# Qdrant Scaling Query Volume

> Use when Qdrant queries with large limits fan out across many shards and over-fetch — each shard returning the full limit. Applies Poisson-based per-shard limit reduction, valid when auto-sharding guarantees random distribution.

- Skill: `mouadja02/qdrant-scaling-query-volume` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mouadja02/qdrant-scaling-query-volume`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mouadja02/qdrant-scaling-query-volume/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: mouadja02 (https://skillmd.com/u/mouadja02)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mouadja02/qdrant-scaling-query-volume

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> **Attribution:** Sourced from [qdrant/skills](https://github.com/qdrant/skills) by [Qdrant](https://qdrant.tech).

# Scaling for Query Volume

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.