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Decentralized vs distributed systems: What's the difference?
OK, so you might’ve noticed the word ‘distributed’ on our website. You’re probably thinking, ‘Wait a minute, does that mean Hive is a decentralized cloud?’.
Well, here’s the thing. The terms ‘decentralized’ and ‘distributed’ are often used interchangeably, but it’s not a case of potay-to, potah-to. The two words are two separate concepts. We’re going to talk about how they’re different, their pros and cons, and the benefits of a distributed cloud.
Deep breath. Let’s jump into it, hive mind!
Decentralized systems: power to the many, not the few
Web2 is dominated by Big Tech. You know who they are. The Googles, the Metas, the Apples. They exert full control over the functionality of their services, and over users’ data. These are what we call centralized systems. Now, take that central owner out of the system. What do you get? A decentralized system. It’s a system in which there is no central authority or centralized component that has control over the entire system. Instead, control is distributed amongst the various components of the system.
TL;DR: centralized systems have one, let’s say, overlord. Decentralized systems offer power to the many, not just the few.
Distributed systems: buzz off, centralization
A distributed system consists of components that are physically separated. They communicate with each other over a network. This allows for resources to be shared and workloads to be distributed across multiple machines, often improving efficiency and performance. But a distributed system may still have a central authority that coordinates and controls the various components.
TL;DR: by harnessing the power of many separate components, distributed systems can enable resource sharing and improve efficiency and performance. These systems can function with or without a central authority.
Distributed v decentralized: choose your fighter
So…what are the differences between the two systems? In a decentralized system, control is distributed amongst the components of the system, while in a distributed system, the components are physically separated and they communicate over a network. Both types of systems can be used to enable the sharing of resources and workloads. Where they differ is in the way that control is managed within the system.
Like with anything else, there are upsides and downsides to both. And like with anything else, maybe a pros and cons list might help you better understand the decentralized versus distributed debate.
It's also important to understand that potential drawbacks are not unique to decentralized systems. Centralized systems can also have issues with complexity, lack of transparency, and scalability, among other things. Ultimately, the choice between a decentralized and a centralized system will depend on the specific needs and goals of an organization or application.
The natural evolution of systems
If recent history is anything to go by, technology evolves and makes things better. While centralized systems enabled the first networks to grow, the thinking behind them evolved, giving way to decentralized systems. And then, by distributing resources and rights across whole networks, distributed systems are proving to be tolerant to faults, more secure, and more transparent. As the clamor for distributed systems grows, their deployment and maintenance costs will plummet. We may be biased here, but we believe distributed systems are the future 😉.
The future, distributed by Hive
Now that you know the difference between the two Ds, we can tell you that Hive is a distributed storage system. It’s hosted on everyone’s computer. Hive also relies on a decentralized data exchange layer. So a Hiver will always be able to retrieve their data directly from peers, without interacting with a central authority. But much like ‘permissioned blockchains’, some of our advanced services will rely on Hive’s control layer.
Wait, you’ve got more questions?
FAQs: Take 5 with Hive
What are some examples of centralized, decentralized and distributed systems?
OK, Dropbox and Google Drive are two examples of centralized systems.
One example of a decentralized system is a peer-to-peer network, like the one used by BitTorrent. In a peer-to-peer network, there is no central server that controls the flow of information. Instead, every participant in the network acts as both a client and a server, sharing resources and workloads with other participants.
An example of a distributed system is a cluster of computers that are used to perform a specific task, such as rendering a 3D animation. In this case, the computers in the cluster are connected to a central server that coordinates their efforts and distributes the workload among them.
How is Hive a peer-to-peer platform?
Good question. Hive splits your stored files into shards of data spread across its P2P network. Additional shards are created to account for peers disappearing from Hive or content being destroyed by hardware failures. For example, let’s assume 100 encrypted shards are generated from your file and sent to 100 peers. These are generated in such a way that only 70 are needed to rebuild the original file. Missing shards are regenerated as soon as we discover peers leaving. With only 30% overhead, the probability of not being able to access the content is then several orders of magnitude lower compared to the simple replication strategy. You can read more about this here.
Check out our articles for more. It’s an absolute beehive of knowledge.
How do centralized, decentralized, and distributed systems differ in terms of their architecture and control mechanisms, and what are some real-world examples of each type?
Centralized, decentralized, and distributed systems vary in their architecture and control mechanisms. Centralized systems, exemplified by platforms like Dropbox and Google Drive, have a single central authority controlling data flow. Decentralized systems, such as peer-to-peer networks like BitTorrent, distribute control among network participants. Distributed systems, like clusters used for rendering 3D animations, involve physically separated components communicating over a network. Real-world examples illustrate how each system type operates and their implications for data management and control.
Can you provide more insights into how Hive's distributed storage system operates and how it ensures data availability and resilience in a peer-to-peer network?
Hive operates as a distributed storage system, distributing files into shards across its peer-to-peer network. This architecture ensures data availability and resilience by generating additional shards to compensate for peers leaving the network or hardware failures. For instance, if 100 encrypted shards are created from a file and distributed to 100 peers, only 70 shards are required to reconstruct the original file. Missing shards are regenerated promptly upon detecting peer departures. With minimal overhead, Hive significantly reduces the risk of data loss compared to traditional replication strategies, providing robust data accessibility and integrity within its distributed framework.
While the article touches on the benefits of distributed systems like Hive, what potential challenges or limitations might organizations encounter when transitioning from centralized or decentralized architectures to distributed systems, and how can these challenges be addressed?
Transitioning from centralized or decentralized architectures to distributed systems like Hive may pose challenges for organizations. These challenges could include adapting existing infrastructure, ensuring compatibility with legacy systems, and managing complexities associated with distributed environments. Additionally, organizations may encounter difficulties in addressing security concerns, maintaining data consistency, and managing network scalability. However, these challenges can be addressed through comprehensive planning, strategic resource allocation, and leveraging expertise in distributed system design and implementation. Collaborative efforts among stakeholders, coupled with thorough risk assessment and mitigation strategies, can facilitate a smooth transition to distributed architectures while maximizing the benefits of improved resilience, scalability, and data availability.
