Distributed storage is a method of storing and managing data across multiple physical devices or servers in a network. Instead of relying on a single centralized storage system, distributed storage spreads the data across various locations, allowing for better scalability, fault tolerance, and improved performance.
In a distributed storage system, data is divided into smaller parts, known as “chunks” or “shards,” which are then distributed and replicated across multiple nodes in the network. Each node stores only a portion of the data, and the system coordinates the retrieval and storage of data to ensure redundancy and fault tolerance.
Key features:
- Scalability: As data grows, more storage nodes can be added to the network to accommodate the increasing storage requirements, making it easy to scale up the storage capacity without disrupting the existing system.
- Fault Tolerance: Since data is replicated across multiple nodes, the system can continue to function even if some nodes fail or become unavailable. The redundancy ensures data availability and minimizes the risk of data loss.
- Performance: Distributed storage allows data to be accessed in parallel from multiple nodes, improving read and write speeds. This distributed architecture reduces the chances of bottlenecks that can occur in a centralized storage system.
- Geographical Distribution: Data can be stored in different geographical locations, which improves data accessibility for users in different regions and enhances disaster recovery capabilities.
Common examples of distributed storage systems are Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage, and Apache Cassandra.
In summary, distributed storage is a method of storing data across multiple servers or nodes in a network, providing scalability, fault tolerance, improved performance, and the ability to handle large amounts of data efficiently.