From 8378c4b6534439ab4f68947310caa01ed5a7a0c2 Mon Sep 17 00:00:00 2001 From: Martin Gaievski Date: Wed, 26 Jun 2024 17:20:26 -0700 Subject: [PATCH 1/6] Adding blog post for performance improvements in hybrid query in 2.15 Signed-off-by: Martin Gaievski --- _community_members/varunudr.md | 22 ++ ...performance-improvment-hybrid-query-215.md | 195 ++++++++++++++++++ assets/media/community/members/varunudr.jpg | Bin 0 -> 304700 bytes 3 files changed, 217 insertions(+) create mode 100644 _community_members/varunudr.md create mode 100644 _posts/2014-06-26-performance-improvment-hybrid-query-215.md create mode 100644 assets/media/community/members/varunudr.jpg diff --git a/_community_members/varunudr.md b/_community_members/varunudr.md new file mode 100644 index 0000000000..c065752adf --- /dev/null +++ b/_community_members/varunudr.md @@ -0,0 +1,22 @@ +--- +name: Varun Jain +short_name: varunudr +title: 'OpenSearch Community Member: Varun Jain' +primary_title: Varun Jain +breadcrumbs: + icon: community + items: + - title: Community + url: /community/index.html + - title: Members + url: /community/members/index.html + - title: 'Varun Jain's Profile' + url: '/community/members/varunudr.html' +photo: '/assets/media/community/members/haoyng.jpg' +job_title_and_company: 'Software development engineer at AWS' +personas: + - author +permalink: '/community/members/varunudr.html' +--- + +**Varun Jain** is a Software Engineer at AWS, focused on working upon opensearch and its plugins. \ No newline at end of file diff --git a/_posts/2014-06-26-performance-improvment-hybrid-query-215.md b/_posts/2014-06-26-performance-improvment-hybrid-query-215.md new file mode 100644 index 0000000000..8ee2490d91 --- /dev/null +++ b/_posts/2014-06-26-performance-improvment-hybrid-query-215.md @@ -0,0 +1,195 @@ +--- +layout: post +title: "Boosting Hybrid Query Performance in OpenSearch 2.15" +authors: + - gaievski + - vamshin + - macrakis +date: 2024-07-02 +categories: + - technical-posts +meta_keywords: Multimodal search, searching with semantic and visual understanding, improve search relevance, hybrid search in OpenSearch 2.10, keyword and image search +meta_description: Improve hybrid query performance in OpenSearch 2.15. +has_science_table: true +--- + + + +Since its introduction in OpenSearch 2.10, hybrid search has become popular among customers who want to improve the relevance of their semantic search results. Hybrid queries combine full-text search and semantic search to provide better results than either method alone for a wide variety of applications in e-commerce, document search, log analytics, and data exploration. However, its complexity can lead to performance issues, especially when dealing with large datasets or complex query structures. + +With each new release, OpenSearch has implemented numerous enhancements to improve the performance of hybrid search at scale. In version 2.15, the improvements in hybrid queries have resulted in up to a 70% performance improvement compared to version 2.13. + +These improvements were achieved by analyzing and optimizing hot spots in the code. The development team focused on the following areas: + +- **Conditional scoring logic:** + We have added conditional scoring logic. Previously, the core logic for collecting scores during a query was fixed, so all computations were performed regardless of their necessity. This often led to unnecessary calculations, especially when certain scoring computations were redundant for specific plugins or query types. + + To address this inefficiency, we made the scoring logic conditional. Now, certain compute operations can be skipped if they are not required by the plugin in use. This optimization reduces computational overhead and accelerates query processing, leading to faster search results and better resource utilization. + + The performance improvements from this change are substantial. According to our benchmarks, query processing speed has increased by up to 20% for certain use cases. This enhancement significantly boosts the efficiency of handling complex queries, making OpenSearch more robust and responsive. Details can be found in the following GitHub issues: + - [[Feature Request] Provide capability for not adding top docs collector in the query search path #13170](https://github.com/opensearch-project/OpenSearch/issues/13170) + - [Pass empty QueryCollectorContext in case of hybrid query to improve latencies by 20% #731](https://github.com/opensearch-project/neural-search/pull/731) + +- **Replacing inefficient constructs:** + In analyzing the performance of 2.13, we found that the Java Streams API, while convenient and expressive, introduced unnecessary overhead in certain high-performance scenarios. This was particularly evident in areas with intensive data processing requirements. + + To address this, 2.15 replaces Java Streams constructs with more performant alternatives, such as for-loops and optimized data handling techniques. These optimizations resulted in a performance gain of up to 25% in specific data processing tasks. This improvement helps OpenSearch handle larger datasets and more complex queries more efficiently. For more details on these changes, refer to the GitHub issue: + - [In hybrid query optimize the way we iterate over results and collect scores of sub queries #745](https://github.com/opensearch-project/neural-search/issues/745) + +- **Eliminating unnecessary calculations:** + We also found that certain expensive calculations, such as computing hash codes for Query objects, were being performed unnecessarily. We removed these unnecessary calculations, so resources are now allocated more efficiently, speeding up hybrid queries. + + The detailed analysis in the GitHub issue shows a 20% improvement in query processing speed: + - [Improve efficiency by eliminating unnecessary hash code computations #705](https://github.com/opensearch-project/neural-search/issues/705) + +- **Optimized data structures:** + We also found a more efficient way to use priority queues, which are used for some sorting operations. In particular, we improved hybrid query latency by changing the allocation strategy of query hits objects. Previously, those objects were pre-populated. We now perform lazy initialization, removing the lowest-score element when the queue reaches its full capacity. + + Benchmarking shows that these optimizations resulted in a performance gain of up to 10% in query processing times for specific data processing tasks. For more details, see the GitHub issue: + - [In hybrid query optimize the way we iterate over results and collect scores of sub queries #745](https://github.com/opensearch-project/neural-search/issues/745) + +- **Reducing repetitive calculations:** + We have mitigated redundant internal computations by implementing value caching and reuse strategies. This has reduced the overall computational overhead within the system. + + By optimizing the handling of repetitive calculations and promoting value reuse, we have sped up the system by 5%. For full details, see the GitHub issues: + - [Improve efficiency by caching and reusing internal calculations #756](https://github.com/opensearch-project/neural-search/issues/756) + - [Enhance performance through value caching and reuse strategies #764](https://github.com/opensearch-project/neural-search/issues/764) + +Benchmark results have shown up to a 70% performance improvement for large (over 10M) datasets for hybrid queries in OpenSearch 2.15 compared to version 2.13. These benchmarks were conducted using a new OpenSearch Benchmark workload that the team has created specifically for evaluating semantic-search use cases. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Number of documents retrievedNumber of hybrid sub-queriesOpenSearch version 2.13OpenSearch version 2.15Performance improvement
p50, msp90, msp99, msp50, msp90, msp99, ms%
1.6K17577787576761
1.6M122424024510911411952
10M172984186823725726470
15M312241300136729433034375
% average boost in 2.15 vs 2.1349
+ +--- + +## Future Improvements + +We continue to analyze the performance of OpenSearch and identify opportunities for further enhancements. We are considering the following improvements for future versions: + +- **Advanced optimization techniques for hybrid query:** + We may iterate over blocks of documents rather than individual ones to further reduce latency and enhance performance. These techniques aim to streamline the processing of hybrid queries by minimizing the computational overhead associated with handling large volumes of data. + +- **Algorithmic refinements:** + Refining existing algorithms and introducing new ones that are better suited for hybrid search. This includes optimizing the ranking and scoring mechanisms to ensure more accurate and faster results. + +Additionally, the team is considering the following ongoing initiatives to provide continuous performance insights and improvements: + +- **Nightly benchmark runs:** + Starting from version 2.15, the team will be publishing the results of hybrid query nightly benchmark runs so users can track changes in performance between versions. These results will be available on the [OpenSearch Benchmarks page](https://opensearch.org/benchmarks/). + +- **Enhanced benchmark workloads:** + The addition of more extensions for benchmark workloads to gather metrics for vector search queries in addition to text search queries. + +These enhancements aim to broaden the capabilities and improve the efficiency of OpenSearch even further. + +--- + +## References + +1. [[META] Improve Hybrid query latency](https://github.com/opensearch-project/neural-search/issues/704) +2. [OpenSearch Benchmark workload for semantic search](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/noaa_semantic_search) +3. [OpenSearch Benchmarks page](https://opensearch.org/benchmarks/) +4. [Improve search relevance with hybrid search, generally available in OpenSearch 2.10](https://opensearch.org/blog/hybrid-search/) +5. 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zP`n(Dn=AFlN}}Qq=lo0m0N+Ia0Qt()TtEAXaM%8f-~OXjCD{|pxq7Fe3I2@(A0b|) z#+(gpuyCX+#j1BavbLbg(b{R}08mofK1Dq@&vkj6U;AVGAMn?zx&Hw6c2$f;L}01QRXWw$wslH_ix-hG38rM8s(wWB`SuXQ6v?lupBNIO;~ zKlX#QvGi5{0Nol7!SDAKTBXXeg~OtWur#4bcVaVLUX0uNwI?46hR^dhYG~*vT%cuq zm|-c)Yf7~DR6Ju=t$_f2)o(NNtEdwF#8PMtT=v@IE)Rn`BKeog9Ah4ILHWhU2Pt_! zYXf2jMDJdDvy3Uf@EDM0Se kRs02Biw)BoS>_Sr%g5nIhrjRbzPI;(`(97$(Aenz+1x Date: Sun, 30 Jun 2024 18:57:55 -0700 Subject: [PATCH 2/6] Updated meta info after review comments Signed-off-by: Martin Gaievski --- _posts/2014-06-26-performance-improvment-hybrid-query-215.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2014-06-26-performance-improvment-hybrid-query-215.md b/_posts/2014-06-26-performance-improvment-hybrid-query-215.md index 8ee2490d91..1b9695c1e4 100644 --- a/_posts/2014-06-26-performance-improvment-hybrid-query-215.md +++ b/_posts/2014-06-26-performance-improvment-hybrid-query-215.md @@ -8,8 +8,8 @@ authors: date: 2024-07-02 categories: - technical-posts -meta_keywords: Multimodal search, searching with semantic and visual understanding, improve search relevance, hybrid search in OpenSearch 2.10, keyword and image search -meta_description: Improve hybrid query performance in OpenSearch 2.15. +meta_keywords: hybrid search, boost hybrid query performance, OpenSearch 2.15, semantic search +meta_description: The launch of OpenSearch 2.15 greatly improves hybrid search with numerous enhancements that boost hybrid query performance by as much as 70% in comparison to version 2.13. has_science_table: true --- From 9e5f40a431192d7942575a9596152c74807469b3 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Mon, 1 Jul 2024 09:59:49 -0400 Subject: [PATCH 3/6] Doc review Signed-off-by: Fanit Kolchina --- _community_members/varunudr.md | 4 +- ...performance-improvment-hybrid-query-215.md | 195 ------------------ ...performance-improvment-hybrid-query-215.md | 179 ++++++++++++++++ 3 files changed, 181 insertions(+), 197 deletions(-) delete mode 100644 _posts/2014-06-26-performance-improvment-hybrid-query-215.md create mode 100644 _posts/2024-06-26-performance-improvment-hybrid-query-215.md diff --git a/_community_members/varunudr.md b/_community_members/varunudr.md index c065752adf..3e1f455f29 100644 --- a/_community_members/varunudr.md +++ b/_community_members/varunudr.md @@ -12,11 +12,11 @@ breadcrumbs: url: /community/members/index.html - title: 'Varun Jain's Profile' url: '/community/members/varunudr.html' -photo: '/assets/media/community/members/haoyng.jpg' +photo: '/assets/media/community/members/varunudr.jpg' job_title_and_company: 'Software development engineer at AWS' personas: - author permalink: '/community/members/varunudr.html' --- -**Varun Jain** is a Software Engineer at AWS, focused on working upon opensearch and its plugins. \ No newline at end of file +**Varun Jain** is a Software Engineer at AWS, focusing on OpenSearch and its plugins. \ No newline at end of file diff --git a/_posts/2014-06-26-performance-improvment-hybrid-query-215.md b/_posts/2014-06-26-performance-improvment-hybrid-query-215.md deleted file mode 100644 index 8ee2490d91..0000000000 --- a/_posts/2014-06-26-performance-improvment-hybrid-query-215.md +++ /dev/null @@ -1,195 +0,0 @@ ---- -layout: post -title: "Boosting Hybrid Query Performance in OpenSearch 2.15" -authors: - - gaievski - - vamshin - - macrakis -date: 2024-07-02 -categories: - - technical-posts -meta_keywords: Multimodal search, searching with semantic and visual understanding, improve search relevance, hybrid search in OpenSearch 2.10, keyword and image search -meta_description: Improve hybrid query performance in OpenSearch 2.15. -has_science_table: true ---- - - - -Since its introduction in OpenSearch 2.10, hybrid search has become popular among customers who want to improve the relevance of their semantic search results. Hybrid queries combine full-text search and semantic search to provide better results than either method alone for a wide variety of applications in e-commerce, document search, log analytics, and data exploration. However, its complexity can lead to performance issues, especially when dealing with large datasets or complex query structures. - -With each new release, OpenSearch has implemented numerous enhancements to improve the performance of hybrid search at scale. In version 2.15, the improvements in hybrid queries have resulted in up to a 70% performance improvement compared to version 2.13. - -These improvements were achieved by analyzing and optimizing hot spots in the code. The development team focused on the following areas: - -- **Conditional scoring logic:** - We have added conditional scoring logic. Previously, the core logic for collecting scores during a query was fixed, so all computations were performed regardless of their necessity. This often led to unnecessary calculations, especially when certain scoring computations were redundant for specific plugins or query types. - - To address this inefficiency, we made the scoring logic conditional. Now, certain compute operations can be skipped if they are not required by the plugin in use. This optimization reduces computational overhead and accelerates query processing, leading to faster search results and better resource utilization. - - The performance improvements from this change are substantial. According to our benchmarks, query processing speed has increased by up to 20% for certain use cases. This enhancement significantly boosts the efficiency of handling complex queries, making OpenSearch more robust and responsive. Details can be found in the following GitHub issues: - - [[Feature Request] Provide capability for not adding top docs collector in the query search path #13170](https://github.com/opensearch-project/OpenSearch/issues/13170) - - [Pass empty QueryCollectorContext in case of hybrid query to improve latencies by 20% #731](https://github.com/opensearch-project/neural-search/pull/731) - -- **Replacing inefficient constructs:** - In analyzing the performance of 2.13, we found that the Java Streams API, while convenient and expressive, introduced unnecessary overhead in certain high-performance scenarios. This was particularly evident in areas with intensive data processing requirements. - - To address this, 2.15 replaces Java Streams constructs with more performant alternatives, such as for-loops and optimized data handling techniques. These optimizations resulted in a performance gain of up to 25% in specific data processing tasks. This improvement helps OpenSearch handle larger datasets and more complex queries more efficiently. For more details on these changes, refer to the GitHub issue: - - [In hybrid query optimize the way we iterate over results and collect scores of sub queries #745](https://github.com/opensearch-project/neural-search/issues/745) - -- **Eliminating unnecessary calculations:** - We also found that certain expensive calculations, such as computing hash codes for Query objects, were being performed unnecessarily. We removed these unnecessary calculations, so resources are now allocated more efficiently, speeding up hybrid queries. - - The detailed analysis in the GitHub issue shows a 20% improvement in query processing speed: - - [Improve efficiency by eliminating unnecessary hash code computations #705](https://github.com/opensearch-project/neural-search/issues/705) - -- **Optimized data structures:** - We also found a more efficient way to use priority queues, which are used for some sorting operations. In particular, we improved hybrid query latency by changing the allocation strategy of query hits objects. Previously, those objects were pre-populated. We now perform lazy initialization, removing the lowest-score element when the queue reaches its full capacity. - - Benchmarking shows that these optimizations resulted in a performance gain of up to 10% in query processing times for specific data processing tasks. For more details, see the GitHub issue: - - [In hybrid query optimize the way we iterate over results and collect scores of sub queries #745](https://github.com/opensearch-project/neural-search/issues/745) - -- **Reducing repetitive calculations:** - We have mitigated redundant internal computations by implementing value caching and reuse strategies. This has reduced the overall computational overhead within the system. - - By optimizing the handling of repetitive calculations and promoting value reuse, we have sped up the system by 5%. For full details, see the GitHub issues: - - [Improve efficiency by caching and reusing internal calculations #756](https://github.com/opensearch-project/neural-search/issues/756) - - [Enhance performance through value caching and reuse strategies #764](https://github.com/opensearch-project/neural-search/issues/764) - -Benchmark results have shown up to a 70% performance improvement for large (over 10M) datasets for hybrid queries in OpenSearch 2.15 compared to version 2.13. These benchmarks were conducted using a new OpenSearch Benchmark workload that the team has created specifically for evaluating semantic-search use cases. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Number of documents retrievedNumber of hybrid sub-queriesOpenSearch version 2.13OpenSearch version 2.15Performance improvement
p50, msp90, msp99, msp50, msp90, msp99, ms%
1.6K17577787576761
1.6M122424024510911411952
10M172984186823725726470
15M312241300136729433034375
% average boost in 2.15 vs 2.1349
- ---- - -## Future Improvements - -We continue to analyze the performance of OpenSearch and identify opportunities for further enhancements. We are considering the following improvements for future versions: - -- **Advanced optimization techniques for hybrid query:** - We may iterate over blocks of documents rather than individual ones to further reduce latency and enhance performance. These techniques aim to streamline the processing of hybrid queries by minimizing the computational overhead associated with handling large volumes of data. - -- **Algorithmic refinements:** - Refining existing algorithms and introducing new ones that are better suited for hybrid search. This includes optimizing the ranking and scoring mechanisms to ensure more accurate and faster results. - -Additionally, the team is considering the following ongoing initiatives to provide continuous performance insights and improvements: - -- **Nightly benchmark runs:** - Starting from version 2.15, the team will be publishing the results of hybrid query nightly benchmark runs so users can track changes in performance between versions. These results will be available on the [OpenSearch Benchmarks page](https://opensearch.org/benchmarks/). - -- **Enhanced benchmark workloads:** - The addition of more extensions for benchmark workloads to gather metrics for vector search queries in addition to text search queries. - -These enhancements aim to broaden the capabilities and improve the efficiency of OpenSearch even further. - ---- - -## References - -1. [[META] Improve Hybrid query latency](https://github.com/opensearch-project/neural-search/issues/704) -2. [OpenSearch Benchmark workload for semantic search](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/noaa_semantic_search) -3. [OpenSearch Benchmarks page](https://opensearch.org/benchmarks/) -4. [Improve search relevance with hybrid search, generally available in OpenSearch 2.10](https://opensearch.org/blog/hybrid-search/) -5. [Hybrid query](https://opensearch.org/docs/latest/query-dsl/compound/hybrid/) diff --git a/_posts/2024-06-26-performance-improvment-hybrid-query-215.md b/_posts/2024-06-26-performance-improvment-hybrid-query-215.md new file mode 100644 index 0000000000..574a78d4d7 --- /dev/null +++ b/_posts/2024-06-26-performance-improvment-hybrid-query-215.md @@ -0,0 +1,179 @@ +--- +layout: post +title: "Boosting Hybrid Query Performance in OpenSearch 2.15" +authors: + - gaievski + - varunudr + - vamshin + - macrakis +date: 2024-07-02 +categories: + - technical-posts +meta_keywords: hybrid search, boost hybrid query performance, OpenSearch 2.15, semantic search +meta_description: The launch of OpenSearch 2.15 greatly improves hybrid search with numerous enhancements that boost hybrid query performance by as much as 70% in comparison to version 2.13. +has_science_table: true +--- + + + +Since its introduction in OpenSearch 2.10, hybrid search has become popular among users looking to enhance the relevance of their semantic search results. By combining full-text search and semantic search, hybrid queries deliver superior results for various applications, including e-commerce, document search, log analytics, and data exploration. However, managing large datasets and complex queries can sometimes lead to performance issues. + +With each new release, OpenSearch has implemented numerous enhancements to improve the performance of hybrid search at scale. In version 2.15, these enhancements led to hybrid query performance improving by up to 70% compared to version 2.13. + +## Impelmented improvements + +The development team made improvements by analyzing the code and optimizing performance bottlenecks. We focused on the following areas: + +- **Conditional scoring logic**: Previously, the core logic for collecting scores during a query was fixed, so computations were performed regardless of whether they were needed. This often led to unnecessary calculations, especially when scoring computations were redundant for specific query types or plugins. In OpenSearch 2.15, we made the scoring logic conditional, allowing certain computations to be skipped if they are not needed by the plugin in use. This optimization reduces computational overhead, accelerates query processing, and improves resource utilization. + + The performance improvements from this change are substantial. Our benchmarks show a 20% increase in query processing speed for some use cases. For more information, see the following GitHub issues: + - [[Feature Request] Provide capability for not adding top docs collector in the query search path #13170](https://github.com/opensearch-project/OpenSearch/issues/13170) + - [Pass empty QueryCollectorContext in case of hybrid query to improve latencies by 20% #731](https://github.com/opensearch-project/neural-search/pull/731) + +- **Replacing inefficient constructs**: We analyzed the performance of version 2.13 and found that the Java Streams API, while convenient, introduced unnecessary overhead in high-performance scenarios. This was particularly evident in areas with intensive data processing requirements. +In version 2.15, we replaced Java Streams constructs with more efficient alternatives, such as for loops and optimized data handling techniques. This change resulted in a performance gain of up to 25% in specific data processing tasks, helping OpenSearch handle larger datasets and more complex queries more efficiently. For more information, see the following GitHub issue: + - [In hybrid query optimize the way we iterate over results and collect scores of sub queries #745](https://github.com/opensearch-project/neural-search/issues/745) + +- **Eliminating unnecessary calculations:** We found that certain expensive calculations, such as computing hash codes for Query objects, are unnecessary. By removing these calculations, resources are allocated more efficiently, speeding up hybrid queries. This change has improved query processing speed by 20%. For more information, see the following GitHub issue: + - [Improve efficiency by eliminating unnecessary hash code computations #705](https://github.com/opensearch-project/neural-search/issues/705) +- **Optimized data structures:** We improved the use of priority queues, which are used for some sorting operations. By changing the allocation strategy of query hits objects to perform lazy initialization, we removed the lowest-score element when the queue reaches full capacity. Our benchmarks show that this optimization resulted in an up to 10% performance gain in query processing times for specific data processing tasks. For more information, see the following GitHub issue: + - [In hybrid query optimize the way we iterate over results and collect scores of sub queries #745](https://github.com/opensearch-project/neural-search/issues/745) + +- **Reducing repetitive calculations:** To mitigate redundant internal computations, we have implemented value caching and reuse strategies, reducing the overall computational overhead within the system. By optimizing the handling of repetitive calculations and promoting value reuse, we have sped up the system by 5%. For more information, see the following GitHub issues: + - [Improve efficiency by caching and reusing internal calculations #756](https://github.com/opensearch-project/neural-search/issues/756) + - [Enhance performance through value caching and reuse strategies #764](https://github.com/opensearch-project/neural-search/issues/764) + +## Benchmark results + +Benchmark results show up to a 70% performance improvement for large datasets (over 10M) in hybrid queries with OpenSearch 2.15 compared to version 2.13. These benchmarks were conducted using a new OpenSearch Benchmark workload created specifically for evaluating semantic-search use cases. The following table summarizes benchmark results. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Number of documents retrievedNumber of hybrid sub-queriesOpenSearch version 2.13OpenSearch version 2.15Performance improvement
p50, msp90, msp99, msp50, msp90, msp99, ms%
1.6K17577787576761
1.6M122424024510911411952
10M172984186823725726470
15M312241300136729433034375
% average boost in 2.15 vs 2.1349
+ +## Planned improvements + +We continue to analyze OpenSearch performance and identify opportunities for further enhancements. Future improvements may include: + +- **Advanced optimization techniques for hybrid query**: Iterating over blocks of documents rather than individual ones to further reduce latency and enhance performance. This technique aim to streamline hybrid query processing by minimizing the computational overhead associated with handling large volumes of data. + +- **Algorithmic refinements**: Refining existing algorithms and introducing new ones better suited for hybrid search. This includes optimizing ranking and scoring mechanisms to ensure more accurate and faster results. + +Ongoing initiatives to provide continuous performance insights and improvements include: + +- **Nightly benchmark runs**: Starting with version 2.15, the team will publish the results of hybrid query nightly benchmark runs so you can track performance changes between versions. These results will be available on the [OpenSearch Benchmarks page](https://opensearch.org/benchmarks/). + +- **Enhanced benchmark workloads**: We'll expand benchmark workloads with extensions in order to gather metrics for vector search queries, in addition to text search queries. + +These enhancements will make OpenSearch more powerful and efficient. + +--- + +## References + +1. [[META] Improve Hybrid query latency](https://github.com/opensearch-project/neural-search/issues/704) +2. [OpenSearch Benchmark workload for semantic search](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/noaa_semantic_search) +3. [OpenSearch Benchmarks page](https://opensearch.org/benchmarks/) +4. [Improve search relevance with hybrid search, generally available in OpenSearch 2.10](https://opensearch.org/blog/hybrid-search/) +5. [Hybrid query](https://opensearch.org/docs/latest/query-dsl/compound/hybrid/) From 72e1be95a049a85f05dd69fa65abe740d70b159d Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Mon, 1 Jul 2024 10:06:27 -0400 Subject: [PATCH 4/6] Remove unnecessary styles Signed-off-by: Fanit Kolchina --- .../2024-06-26-performance-improvment-hybrid-query-215.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/_posts/2024-06-26-performance-improvment-hybrid-query-215.md b/_posts/2024-06-26-performance-improvment-hybrid-query-215.md index 574a78d4d7..99fbc609ee 100644 --- a/_posts/2024-06-26-performance-improvment-hybrid-query-215.md +++ b/_posts/2024-06-26-performance-improvment-hybrid-query-215.md @@ -15,18 +15,11 @@ has_science_table: true ---