Scaling for Query Volume
SkillSearchLets your agent follow proven guidance for Qdrant queries that return too many results or paginate slowly.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Scaling for Query Volume skill
About this capability
Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.
What this skill tells your AI
The instructions your AI receives, as published by qdrant/skills in skills/qdrant-scaling/scaling-query-volume/SKILL.md and read by ahel’s review.
Problem: When a query has a large limit (e.g. 1000) and there are multiple shards (e.g. 10), naively each shard must return the full 1000 results — totaling 10,000 scored points transferred and merged. This is wasteful since data is randomly distributed across auto-shards.
Core idea
Instead of asking every shard for the full limit, ask each shard for a smaller limit computed via Poisson distribution statistics, then merge. This is safe because auto-sharding guarantees random, independent data distribution.
When it activates
- More than 1 shard
- Auto-sharding is in use (all queried shards share the same shard key)
- The request's limit + offset >= SHARD_QUERY_SUBSAMPLING_LIMIT (128)
- The query is not exact
Key tradeoff
The strategy trades a small probability of slightly incomplete results for a large reduction in inter-shard data transfer, especially for high-limit queries across many shards. The 1.2x safety factor and the 99.9% Poisson threshold keep the error rate very low — comparable to inaccuracies already introduced by approximate vector indices like HNSW.
Signals
- GitHub stars
- 232
- Forks
- 27
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
qdrant-scaling-query-volume- Source
- github.com/qdrant/skills