A Beginner's Guide To Distributed Systems

Have you ever wondered how your favorite online services manage to work so seamlessly, whether it's streaming movies, shopping online, or just browsing the web?

Behind the scenes, an incredible technology called distributed systems makes all this possible. It connects a group of independent computers so effectively that they function as a single unit. This incredible teamwork allows for handling big data, managing loads of online transactions, and ensuring that your video streams flawlessly.

In this blog, we are diving into the world of distributed systems. You will learn what they are, why they are crucial in our digital-first world, and how they manage to keep things running smoothly. So let’s get started!

Introduction to Distributed Systems

Imagine you're using a group of computers that work together so well that to the outside world, they appear as a single system. That's what we call a distributed system.

A distributed system is essentially a group of computers that performs a task more efficiently than a single computer could manage on its own.

Each computer in the system, also known as a node, works on a piece of the task independently but coordinates with the other computers to complete the overall job. This coordination is usually managed through the exchange of messages over a network.

Think of it like a group project where each person is responsible for a different part of the assignment. They work separately but share information and collaborate to produce one final project.

In the case of distributed systems, these 'group members' are computers, and their 'project' can be anything from processing large amounts of data to hosting the files and services needed to run a website.

One of the main reasons we use distributed systems is because they can handle more data and process requests faster than a single machine. They can also continue to operate even if one or more of the computers in the system fail, which makes them very reliable.

In simple terms, distributed systems help make our apps and services quicker, more efficient, and more available so that everything from your bank to your favorite online store runs smoothly even under heavy demand.

These systems are crucial because they help handle jobs that are too big for a single machine.

For example, when you search something on Google, multiple servers are working together to get you results quickly.

Distributed systems are everywhere—from the internet to mobile networks to the vast array of cloud services. Every time you stream a video, shop online, or even swipe your card at a store, there's a distributed system working in the background to make sure everything goes smoothly.

Key Components of Distributed Systems

Here is the list of key components of a distributed system:

Nodes are the individual computers within a distributed system. Each node carries out part of the computing work, similar to how each appliance in your kitchen has a different job.

• Networks: Networks are the communication lines that connect nodes. They ensure that nodes can talk to each other, much like phones let us talk to people across the world.
• Protocols: Protocols are the rules that nodes follow to communicate effectively. They are like the grammar rules of a language, making sure the nodes understand each other.

Core Principles of Distributed Systems

Let us discuss some core principles of distributed systems one by one:

Decentralization

Decentralization refers to the distribution of functions and powers away from a central location or authority.

In the context of distributed systems, it means that instead of having a single central server that handles all tasks, the tasks are spread across multiple, independent computers (or nodes).

Each node works on its tasks and has autonomy, which helps in scaling the system's capacity and performance. This setup enhances the system's ability to handle more transactions or operations simultaneously, as multiple nodes process data and requests in parallel.

A simple way to understand decentralization is by thinking of a busy restaurant. Instead of having one chef who cooks everything, imagine if there were several chefs, each specializing in different types of dishes.

This setup would allow the restaurant to serve more customers simultaneously, making the kitchen's overall operation more efficient and faster.

Fault Tolerance

Fault tolerance is the ability of a system to continue operating properly in the event of the failure of some of its components.

In the world of distributed systems, this means that the system is designed in such a way that, should any individual node fail, the rest of the system can still function without losing data or processing capabilities.

Fault tolerance is crucial for maintaining the system’s reliability and availability, especially in critical applications like financial services or telecommunications.

To illustrate fault tolerance, think of it as a team working on a critical project with a tight deadline. If one team member falls ill, the project shouldn’t come to a halt.

Ideally, the team would be set up in such a way that other members could cover the absent member's responsibilities, ensuring that the project moves forward as planned.

Similarly, in distributed systems, mechanisms are in place to ensure that if one node fails, others can take over the tasks of the failed node without disrupting the entire system's operations.

Scalability in Distributed Systems

Scalability is about how well an application can handle an increasing load without sacrificing performance.

Scalability ensures your application can handle growth without slowing down or crashing. This is crucial for any successful business, as a slow or unreliable application can lead to lost customers and revenue.

Let's break it down using simpler terms and examples.

What Is Load

Load refers to anything that uses up a system's resources like CPU, memory, or network bandwidth. For example, imagine you run a popular online store.

The load could be the number of people visiting your site at the same time, or how many times they make purchases (writes) compared to just browsing (reads).

Measuring Performance

Performance in system design is usually measured in two ways:

• Throughput: This is the number of requests your application can handle per second. For example, if your store can process 100 purchases per second, your throughput is 100 requests/second.

• Response Time: This is how long it takes for your system to respond to a request. For example, when a user clicks "buy," the time it takes for the confirmation page to load is the response time.

Handling Increased Load

As more users visit your store, the load increases. Your application has a maximum load it can handle before it starts to slow down or fail. This is its capacity. If too many users try to buy things at the same time, your store might take longer to respond or even crash.

Scaling Up vs. Scaling Out

There are two main ways to increase your application's capacity to handle more load:

• Scaling Up: This means getting better, more powerful hardware. Imagine upgrading your single store to a superstore with more cash registers and faster servers. But, this has limits because you can't keep upgrading forever.

• Scaling Out: This means adding more machines to your system and making them work together. Instead of upgrading one superstore, you open several smaller stores. Each store handles part of the load, so no single store gets overwhelmed. This is like adding more servers to your application. For instance, if your store gets very busy during sales, you can add more servers to handle the extra traffic.

Example of Scaling Out

Cloud services make scaling out much easier. Companies like Amazon Web Services (AWS) let you rent virtual machines. If your online store gets a lot of traffic during a sale, you can quickly rent more servers to handle the load.

When the sale is over, you can reduce the number of servers, so you don't pay for resources you don't need.

Find more insights about scalability in distributed systems.

Understanding Resiliency in Distributed Systems

Resiliency means that a system can keep working even when parts of it fail. In a distributed system, where multiple components work together, things can and will go wrong.

Let’s break it down:

Why Failures Happen

Imagine a large network of computers (nodes) working together to keep a website running. Each node, or computer, has a chance of failing—maybe it crashes or loses its connection. Even if the chances of each node failing are small, the more nodes you have, the more likely it is that some will fail over time.

If one node fails and it’s not well isolated from others, it can cause more failures, like a domino effect.

Impact on Availability

Availability is about how often the system is up and running, and able to serve requests. It’s measured as a percentage of the total time.

For example:

• 90% availability means the system is down for 2.4 hours a day.

• 99% availability means it’s down for 14.4 minutes a day.

• 99.9% (three nines) means 1.44 minutes of downtime a day.

• 99.99% (four nines) means 8.64 seconds of downtime a day.

• 99.999% (five nines) means less than a second of downtime a day.

Higher availability means the system is more reliable. Users generally expect at least 99.9% availability.

Discover the strategies to enhance availability in distributed systems.

How to Achieve Resiliency

To keep the system running smoothly despite failures, we use techniques like: Redundancy: Have backup components ready to take over if one fails. For example, if one server goes down, another can take its place without users noticing.

• Fault Isolation: Make sure failures in one part of the system don’t affect other parts. This is like having fire doors in a building to prevent a fire from spreading.

• Self-Healing Mechanisms: The system can automatically detect failures and fix them. For instance, if a server crashes, the system can restart it or redirect tasks to other servers.

Example of Resiliency

Consider a popular social media platform. To ensure users can always post and view content, the platform uses many servers around the world.

If one server in a data center fails, another server can take over immediately, and users won’t notice any interruption. The system also monitors server health and can quickly replace or fix failed servers automatically.

Role of Communication in Distributed Systems

Communication is how different parts of a distributed system talk to each other.

Let’s use a simple example to explain this concept.

Basic Idea

When you use your web browser to visit a website, your browser needs to talk to the website’s server. Here’s how it works:

Your browser takes the website’s URL (like www.example.com) and finds the server’s IP address. Your browser sends a request to this IP address, asking for the web page. The server receives this request, processes it, and sends back the web page content to your browser.

Challenges in Communication

1. Message Representation: How do the request and response messages travel over the network? These messages are broken down into smaller packets that travel through the network and are reassembled at the destination.

2. Network Issues: What if there’s a temporary network outage, or a faulty network switch messes up some data in the messages? These issues can cause messages to be lost or corrupted. The system needs ways to detect and handle these problems, like resending lost messages.

3. Security: How does the server make sure no one can intercept and read the messages? This is where encryption comes in. HTTPS (the secure version of HTTP) encrypts the data, so even if someone intercepts it, they can’t read it.

Example of Network Communication

Imagine you’re sending a file to a friend using an online service. Here’s what happens:

1. Your computer breaks the file into smaller packets.

2. Each packet is sent over the internet, which might involve traveling through multiple routers and switches.

3. Your friend’s computer receives the packets and reassembles them into the original file.

If a packet gets lost or corrupted along the way, your computer will resend it. The service might use encryption to keep the file secure during transmission.

Why Understanding Communication Matters

Knowing how communication works helps you design systems that are more reliable and secure.

For example, if you understand how packets can get lost, you can implement methods to detect and resend them. If you know about encryption, you can ensure your data stays private.

Coordination in Distributed Systems

Coordination in distributed systems is about ensuring all parts of the system work together smoothly. Think of it like organizing a group of people to achieve a common goal. Here’s a simple way to understand it:

The Challenge

In a distributed system, you have multiple nodes (computers) that need to coordinate their actions to work toward a shared objective. This is tricky, especially when failures happen.

To illustrate, let’s use a famous thought experiment called the “two generals” problem.

The Two Generals Problem

Imagine two generals, each leading their own army, need to agree on a time to attack a city. The only way they can communicate is by sending messengers. But there’s a risk: the enemy might capture the messengers, leading to communication failures. Here’s how it plays out:

1. General 1 sends a messenger to General 2 with a proposed attack time.

2. The messenger might get captured, so General 1 doesn’t know if the message was delivered.

3. General 2 could send a confirmation back to General 1 to say they received the message.

4. However, the second messenger could also be captured, so General 2 won’t know if their confirmation was received.

No matter how many messages they send back and forth, there’s always uncertainty about whether the other general got the message. This shows how hard it is to ensure both sides are perfectly coordinated.

Why Coordination is Important

In distributed systems, lack of coordination can lead to inconsistent states and errors.

For instance, if different parts of an online banking system don’t agree on the same transaction details, it could lead to issues like double spending or incorrect balances.

Coordination Techniques

To overcome these challenges, distributed systems use various algorithms and techniques. Here are a few key ones:

• Consensus Algorithms: These help all nodes agree on a single data value or decision. Examples include Paxos and Raft.

• Distributed Locks: These prevent multiple nodes from making conflicting changes at the same time.

• Leader Election: This designates one node as the leader to make decisions, ensuring a single point of coordination.

Example of Coordination

Consider an online multiplayer game where players are on different servers.

If a player picks up an item, the game needs to make sure that item is marked as "picked up" across all servers. This requires coordination to ensure all servers agree on the game's state.

The Concept of Maintainability in Distributed Systems

Maintainability is about making software easy to fix, update, and run smoothly over time.

This is important because most of the cost of software comes after it's first built, during its maintenance.

Once you create software, you will need to spend time fixing bugs, adding new features, and ensuring it runs properly.

If the software is hard to update or fix, these tasks become time-consuming and expensive. Therefore, it's crucial to make systems that are easy to change and extend.

Key Aspects of Maintainability

1. Easy to Modify: When you need to fix a bug or add a new feature, the code should be easy to understand and change. Clear, well-organized code helps a lot with this.

2. Good Testing: Every change you make can introduce new issues. Good testing practices help catch these problems before they affect users. This includes:

   • Unit Tests: Testing individual parts of the code.

   • Integration Tests: Testing how different parts of the code work together.

   • End-to-End Tests: Testing the entire system to ensure it works as expected.

3. Safe Deployment: After making changes, you need to release the new version without disrupting the service. This means having a reliable process for deploying updates.

4. Monitoring and Operations: Once the software is running, it needs to be monitored to ensure it's healthy and performing well. If problems occur, operators need tools to diagnose and fix issues quickly.

Example of Maintainability

Imagine you have an online store. Over time, you need to:

• Fix bugs that customers report.
• Add new features, like a wish list or product reviews.
• Ensure the store stays fast and reliable as more users visit.

Need for Reliability in Distributed Systems

Reliability in communication means ensuring that data sent from one place to another arrives correctly and in order.

Let's break this down:

How TCP Ensures Reliable Communication

TCP (Transmission Control Protocol) is a method used on the internet to make sure data gets from one computer to another without errors. Here’s how it works:

1. Breaking Data into Segments: Imagine you’re sending a long letter to a friend, but you can only send it one page at a time. TCP does something similar by breaking your data into smaller pieces called segments.

2. Numbering Segments: Each segment is like a page in your letter, numbered in order. This numbering helps the receiver (your friend) know the correct order of the segments and if any are missing.

3. Acknowledging Receipt: When your friend receives a page, they send you a message saying they got it. This is called an acknowledgment. If you don’t get this acknowledgment, you know the page didn’t arrive.

4. Retransmitting Missing Segments: If you don’t get a message back, you send the page again. This ensures that even if some pages get lost, your friend will eventually get the whole letter.

5. Checking for Errors: To make sure the pages weren’t damaged or changed on the way, your friend checks each one using a method called a checksum. If a page doesn’t match the expected content, your friend knows it’s corrupted and can ask you to resend it.

Example of Reliability

Imagine you’re sending an important document to a colleague via email.

Here’s how you ensure it’s reliable:

• Segmenting the Document: Break the document into smaller parts.

• Numbering Pages: Number each part so your colleague knows the order.

• Confirmation: Ask your colleague to confirm receipt of each part.

• Resending Missing Parts: If they miss any part, resend it.

• Checking Integrity: Your colleague checks each part to make sure it’s not altered or corrupted.

For a quick introduction, check out the 4 pillars of System Design.

Types of Distributed Systems

Some of the different types of distributed systems are:

1. Client-Server Systems

This is one of the most straightforward types of distributed systems. In this setup, there are 'clients' (computers or software applications) that request services, and 'servers' that provide those services.

For instance, when you use a web browser (the client) to access a webpage, the server is the computer somewhere else that sends the data back to your browser.

2. Peer-to-Peer Systems (P2P)

Unlike the client-server model, there is no dedicated server in peer-to-peer systems. Each node, or participant, acts as both a client and a server. This type is often used for sharing files or data directly between systems on a network without needing a central server.

Examples include applications for file-sharing or cryptocurrencies like Bitcoin.

3. Multi-tier Systems

In these systems, different tasks are processed at different places. Common in web applications, a typical multi-tier system might have one tier handling the database, another handling the business logic, and another managing the user interface.

This separation helps organize programming and can improve performance and scalability.

4. Cloud-Based Systems

These systems use cloud computing to run their applications on a collection of servers that might be spread across the globe.

Cloud systems offer flexibility, scalability, and reliability, allowing resources to be added or removed as needed depending on the demand.

5. Grid Computing

This type involves a distributed architecture of large numbers of computers connected to solve a complex problem together. The computers might be physically very far apart and dedicated to performing a set of tasks.

Grid computing is often used in scientific research, where a large amount of processing power is needed.

6. Hybrid Systems

These are combinations of different types of distributed systems. For example, a system might combine elements of both peer-to-peer and client-server models to optimize functionality and performance.

Each type of distributed system offers different advantages and might be chosen based on the specific needs of the application or the problem being solved. These systems are fundamental in areas ranging from everyday internet browsing to complex scientific calculations and large-scale web services.

Challenges in Distributed Systems

Distributed systems come with several challenges that can affect their efficiency and reliability.

Here are some of the common and critical challenges faced in these systems:

1. Latency Issues

In a distributed system, data needs to travel between different nodes that may be spread out geographically. This travel time, known as latency, can cause delays.

For example, if you're playing an online game hosted on a server in another country, you might experience a delay between your actions and the game's response.

2. Data Consistency

Keeping data consistent across all nodes in a distributed system can be challenging. If several nodes are updating the same data at the same time, there needs to be a way to ensure that they don't conflict with each other.

For instance, if two people are editing a document online simultaneously, the system needs to ensure that both sets of changes are saved without overwriting each other.

3. Handling Failures

Distributed systems must handle failures gracefully. This includes dealing with hardware failures, network issues, or software bugs.

For example, if an e-commerce website's server fails during a transaction, the system should be able to reroute the task to another server without disrupting the user's experience.

4. Scalability

As more nodes are added to handle more work, the system needs to manage these nodes efficiently. Scalability is about the system's ability to handle a growing amount of work or its ability to be enlarged to accommodate that growth.

For instance, as more users join a social media platform, the system must scale to handle the increased load without performance degradation.

5. Security Concerns

Security is a significant challenge in distributed systems because the data is spread across multiple nodes, often across different networks, which can expose it to various security vulnerabilities.

For example, a system that processes sensitive information, like bank transactions, needs robust security measures to prevent unauthorized access or data breaches.

6. Network Issues

Since distributed systems rely heavily on network connections, any network failure can cripple the system. Managing network configurations, ensuring sufficient bandwidth, and minimizing downtime are crucial.

During high traffic times, like Black Friday sales, e-commerce sites must ensure their network can handle the surge in users to avoid service disruptions.

Addressing these challenges requires careful planning, robust system design, and ongoing management to ensure that the distributed system performs well and meets the needs of its users.

Security Challenges in Distributed Systems

In distributed systems, security and privacy challenges can be quite diverse and complex. Here are some key types:

1. Backdoor Attacks

These attacks involve inserting hidden vulnerabilities or malicious triggers in a system that can later be exploited.

For example, in federated learning environments, an attacker could manipulate local models so that the global model behaves maliciously under specific conditions.

2. Membership Inference Attacks

These attacks aim to determine if particular data was used in a machine learning model's training set. By exploiting model outputs, attackers can potentially uncover sensitive information about the training data.

3. Generative Adversarial Network (GAN) Based Attacks

GANs can be used maliciously to generate synthetic data that mimics real data, leading to privacy breaches and data falsification.

These attacks challenge traditional privacy protection methods like differential privacy by closely imitating the distribution of original training data.

4. Differential Privacy Attacks

Even with measures like differential privacy, which aims to protect individual privacy by adding noise to data, there remain vulnerabilities. These attacks attempt to exploit the balance between data utility and privacy by manipulating the noise to glean sensitive information.

Measures To Improve Security and Privacy Issues in Distributed Systems

Here are some measures to improve security and privacy in distributed systems:

• Regular Updates and Patch Management: Keep all system software up to date to protect against known vulnerabilities.

• Advanced Encryption Techniques: Use strong encryption for data at rest and in transit to protect sensitive information from unauthorized access.

• Access Control Policies: Implement strict access control measures to ensure only authorized users and systems can access certain data.

• Network Segmentation: Divide the network into segments to contain and limit access to sensitive information in case of a breach.

• Continuous Monitoring and Anomaly Detection: Use monitoring tools to detect unusual activities that could indicate a security breach.

• Employee Training and Awareness: Regularly train employees on security best practices and phishing attack prevention.

• Use of Multi-Factor Authentication (MFA): Enhance security by requiring multiple methods of authentication from users to access sensitive systems and data. Regular Security Audits: Conduct thorough audits to identify and rectify security vulnerabilities in the system.

Implementing these measures can significantly enhance the security and privacy of distributed systems.

Real-World Applications of Distributed Systems

Distributed systems have a wide array of real-world applications across various sectors, showcasing their versatility and critical role in modern technology landscapes.

Here are some of the latest applications:

1. Autonomous Robotic Systems

Distributed systems are pivotal in the operation of autonomous robotic systems, which are used in diverse settings such as warehouses for inventory management, agriculture for crop monitoring, and disaster relief operations where robots perform search and rescue missions.

These systems rely on the coordination of multiple robotic units to perform complex tasks efficiently​​.

2. Artificial Intelligence and Machine Learning

Distributed computing plays a central role in advancing AI and machine learning technologies.

By distributing the computational load across multiple systems, AI models, especially those requiring intensive computation like deep learning models, can be trained faster and more efficiently.

This is crucial for applications in autonomous driving and smart healthcare systems, where real-time data processing and decision-making are essential​​.

3. Blockchain Technology

Distributed ledgers, or blockchains, are another prime example of distributed systems in action.

They provide a secure and transparent way to record transactions in scenarios like cryptocurrency exchanges and supply chain tracking.

The decentralized nature of blockchain ensures that the data is resistant to tampering and fraud​​.

4. Cloud Computing

Distributed systems form the backbone of cloud computing services, which offer scalable and on-demand resources over the Internet.

These systems allow for efficient management of data centers that power applications ranging from web hosting to complex data analytics platforms​​.

5. Telecommunications

Distributed systems are essential in the operation of telecommunication networks, including the Internet and cellular networks.

They manage data routing, signal distribution, and service delivery across vast geographic areas, ensuring that communication remains seamless and efficient​.

6. Social Media

Platforms like Facebook and Twitter use distributed systems to handle the massive amounts of data generated by their users, from storing photos and videos to delivering content seamlessly across the globe.

7. Streaming Services

Companies like Netflix and Spotify use distributed systems to stream audio and video content to millions of users worldwide without lag, ensuring high availability and network performance.

8. Gaming

Online multiplayer games use distributed systems to enable real-time, synchronized gameplay for players around the world, managing game state data across multiple servers.

Future Trends in Distributed Systems

Looking ahead, several trends are likely to shape the future of distributed systems:

1. Edge Computing

As devices at the edge of networks, like smartphones and IoT devices, become more powerful, more data processing is moving closer to where data is generated rather than in centralized data centers.

This reduces latency (delays in data processing) and can lead to faster insights from data.

2. 5G Technology

The rollout of 5G networks will greatly enhance the capabilities of distributed systems, providing faster speeds and more reliable connections.

This will enable more complex applications, such as augmented reality and autonomous vehicles, which require rapid data processing across distributed nodes.

3. Artificial Intelligence and Machine Learning

Distributed systems are becoming increasingly integrated with AI to process data more intelligently and efficiently.

This integration helps in managing the vast amounts of data generated by IoT devices and in making smarter decisions in real-time.

4. Blockchain and Distributed Ledgers

These technologies are expected to expand beyond cryptocurrencies to other applications like supply chain management, where they can provide transparency and security in transactions.

5. Increased Focus on Security

As distributed systems become more prevalent, ensuring their security against attacks will become even more critical. Innovations in encryption and secure communication protocols will likely develop to address these concerns.

6. Serverless Architectures

This trend involves abstracting server hardware and operating systems from developers, focusing instead on the deployment and management of applications.

This can simplify the scaling of applications and reduce costs by charging only for the resources the applications use.

These trends suggest a future where distributed systems are more integrated, intelligent, and efficient, driving advancements across various industries.

Final Words

Understanding distributed systems isn't just for tech professionals—it’s for anyone who wants to grasp how digital services are delivered today. And as technology continues to evolve, so too will the capabilities and complexities of distributed systems. It's an exciting area to explore, full of opportunities for innovation and growth.

This blog aims to make the concept of distributed systems accessible and engaging, offering a solid foundation for those curious about one of the pillars of modern computing.