A Thorough Introduction to Distributed Systems

In today‘s world of big data, cloud computing, and global-scale web services, distributed systems have become an essential part of the computing landscape. Tech giants like Google, Amazon, and Facebook have built their empires on the power of distributed systems, and an increasing number of applications are being built to run on clusters of machines rather than a single server.

As a full-stack developer, having a solid understanding of distributed systems is crucial to designing and building applications that are scalable, resilient, and high-performance. In this article, we‘ll take a comprehensive look at the world of distributed systems, covering the key concepts, characteristics, types of systems, challenges, and trends. Whether you‘re building a small web application or a global-scale service, this guide will give you the knowledge you need to make informed decisions about your architecture.

What is a Distributed System?

At its core, a distributed system is a group of computers that work together to appear as a single coherent system to the end user. These computers, or nodes, are connected by a network and communicate with each other by passing messages. Each node has its own private memory and operates concurrently, but they coordinate their actions to achieve a common goal.

More formally, a distributed system is defined as having the following properties:

1. No shared memory – Nodes do not have direct access to each other‘s memory and must communicate via message passing over a network.

2. Concurrency – Nodes operate concurrently and asynchronously, each with its own independent flow of control. There is no global clock synchronizing the actions of nodes.

3. Failure independence – Nodes can fail independently without necessarily bringing down the entire system. The system should be designed to tolerate and recover from the failure of individual nodes.

4. Geographical distribution – Nodes are often spread across multiple data centers or geographic regions to provide high availability and low latency to users in different locations.

Distributed systems can range in size from a handful of nodes to millions of nodes spanning the globe. Some well-known examples of distributed systems include:

• Google‘s search engine – Handles billions of searches per day using a massive cluster of nodes
• Amazon‘s e-commerce platform – Processes millions of transactions per day across a global network of data centers
• Facebook‘s social network – Serves billions of users with a highly scalable architecture of caching, load balancing, and data storage
• Bitcoin‘s cryptocurrency network – Maintains a decentralized ledger of transactions across a global peer-to-peer network of nodes

Why Use a Distributed System?

Building and operating a distributed system is not a trivial undertaking – it requires careful design, advanced infrastructure, and a deep understanding of the unique challenges involved. So why bother? There are four key motivations for using a distributed system:

1. Scalability – Distributed systems allow you to scale horizontally by adding more nodes to the system, rather than vertically by upgrading the hardware of a single node. This provides virtually unlimited capacity on demand, without the bottlenecks of a single machine. For example, Google‘s search engine is able to handle billions of searches per day by distributing the load across a massive cluster of nodes.

2. Fault Tolerance – Distributed systems are designed to tolerate the failure of individual nodes without bringing down the entire system. By replicating data and computation across multiple nodes, the system can continue operating even if some nodes fail. For example, Amazon‘s S3 storage service is designed to provide 99.999999999% durability by replicating data across multiple data centers.

3. Low Latency – Distributing nodes geographically allows you to serve users from the location closest to them, reducing the latency of network communication. For example, content delivery networks (CDNs) like Akamai and Cloudflare distribute web content across a global network of nodes to provide fast load times for users around the world.

4. Cost Efficiency – While distributed systems require more infrastructure and operational complexity than single-node systems, they can be more cost-efficient in the long run. Horizontal scaling with commodity hardware is often cheaper than vertical scaling with high-end hardware, and the ability to dynamically scale up and down based on demand can reduce wasted capacity.

The CAP Theorem

One of the fundamental challenges of distributed systems is the trade-off between consistency, availability, and partition tolerance, known as the CAP theorem. Formalized by computer scientist Eric Brewer in 2000, the CAP theorem states that a distributed system can only provide two of the following three properties:

• Consistency – All nodes see the same data at the same time. Reads and writes are atomic and appear to take place instantaneously across the whole system.

• Availability – Every request receives a non-error response, even in the presence of node failures. The system is always operational from the client‘s perspective.

• Partition tolerance – The system continues operating even if there is a network partition that prevents communication between nodes. It can tolerate an arbitrary number of messages being lost or delayed.

In practice, network partitions are unavoidable in distributed systems, so the choice comes down to prioritizing consistency or availability. A system that prioritizes consistency will sacrifice availability during a partition, waiting for the partition to resolve before responding to requests. A system that prioritizes availability will sacrifice consistency during a partition, providing a response based on the available data even if it‘s out of date.

This trade-off has significant implications for the design of distributed databases and other stateful systems. Traditional relational databases prioritize consistency with features like ACID transactions and strong consistency models. NoSQL databases like Cassandra and Riak prioritize availability with eventual consistency models and the ability to read and write during partitions.

The CAP theorem has been somewhat controversial and often misinterpreted to mean that a system must choose between consistency and availability in all cases. In reality, the trade-off only applies during a network partition, and many systems can provide both consistency and availability when the network is healthy. Nonetheless, the CAP theorem remains a useful framework for reasoning about the trade-offs involved in distributed systems.

Consistency Models

One of the key challenges of distributed systems is maintaining consistency of data across multiple nodes. With data replicated across nodes, there is the potential for conflicts and inconsistencies to arise due to network delays, node failures, and concurrent updates. To address this challenge, distributed systems use various consistency models that define the guarantees provided by the system for reading and writing data.

The strongest consistency model is linearizability or strict consistency, which provides the illusion that each operation takes effect instantaneously across the whole system. In a linearizable system, all nodes see the same view of the data at all times, and reads always return the most recent write. Linearizability is the gold standard for many applications that require strong consistency, such as financial systems and collaborative editing.

However, linearizability comes at a cost – it requires a high degree of coordination between nodes and is sensitive to network delays and failures. In a distributed system, providing linearizability can significantly reduce performance and availability, especially in the presence of network partitions.

At the other end of the spectrum is eventual consistency, which allows for temporary inconsistencies between replicas but guarantees that all replicas will eventually converge to the same state. In an eventually consistent system, writes are propagated to replicas asynchronously, and reads may return stale data for a period of time. Eventually consistent systems are highly available and partition tolerant but sacrifice the strong consistency guarantees of linearizability.

Between these two extremes are a range of intermediate consistency models that provide different trade-offs between consistency, availability, and performance. Some examples include:

• Causal consistency – Operations are ordered according to their causal relationships, such that if one operation happens before another, all nodes will see them in that order. However, concurrent operations may be seen in different orders on different nodes.

• Sequential consistency – All nodes see the same order of operations, but that order may not reflect the real-time ordering of operations across the system.

• Bounded staleness – Reads are guaranteed to return data that is no more than a fixed time interval (e.g. 5 minutes) behind the most recent write.

Choosing the right consistency model for a distributed system depends on the specific requirements of the application. Strong consistency models like linearizability are necessary for some use cases but come with significant performance and availability costs. Weaker consistency models like eventual consistency are more scalable and partition tolerant but may not be suitable for applications that require strict ordering or real-time data.

As a developer, it‘s important to understand the consistency models provided by the distributed systems you work with and choose the appropriate model for your use case. In some cases, it may be necessary to use a combination of consistency models for different parts of the system or to implement application-level mechanisms for handling inconsistencies.

Consensus Algorithms

Another fundamental challenge of distributed systems is getting nodes to agree on a single value or sequence of values, even in the presence of node failures and network partitions. This problem, known as consensus, is critical for many distributed systems tasks such as leader election, state machine replication, and distributed locking.

Achieving consensus in a distributed system is a non-trivial problem, as nodes may fail or be unreachable at any time, and messages between nodes can be lost, delayed, or reordered. The most well-known result in this area is the FLP impossibility result, which proves that no deterministic algorithm can achieve consensus in a asynchronous system with even a single node failure.

Despite this theoretical limitation, there are several practical algorithms for achieving consensus in distributed systems with different trade-offs and assumptions. Some of the most well-known consensus algorithms include:

• Paxos – Paxos is a family of protocols for solving consensus in a network of unreliable processors. It is based on a message-passing model and assumes that nodes can fail by crashing but not by behaving maliciously. Paxos is used in many distributed systems, including Google‘s Chubby lock service and the Cassandra database.

• Raft – Raft is a consensus algorithm designed to be easier to understand and implement than Paxos. It is based on a leader-follower model and provides a simple, understandable mechanism for leader election and log replication. Raft is used in many open-source distributed systems, including etcd and Consul.

• Zab – Zab (Zookeeper Atomic Broadcast) is a consensus algorithm used in the Apache Zookeeper distributed coordination service. It is based on a primary-backup model and provides a total order of updates across the system.

• Byzantine fault tolerance (BFT) – BFT algorithms are designed to tolerate "Byzantine" failures, where nodes may behave arbitrarily or maliciously. BFT algorithms typically require a larger number of nodes and more complex message-passing protocols than crash fault-tolerant algorithms like Paxos and Raft. Examples of BFT algorithms include PBFT (Practical Byzantine Fault Tolerance) and Tendermint.

Implementing consensus algorithms in a distributed system is a complex task that requires careful design and testing. As a developer, it‘s important to understand the trade-offs and assumptions of different consensus algorithms and choose the appropriate one for your use case. In many cases, it‘s best to use an existing distributed system or library that provides a well-tested implementation of a consensus algorithm rather than trying to implement one from scratch.

Skills and Mindset for Building Distributed Systems

Building and operating distributed systems requires a different set of skills and mindset than traditional single-node systems. Here are some key skills and approaches that are important for developers working on distributed systems:

1. Systems thinking – Distributed systems are complex systems with many interacting components, feedback loops, and emergent behaviors. Developers need to be able to think holistically about the system and understand how changes to one part of the system can impact other parts.

2. Failure-oriented mindset – In a distributed system, failures are the norm rather than the exception. Developers need to design systems with failure in mind, using techniques like redundancy, isolation, and graceful degradation to minimize the impact of failures.

3. Asynchronous programming – Distributed systems rely heavily on asynchronous communication and coordination between nodes. Developers need to be comfortable with asynchronous programming models and tools like futures, promises, and reactive streams.

4. Data consistency and concurrency – Distributed systems often involve replicating and partitioning data across multiple nodes, which can lead to consistency and concurrency issues. Developers need to understand concepts like consistency models, transaction isolation levels, and distributed locking.

5. Network programming – Distributed systems are built on top of networks, which can be unreliable, slow, and insecure. Developers need to be familiar with network protocols, messaging patterns, and techniques for handling network failures and latency.

6. Observability and debugging – Debugging a distributed system can be much more difficult than a single-node system, as issues can span multiple nodes and be non-deterministic. Developers need to be familiar with tools and techniques for monitoring, tracing, and debugging distributed systems, such as distributed tracing, log aggregation, and time-travel debugging.

7. Collaboration and communication – Building and operating a distributed system requires collaboration and communication across multiple teams, including developers, operations, and security. Developers need to be able to work effectively in a team environment and communicate clearly about the system‘s architecture, trade-offs, and operational procedures.

Cultivating these skills and mindsets can help developers build more reliable, scalable, and maintainable distributed systems. It‘s also important to stay up-to-date with the latest tools, techniques, and best practices in the field, as distributed systems are an active area of research and development.

Conclusion

Distributed systems are a complex but increasingly essential part of the modern computing landscape. As applications and services continue to grow in scale and complexity, the ability to design, build, and operate distributed systems is becoming a critical skill for developers.

In this article, we‘ve explored the key concepts, challenges, and techniques of distributed systems, including the CAP theorem, consistency models, consensus algorithms, and the skills and mindset needed to work effectively in this field. We‘ve seen how distributed systems enable scalability, fault tolerance, and low latency, but also introduce new challenges and trade-offs that developers need to carefully consider.

As a developer working on distributed systems, it‘s important to have a deep understanding of these concepts and techniques, as well as the ability to think holistically about the system and design for failure. By staying up-to-date with the latest tools and best practices, collaborating effectively with others, and cultivating a failure-oriented mindset, developers can build distributed systems that are reliable, scalable, and maintainable over the long term.

Ultimately, the power and potential of distributed systems lies in their ability to enable new kinds of applications and services that were previously impossible or impractical. By embracing the challenges and opportunities of distributed systems, developers can push the boundaries of what‘s possible and create systems that are truly transformative.

References

1. Kleppmann, Martin. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O‘Reilly Media, 2017.
2. Takada, Mikito. Distributed Systems for Fun and Profit. Online book, available at http://book.mixu.net/distsys/
3. Lamport, Leslie. "Paxos Made Simple." ACM SIGACT News, vol. 32, no. 4, 2001, pp. 51-58.
4. Ongaro, Diego, and John Ousterhout. "In Search of an Understandable Consensus Algorithm." Proceedings of the USENIX Annual Technical Conference, 2014, pp. 305-319.
5. Junqueira, Flavio P., and Benjamin Reed. ZooKeeper: Distributed Process Coordination. O‘Reilly Media, 2013.