qdrant-tenant-scaling
$
npx mdskill add github/awesome-copilot/qdrant-tenant-scalingAvoid collection-per-tenant; use shared collections with tenant keys.
- Prevents resource waste and collection limits when tenants grow.
- Integrates with Qdrant's multitenancy and sharding documentation.
- Selects partitioning strategy based on tenant count thresholds.
- Provides specific scaling patterns for 10k and 100k+ tenants.
SKILL.md
.github/skills/qdrant-tenant-scalingView on GitHub ↗
--- name: qdrant-tenant-scaling description: "Guides Qdrant multi-tenant scaling. Use when someone asks 'how to scale tenants', 'one collection per tenant?', 'tenant isolation', 'dedicated shards', or reports tenant performance issues. Also use when multi-tenant workloads outgrow shared infrastructure." --- # What to Do When Scaling Multi-Tenant Qdrant Do not create one collection per tenant. Does not scale past a few hundred and wastes resources. One company hit the 1000 collection limit after a year of collection-per-repo and had to migrate to payload partitioning. Use a shared collection with a tenant key. - Understand multitenancy patterns [Multitenancy](https://search.qdrant.tech/md/documentation/manage-data/multitenancy/) Here is a short summary of the patterns: ## Number of Tenants is around 10k Use the default multitenancy strategy via payload filtering. Read about [Partition by payload](https://search.qdrant.tech/md/documentation/manage-data/multitenancy/?s=partition-by-payload) and [Calibrate performance](https://search.qdrant.tech/md/documentation/manage-data/multitenancy/?s=calibrate-performance) for best practices on indexing and query performance. ## Number of Tenants is around 100k and more At this scale, the cluster may consist of several peers. To localize tenant data and improve performance, use [custom sharding](https://search.qdrant.tech/md/documentation/operations/distributed_deployment/?s=user-defined-sharding) to assign tenants to specific shards based on tenant ID hash. This will localize tenant requests to specific nodes instead of broadcasting them to all nodes, improving performance and reducing load on each node. ## If tenants are unevenly sized If some tenants are much larger than others, use [tiered multitenancy](https://search.qdrant.tech/md/documentation/manage-data/multitenancy/?s=tiered-multitenancy) to promote large tenants to dedicated shards while keeping small tenants on shared shards. This optimizes resource allocation and performance for tenants of varying sizes. ## Need Strict Tenant Isolation Use when: legal/compliance requirements demand per-tenant encryption or strict isolation beyond what payload filtering provides. - Multiple collections may be necessary for per-tenant encryption keys - Limit collection count and use payload filtering within each collection - This is the exception, not the default. Only use when compliance requires it. ## What NOT to Do - Do not create one collection per tenant without compliance justification (does not scale past hundreds) - Do not skip `is_tenant=true` on the tenant index (kills sequential read performance) - Do not build global HNSW for multi-tenant collections (wasteful, use `payload_m` instead)
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|