qdrant-performance-optimization

name	qdrant-performance-optimization
description	Navigation hub linking sub-skills for proactive Qdrant tuning: search speed, indexing performance, and memory usage optimization. Use when planning configuration or capacity changes to improve speed and efficiency. For diagnosing an active production slowdown or analyzing live metrics, use qdrant-monitoring instead.
allowed-tools	["Read","Grep","Glob"]

Qdrant Performance Optimization

Symptom to Sub-skill Map

Symptom	Sub-skill
Filtered queries much slower than unfiltered	Search Speed Optimization
Low QPS, cannot handle the query load	Search Speed Optimization
Individual queries take too long to return	Search Speed Optimization
Index build or vector upload takes too long	Indexing Performance Optimization
Collection stays yellow, optimizer runs for a long time	Indexing Performance Optimization
Bulk upsert of vectors is slow	Indexing Performance Optimization
RAM usage too high or out-of-memory crashes	Memory Usage Optimization
Want to fit a larger dataset on the same hardware	Memory Usage Optimization
Reducing cost by moving data to disk	Memory Usage Optimization

There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.

Search Speed Optimization

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.

Indexing Performance Optimization

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.

Memory Usage Optimization

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.