What is the typical capacity and latency of a single Redis instance used for caching?

What is the typical capacity and latency of a single Redis instance used for caching?

• A. A single Redis instance holds up to 1 TB of data and handles 100k-200k operations per second with under 1 ms latency.
• B. Redis can store unlimited data on a single node.
• C. Redis latency is typically tens of milliseconds.
• D. Redis is only suitable for petabyte-scale caching.

Answer: (A)

Redis caching relies on in-memory storage, so capacity is limited by the amount of RAM on the machine and latency stays extremely low for local access. Because data lives in memory rather than on disk, a single Redis instance can serve very fast reads and writes, typically delivering sub-millisecond response times for simple operations when accessed over a fast network and with a light workload. The capacity can reach large data sizes if you provision enough RAM; 1 terabyte of cached data on one node is within the realm of possibility on high-end hardware, and throughputs of roughly 100k–200k simple operations per second are achievable on a well-tuned setup. The key takeaway is that a single Redis instance, used as a cache, trades off infinite scalability for blazing speed and RAM-based capacity, with real-world deployments often expanding beyond a single node only when datasets exceed a single-node RAM footprint.

Redis caching relies on in-memory storage, so capacity is limited by the amount of RAM on the machine and latency stays extremely low for local access. Because data lives in memory rather than on disk, a single Redis instance can serve very fast reads and writes, typically delivering sub-millisecond response times for simple operations when accessed over a fast network and with a light workload. The capacity can reach large data sizes if you provision enough RAM; 1 terabyte of cached data on one node is within the realm of possibility on high-end hardware, and throughputs of roughly 100k–200k simple operations per second are achievable on a well-tuned setup. The key takeaway is that a single Redis instance, used as a cache, trades off infinite scalability for blazing speed and RAM-based capacity, with real-world deployments often expanding beyond a single node only when datasets exceed a single-node RAM footprint.