krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-04 10:28:29 +02:00
Code Issues Packages Projects Releases Wiki Activity

Restructure Docs - Stage 4a (#2280)

Browse Source 
* create Develop and Deploy tabs; move Operations; re-weight pages

* move capacity planning page; create section dropdown content

* added aliases to frontmatter

* update link references to new canonical links; maintain anchoring

* address remaining link issues and errors

* fix outlier tutorial reference issue

* Treat 'develop' and 'deploy' as a unified search space

* fix some frontmatter aliases

* add section header redirects

* fix 'Operations' redirect to go to 'Deploy' tab

* update redirects file for * pattern

* add :splat to redirect references

* Add wildcard to each entry in _redirects file

---------

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
This commit is contained in:
kanungle
2026-04-20 18:03:45 +02:00
committed by GitHub
co-authored by Abdon Pijpelink
parent 7b463c5ae5
commit 544708f293
155 changed files with 654 additions and 414 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/articles/vector-search-resource-optimization.md
+7 -7
View File
@@ -32,12 +32,12 @@ Let's take a look at some common goals and optimization strategies:
| Intended Result | Optimization Strategy |
|--------------------------------|------------------------------|
| [**High Search Precision + Low Memory Expenditure**](/documentation/operations/optimize/#1-high-speed-search-with-low-memory-usage) | [**On-Disk Indexing**](/documentation/operations/optimize/#1-high-speed-search-with-low-memory-usage) |
| [**High Search Precision + Low Memory Expenditure**](/documentation/ops-optimization/optimize/#1-high-speed-search-with-low-memory-usage) | [**On-Disk Indexing**](/documentation/ops-optimization/optimize/#1-high-speed-search-with-low-memory-usage) |
| [**Low Memory Expenditure + Fast Search Speed**](/documentation/manage-data/quantization/) | [**Quantization**](/documentation/manage-data/quantization/) |
| [**High Search Precision + Fast Search Speed**](/documentation/operations/optimize/#3-high-precision-with-high-speed-search) | [**RAM Storage + Quantization**](/documentation/operations/optimize/#3-high-precision-with-high-speed-search) |
| [**Balance Latency vs Throughput**](/documentation/operations/optimize/#balancing-latency-and-throughput) | [**Segment Configuration**](/documentation/operations/optimize/#balancing-latency-and-throughput) |
| [**High Search Precision + Fast Search Speed**](/documentation/ops-optimization/optimize/#3-high-precision-with-high-speed-search) | [**RAM Storage + Quantization**](/documentation/ops-optimization/optimize/#3-high-precision-with-high-speed-search) |
| [**Balance Latency vs Throughput**](/documentation/ops-optimization/optimize/#balancing-latency-and-throughput) | [**Segment Configuration**](/documentation/ops-optimization/optimize/#balancing-latency-and-throughput) |
After this article, check out the code samples in our docs on [**Qdrant’s Optimization Methods**](/documentation/operations/optimize/).
After this article, check out the code samples in our docs on [**Qdrant’s Optimization Methods**](/documentation/ops-optimization/optimize/).
---
@@ -57,7 +57,7 @@ Qdrant uses the [**HNSW (Hierarchical Navigable Small World Graph) algorithm**](
Working with massive datasets that contain billions of vectors demands significant resources—and those resources come with a price. While Qdrant provides reasonable defaults, tailoring them to your specific use case can unlock optimal performance. Here’s what you need to know.
The following parameters give you the flexibility to fine-tune Qdrant’s performance for your specific workload. You can modify them directly in Qdrant's [**configuration**](https://qdrant.tech/documentation/operations/configuration/) files or at the collection and named vector levels for more granular control.
The following parameters give you the flexibility to fine-tune Qdrant’s performance for your specific workload. You can modify them directly in Qdrant's [**configuration**](https://qdrant.tech/documentation/ops-configuration/configuration/) files or at the collection and named vector levels for more granular control.
**Figure 3:** A description of three key HNSW parameters.
@@ -325,7 +325,7 @@ Here’s how to choose the shard_number:
| **Plan for Scalability** | Start with at least **2 shards per node** to allow room for future growth. |
| **Future-Proofing** | Starting with around **12 shards** is a good rule of thumb. This setup allows your system to scale seamlessly from 1 to 12 nodes without requiring re-sharding. |
Learn more about [**Sharding in Distributed Deployment**](/documentation/operations/distributed_deployment/)
Learn more about [**Sharding in Distributed Deployment**](/documentation/distributed_deployment/)
---
@@ -584,7 +584,7 @@ Here are some important metrics to monitor:
| grpc_responses_avg_duration_seconds | | Average response duration in gRPC API |
| rest_responses_fail_total | | Total number of failed responses (REST) |
Read more about [**Qdrant Open Source Monitoring**](/documentation/operations/monitoring/) and [**Qdrant Cloud Monitoring**](/documentation/cloud/cluster-monitoring/) for managed clusters.
Read more about [**Qdrant Open Source Monitoring**](/documentation/ops-monitoring/monitoring/) and [**Qdrant Cloud Monitoring**](/documentation/cloud/cluster-monitoring/) for managed clusters.
_________________________________________________________________________
## Recap: When Should You Optimize?