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Last updated: Aug 02, 2023

Thick vs Thin Provisioning: All You Wanted to Know

Here you will find out:

  • what is thin provisioning
  • thin provisioning usage in VMFS disks
  • how DiskInternals can help you

Are you ready? Let's read!

Thick and thin provisioning

Storage technology innovation seeks to improve value for money, performance, and best use of acquired storage space. As a result of this invention, thin provisioning (TP), a data service, is positioned to achieve optimal storage utilization rates.

We'll examine thin provisioning in this piece, including what it is, how it functions, and why organizations need it. We'll contrast TP with conventional provisioning, sometimes referred to as thick provisioning, in order to better understand how TP operates.

About Thin Provisioning

In storage area networks (SAN), centralized storage disks, and storage virtualization systems, thin provisioning, also known as virtual provisioning or thin storage, is a form of on-demand storage distribution depending on user requirements.

When a user uses up their assigned storage space in thin storage, they are given additional. When users free up space by deleting files or moving data, the unused space is recovered, repurposed, and used for other workloads that require storage.

The Difference Between Thin and Thick Provisioning: How They Work

Thick provisioning (also known as fat provisioning) pre-allocates storage space in anticipation of future capacity requirements, as opposed to thin provisioning. This storage allocation approach results in unused store capacity, ineffective storage use, and excessive infrastructure and maintenance expenditures.

In other words, while both thin and thick provisioning are storage allocation strategies, the difference is in the configuration of the Logical Unit Number (LUN).

Creating and Managing Thick Provisioned LUNs

Thick provisioning is used to create LUNs and assign them to particular hosts or applications. A specific amount of storage space is reserved by the LUN for the specific host or application. While the procedure is running, the allocated storage space uses all of the available storage space.

Consider a business with a 100-person workforce. One terabyte of storage is required for each worker. The business will need to make an investment in a storage system that can provide 100TB of usable storage space in order to accommodate all of its employees. Each employee will have a 1TB LUN assigned to them via thick provisioning. It's important to note that employees won't need all of the 1TB of storage space right away; rather, they'll need that much overall as data increases. In other words, a large portion of the allocated storage will be inactive until required. The Return on Investments (ROIs) will be subpar as a result.

Furthermore, as employees begin to use up the allowed 1TB of storage and want more, further issues will occur.

The problem with thick provisioned LUNs is that they can't be scaled up once they've been provisioned. A thick provisioned LUN must be rebuilt in order to add extra storage. In other words, host applications and workloads must be halted, data must be backed up, and new LUNs with more storage capacity must be created and assigned.

Imagine the downtime and inefficiency in the aforementioned storage once employees have used up their 1TB allocation.

Creating and Using Thin Provisioned LUNs

Thin provided LUNs, unlike thick provisioned LUNs, can be set to be bigger than the physical storage space available. Thin LUNs continue to operate and distribute data over the available storage space, while the array maintains track of it, thanks to the thin provisioned pool idea.

Using thin provisioning , the firm may acquire a storage infrastructure with 50TBs of consumption capacity and assign 100TBs to the workers. They can buy extra storage and scale up configured thin LUNs once the 50TBs are nearly full.

Another distinction between thin and thick provisioned LUNs is that thin LUNs are more readily scaled up as needed. It is not necessary to suspend host programs or workloads that utilise the allotted storage.

In other words, thin provisioning not only improves ROIs but also makes future data growth simple — with no downtime.

Enterprise Benefits of Thin Provisioning

  • Thin provisioning accelerates storage provisioning, simplifies management, and lowers costs by maximizing storage use in IT settings with numerous hosts and applications.
  • IT settings that do not use thin provisioning either experience interruption when dedicated storage capacity for a LUN is used up, or they overprovision their storage infrastructure. Both approaches are inefficient, expensive, and waste resources. Thin provisioning allows available resources to be fully utilized, and more storage may be provided without delay or disturbance when assigned storage is depleted.
  • To improve storage utilization, thin provisioning can be used with other data services. Compression services, for example, go well with thin provisioning. Thin provisioning makes the most of available storage space while compression minimizes the data's total size.

Thin provisioning in VMFS formatted disk: data recovery

You can return any information from data storage based on the VMware VMFS file system. One application, with a record of 15 years of effectiveness in this field — DiskInternals VMFS Recovery — will help you with this. It also restores VMDK files stored on disks formatted using VMware ESX/ESXi Server. The most common limitations of Windows are bypassed during recovery, and virtual disks can be mounted as local disks available in Windows Explorer, etc.

Downloading and installing the utility, of course, is a priority.

Next, connect via SSH, if necessary.

Then open the disk (local disk or remote by SSH).

Next is the scanning process, after which you will need to find the necessary VMDK files.

Then proceed to mount the VMDK file and open it.

View files for free to check their integrity.

The final step is instant automatic recovery; to perform it you need to purchase a license to complete data export.

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