entire application goes down if the server goes down.

·         Enhanced speed: Heavy traffic can bog down single servers when traffic gets heavy, impacting performance for everyone. The scalability of distributed databases and other distributed systems makes them easier to maintain and also sustain high-performance levels.

·         Geo-distribution: Distributed content delivery is both intuitive for any internet user, and vital for global organizations.

Challenges of distributed systems:

Distributed systems are considerably more complex than monolithic computing environments, and raise a number of challenges around design, operations and maintenance. These include:

·         Increased opportunities for failure: The more systems added to a computing environment, the more opportunity there is for failure. If a system is not carefully designed and a single node crashes, the entire system can go down. While distributed systems are designed to be fault tolerant, that fault tolerance isn’t automatic or foolproof. 

·         Synchronization process challenges: Distributed systems work without a global clock, requiring careful programming to ensure that processes are properly synchronized to avoid transmission delays that result in errors and data corruption. In a complex system — such as a multiplayer video game — synchronization can be challenging, especially on a public network that carries data traffic.

·         Imperfect scalability: Doubling the number of nodes in a distributed system doesn’t necessarily double performance. Architecting an effective distributed system that maximizes scalability is a complex undertaking that needs to take into account load balancing, bandwidth management and other issues.

·         More complex security: Managing a large number of nodes in a heterogeneous or globally distributed environment creates numerous security challenges. A single weak link in a file system or larger distributed system network can expose the entire system to attack.

·         Increased complexity: Distributed systems are more complex to design, manage and understand than traditional computing environments.

Risks of distributed systems:

The challenges of distributed systems as outlined above create a number of correlating risks. These include:

·         Security: Distributed systems are as vulnerable to attack as any other system, but their distributed nature creates a much larger attack surface that exposes organizations to threats.

·         Risk of network failure: Distributed systems are beholden to public networks in order to transmit and receive data. If one segment of the internet becomes unavailable or overloaded, distributed system performance may decline.

·         Governance and control issues: Distributed systems lack the governability of monolithic, single-server-based systems, creating auditing and adherence issues around global privacy laws such as GDPR. Globally distributed environments can impose barriers to providing certain levels of assurance and impair visibility into where data resides.

·         Cost control: Unlike centralized systems, the scalability of distributed systems allows administrators to easily add additional capacity as needed, which can also increase costs. Pricing for cloud-based distributed computing systems are based on usage (such as the number of memory resources and CPU power consumed over time). If demand suddenly spikes, organizations can face a massive bill.

How do you apply access control in distributed systems?

Administrators use a variety of approaches to manage access control in distributed computing environments, ranging from traditional access control lists (ACLs) to role-based access control (RBAC). One of the most promising access control mechanisms for distributed systems is attribute-based access control (ABAC), which controls access to objects and processes using rules that include information about the user, the action requested and the environment of that request. Administrators can also refine these types of roles to restrict access to certain times of day or certain locations.

Distributed Systems Use Cases

How are distributed systems used?

Distributed systems are used when a workload is too great for a single computer or device to handle. They’re also helpful in situations when the workload is subject to change, such as e-commerce traffic on Cyber Monday. Today, virtually every internet-connected web application that exists is built on top of some form of distributed system.

Some of the most common examples of distributed systems:

·         Telecommunications networks (including cellular networks and the fabric of the internet)

Read More

·         Graphical and video-rendering systems

·         Scientific computing, such as protein folding and genetic research

·         Airline and hotel reservation systems

·         Multiuser video conferencing systems

·         Cryptocurrency processing systems (e.g. Bitcoin)

·         Peer-to-peer file-sharing systems (e.g. BitTorrent)

·         Distributed community compute systems (e.g. Folding@Home)

·         Multiplayer video games

·         Global, distributed retailers and supply chain management (e.g. Amazon)

What are different types of distributed deployments?

 Distributed deployments can range from tiny, single department deployments on local area networks to large-scale, global deployments. In addition to their size and overall complexity, organizations can consider deployments based on the size and capacity of their computer network, the amount of data they’ll consume, how frequently they run processes, whether they’ll be scheduled or ad hoc, the number of users accessing the system, capacity of their data center and the necessary data fidelity and availability requirements.

Based on these considerations, distributed deployments are categorized as departmental, small enterprise, medium enterprise or large enterprise. While there are no official taxonomies delineating what separates a medium enterprise from a large enterprise, these categories represent a starting point for planning the needed resources to implement a distributed computing system. Distributed systems can also evolve over time, transitioning from departmental to small enterprise as the enterprise grows and expands.

Why do we need distributed systems now?

 Modern computing wouldn’t be possible without distributed systems. They’re essential to the operations of wireless networks, cloud computing services and the internet. If distributed systems didn’t exist, neither would any of these technologies.

But do we still need distributed systems for enterprise-level jobs that don’t have the complexity of an entire telecommunications network? In most cases, the answer is yes. Distributed systems provide scalability and improved performance in ways that monolithic systems can’t, and because they can draw on the capabilities of other computing devices and processes, distributed systems can offer features that would be difficult or impossible to develop on a single system.

Kafka Event Exchange Between Local and Azure

While it may not be a daunting task to set up Kafka on a local machine or within a particular network and produce/consume messages, people do face challenges when they try to make it work across the network. Let’s consider a hybrid scenario where your software solution is distributed across two different platforms (say AWS and Azure or on-premise and Azure), and there is a need to route messages…

Chaos Engineering: Planning Your First Chaos Experiment - ITChronicles

When it comes to quality assurance (QA) testing distributed software systems, the simple fact of the matter is no amount of preproduction QA testing can unearth all the possible scenarios and failures that may crop up in your real production deployment. And here’s another fact: failures in production are inevitable – network failures, infrastructure failures, application failures. As such, you will see failures in production. It’s hardly a wonder, then, that the practice of Testing in Production (TiP) is gaining steam in DevOps and testing communities. However, rather than waiting around – totally unprepared – for failures to happen, only to deal with them after the fact, there is a certain TiP practice which dictates that engineers should intentionally inject failures into a distributed system to test its resilience and learn from the experience. This approach is known as chaos engineering.

In our previous post – ‘A Complete Introduction to Chaos Engineering’ – we explored the principles of chaos, where it can add value, why we need it, and how chaos engineering can be used to build safer, more performant and more secure systems. In today’s post, we’re going to build on what we’ve learnt so far, and consider the steps you’ll need to take when planning your first chaos experiment.

Chaos Engineering

In essence, chaos engineering is the practice of conducting thoughtful, carefully-planned experiments designed to reveal weaknesses in our systems. Let’s say you’ve developed a new web application – the latest and greatest thing that the whole world has been waiting for. You’ve done all the hard work, and now the time has finally come to launch the service to customers.

But how can you be sure – really sure – that the distributed system you’ve built is resilient enough to survive use in production? The truth is you can’t. Why? Because you don’t know what disasters may strike – outages, network failures, denial-of-service attacks. What’s more, no matter how hard you try, you can’t build perfect software – and the companies and services you depend on can’t build perfect software either.

So we’re back to the start again – failures in production are inevitable. You can’t control that. What you can do, however, is build a quality product that is resilient to failures – software that is able to cope with unexpected events, and ready and prepared for those inevitable disasters.