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Guides Qdrant data volume scaling decisions.
Passed 21 of 22 test cases with the skill, and 18 of 22 without it.
3 fixed18 passed either way1 still failing0 broke
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One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
When a Qdrant node runs out of physical RAM, what memory management feature allows vector and payload data to be accessed directly from disk without immediately forcing a cluster expansion?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies memory-mapped files as a single-node vertical scaling mechanism. | Missed | Passed |
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This document covers data volume scaling scenarios, where the total size of the dataset exceeds the capacity of a single node.
If the use case is multi-tenant, meaning that each user only has access to a subset of the data, and we never need to query across all the data, then we can use multi-tenancy patterns to scale.
The recommended way is to use multi-tenant workloads with payload partitioning, per-tenant indexes, and tiered multitenancy.
Learn more Tenant Scaling
Some use-cases are based on a sliding time window, where only the most recent data is relevant. For example an index for social media posts, where only the last 6 months of data require fast search.
Learn more Sliding Time Window
Most general use-cases require global search across all data. In these situations, we might need to fall back to vertical scaling, and then horizontal scaling when we reach the limits of vertical scaling.
When data doesn't fit in a single node, the first approach is to scale the node itself — more RAM, better disk, quantization, mmap. Exhaust vertical options before going horizontal, as horizontal scaling adds permanent operational complexity.
Learn more Vertical Scaling
When a single node can't hold the data even with quantization and mmap, distribute data across multiple nodes via sharding.
Learn more Horizontal Scaling
test cases passed, vs 18 / 22 without
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