One of the most common—and often most challenging—questions I hear is: “How do I size my GemFire deployment?” While the question itself is straightforward, the discovery process behind it often sparks deep and thought-provoking discussions. Will we need to over-provision to handle a proverbial storm of data, or will our data remain more like a calm lake with a mirror-like reflection?
In this first installment of our three-part blog series on VMware Tanzu GemFire sizing, we’ll explore the foundational considerations and key questions you should address as you begin your data journey with GemFire.
Understanding Your Data Requirements
- How much are we going to store?
This is usually the biggest stumbling block. Pinning down exactly how much data will live in GemFire can be trickier than you might think. Not all data is equally important or frequently accessed, so deciding what truly belongs in GemFire is critical. - What are our performance requirements or SLAs?
Your Service Level Agreement (SLA) might dictate how quickly GemFire needs to respond to reads and writes, which in turn influences how many nodes and how much memory you’ll need. - Will GemFire persist all the data?
Persistent stores provide durability but also increase disk usage and potentially impact performance. You’ll have to weigh the cost of disk resources against the benefits of persistent data. - Is overflow an option?
Overflowing data to disk can save valuable in-memory space, but it also introduces additional disk I/O overhead. It’s a useful strategy when dealing with infrequently accessed data or bursts in data size. - What operations will GemFire handle?
Queries, functions, and indexes can impose additional overhead. The more complex the operations, the greater the CPU and memory requirements. - Network and hardware considerations
The performance of your persistent storage layer, network throughput, and any disaster recovery (DR) or high availability (HA) strategies has tremendous influence on your capacity planning.
Real-World Example: Deciding What to Store
I recall working with a client determined to put petabytes of data into GemFire—everything from frequently accessed records to rarely touched historical logs. Through careful discussion, we came to the conclusion we only needed a few months’ worth of data in GemFire; the rest could remain in their data warehouse.
This kind of conversation is common, what is the working set size we are going to use with GemFire. It’s perfectly okay to start with an assumption and then refine it once you gather more details about your use case. Start broad, narrow down, and then estimate. That approach is far simpler than trying to be hyper-precise “golden answer” out of the gate.
Scaling: A Key GemFire Strength
One of the greatest strengths of GemFire is how straightforward it is to scale, whether you’re aiming to boost performance or increase storage. Even if your initial estimates for data size or usage patterns prove to be too large or too small, GemFire won’t lock you into a rigid configuration. Simply adding or removing nodes—one or several as needed—prompts GemFire to automatically redistribute data across the cluster, making the most of the available resources. This flexibility becomes a real advantage if you find you need more capacity than originally planned, as it minimizes downtime and complexity while ensuring that both performance and storage requirements continue to be met.
It’s equally important to realize you don’t need a perfect capacity forecast or a fully optimized architecture at the outset. A solid, functional baseline is enough to get started. As your application grows or your data needs shift, GemFire can be expanded incrementally to accommodate evolving demands. This incremental approach spares you the stress and expense of completely overhauling your environment if your early projections fall short, enabling you to adapt more swiftly and cost-effectively to real-world conditions.
GemFire makes system architects’ jobs easier and dynamic scaling is key—no overhauls needed from prototype to production.
Coming Up Next
In the next post, we’ll dive deeper into redundancy planning, why having 50% free capacity is often recommended for a GemFire server, and a simple example to illustrate how these principles come together.
Stay tuned for the next blog, where we’ll explore how to estimate storage capacity, decide on redundant copies, and handle unexpected outages with minimal impact to your GemFire data grid.
