What is Distributed Computing (Distributed Systems)
Distributed computing is the method of making multiple computers work together to solve a common problem. This model divides a task into smaller chunks, which are then processed simultaneously by a network of computers rather than a single centralized server. The primary goal of distributed computing is to improve processing speed and data storage capacity, making it possible to handle large-scale computations and vast amounts of data efficiently.
How does Distributed Computing work?
The essence of distributed computing lies in its architecture, which comprises multiple software and hardware components interconnected through a communication network. These components, known as nodes, can be located in the same physical location or spread across different geographical areas, including those at the network's edge, known as edge computing. Each node operates independently, executing part of the application or task. For this setup to work effectively, mechanisms for task allocation, synchronization, and communication among the nodes are crucial. Distributed computing systems often employ algorithms that ensure fault tolerance and consistent operation despite individual node failures or network issues. For more information, check out the concepts of horizontal and vertical scaling.
Examples of distributed systems:
World Wide Web (WWW): The internet is a massive distributed computing system. Websites are hosted on servers spread worldwide, and users access them through web browsers. The web operates on a client-server model, where web servers deliver content to client browsers upon request.
Cloud Computing Platforms: services like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide distributed computing resources to users over the internet. These platforms offer various services such as virtual machines, storage, databases, and AI services, all distributed across multiple data centers worldwide.
Distributed Databases: store data across multiple nodes in a network, providing benefits such as fault tolerance, scalability, and improved performance. Distributed databases use Sharding, a method of partitioning that divides a large database into smaller, faster, and more manageable pieces called shards.
Content Delivery Networks (CDNs): CDNs distribute web content (e.g., images, videos, scripts) across multiple servers in different geographic locations. This helps reduce latency and improve the performance of web applications by serving content from servers closer to the user. Examples include Cloudflare, Akamai, and Amazon CloudFront.
Peer-to-Peer (P2P) Networks: enable distributed sharing of resources such as files, computing power, or bandwidth directly between individual nodes without centralized servers. Examples include BitTorrent for file sharing and Bitcoin for decentralized cryptocurrency transactions.
Distributed File Systems: Distributed file systems allow files to be stored and accessed across multiple machines in a network as if they were stored on a single device. Examples include Hadoop Distributed File System (HDFS) and Google File System (GFS).
Blockchain Networks: Blockchain is a distributed ledger technology that maintains a continuously growing list of records (blocks) in a decentralized manner. Each block contains a timestamp and a link to the previous block, creating a tamper-evident and transparent record of transactions. Examples include Bitcoin and Ethereum blockchains.
How does PubNub use Distributed Computing?
PubNub provides real-time infrastructure as a service, enabling developers to build applications that require live interactions, such as chat apps, live updates, and real-time tracking. At the core of PubNub's technology is the use of distributed computing to handle massive amounts of data and messages that traverse the globe in real time.
PubNub operates a Data Stream Network (DSN), a pivotal example of distributed computing in action. The network uses various servers to route and manage messages across various geographical locations. This architecture allows PubNub to distribute the load evenly across its network, ensuring high performance and minimal delays. Each node in the network can handle a part of the data traffic, thus contributing to the overall efficiency and robustness of the service.
