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Qdrant Horizontal Scaling

GitHubOfficialFirst committed by anush008Distributed by GitHub

  • Skill
  • Tool integration
  • Data and AI
  • MIT
  • No issues found

From the author

Diagnoses and guides Qdrant horizontal scaling decisions. Use when someone asks 'vertical or horizontal?', 'how many nodes?', 'how many shards?', 'how to add nodes', 'resharding', 'data doesn't fit', or 'need more capacity'. Also use when data growth outpaces current deployment.
— the description by anush008

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to scaling Qdrant vector databases. It helps users determine when to transition from vertical to horizontal scaling, how to configure nodes and shards for optimal performance, and how to manage fault tolerance. It emphasizes best practices for cluster maintenance, including the implications of replication factors and the complexities of resharding.

What it does

  • Evaluates the necessity of vertical versus horizontal scaling for Qdrant clusters.
  • Provides configuration recommendations for node counts and replication factors.
  • Advises on optimal shard distribution to balance performance and overhead.
  • Outlines the risks and operational requirements of resharding.
  • Identifies common pitfalls in cluster management and capacity planning.

When to use it

  • When deciding whether to scale a Qdrant deployment vertically or horizontally.
  • When planning the initial cluster architecture for production environments.
  • When experiencing performance bottlenecks or capacity limits in existing clusters.
  • When needing to ensure high availability and fault tolerance for vector data.
  • When troubleshooting uneven data distribution across cluster nodes.

Advantages

  • Promotes operational stability by prioritizing vertical scaling before adding complexity.
  • Ensures fault tolerance through recommended replication strategies.
  • Helps prevent common configuration errors like uneven shard distribution.
  • Provides clear guidance on the limitations and alternatives to resharding.

Usage examples

“Should I scale my Qdrant cluster horizontally or vertically?”
The agent will advise checking if vertical scaling (increasing node resources) is sufficient first, as it is simpler, and explain that horizontal scaling is for when data exceeds single-node capacity or fault tolerance is required.
“How many shards should I set for a 4-node Qdrant cluster?”
The agent will recommend a shard count between 6 and 12 to ensure even distribution and balance performance overhead, while warning against shard counts that are not multiples of the node count.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/qdrant-horizontal-scaling/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip qdrant-horizontal-scaling.zip -d .agents/skills

Documentation by anush008

What to Do When Qdrant Needs More Capacity

Vertical first: simpler operations, no network overhead, good up to ~100M vectors per node depending on dimensions and quantization. Horizontal when: data exceeds single node capacity, need fault tolerance, need to isolate tenants, or IOPS-bound (more nodes = more independent IOPS).

Most basic distributed configuration

  • 3 nodes, 3 shards with replication_factor: 2 for zero-downtime scaling

Minimum of 3 nodes is important for consensus and fault tolerance. With 3 nodes, you can lose 1 node without downtime. With 2 nodes, losing 1 node causes downtime for collection operations. Replication factor of 2 means each shard has 1 replica, so you have 2 copies of data. This allows for zero-downtime scaling and maintenance. With replication_factor: 1, zero-downtime is not guaranteed even for point-level operations, and cluster maintenance requires downtime.

Choosing number of shards

Shards are the unit of data distribution. More shards allows more nodes and better distribution, but adds overhead. Fewer shards reduces overhead but limits horizontal scaling.

For cluster of 3-6 nodes the recommended shard count is 6-12. This allows for 2-4 shards per node, which balances distribution and overhead.

Changing number of shards

Use when: shard count isn't evenly divisible by node count, causing uneven distribution, or need to rebalance.

Resharding is expensive and time-consuming, it should be used as a last resort if regular data distribution is not possible. Resharding is designed to be transparent for user operations, updates and searches should still work during resharding with some small performance impact.

But resharding operation itself is time-consuming and requires to move large amounts of data between nodes.

  • Available in Qdrant Cloud Resharding

  • Resharding is not available for self-hosted deployments.

Better alternatives: over-provision shards initially, or spin up new cluster with correct config and migrate data.

What NOT to Do

  • Do not jump to horizontal before exhausting vertical (adds complexity for no gain)

  • Do not set shard_number that isn't a multiple of node count (uneven distribution)

  • Do not use replication_factor: 1 in production if you need fault tolerance

  • Do not add nodes without rebalancing shards (use shard move API to redistribute)

  • Do not scale down RAM without load testing (cache eviction causes days-long latency incidents)

  • Do not hit the collection limit by using one collection per tenant (use payload partitioning)

Files

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "qdrant-horizontal-scaling", distributed by AutoGineer (https://autogineer.com/agent-skills).

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

  • ATTRIBUTION.md: this file, written by AutoGineer

  • LICENSE: copied from LICENSE

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of anush008anush008
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/qdrant-scaling/scaling-data-volume/horizontal-scaling · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
anush008

AutoGineer is not affiliated with or endorsed by anush008 or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download qdrant-horizontal-scaling.zip