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Understanding Object Storage vs. Block Storage

Block, File storage, or Object — Which fundamental storage system is the right fit for today’s data storage environments?

That has probably left even the most experienced IT storage admins to scratch their heads.

The reason? Enterprise data storage technologies choices being Block, File storage, and Object, it’s often the Object storage vs. Block storage debate that collides. Blame it on the data at scale making future data storage a massive challenge. Besides, processing the data, storing it, and accessing it based on the use cases - imagine the complexity it drives up for deployment of each type of architecture!

So, where do you choose to store your data? What business value can you derive from it?

In this article, we shall discuss Object-based storage vs. Block based Storage, the access methods that support block-based and object-based storage technology, their use cases, how do they fit into businesses, and why they might not be the best choice always.

So what is the difference between block storage and object storage? Let’s explore.

Object Storage

Object-based storage, shortly known as object storage is a data storage architecture that employs a flat memory model to store distinct units of data or objects as isolated containers. These isolated containers are known as buckets. The flat structure works like a single self-contained repository wherein each object gets stored with equal access across multiple networked systems. The best part is that you can locate the object even without knowing the physical location of the data.

This is because every single object features three important attributes:

  1. The data. It can be anything you want to store ranging from a family photo, music, videos, 5,00000-page manual document file, to any unstructured data.

  2. Relevant metadata that describes the data (includes details like age, privacy, access contingencies); and

  3. A custom identifier that contains a unique ID address to let the OS locate it over a distributed system.

Access Methods

The access method is a technical advantage for storage admins that makes the Object Storage technology tick. In the Object Storage platform, you can access objects via HTTP Application Programming Interfaces (APIs) that rely on Representational State Transfer (RESTful) APIs for access. When you want to retrieve a file, it sends an API request to the cloud block storage to locate the desired object. This makes object-based storage a great choice for public cloud workloads. Furthermore, you can distribute objects across multiple geographic locations. This lets you move objects across different tiers or even different geographies.

The good news is that you can define the metadata, meaning for each piece of data, you can add identifiers to build more context to it. Once you know the object’s metadata information, you can easily query it. Besides, you can classify/ organize the files with the file information, easily index it and access to retrieve the data whenever you want. And for big data analytics, the opportunities are endless!

However, you can directly access this data as mounted drive volume through an OS server that is familiar with the object device. AWS, the market leader of the cloud provides Amazon S3 which is an object storage offering.

Use cases

  • Unstructured data- Since object storage follows no hierarchy, it’s ideal for storing data such as multimedia content, files, folders, archives, and static web content distributed across geographical locations.

  • Cloud application development- You can access object storage via HTTPS API. So you can build native system applications with massive scale data that can be stored, tagged, and analyzed for big data analytics.

  • Archival storage- With object storage, you can add storage nodes for scaling unstructured data updated frequently. This allows you to archive files while still retaining instant access.

  • Backup of files- You can use object storage to backup files, log files, and database dumps.

  • Objects can be read multiple times- Object storage data is written once but can be read by multiple clients. It works very well for globally distributed rich media storage as multiple clients can access and read data across all locations.

  • Optimized for static data- You can manage high volumes of static and unstructured data with object storage. E.g. images, video files, music, or transactional records.

Why Object Storage for Businesses?

When it comes to the difference between Object and Block storage, the former wins as a viable option for unstructured data storage. It’s complex to organize, manage and search the avalanche of unstructured data growing day by day. This is where Object storage makes sense in extracting data insights from high volume storage and data distribution across geographies that helps in the development of business goals.

Below are the reasons for choosing object storage technology in the block-level storage vs. object-level storage scenario:

Searchability- Metadata residing in the Objects themselves powers extensive search results. E.g. you can search for a certain type of file that meets specific criteria. Also, you can easily create custom metadata and add attributes over time without having to build databases to associate metadata with the objects.

Unlimited scalability- Object storage allows scaling out horizontally by adding nodes. This ensures the high availability of object data as multiple copies of the same objects get dispersed across multiple nodes. So, now you can leverage more storage space by adding nodes to the cluster and scale the storage system up and down (addition/removal of storage units) as per the enterprise need.

Big data analytics- To take advantage of big data analytics, enter object storage. Every individual object is tagged with metadata that supports relevance while adding more context to the underlying data. This lets you extract actionable insights from the big data which you cannot expect from traditional blocks.

Distributed storage across geographies- You can take advantage of distributed access feature of the multi-petabyte scale data storage bigtime! Thanks to extensible metadata and the geographic flexibility of object storage. With the keyword-searchable global namespace, not only can you locate, migrate, and protect the data but also load-balance them among on-premises and cloud storage tiers. For businesses, this optimizes the capacity, cost, availability, and compliance, thereby helping them to meet your business goals.

Meets heavy data storage needs: You can store large files, customer data, and unstructured enterprise data in a storage pool. It can scale hundreds of petabytes of data. This eliminates the scaling limitations because of flat namespaces - a very attractive option for enterprises.

Application development using HTTP(s) protocol: Since Object storage supports access via HTTP(s) protocol, you can easily integrate it into your applications as all requests are made over HTTP(s) API. So, now you can build, develop, deploy cloud-native applications for mobile, responsive, and even traditional app development.

Why Object Storage Is Not the Best Choice Always?

To understand object-based storage vs. block-based storage, you should assess the instances in which Object storage is not well suited. Here you go.

  • With Object storage, you cannot easily modify files as objects are designed to read/write or overwrite entire files, and not part of it. If you are uploading a new revision of the entire file, it affects the IO performance. Henceforth, it’s a bad choice for database operations.

  • Object storage doesn’t guarantee that you will receive the latest version of the file upon the read request. This is because updates propagated across all locations are not latest or (eventual consistent) always as data is not constantly altered.

  • For organizations that prioritize storage performance, object storage delivers slow I/O activity performance for workloads across the storage. Blame it on the object-based architecture that requires metadata analysis. Since data is bundled together with customized metatags, this slows down the performance of applications and workflows.

Block Storage

Block storage (also known as block-level storage) is the simplest form of data storage technology used for storing structured data such as databases, applications, etc. It is commonly deployed in Storage Area Networks (SANs) systems or cloud-based storage environments. So, when you buy the SAN/block storage, you get a high-speed storage architecture in which the data gets stored as fixed-sized chunks known as blocks.

In block storage technology, each block is split into equal-sized blocks that function as individual hard disk drives in a PC. Here, the blocks are controlled by an external server OS that lets you access these storage drives. Through this, you get the flexibility to store any kind of application including file, database, VM volumes, and more. The best part is that you can even share the storage files or backup your data placed in the block storage using supported third-party tools or OS native backup tools. A good example of block storage AWS is Amazon Elastic Block Store (EBS) which is a persistent block storage service designed for Amazon Elastic Cloud Compute (EC2).

Access Methods

The client operating system controls the block via high-performance protocols such as Fibre Channel and Internet Small Computer Systems Interface (iSCSI). So the storage is easily accessible. Again, the SAN places these blocks across multiple storage nodes. This makes access to block storage data faster, especially when the application is local.

Another key point is that each block has its unique ID address that lets you search a specific object or retrieve the block data quickly. Since the OS can directly read/write/re-write the blocks as needed, you can easily configure, manage and organize the data as a (structure) file system or an application-specific structure. Besides, object-based storage relies on filesystem protocol (NTFS, XFS, or ext4), meaning you can easily modify the blocks to access specifically required blocks while retaining the older version. This is where block-based storage wins in I/O speed.

Use Cases

  • Create raw storage volume for any application- With block storage, you can create individual hard drives for any application such as database, files, VM filesystem, and more.

  • RAID arrays- You can employ block storage systems as RAID volumes (*RAID is a data virtualization storage technology) that bolster data protection. This is done by configuring individual disks into RAID arrays.

  • Consistent I/O operations- You can use block storage for database-oriented applications that require a very low-latency and consistent storage operations I/O (Input/Output or Read/Write).

  • Email servers- Block-level storage supports Microsoft Exchange for email servers, unlike NAS file storage systems.

  • VMware servers- Using block-level storage, you can deploy VMware servers for storing VM filesystems (VMFS) volumes.

  • Booting- You can use a block storage architecture to boot up an operating system or external server directly from block storage.

Why Block Storage for Businesses?

So why block-level storage makes sense for the IT environment? Below are some reasons for being a popular choice:

  • Versatility- You can format block-level storage to accept any usable filesystem. E.g, VMware servers will use VMFS; for running Windows, NTFS is the primary format.

  • Flexibility- Block storage allows quick configuration to update storage capacity. You can add storage volumes or move storage between servers without sacrificing performance.

  • Fast I/O data performance- Block storage mechanisms support underlying file protocols (NFS, CIFS, ext3/ext4, and others) for rapid I/O data access and low latency for high-performance applications. So, you can perform high-activity IO operations such as caching, database operations, log files, etc.

  • Add storage capacity- You can easily upgrade to standard speed storage by adding high-performance storage for customers.

  • Pay as you use- You just need to pay for the block storage space that you have allotted. This means you can easily attach/detach or reattach block storage volume that keeps your cost down.

  • Scale-out performance- Since block storage volume works independently with separate blocks of data, you can create additional block volumes to scale out. The performance scales with the disk size or the limit of the VM instances. The good news is that you don’t have to pay for more compute capability.

  • Easy management- You can easily manage access and control privileges as the host in the Operating system or block storage volumes directly control the data permissions.

Why Block-Based Storage, Not the Best Choice Always?

Block storage might not be the best alternative for some instances.

  • An internet-connected client cannot download a file stored in block storage at any time. This is because block storage architecture is limited to specified volume capacity as default limits. However, customers can request increasing limits if they need to extend the capacity beyond the default limits.

  • Unlike tiered-based or volume-based pricing, the entire block storage volume pricing is pre-defined. That is to say, to access one piece of data, you need to pay independently for the entire block storage space that includes the volume of data stored, types of operations performed, and data transfer cost.

  • File distribution is complex and expensive in block storage as each unit of data is split and stored separately. This leads to wastage of infrastructure costs and inefficient utilization of resources.

The following comparison chart summarizes the difference between block and object storage. Take a look.

Object Storage
Block Storage
Data is stored as objects in scalable buckets. Data is stored as fixed-sized blocks.
Can scale infinitely to Petabytes and beyond. Limited scalability with fixed-sized blocks as per requirements.
With more context to data (metadata), you can easily organize, locate, or retrieve data. No metadata.
Unstructured data can be stored efficiently across multiple geographical locations. The greater the distance between storage, the higher the latency.
Best performance for unstructured content and high stream throughput. Best performance for relational database and transactional data.
HTTP(S) based API connectivity. Accessible via Fibre Channel and Internet Small Computer Systems Interface (iSCSI).
Unlimited file storage capacity. Can add nodes to increase capacity.
Best suited for static files and applications such as data backups, static content, archival images, rich multimedia content (videos, pictures, or music). Ideal for applications such as enterprise databases, and transactional data that require high IOPS and low latency.

Effective Storage Backup and Recovery With Zmanda

Whichever storage option you are comfortable with, you are likely to store your data for long-term archival. This holds for data used less frequently, or not accessed at all but consumes a valuable storage place. But what if your primary storage becomes unavailable? Relax! Now, you can easily access, recover your complete set of data or even spin up a virtual machine to store data on the backup server in minutes!

With this in mind, Zmanda has been designed for comprehensive storage, backup and DR capability across the object and block storage appliances. You can replicate the backed-up data to an offsite location of your choice.

Presently, Zmanda backup engine supports the following types of object storage repositories for long-term data storage:

Try them out! Or if you are still torn between the type of architectural approaches as an ideal scalable storage solution, we have a hybrid/converged solution to fit your needs. Get in touch with us to understand how we leverage each solution while lowering your TCO (total cost of ownership).