Gartner defines Scalability as the measure of a system’s ability to increase or decrease in performance and cost in response to changes in application and system processing demands. Considering this is essential when choosing a storage solution as prioritizing it from the start leads to lower maintenance costs, better user experience, and higher agility. But before you can make a decision, you need to first understand the differences in how you can scale your solution.
Scale-out refers to the ability of a system to scale certain dimensions when you add more components. In a storage or file system – sometimes also called scale-out NAS (network-attached storage) – these components are hard drives (hard disk, NVMe) and servers. The more interesting part are the dimensions to scale in a storage system when you add more components:
- Throughput performance (bandwidth)
- Random IO performance (IOPS, random 4k IO)
- Metadata performance
- Capacity, i.e., the storage capacity for files
- Metadata capacity, e.g., the number of files or file systems
A proper scale-out file system should scale in all of these dimensions. For example, suppose you can only scale the capacity, like with many storage appliances. In that case, you will often run out of performance for the applications that want to access the growing amount of storage. Most use-cases and applications grow performance and capacity in lockstep; however, archival storage is one of the few exceptions.
Scalability limits of scale-out storage
Another important aspect of a scale-out NAS system is determining how far it can actually scale. All distributed systems have limits regarding the number of servers and/or drives they can have. Good systems have limits in the thousands or tens of thousands of servers, so those limits are more of a theoretical issue.
Other systems, especially those where scale-out was added later, have much lower limits, like 16 servers. You might say 16 servers are enough for you today but are you ready to move to a new system when you go to 17? Or even worse, start a new cluster that is completely independent?
Similarly, it’s essential to look for practical scalability limits, which might be much lower than what the theoretical limit says. Some examples are file or storage systems that rely on so-called “consistent hashing” to determine the location of data. Whenever the storage cluster changes (outage, new or removed server), the data needs to be moved. The more servers, the more outages or failures you’ll see, which causes the clusters to become unstable and results in higher latencies and partial unavailability.
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How to consolidate your NAS with Quobyte - a scale-out software NAS.
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