What is object storage?

Deep learning requires massive, diverse datasets and quick data retrieval that traditional storage solutions, like file systems and databases, struggle to keep up with. In contrast, object storage offers a scalable and efficient alternative, perfectly suited for the unstructured data used to train generative AI models.

Unlike hierarchical file systems, which organize data in a tree structure with directories and subdirectories, object storage uses a flat address space, eliminating bottlenecks that arise from complex file hierarchies due to the complexity of managing nested folders and paths and enabling limitless scalability. Plus, its optimized architecture allows for high bandwidth access, which is important for deep learning training.

In this article, we will discuss object storage, what it is, its architecture, how it works, its advantages, use cases, and comparisons with other storage systems.

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What is object storage?

Object storage breaks from the traditional model of hierarchical file systems by storing digital data as discrete units called "objects" within a single, flat repository. Instead of navigating through folders and directories to locate a file, object storage places all objects in a single address space, often referred to as a "bucket" or "container." This concept of buckets is fundamental to how object storage organizes and manages data.

Think of it like a vast, organized warehouse where each item is individually labeled and accessible without knowing its precise location on a shelf.

Let's look at what constitutes an object in this storage system. Each object comprises three key elements:

• Data: This is the actual content being stored. It could be a text file, image, video, or any other type of data.
• Metadata: This is a set of key-value pairs that describe the object. Metadata can include information like the object's creation date, size, content type, and other relevant attributes. Metadata is crucial for organizing, searching, and managing objects effectively.
• Unique identifier: Every object has a unique identifier, typically a long string of characters, allowing direct access to the object without traversing a file path.

Source: Paper

The flat structure of object storage eliminates the complexities and limitations of hierarchical organization, enabling object storage to scale horizontally, which means it can easily accommodate massive amounts of data by simply adding more storage devices to the system.

Such inherent scalability, combined with the flexibility provided by metadata and unique identifiers, makes object storage well-suited for managing the large, diverse datasets that drive modern AI applications.

Before we discuss the architecture of object storage let’s compare object storage to other storage systems.

Object storage vs. file storage vs. block storage

While object storage offers compelling advantages, it's essential to understand how it compares to other storage systems like file storage and block storage. Each has its strengths and weaknesses, making them suitable for different use cases.

File Storage

Structure: Organizes data in a hierarchical structure of files and folders, resembling the file system on a typical computer.

Access: Access is path-based, requiring traversal of directories to locate files.

Metadata: Supports basic metadata, such as file name, size, creation/modification date, and permissions.

Strengths:

• Ideal for storing and sharing files.
• Suited for user home directories.
• Supports applications that depend on familiar file system interfaces.

Weaknesses:

• Becomes inefficient for managing large volumes of unstructured data.
• Limited scalability and performance, especially with deep or complex file hierarchies.

Source: Paper

Block Storage

Structure: Data is divided into fixed-size blocks stored independently on storage devices. Blocks lack a file structure or hierarchy.

Access: Access occurs directly at the block level, typically through raw disk access protocols, enabling low-latency operations.

Metadata: Stores minimal metadata, primarily concerning block locations and status.

Strengths:

• Delivers excellent performance for applications requiring random data access (e.g., databases and transaction systems).
• Optimized for low-latency, high-speed operations.

Weaknesses:

• Not efficient for unstructured data or large file management.
• Scalability can be constrained by physical storage device limits.

Object Storage

Structure: Stores data as discrete objects within a flat address space (e.g., in buckets or containers).

Access: Objects are accessed through unique identifiers, bypassing hierarchical navigation.

Metadata: Supports extensive, customizable metadata for each object, allowing advanced data organization, searchability, and management.

Strengths:

• Highly scalable and cost-effective, designed for massive volumes of unstructured data.
• Provides high availability and durability via data replication and redundancy mechanisms.
• Flexible metadata capabilities enable rich data insights and tagging.

Weaknesses:

• Not suitable for applications that require low-latency random data access.
• Does not provide traditional file system semantics, which can limit compatibility with legacy applications.

Here's a table summarizing the key differences:

Choosing the right storage system depends on your specific needs and application requirements. Object storage is good when dealing with large volumes of unstructured data, while file storage is suitable for traditional file management, and block storage excels in performance-critical applications with random access patterns.

The architecture of object storage

To achieve the scalability, durability, and high availability object storage systems are designed to provide, they typically employ a distributed architecture that spreads data across multiple physical storage devices and servers. Here's a breakdown of the key components:

1. Storage nodes:

In object storage systems, storage nodes are physical servers designed to store data objects. Each node handles a portion of the overall storage pool, distributing the workload across multiple servers for scalability and reliability.

These nodes can support various types of storage media, such as hard disk drives (HDDs) for high-capacity storage and solid-state drives (SSDs) for high-speed data access. This architecture enables efficient management of large volumes of unstructured data, ensuring flexibility, scalability, and fault tolerance in modern storage solutions.

Once data is stored on the storage nodes, the system needs an efficient way to locate and manage it, and for that, it uses a metadata server.

2. Metadata server:

In object storage systems, metadata servers play a crucial role in managing metadata, which includes information about objects, such as unique identifiers, attributes, and permissions. These servers maintain mappings between object identifiers and their physical or logical locations within the storage system. Depending on the architecture:

• Centralized metadata management: In some systems, a central metadata server acts as a directory, storing metadata and directing requests. When an application requests an object, the metadata server provides the object's location and facilitates retrieval.
• Distributed metadata management: In other implementations, metadata responsibilities are distributed across multiple servers or nodes to improve scalability and fault tolerance. In such cases, metadata may be stored alongside data objects or handled collectively by a network of metadata servers.

By managing metadata effectively, these servers ensure efficient object retrieval, enhance system scalability, and provide flexibility in data organization. Additionally, the metadata server plays a vital role in maintaining data consistency by ensuring all updates and changes to objects are properly tracked and reflected in the metadata, helping prevent conflicts, and ensuring that applications consistently access the latest version of an object.

Furthermore, the metadata server enables the versioning of objects. By storing historical metadata for each object, the system can maintain multiple versions of the same object, allowing users to access or revert to previous versions as needed, which is essential for data backup, recovery, and tracking changes over time.

3. Object storage devices (OSDs):

Object Storage Devices (OSDs) are intelligent storage components that integrate storage media, processing capabilities, and network connectivity into a unified system. OSDs manage the data stored on individual storage nodes, handling tasks such as reading and writing data, replication, and ensuring integrity.

Source: Paper

They coordinate with metadata servers (or distributed metadata systems) to maintain data consistency, enforce access rules, and ensure high availability across the object storage architecture. The combination of functionality allows OSDs to support scalable, fault-tolerant, and efficient data management in object storage systems.

4. Application Programming Interface (API):

Object storage systems have APIs that allow applications to interact seamlessly with the storage system. These APIs provide functionalities for creating, reading, updating, and deleting objects, managing object metadata, and enforcing access control policies.

APIs are typically based on standard protocols such as REST or S3 for AWS, enabling interoperability with various applications and services while supporting efficient and flexible data management in object storage environments.

5. Load Balancer:

The load balancer is a critical component of object storage systems, designed to distribute incoming requests across multiple metadata servers and storage nodes. Its primary purpose is to optimize performance, balance workloads, and eliminate bottlenecks by preventing any single node or server from being overwhelmed.

How object storage works

Object storage operates quite differently from traditional file-based storage. Here's a simplified breakdown of how it works:

• Data Upload: When you upload data to an object storage system, it is broken down into objects. Each object is assigned a unique identifier and paired with relevant metadata.
• Object Placement: The system determines the optimal storage location for the object based on factors like available capacity, data replication policies, and performance requirements. The object and its metadata are then stored on one or more storage nodes.
• Metadata Management: The metadata server records the object's unique identifier location on the storage nodes, as well as associated metadata. This information is crucial for retrieving the object later.
• Data Retrieval: When you need to access an object, you provide its unique identifier. The system uses the metadata server to locate the object on the storage nodes and retrieve it for you.
• API Interactions: Applications interact with the object storage system through APIs. These APIs provide a standardized way to perform actions like uploading, downloading, deleting objects, and managing metadata.

Source: Wikipedia

This streamlined process, combined with the architectural advantages of object storage, enables efficient and scalable data management, especially for large volumes of unstructured data common in modern applications.

Let’s talk a bit more about the benefits of using object storage.

Benefits of object storage

Object storage offers a compelling array of advantages over traditional storage systems, making it a popular choice for a wide range of applications. Here are some of the key benefits:

• Massive Scalability: Object storage is designed to scale horizontally to accommodate petabytes or even exabytes of data, making it ideal for applications with vast storage needs. You can seamlessly expand capacity by simply adding more storage nodes to the system without disrupting operations.
• Cost-Efficiency: Object storage can be more cost-effective than traditional storage, especially for large datasets. Cloud providers offer pay-as-you-go pricing models, allowing you to optimize costs by only paying for the storage you use. Additionally, object storage reduces the expenses associated with managing and maintaining on-premise hardware.
• High availability and reliability: Data is replicated across multiple nodes and geographic locations, ensuring high availability even in the event of hardware failures or natural disasters. This redundancy also enhances data reliability, protecting against data loss and maintaining data integrity over time. Built-in mechanisms like replication and erasure coding further strengthen data protection.
• Enhanced data management: Object storage simplifies data management through metadata tagging. Users can efficiently search, retrieve, and organize data without worrying about complex directory structures. Metadata also allows for granular control over data access policies and lifecycle management.
• Flexibility: Object storage can handle various data types, from small text files to large multimedia files, making it suitable for diverse workloads, especially those involving unstructured data like images, videos, and log files.
• Performance: While not ideal for low-latency applications, object storage excels at handling large-scale unstructured data with sequential access patterns. It is optimized for high-bandwidth access, facilitating rapid data ingestion and retrieval, which is essential for data-intensive tasks like deep learning and big data analytics.
• Global accessibility and cloud integration: Object storage is often the backbone of cloud storage services, offering global access to data through RESTful APIs. This facilitates collaboration, remote work, and seamless integration with cloud-native applications.

These benefits make object storage a powerful solution for organizations looking to modernize their storage infrastructure and address the challenges of managing growing volumes of data in today's data-driven world.

Use cases of object storage.

The versatility and advantages of object storage make it suitable for a wide range of use cases across various industries. Here are some prominent examples:

1. Deep learning and AI:

• Training data: Object storage provides a scalable and cost-effective solution for storing and managing the massive datasets required to train deep learning models. Its ability to handle diverse data formats like images, videos, and text makes it ideal for AI applications.

Source: Paper

• Model deployment: Object storage can be used to store and deploy trained AI models, making them readily accessible for inference and predictions.

2. Big data and analytics:

• Data lake: Object storage serves as a foundation for data lakes, providing a centralized repository for storing and analyzing large volumes of structured and unstructured data from various sources.
• Log processing and analytics: Object storage can efficiently store and process log files generated by applications and systems, enabling real-time analysis and insights.

3. Backup and archiving:

• Data backup and recovery: Object storage offers a reliable and cost-effective solution for backing up critical data, ensuring business continuity in case of data loss or disasters.
• Long-term archiving: Object storage's scalability and durability make it suitable for archiving large volumes of data for long-term preservation and compliance.

4. Content delivery and streaming:

• Media streaming: Object storage can be used to store and deliver media content, such as videos and images, to users across the globe with low latency and high throughput.
• Content distribution: Object storage enables efficient distribution of software updates, documents, and other content to a large user base.

5. Cloud-native applications:

• Microservices: Object storage provides a scalable and reliable storage layer for microservices architectures, enabling independent scaling and deployment of services.
• Serverless computing: Object storage integrates seamlessly with serverless computing platforms, providing a cost-effective and scalable storage solution for serverless functions.

6. Other use cases:

• Healthcare: Storing and managing medical images, patient records, and research data.
• Financial services: Archiving financial transactions, regulatory reports, and customer data.
• Government: Preserving public records, geospatial data, and other critical information.

These are just a few examples of the applications of object storage as it can be used in a lot of industries.

Conclusion

Being able to handle massive amounts of unstructured data efficiently, combined with its scalability, reliability, and cost-effectiveness, makes object storage an important component of modern IT infrastructures. It provides the foundation for data-driven innovation, especially if you're backing up enterprise data, hosting a large-scale cloud application, or managing multimedia content.

With object storage, you can tackle the biggest data challenges and drive innovation in deep learning and beyond. You can now use S3-compatible object storage on CUDO Compute. With our storage, you are guaranteed the speed and reliability needed for AI training and inference. Get started now!