Scaling data volume
Skill github/awesome-copilot/skills/qdrant-scaling/scaling-data-volume
Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot.
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Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.
SKILL.md
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Scaling Data Volume
This document covers data volume scaling scenarios, where the total size of the dataset exceeds the capacity of a single node.
Tenant Scaling
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
Sliding Time Window
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
Global Search
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.
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
Horizontal 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