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fix qdrant 1.8
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davidmyriel
@@ -1,9 +1,9 @@
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---
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title: "Qdrant 1.8.0 - Major Performance Enhancements"
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title: "Qdrant 1.8.0: Enhanced Search Capabilities for Better Results"
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draft: false
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slug: qdrant-1.8.x
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short_description: "Faster sparse vectors.Optimized indexation. Optional CPU resource management."
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description: "Much faster sparse vectors, optimized indexation of text fields and optional CPU resource management configuration. "
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description: "Explore the latest in search technology with Qdrant 1.8.0! Discover faster performance, smarter indexing, and enhanced search capabilities."
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social_preview_image: /articles_data/qdrant-1.8.x/social_preview.png
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small_preview_image: /articles_data/qdrant-1.8.x/icon.svg
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preview_dir: /articles_data/qdrant-1.8.x/preview
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@@ -20,12 +20,14 @@ tags:
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- text field index
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---
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[Qdrant 1.8.0 is out!](https://github.com/qdrant/qdrant/releases/tag/v1.8.0).
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This time around, we have focused on Qdrant's internals. Our goal was to optimize performance, so that your existing setup can run faster and save on compute. Here is what we've been up to:
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# Unlocking Next-Level Search: Exploring Qdrant 1.8.0's Advanced Search Capabilities
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- **Faster sparse vectors:** Hybrid search is up to 16x faster now!
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[Qdrant 1.8.0 is out!](https://github.com/qdrant/qdrant/releases/tag/v1.8.0).
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This time around, we have focused on Qdrant's internals. Our goal was to optimize performance so that your existing setup can run faster and save on compute. Here is what we've been up to:
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- **Faster [sparse vectors](https://qdrant.tech/articles/sparse-vectors/):** [Hybrid search](https://qdrant.tech/articles/hybrid-search/) is up to 16x faster now!
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- **CPU resource management:** You can allocate CPU threads for faster indexing.
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- **Better indexing performance:** We optimized text indexing on the backend.
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- **Better indexing performance:** We optimized text [indexing](https://qdrant.tech/documentation/concepts/indexing/) on the backend.
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## Faster search with sparse vectors
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@@ -36,7 +38,7 @@ What this means for your setup:
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- **Query speed:** The time it takes to run a search query has been significantly reduced.
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- **Search capacity:** Qdrant can now handle a much larger volume of search requests.
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- **User experience:** Results will appear faster, leading to a smoother experience for the user.
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- **Scalability:** You can easily accomodate rapidly growing users or an expanding dataset.
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- **Scalability:** You can easily accommodate rapidly growing users or an expanding dataset.
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### Sparse vectors benchmark
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@@ -49,7 +51,7 @@ Latency (y-axis) has dropped significantly for queries. You can see the before/a
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**Figure 1:** Dropping latency in sparse vector search queries across versions 1.7-1.8.
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The colors within both scatter plots show the frequency of results. The red dots show that the highest concentration is around 2200ms (before) and 135ms (after). This tells us that latency for sparse vectors queries dropped by about a factor of 16. Therefore, the time it takes to retrieve an answer with Qdrant is that much shorter.
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The colors within both scatter plots show the frequency of results. The red dots show that the highest concentration is around 2200ms (before) and 135ms (after). This tells us that latency for sparse vector queries dropped by about a factor of 16. Therefore, the time it takes to retrieve an answer with Qdrant is that much shorter.
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This performance increase can have a dramatic effect on hybrid search implementations. [Read more about how to set this up.](/articles/sparse-vectors/)
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@@ -76,7 +78,7 @@ optimizer_cpu_budget: 0
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For most users, the default `optimizer_cpu_budget` setting will work well. We only recommend you use this if your indexing load is significant.
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Our backend leverages dynamic CPU saturation to increase indexing speed. For that reason, the impact on search query performance ends up being minimal. Ultimately, you will be able to strike a the best possible balance between indexing times and search performance.
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Our backend leverages dynamic CPU saturation to increase indexing speed. For that reason, the impact on search query performance ends up being minimal. Ultimately, you will be able to strike the best possible balance between indexing times and search performance.
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This configuration can be done at any time, but it requires a restart of Qdrant. Changing it affects both existing and new collections.
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@@ -84,13 +86,13 @@ This configuration can be done at any time, but it requires a restart of Qdrant.
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## Better indexing for text data
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In order to minimize your RAM expenditure, we have developed a new way to index specific types of data. Please keep in mind that this is a backend improvement, and you won't need to configure anything.
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In order to [minimize your RAM expenditure](https://qdrant.tech/articles/memory-consumption/), we have developed a new way to index specific types of data. Please keep in mind that this is a backend improvement, and you won't need to configure anything.
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> Going forward, if you are indexing immutable text fields, we estimate a 10% reduction in RAM loads. Our benchmark result is based on a system that uses 64GB of RAM. If you are using less RAM, this reduction might be higher than 10%.
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Immutable text fields are static and do not change once they are added to Qdrant. These entries usually represent some type of an attribute, description or a tag. Vectors associated with them can be indexed more efficiently, since you don’t need to re-index them anymore. Conversely, mutable fields are dynamic and can be modified after their initial creation. Please keep in mind that they will continue to require additional RAM.
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Immutable text fields are static and do not change once they are added to Qdrant. These entries usually represent some type of attribute, description or tag. Vectors associated with them can be indexed more efficiently, since you don’t need to re-index them anymore. Conversely, mutable fields are dynamic and can be modified after their initial creation. Please keep in mind that they will continue to require additional RAM.
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This approach ensures stability in the vector search index, with faster and more consistent operations. We achieved this by setting up a field index which helps minimize what is stored. To improve search performance we have also optimized the way we load documents for searches with a text field index. Now our backend loads documents mostly sequentially and in increasing order.
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This approach ensures stability in the [vector search](https://qdrant.tech/documentation/overview/vector-search/) index, with faster and more consistent operations. We achieved this by setting up a field index which helps minimize what is stored. To improve search performance we have also optimized the way we load documents for searches with a text field index. Now our backend loads documents mostly sequentially and in increasing order.
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## Minor improvements and new features
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@@ -103,7 +105,11 @@ Beyond these enhancements, [Qdrant v1.8.0](https://github.com/qdrant/qdrant/rele
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4. **Find points** whose payloads match more than the minimal amount of conditions. We included the `min_should` match feature for a condition to be `true` ([PR#3331](https://github.com/qdrant/qdrant/pull/3466/)).
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5. **Modify nested fields:** We have improved the `set_payload` API, adding the ability to update nested fields ([PR#3548](https://github.com/qdrant/qdrant/pull/3548)).
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## Experience the Power of Qdrant 1.8.0
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Ready to experience the enhanced performance of Qdrant 1.8.0? Upgrade now and explore the major improvements, from faster sparse vectors to optimized CPU resource management and better indexing for text data. Take your search capabilities to the next level with Qdrant's latest version. [Try a demo today](https://qdrant.tech/demo/) and see the difference firsthand!
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## Release notes
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For more information, see [our release notes](https://github.com/qdrant/qdrant/releases/tag/v1.8.0).
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Qdrant is an open source project. We welcome your contributions; raise [issues](https://github.com/qdrant/qdrant/issues), or contribute via [pull requests](https://github.com/qdrant/qdrant/pulls)!
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Qdrant is an open-source project. We welcome your contributions; raise [issues](https://github.com/qdrant/qdrant/issues), or contribute via [pull requests](https://github.com/qdrant/qdrant/pulls)!
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