mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-26 14:38:30 +02:00
Dmitry Kan
f62f8e62c9
changed "higher" to "lower". If I have less vectors in RAM, less RAM is used and more available for concurrent search => latency is lower, QPS is higher.
52 lines
2.2 KiB
Markdown
52 lines
2.2 KiB
Markdown
---
|
|
title: Capacity and sizing
|
|
weight: 20
|
|
---
|
|
|
|
# Capacity and sizing
|
|
|
|
We have been asked a lot about the optimal cluster configuration to serve a number of vectors.
|
|
The only right answer is “It depends”.
|
|
|
|
It depends on a number of factors and options you can choose for your collections.
|
|
|
|
## Basic configuration
|
|
|
|
If you need to keep all vectors in memory for maximum performance, there is a very rough formula for estimating the needed memory size looks like this:
|
|
|
|
```
|
|
memory_size = number_of_vectors * vector_dimension * 4 bytes * 1.5
|
|
```
|
|
|
|
Extra 50% is needed for metadata (indexes, point versions, etc.) as well as for temporary segments constructed during the optimization process.
|
|
|
|
If you need to have payloads along with the vectors, it is recommended to store it on the disc, and only keep [indexed fields](../../concepts/indexing/#payload-index) in RAM.
|
|
Read more about the payload storage in the [Storage](../../concepts/storage/#payload-storage) section.
|
|
|
|
|
|
## Storage focused configuration
|
|
|
|
If your priority is to serve large amount of vectors with an average search latency, it is recommended to configure [mmap storage](../../concepts/storage/#configuring-memmap-storage).
|
|
In this case vectors will be stored on the disc in memory-mapped files, and only the most frequently used vectors will be kept in RAM.
|
|
|
|
The amount of available RAM will significantly affect the performance of the search.
|
|
As a rule of thumb, if you keep 2 times less vectors in RAM, the search latency will be 2 times lower.
|
|
|
|
The speed of disks is also important. [Let us know](mailto:cloud@qdrant.io) if you have special requirements for a high-volume search.
|
|
|
|
## Sub-groups oriented configuration
|
|
|
|
|
|
If your use case assumes that the vectors are split into multiple collections or sub-groups based on payload values,
|
|
it is recommended to configure memory-map storage.
|
|
For example, if you serve search for multiple users, but each of them has an subset of vectors which they use independently.
|
|
|
|
In this scenatio only the active subset of vectors will be kept in RAM, which allows
|
|
the fast search for the most active and recent users.
|
|
|
|
In this case you can estimate required memory size as follows:
|
|
|
|
```
|
|
memory_size = number_of_active_vectors * vector_dimension * 4 bytes * 1.5
|
|
```
|