Capacity Planning

Calculate memory requirements based on projected growth and retention policies.

In this lesson, you will learn how to mathematically calculate your cluster’s memory requirements to ensure your cache remains performant as your dataset grows.

The Foundation of Capacity Planning

Capacity planning is the process of predicting your hardware needs by balancing data volume growth against your retention policy. In a key-value store, memory is your most expensive and finite resource. If you underestimate your growth, you risk memory exhaustion, which forces the engine into aggressive eviction or disk-swapping, destroying your latency targets.

The Calculation Formula

To determine the necessary memory, you need to solve for the total size of your working set. Start with these three variables:

$N$ : Average number of objects projected per month.
$S$ : Average size of a single object (including key overhead and metadata).
$T$ : Retention period (TTL) in months.

The base memory required is: Memory = (N * S) * T

However, this is only the "payload" size. You must also factor in the overhead of the storage engine. If your cache engine uses a hash table structure, you should add a buffer (typically 15-20%) for metadata, pointers, and memory fragmentation (as discussed in previous lessons).

Concrete Illustration

Imagine you expect to add 1 million items per month, each object averages 2 KB, and you have a 6-month retention policy.

Projected items: 6,000,000 (1M * 6 months)
Payload size: 6,000,000 * 2 KB = 12 GB
Overhead buffer (20%): 12 GB * 0.20 = 2.4 GB
Total Required RAM: 14.4 GB

Strategic Adjustments

Capacity planning isn't a "set and forget" task. If your Analyzing Key Space Distribution metrics show that your keys are growing larger over time, your $S$ variable will drift upward, invalidating your initial projections. Furthermore, if you implement a Cache Invalidation Strategy that allows for shorter TTLs, you can lower your $T$ variable, effectively reducing your memory footprint without losing cache utility. Always build in a "burst margin"—a percentage of headroom—to account for spikes in write traffic or temporary data surges.

Key Takeaway: Precise capacity planning requires multiplying your projected item volume by the object size and retention duration, then adding a buffer for engine overhead and fragmentation.