Minor FAQ improvements: spelling & formatting (#318)

* Minor spelling and formatting improvements in FAQ * Add unused RAM is wasted RAM quote, improve memory text
2026-10-01 00:48:32 +02:00 · 2023-10-29 23:19:00 +01:00
parent 9854c1443e
commit a60640fb03
2 changed files with 15 additions and 13 deletions
@@ -19,24 +19,26 @@ Read more about [configuring the optimal](../../tutorials/optimize/) use of Qdra

 There are two main scenarios of Qdrant usage in terms of resource consumption:

- **Performance-optimized** - when you need to serve vector search as fast(many) as possible. In this case, you need to have as much vector data in RAM as possible. Use our [calculator](https://cloud.qdrant.io/calculator) to estimate the required RAM.
- **Storage-optimized** - when you need to store many vectors and minimize costs by compromising some search speed. In this case, pay attention to the disk speed instead. More about it in the article about [Memory Consumption](../../../articles/memory-consumption/).
+- **Performance-optimized** -- when you need to serve vector search as fast (many) as possible. In this case, you need to have as much vector data in RAM as possible. Use our [calculator](https://cloud.qdrant.io/calculator) to estimate the required RAM.
+- **Storage-optimized** -- when you need to store many vectors and minimize costs by compromising some search speed. In this case, pay attention to the disk speed instead. More about it in the article about [Memory Consumption](../../../articles/memory-consumption/).

 ### I configured on-disk vector storage, but memory usage is still high. Why? 

-First of all, memory usage metrics as reported by `top` or `htop` might be misleading. They are not showing the amount of memory required to run the service.
-If you see RSS memory usage of 10GB, it doesn't mean that it won't work on a machine with 8GB of RAM.
+Firstly, memory usage metrics as reported by `top` or `htop` may be misleading. They are not showing the minimal amount of memory required to run the service.
+If the RSS memory usage is 10 GB, it doesn't mean that it won't work on a machine with 8 GB of RAM.

-Qdrant uses many techniques to reduce search latency, including caching the disk data in RAM and preloading the data from disk to RAM.
+Qdrant uses many techniques to reduce search latency, including caching disk data in RAM and preloading data from disk to RAM.
 As a result, the Qdrant process might use more memory than the minimum required to run the service.

-If you want to limit the memory usage of the service, we recommend using [limits in docker](https://docs.docker.com/config/containers/resource_constraints/#memory) or Kubernetes.
+> Unused RAM is wasted RAM
+
+If you want to limit the memory usage of the service, we recommend using [limits in Docker](https://docs.docker.com/config/containers/resource_constraints/#memory) or Kubernetes.


 ### My requests are very slow or time out. What should I do?

 There are several possible reasons for that:

- **Using filters without payload index** - If you're performing a search with a filter but you don't have a payload index, Qdrant will have to load whole payload data from disk to check the filtering condition. Ensure you have adequately configured [payload indexes](../../concepts/indexing/#payload-index).
- **Usage of on-disk vector storage with slow disks** - If you're using on-disk vector storage, ensure you have fast enough disks. We recommend using local SSDs with at least 50k IOPS. Read more about the influence of the disk speed on the search latency in the article about [Memory Consumption](../../../articles/memory-consumption/).
- **Large limit or nonoptimal query parameters** - A large limit or offset might lead to significant performance degradation. Please pay close attention to the query/collection parameters that significantly diverge from the defaults. They might be the reason for the performance issues.
+- **Using filters without payload index** -- If you're performing a search with a filter but you don't have a payload index, Qdrant will have to load whole payload data from disk to check the filtering condition. Ensure you have adequately configured [payload indexes](../../concepts/indexing/#payload-index).
+- **Usage of on-disk vector storage with slow disks** -- If you're using on-disk vector storage, ensure you have fast enough disks. We recommend using local SSDs with at least 50k IOPS. Read more about the influence of the disk speed on the search latency in the article about [Memory Consumption](../../../articles/memory-consumption/).
+- **Large limit or non-optimal query parameters** -- A large limit or offset might lead to significant performance degradation. Please pay close attention to the query/collection parameters that significantly diverge from the defaults. They might be the reason for the performance issues.
@@ -10,7 +10,7 @@ weight: 1
 As much as you want, but be aware that each collection requires additional resources.
 It is _highly_ recommended not to create many small collections, as it will lead to significant resource consumption overhead.

-We consider creating a collection for each user/dialog/document as an anti-pattern.
+We consider creating a collection for each user/dialog/document as an antipattern.

 Please read more about collections, isolation, and multiple users in our [Multitenancy](../../tutorials/multiple-partitions/) tutorial.

@@ -38,13 +38,13 @@ What Qdrant can do:

 What Qdrant plans to introduce in the future:

- Support for sparse vectors, as used in [SPADE](https://github.com/naver/splade) or similar model
+- Support for sparse vectors, as used in [SPADE](https://github.com/naver/splade) or similar models

 What Qdrant doesn't plan to support:

 - BM25 or other non-vector-based retrieval or ranking functions
 - Built-in ontologies or knowledge graphs
- Query analizers and other NLP tools
+- Query analyzers and other NLP tools

 Of course, you can always combine Qdrant with any specialized tool you need, including full-text search engines.
 Read more about [our approach](../../../articles/hybrid-search/) to hybrid search.  
@@ -68,7 +68,7 @@ But in some cases, we might be able to help you with that through manual interve

 ### How do I avoid issues when updating to the latest version?

-We only guarantee compatibility if you update between consequent versions. You would need to upgrade versions one at a time 1.1 -> 1.2, then 1.2 -> 1.3, then 1.3 -> 1.4.
+We only guarantee compatibility if you update between consequent versions. You would need to upgrade versions one at a time: `1.1 -> 1.2`, then `1.2 -> 1.3`, then `1.3 -> 1.4`.

 ### Do you guarantee compatibility across versions?