Architectures of Distributed Storage Systems

Below are some common architectures used in distributed storage systems:

1. Replication-based architecture

In this architecture, data is replicated across multiple nodes in the system. This ensures fault tolerance, as the loss of one node does not result in data loss. Replication can be synchronous or asynchronous, depending on whether the data is copied to all nodes before the write operation is acknowledged.

Replication-based architectures frequently use methods like consensus protocols or quorum-based consistency.

• Synchronous replication: Before the write action is acknowledged to the client, the data is transferred to every node in synchronous replication. This guarantees data consistency at all times across all replicas. But because the write process has to wait for acknowledgement from every duplicate before finishing, it might cause latency.
• Asynchronous replication: This type of replication does not wait for all replicas to be updated; instead, it acknowledges the write operation to the client as soon as the data is written to the primary node. Next, an asynchronous copy of the data is made to the replica nodes. Although this lowers latency, if the primary node fails before the copies are updated, it may result in inconsistent data.

2. Sharding architecture

Sharding involves partitioning data into smaller subsets called shards and distributing these across multiple nodes. Each node is responsible for storing and managing a subset of the data. This architecture helps distribute the storage and processing load evenly across nodes, improving scalability.

• Horizontal partitioning: In sharding, data is divided horizontally among several nodes according to a predetermined criterion (e.g., range of values, hash of the key). Every shard is overseen by a distinct node and comprises a portion of the data.
• Coordination and routing: A sharding architecture usually include a routing mechanism to identify the shard from which the requested data is being retrieved and route the request appropriately. Coordination techniques are also required to manage events like shard migrations and rebalancing and to guarantee data consistency.

3. Distributed File System (DFS)

DFS offers a single, unified view of file storage on several servers. It gives users and apps a single, logical file system while abstracting the underlying complexity of storage distribution. Hadoop Distributed File System (HDFS) and Google File System (GFS) are two examples.

• Client/Server Architecture: To access and modify files in a DFS, clients communicate with servers. Each server oversees a subset of the entire file system, and they are dispersed throughout a network. Clients use a defined interface that the DFS provides to request file operations (read, write, and delete).
• Uniform View: By giving users and applications a uniform view of file storage, DFS simplifies the intricacies of storage distribution. Users see a single, logical file system even when the data is physically located on multiple servers.
• Fault Tolerance and Scalability: DFSs are made to grow horizontally by adding more servers to the network. In the event of server outages, they also have fault tolerance techniques to guarantee data availability. Techniques for redundancy and replication are frequently.

Hadoop Distributed File System (HDFS) is a popular DFS used in the Hadoop ecosystem for storing large volumes of data across a cluster of commodity hardware. Google File System (GFS) is another notable example, developed by Google to support its infrastructure and services.

4. Object Storage architecture

Data is arranged as objects in object storage, each with its own information, data, and unique identity. Instead of being organized like files, these objects are kept in a flat hierarchy. Systems for object storage may store unstructured data, including documents, films, and photos, and they are very scalable. OpenStack Swift, Azure Blob Storage, and Amazon S3 are a few examples.

• Objects and Metadata: Data is arranged into distinct components called objects in object storage architecture. Every object is made up of related metadata and the actual data, which could be a document, video, or image. The attributes of the item, such as its name, size, content type, creation date, and any other custom metadata, are all contained in the metadata. This metadata makes efficient object management, retrieval, and storage possible while also offering insightful context.
• Flat Hierarchy: Object storage systems use a flat hierarchy to arrange data into folders and subfolders, in contrast to standard file systems that use a hierarchical directory structure. Every object in the storage system has a unique identification, and they are all kept in a flat namespace.

In today’s world where everything revolves around data, we need storage solutions that are fast and reliable and able to handle huge amounts of information. The old way of storing data in one place is no longer enough because there’s just too much data created by all the apps and services we use daily. That’s where distributed storage systems come in. They spread out the data across many different places, making it easier to manage and keeping it safe even if something goes wrong with one part of the system.