From qdrant
Guides Qdrant cluster scaling decisions for data volume, query throughput, latency, and query volume. Use for node count, sharding, vertical/horizontal scaling, or capacity issues.
npx claudepluginhub qdrant/skills --plugin qdrantThis skill is limited to using the following tools:
First determine what you're scaling for:
Optimizes Qdrant performance via search speed (latency/throughput), indexing, memory usage, and hardware strategies. Use to improve vector search deployment speed and efficiency.
Tunes vector indexes for latency, recall, and memory using HNSW parameters, quantization strategies, and scaling guidelines up to billions of vectors.
Guides vector database selection for embeddings and semantic search, compares managed options like Pinecone and self-hosted like pgvector/Milvus, explains ANN algorithms like HNSW.
Share bugs, ideas, or general feedback.
First determine what you're scaling for:
After determining the scaling goal, we can choose scaling strategy based on tradeoffs and assumptions. Each pulls toward different strategies. Scaling for throughput and latency are opposite tuning directions.
This becomes relevant when volume of the dataset exceeds the capacity of a single node. Read more about scaling for data volume in Scaling Data Volume
If your system needs to handle more parallel queries than a single node can handle, then you need to scale for query throughput.
Read more about scaling for query throughput in Scaling for Query Throughput
Latency of a single query is determined by the slowest component in the query execution path. It is in sometimes correlated with throughput, but not always. It might require different strategies for scaling.
Read more about scaling for query latency in Scaling for Query Latency
By query volume we understand the amount of results that a single query returns. If the query volume is too high, it can cause performance issues and increase latency.
Tuning for query volume is opposite might require special strategies.
Read more about scaling for query volume in Scaling for Query Volume