qdrant-horizontal-scaling
$
npx mdskill add github/awesome-copilot/qdrant-horizontal-scalingDiagnose Qdrant scaling needs and recommend optimal node configurations.
- Handles queries about vertical versus horizontal scaling approaches.
- Integrates with Qdrant cluster management APIs and vector storage.
- Analyzes data volume, IOPS requirements, and fault tolerance needs.
- Delivers specific node counts, shard numbers, and replication settings.
SKILL.md
.github/skills/qdrant-horizontal-scalingView on GitHub ↗
--- name: qdrant-horizontal-scaling description: "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." --- # 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](https://search.qdrant.tech/md/documentation/operations/distributed_deployment/?s=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)
More from github/awesome-copilot
- acquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
- acreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
- acreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
- acreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
- add-educational-comments'Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.'
- adobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
- agent-governance|
- agent-owasp-compliance|
- agent-supply-chain|
- agentic-eval|