How to design a reliable distributed system

When it comes to keeping things running, having several systems is better than one. Whether that’s a team of people, a network of shops, or a cluster of servers, spreading the load reduces the risk of everything failing at once. 

This is the ethos behind a distributed system. Instead of having all your computer data and command capability in one spot, tasks get shared across multiple components.

This means that even if one part has issues, the rest can keep running with no downtime. Here’s everything you need to know about this scalable, reliable approach to computing. 

What is a distributed system?

A distributed system is a collection of independent computers that work together as a single entity. 

These computers: 

• Share resources
• Communicate with each other; and
• Coordinate tasks to provide a unified service or perform complex functions. 

The key idea is that no single machine controls everything. Instead, each machine has a role, and the system as a whole appears seamless to end users.

It’s like a team of workers spread across different locations, each doing their part to complete a bigger job. They might not see each other directly, but they rely on clear communication and shared goals to get things done. 

Distributed systems pop up everywhere, from the cloud to big data processing to online services like Google, Facebook, and Amazon. The goal is reliability and scalability, with the system handling more data and users than a single computer could manage solo. 

What are the characteristics of a distributed system?

Distributed systems come with a distinct set of features. 

• Multiple components: A distributed system consists of many independent machines, each with its own local resources (CPU, memory, storage). These machines work together but operate independently.
  
• Concurrency: Many tasks happen at the same time. Each component can run processes simultaneously, which is more efficient.
  
• Scalability: Distributed systems can scale easily by adding more machines to the network. This allows for increased load without sacrificing performance as demand grows.
  
• Fault tolerance: If one machine or element fails, the whole system needn’t go down with the ship. Distributed systems are designed with failover mechanisms in place, which keeps disruption to a minimum. 
  
• Transparency: Despite the complexity of multiple machines all working together, users interact with the system as if it’s a single entity because the front end presents a single, unified front.
  
• Communication: Components of a distributed system communicate over a network. This involves protocols and message-passing mechanisms that coordinate data and tasks.
  
• Resource sharing: Machines in a distributed system share resources like storage and computing power. This makes it possible to handle more tasks than any single machine could.
  
• Decentralization: There is no central control. Instead, each machine or component has some level of autonomy, and they coordinate with others as needed. This helps avoid bottlenecks and single points of failure.

What’s the difference between a centralized system and a distributed system?

The clue is in the name! With a centralized system, a single central unit or server manages everything — data, commands, storage — the lot. The entire system relies on this central point to handle tasks and make decisions. It’s straightforward to manage, but if the central unit fails, the whole system usually crashes.

Meanwhile, a distributed system spreads resources and control across multiple machines.  Even if one machine completely shuts down, the system can keep running. This makes it more reliable and fault-tolerant.

Here’s a quick comparison:

Control

• Centralized: one central point
• Distributed: lots of independent machines.

Failure

• Centralized: failure of a central unit affects the whole system
• Distributed: failure of one machine doesn’t stop the system.

Scalability

• Centralized: harder to scale, limited by the capacity of the central unit
• Distributed: can easily scale by adding more machines.

What’s the difference between a distributed system and microservices?

‘Distributed system’ is a broad concept. It focuses on communication and resource-sharing between different independent machines that may or may not be geographically dispersed. 

Microservices is a more specific term referring to an architectural style used within a distributed system. 

Microservices break down an application into smaller, self-contained services. Teams can develop, deploy, and scale each of these independently. Each microservice performs a specific function and communicates with others via APIs.

Here’s how they differ:

Scope

• Distributed system: a broad concept involving any system where components are spread across multiple machines
• Microservices: a specific way of structuring applications within a distributed system.

Structure

• Distributed systems: can include various types of components, not just services.
• Microservices: always focuses on small, independent services.

Communication

• Distributed system: machines or components communicate to share resources and data
• Microservices: services communicate using lightweight protocols (e.g., HTTP, REST, gRPC) to coordinate tasks.

What is distributed tracing?

One term you’ll hear floating around is distributed tracing. 

This technique helps dev teams monitor and troubleshoot requests as they travel through different services within a distributed system. 

By collecting data throughout a request’s lifecycle, distributed tracing helps the backend team identify bottlenecks and performance issues. It also offers a visual representation of how services interact, making it easier to pinpoint problems and generally improve the system.

Architectures of distributed systems

Let’s take a look at the most common types of distributed system. 

Client-server architecture

With this model, clients request services or resources from a central server. This then processes the requests and returns the results. It’s one of the most basic and widely used types of distributed systems.

Example: Web apps like Gmail, where the client (your web browser) communicates with the server to fetch emails.

Peer-to-Peer (P2P) architecture

Here, every node (peer) in the system can act both as a client and a server. These peers pool resources without needing a central hub.

Example: Cryptocurrencies like Bitcoin also use P2P networks, where users (nodes) validate transactions and maintain the blockchain ledger without needing a central authority. 

Multitier architecture

This architecture splits the system into multiple layers or tiers. Each one is responsible for different functions.

Example: Online banking systems feature a front-end (presentation tier), which interacts with the application tier (business logic), and the database tier (data storage).

Microservices architecture

An application is divided into smaller, loosely coupled services, each running independently and communicating through APIs. It promotes scalability and flexibility.

Example: eCommerce platforms like Amazon, which break their system down into separate services for payment processing, product catalog, and user accounts.

Event-driven architecture

This system reacts to events or changes in the system, triggering workflows or actions. It’s highly responsive and is used in systems that need to process real-time events.

Example: Stock trading platforms, where events like price changes or market orders trigger actions like executing stock buying or selling movements.

Cloud computing architecture

Cloud systems give on-demand services over the internet, often using a combination of client-server and microservices architectures. Cloud providers offer resources like computing power and databases.

Example: Google Cloud or AWS, where users can scale their storage and processing power based on their needs.

Shared memory architecture

In this model, multiple processors share a common memory space, allowing for fast communication and data sharing. It’s mainly used in tightly coupled systems.

Example: High-performance computing systems used in scientific research or simulations, where shared memory allows quick data exchange between processors.

Master-Slave architecture 

One node (the master) controls the operation, while other nodes (slaves) follow the master’s commands. It’s often used for systems requiring high availability or fault tolerance.

Example: Database replication systems, where a master database server handles write operations, and slave servers handle read operations.

The advantages and disadvantages of distributed systems 

As with all systems, there are pros and cons. Let’s take a closer look at what those are. 

Advantages:

• Scalability: Distributed systems can handle growing demand by adding more machines. This makes them good for burgeoning businesses. 
• Fault tolerance: If one component fails, others can take over. This keeps the system available and operational. 
• Resource sharing: Pooling computing power, storage, and data from multiple machines is more efficient than overloading a single, centralized system. 
• Flexibility: Distributed systems support different technologies and services, which makes it easier to adapt to various needs. 

Cons:

• Complexity: Managing multiple machines and making sure they run smoothly demands solid planning and specialized tools. 
• Network dependency: Communication between components depends on the network. This means problems like latency or downtime can hinder performance.
• Consistency challenges: Keeping data consistent across the different machines can be tricky, especially in real-time apps.
• Higher cost: The infrastructure and tools you need to run a distributed system can be expensive, both in terms of setup and maintenance.

Risks and how to handle them

Now let’s look at some of the manageable and mitigatable risks associated with distributed systems. 

Data loss

In distributed systems, data can get lost during network problems or communication failures. This happens when messages fail to reach their destination, or the system doesn’t save updates in time.

How to handle it: Use redundancy. Store data in more than one place and use tools like distributed databases that sync updates across nodes. Also, set up backups and monitor for network problems to spot issues early.

Security concerns

With so many moving parts and data flowing between them, keeping everything secure can be tricky. Each communication point is a potential weak spot that hackers could target.

How to handle it: Encrypt data both at rest and in transit. Limit who or what can access certain parts of the system with strong authentication and role-based permissions. Regularly test for security holes to stay ahead of threats.

Synchronization issues

Keeping everything in sync with a system spread across multiple locations can be tough. If updates don’t reach all parts of the system in time, this can cause delays or conflicts.

How to handle it: Use tools that support eventual consistency, where updates are applied over time but follow strict rules to avoid conflicts. For systems that need real-time sync, consider protocols like Paxos or Raft, so updates happen in the right order.

Single points of failure

Even though distributed systems aim to spread risk, a poorly designed system can still have weak spots. If one critical component fails, it might bring the whole system down.

How to handle it: Replicate key parts of your system. And use load balancers to spread work across multiple components, so no one part becomes a bottleneck.

How diagramming tools can help 

Distributed systems bring undeniable benefits. But they’re not without their challenges. Luckily, with the right tools and processes in place, you can build systems that are both reliable and scalable.

Visualizing your system architecture is a good starting point. It helps teams understand how different components interact, while giving you a way to spot potential problem areas before they snowball.

Cacoo, our own diagramming tool, comes with templates and features tailored to dev teams. Choose your template, edit it with the handy drag-and-drop interface, share it with the team, and edit as your creation evolves. Map out data flow, redundancy strategies, and fault-tolerance mechanisms in one central hub. By using collaborative online tools, you make complex systems more accessible and help the team work together, and not just your systems.