npx skills add ...
npx skills add github/awesome-copilot --skill qdrant-performance-optimization
npx skills add github/awesome-copilot --skill qdrant-performance-optimization
Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.
There are two different criteria for search speed: latency and throughput. Latency is the time it takes to get a response for a single query, while throughput is the number of queries that can be processed in a given time frame. Depending on your use case, you may want to optimize for one or both of these metrics.
More on search speed optimization can be found in the Search Speed Optimization skill.
Qdrant needs to build a vector index to perform efficient similarity search. The time it takes to build the index can vary depending on the size of your dataset, hardware, and configuration.
More on indexing performance optimization can be found in the Indexing Performance Optimization skill.
Vector search can be memory intensive, especially when dealing with large datasets. Qdrant has a flexible memory management system, which allows you to precisely control which parts of storage are kept in memory and which are stored on disk. This can help you optimize memory usage without sacrificing performance.
More on memory usage optimization can be found in the Memory Usage Optimization skill.