Distributed systems: what they are, how they work, and what they are used for

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The Distributed systems They are everywhereAlthough they often go unnoticed, every time you search for something on Google, pay by card, stream a series, or play an online game, you're relying on this type of architecture without realizing it. They are the silent foundation of the modern digital economy and allow millions of users to use a service simultaneously without the entire system collapsing.

In the last decades, Computing has evolved from single servers to enormous coordinated networks of machines spread across the globe. In this article, we'll take a closer look at what a distributed system is, how it differs from a centralized one, its advantages and disadvantages, how it has evolved, the types of architectures that exist, where it's used in real-world applications, and the challenges it presents in terms of communication, security, management, and data storage.

What is a distributed system?

A distributed system is, in essence, a a set of computers or nodes that cooperate to offer a single service in a coordinated manner, as if they were a single logical machine. Each node has its own processor, memory, and storage, but they all communicate through a network (usually the Internet or a corporate network) to share resources and divide the work.

Instead of relying on a single, gigantic central server, The burden is shared among many smaller teamsThe idea is often compared to an orchestra: each instrument (node) has its role, but what the public perceives is a single coherent performance (the distributed system).

This approach fits perfectly with today's world of big data: Storing and processing huge volumes of information is only viable by distributing the work. among multiple machines. Hence, in data & analytics and Big Data environmentsVirtually everything relies on distributed systems: platforms like Hadoop, Spark, Databricks, Cloudera or query engines like Presto are based on this philosophy.

A key feature of these systems is that They hide the internal complexity from the end user.The person using an e-commerce website, an online bank, or a cloud service doesn't see hundreds or thousands of nodes, but rather an application that "simply works," even though there is a very complex distributed infrastructure underneath.

Difference between a centralized system and a distributed system

In a centralized system, All logic, data, and processing are concentrated on a single machine or main server.If that server goes down, the service is unavailable until it's restored. Scaling up usually involves buying more expensive and powerful equipment, and there's a clear "single point of failure."

Conversely, in a distributed system The functions are distributed among several interconnected nodesThere isn't a single indispensable piece of equipment: if one fails, the rest can continue working and compensate for that loss. This increases fault tolerance and allows for growth by adding more nodes instead of inflating just one.

This difference also affects how we scale capacity. La horizontal scalabilityTypical of distributed systems, this involves adding more nodes to the cluster, placing them "in parallel" to distribute load and storage.

From a cost perspective, it is usually more profitable have many standard servers working together than one or two extremely expensive superservers. Furthermore, the failure of a small node usually has a marginal impact on the overall service, whereas the failure of a large, centralized server can bring everything down.

Are distributed systems the same as microservices?

Although they are closely related, they are not exactly the sameA distributed system is a broader concept: any set of nodes that cooperate via a network to offer a shared service falls under this definition, regardless of how the software is organized within it.

Microservices architecture, on the other hand, It is a specific way of designing distributed applicationsInstead of creating a single “monolith,” the application is divided into small, independent services, each with its own logic and often its own database. These microservices communicate with each other using APIs or messaging.

Therefore, a microservices-based platform is always a distributed system, because Its components are distributed and connected by a networkBut there are also distributed systems that do not follow the microservices pattern, for example, a parallel computing cluster, a classic distributed database, or a peer-to-peer network file sharing.

How have distributed systems evolved?

In the early days of business computing, it was normal to have large centralized systems or mainframes which did almost everything: processing, storage, reporting, etc. Over time, client-server architectures and centralized data warehouses for business analysis appeared.

The problem is that, as the data grew, These centralized warehouses fell short in capacity and speedStoring more detailed, historical data from multiple sources was becoming extremely expensive and slow. New analytical needs demanded faster reaction times, greater granularity, and parallel processing.

That's where modern distributed systems come in, especially with the rise of Big Data from the 2000s onwardsAlthough the idea of ​​distributed computing dates back to the 1960s, projects like Hadoop first and then Spark (which was born in 2009 precisely to improve performance and flexibility) made this paradigm a standard in data analytics.

We went from trying to do everything with a single general-purpose tool to working with technology stacks: combinations of specialized components (distributed storage, batch and stream processing engines, orchestrators, data catalogs, etc.) that are integrated with each other to cover the entire data lifecycle.

How does a distributed system work?

Any distributed system can be viewed as a set of components that manage storage, processing, and communicationEach node receives part of the data or work, executes its task, and then coordinates its results with the rest of the system to provide a unified response.

In many scenarios, the data is divided into blocks and these blocks are distributed among different nodes. Each file or record can be fragmented and replicated so that there are redundant copies on different servers. If a node is lost, the system can reconstruct the information from the existing replicas.

This partitioning and replication strategy makes it Reading and processing times are drastically reducedThis is because it allows for parallel processing of different fragments. At the same time, it offers high fault tolerance: the loss of one node results in only a small reduction in capacity, not a global disaster.

However, all this magic comes at a cost in complexity: Managing, configuring, and monitoring distributed clusters is not trivial.It is necessary to coordinate updates, monitor the status of nodes, manage data redistribution when the cluster size changes, and resolve consistency issues between replicas.

Distributed systems architectures

There are several architectural patterns for organizing a distributed system, each with its own advantages and use cases. The most common ones combine different communication topologies and division of responsibilities between nodes.

One of the most classic architectural styles is that of client-serverIn this model, one or more servers offer resources (data, services, files), and clients make requests and consume those resources. It's like a library: the librarian (server) manages the books, and users (clients) request them.

At the other extreme is architecture or on equal termswhere there is no central node controlling everything. Each participant acts simultaneously as both client and server, sharing resources with the others. This is the typical model for many file-sharing networks or some cryptocurrencies.

The architecture is also noteworthy. service-oriented and microservicesIn these applications, the application consists of multiple distributed services that expose well-defined interfaces. Each service can be deployed, scaled, and updated independently, providing significant flexibility for system evolution.

In all cases, the key lies in how the nodes are coordinated and synchronized: Concurrency, latency, partial failures, and data consistency must be managed., while maintaining a smooth and consistent user experience.

Advantages of distributed systems

Among the reasons why distributed systems have become the standard in so many sectors, several very clear advantages stand out related to performance, availability and growth.

One of the most visible benefits is the performance improvementBy allowing many machines to work in parallel on different parts of a task, response times are reduced and very high workloads are supported. This is crucial in mission-critical applications such as online banking, e-commerce, or real-time services.

Another big advantage is the High availabilityBy distributing both the workload and data across multiple nodes, if one fails, the system can continue operating by relying on the others. This resilience is key where downtime translates directly into financial losses or a poor user experience.

La Scalability Another key strength is that distributed systems can scale by adding nodes to the network without interrupting service. This allows them to adapt to peak demand, sustained business growth, or changes in data volume, avoiding the need to shut down operations to upgrade to a more powerful server.

In addition, they offer a great flexibility in resource managementYou can prioritize certain tasks, allocate more capacity to critical processes, or deploy new services on specific nodes. This fine-tuning capability is invaluable in highly dynamic environments.

Disadvantages and risks of distributed systems

Not everything is an advantage: The distribution introduces new problems that do not appear (or appear less frequently) in centralized systems. Designing and operating these architectures involves taking on certain challenges.

First is the communication complexityWhen working on real networks, one must deal with variable latencies, limited bandwidth, packet loss, and heterogeneity between nodes. Coordinating processes that share data across the network without blocking the system or generating inconsistencies is not trivial.

Another delicate point is the bugs and errorsIn a distributed environment, it's practically inevitable that some node, disk, or network link will fail at some point. Therefore, robust mechanisms for failure detection, automatic recovery, operation retries, and dynamic redistribution of tasks and data are essential.

La security, It also gets more complicated: the more nodes, the larger the attack surface. Distributed systems are especially vulnerable to attacks such as denial-of-service, code injection, interception of communications, or unauthorized access to poorly protected nodes.

Por último, la management and administration It is much more demanding. Configuring, monitoring, and maintaining a geographically distributed cluster composed of heterogeneous technologies requires good tools, mature processes, and technical teams with specific experience in this type of environment.

Real-world applications of distributed systems

The presence of distributed systems in daily life is so widespread that it is difficult to imagine modern digital services without them. Many critical sectors depend on this architecture to function reliably.

In the web world, for example, the major global applications of e-commerce and social media They use distributed systems to serve millions of simultaneous users. Platforms like Amazon or Alibaba distribute requests across data centers around the world and support their scalability with distributed databases and content delivery networks (CDNs).

telecommunications networks for telephony and Internet They rely on distributed infrastructures that route calls, messages, and data packets through numerous intermediate nodes. Thanks to this, communication maintains reasonable levels of latency and reliability even when part of the network experiences outages.

The financial and banking sector is another good example: payment systems, ATMs, trading, or online banking They rely on distributed databases and services that replicate information across regions, apply strong encryption and authentication measures, and support geographically dispersed transactions, minimizing the risk of failure.

In the field of Big Data and advanced analytics, Distributed processing systems allow working with gigantic volumes of records: server logs, sensor data, social networks, transactions, etc. Technologies such as Hadoop Distributed File System (HDFS) or Spark distribute storage and computing across multiple nodes to ensure reasonable processing times.

Distributed database systems

Distributed databases are a special and very important case within distributed systems. Instead of storing all the data on a single server, it is distributed across several nodes., often located in different geographical regions, maintaining a unified logical view for the user.

This strategy allows scaling in both storage capacity and read/write performance. New nodes or regions can be added as demand grows, and the partitioning and replication mechanisms are responsible for redistributing the information more or less automatically.

One of the major difficulties is maintaining the synchronization and data consistency between replicas. This is achieved using consensus algorithms such as Paxos or Raft, which ensure that operations are applied in a compatible order across all nodes that are part of a replica group.

Depending on the application type, some databases prioritize availability and tolerance to network partitions over strict consistency, adopting models such as the eventual consistencyIn other cases, synchronous replication is used to maintain strong consistency, sacrificing some latency in exchange for greater data integrity.

Large e-commerce platforms or cloud services combine distributed databases with caching systems to serve content with low latency and absorb traffic spikes. A classic example of distributed storage that seeks reliability and fault tolerance is Amazon S3, which replicates data across multiple servers within a region.

Parallel computing and high performance in distributed systems

Another area where distributed systems shine is the high-performance parallel computing (HPC)Instead of processing large volumes of data sequentially on a single computer, the calculations are distributed across clusters of hundreds or thousands of nodes.

In these clusters, each node executes a part of the problem and, through finely tuned coordination techniques, The partial results are combined to obtain the final resultThis allows for tackling complex scientific simulations, climate modeling, advanced financial analysis, or processing large medical images at a speed unthinkable with a single machine.

To achieve this efficiency, the following are used: specifically designed parallel algorithms to distribute the load and minimize communication between nodes. Techniques such as CPU affinity or optimization for NUMA architectures help improve performance by adjusting how processes and data are allocated to memory and processors.

In artificial intelligence and deep learning, distributed computing enables train huge neural networks by distributing data and models across multiple GPUs and serversThe system coordinates gradients and parameter updates so that training progresses in parallel without breaking the model's coherence.

The cloud has boosted this approach by offering HPC as a Service (HPCaaS)This allows small companies and teams to temporarily rent large clusters to train models or run intensive simulations, without having to buy and maintain all that infrastructure.

Distributed systems in everyday technology

Beyond data centers, Distributed systems are part of almost everyone's daily life that interacts with technology. Its presence is so normalized that we barely pay attention to it.

Email services, instant messaging platforms, and social networks operate on distributed infrastructures that replicate user data worldwideThanks to this, we can access our messages from any device, with low latency and, generally, without visible interruptions.

Peer-to-peer file-sharing networks are another example: instead of downloading from a single server, The file is fragmented and served from multiple peerswhere each participating person acts simultaneously as a client and server, improving the resilience and performance of the network.

In the field of Internet of Things (IoT) and smart grids, millions of sensors and devices send data to distributed platforms that process information in real time to optimize energy consumption, automate buildings, or coordinate fleets of connected vehicles.

And, of course, the big ones cloud computing platforms AWS, Microsoft Azure, and Google Cloud are the most obvious examples of distributed systems: they group data centers in different regions, offer on-demand resources, and allow companies to deploy their applications on a global scale with just a few clicks and a credit card.

How do I know what type of distributed system I need?

When choosing a specific solution, there is no single recipe: The design of the distributed system must be tailored to the context of the organization., to its objectives and its technological maturity.

It's best to start by analyzing the current and expected data volumeProcessing a few million records a day is not the same as handling continuous flows of real-time data from IoT devices distributed around the world.

It is also key to study the available budget and scaling strategySome companies can afford dedicated teams and specialized staff, while others will rely almost entirely on managed cloud services to reduce operational complexity.

It is also necessary to take into account the peak demand, low activity windows, and processing time constraintsA system that needs to respond in milliseconds will have different requirements than one oriented towards nightly batch processes.

Defining these aspects from the outset helps design a coherent architecture that is easier to manage and less prone to surprises. Today, even small organizations can access a distributed computing power that was previously only available to large corporationsprovided they have the necessary technical knowledge and business acumen to take advantage of it.

Distributed systems have gone from being a specialized solution to becoming the backbone of most digital services. Its ability to distribute load, tolerate faults, scale horizontally, and work with massive volumes of data This makes them an essential component for any organization that wants to compete in an increasingly connected, demanding, and technology-dependent environment.