Coding & Refactoringlow risk

qdrant-scaling-query-volume

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'.

github/awesome-copilot·skills/qdrant-scaling/scaling-query-volume/SKILL.md
95/ 100Quality

Install this skill

Choose your coding agent and copy a project or personal installation command.

Pinned to the indexed commit
Project installation.agents/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a codex -y
Personal installation~/.agents/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a codex -g -y
Manual folder.agents/skills/scaling-query-volumeOfficial docs ↗
Project installation.claude/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a claude-code -y
Personal installation~/.claude/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a claude-code -g -y
Manual folder.claude/skills/scaling-query-volumeOfficial docs ↗
Project installation.agents/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a github-copilot -y
Personal installation~/.copilot/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a github-copilot -g -y
Manual folder.agents/skills/scaling-query-volumeOfficial docs ↗
Project installation.agents/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a cursor -y
Personal installation~/.cursor/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a cursor -g -y
Manual folder.agents/skills/scaling-query-volumeOfficial docs ↗
Project installation.agents/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a gemini-cli -y
Personal installation~/.gemini/skills/scaling-query-volume
npx skills add https://github.com/github/awesome-copilot/tree/0aaced533251f5b86c69dfbc5e55db74c4b4d1af/skills/qdrant-scaling/scaling-query-volume -a gemini-cli -g -y
Native Gemini CLIgemini skills install https://github.com/github/awesome-copilot.git --scope workspace --path skills/qdrant-scaling/scaling-query-volume
Manual folder.agents/skills/scaling-query-volumeOfficial docs ↗
⚠ Installation uses the open-source skills CLI. Inspect the source and permissions before running the command.

Skill instructions

View source on GitHub ↗
# 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.