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Optimized Data Structures:</br>
**Defragmentation:** Storage for multitenant workloads is more optimized and scales better.</br>
**On-Disk Payload:** You can now store less frequently used data on disk, rather than on RAM.</br>
**UUID Support:** Additional data type for payload can result in significant memory savings.
**UUID Support:** Additional data types for payload can result in significant memory savings.
Improved Query API:</br>
**GroupBy Endpoint:** Use this query method to group results by a certain payload field.</br>
**Random Sampling:** Select a subset of data points from a larger dataset in a random manner.</br>
**Random Sampling:** Select a subset of data points from a larger dataset randomly.</br>
**Hybrid Search Fusion:** We are adding the Distribution-Based Score Fusion (DBSF) method.</br>
New Web UI Tools:</br>
**Search Quality Tool:** Test the precision of your semantic search requests in real time.</br>
**Search Quality Tool:** Test the precision of your semantic search requests in real-time.</br>
**Graph Exploration Tool:** Visualize vector search in context-based exploratory scenarios.</br>
### Quick Recap: Multitenant Workloads
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### Defragmentation of Tenant Storage
With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and better resource utilization.
With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and resource utilization.
> Defragmentation can significantly improve performance. In the coming weeks, we will share **benchmark data** to demonstrate performance gains.
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*This feature can help you manage a high number of different payload indexes, which is beneficial if you are working with large varied datasets.*
**Figure 2:** By moving the Workspace 2 index to disk, the system can free up valuable memory resources for Workspaces 1,3 and 4, that are accessed more frequently.
**Figure 2:** By moving the Workspace 2 index to disk, the system can free up valuable memory resources for Workspaces 1, 3 and 4, which are accessed more frequently.
![on-disk-payload](/blog/qdrant-1.11.x/on-disk-payload.png)
@@ -419,7 +419,7 @@ This endpoint will retrieve the best N points for each document, assuming that t
Our [Food Discovery Demo](https://food-discovery.qdrant.tech) always shows a random sample of foods from the larger dataset. Now you can do the same and set the randomization from a basic Query API endpoint.
When calling the Query API, you will be able to select a subset of data points from a larger dataset in a random manner.
When calling the Query API, you will be able to select a subset of data points from a larger dataset randomly.
*This technique is often used to reduce the computational load, improve query response times, or provide a representative sample of the data for various analytical purposes.*
@@ -668,13 +668,13 @@ We have updated the Qdrant Web UI with additional testing functionality. Now you
**Try it:** In the Dashboard, go to collection settings and test the **Precision** from the Search Quality menu tab.
> The feature will conduct semantic search for each point and produce a report below.
> The feature will conduct a semantic search for each point and produce a report below.
<iframe width="560" height="315" src="https://www.youtube.com/embed/PJHzeVay_nQ?si=u-6lqCVECd-A319M" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## Web UI: Graph Exploration Tool
Deeper exploration is highly dependant on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes.
Deeper exploration is highly dependent on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes.
**Try it:** Using the feature is pretty self-explanatory. Each collection's dataset can be explored from the **Graph** tab. As you see the images change, you can steer your search in the direction of specific characteristics that interest you.