From 355642c8965fc11b547166dcb1727212b9941a57 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Wed, 27 Nov 2024 14:07:19 -0500 Subject: [PATCH] Last editorial comments Signed-off-by: Fanit Kolchina --- .../2024-11-26-opensearch-performance-2.17.md | 35 ++++++------------- 1 file changed, 10 insertions(+), 25 deletions(-) diff --git a/_posts/2024-11-26-opensearch-performance-2.17.md b/_posts/2024-11-26-opensearch-performance-2.17.md index 0b8549466..3c395798b 100644 --- a/_posts/2024-11-26-opensearch-performance-2.17.md +++ b/_posts/2024-11-26-opensearch-performance-2.17.md @@ -24,23 +24,16 @@ additional_author_info: We sincerely appreciate the contributions to this blog f Our commitment to enhancing OpenSearch's performance remains unwavering, and this blog post showcases the significant progress we've made. Recently, we've focused our investments on four key areas: text querying, vector storage and querying, ingestion and indexing, and storage efficiency. Additionally, we've published our search and performance roadmap, reaffirming that performance continues to be our top priority. In this blog post, we'll bring you up to date on our continuing performance improvements through [OpenSearch 2.17](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.17.0.md). -OpenSearch 2.17 offers a remarkable **6x performance boost** compared to OpenSearch 1.3, enhancing key operations like text queries, term aggregations, range queries, date histograms, and sorting. Additionally, the improvements in semantic vector search now allow for highly configurable settings, enabling you to balance response time, accuracy, and cost according to your needs. These advancements are a testament to the dedicated community whose contributions and collaboration propel OpenSearch forward. +OpenSearch 2.17 offers a remarkable **6x performance boost** compared to OpenSearch 1.3, enhancing key operations like text queries, terms aggregations, range queries, date histograms, and sorting. Additionally, the improvements in semantic vector search now allow for highly configurable settings, enabling you to balance response time, accuracy, and cost according to your needs. These advancements are a testament to the dedicated community whose contributions and collaboration propel OpenSearch forward. -The first section focuses on key query operations, including text queries, terms aggregations, range queries, date histograms, and sorting. These improvements were evaluated using the [OpenSearch Big5 workload](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/big5), which represents common use cases in both search and analytics applications. The benchmarks provide a repeatable framework for measuring real-world performance enhancements. The next section reports on vector search improvements. Finally, we present our roadmap for 2025, where you'll see that we're making qualitative improvements in many areas, in addition to important incremental changes. We are improving query speed by processing data in real time. We are building a query planner that uses resources more efficiently. We are speeding up intra-cluster communications. And we're adding efficient join operations to query domain-specific language (DSL), Piped Processing Language (PPL), and SQL. To follow our work in more detail, and to contribute comments or code, please participate on the [OpenSearch forum](https://forum.opensearch.org/) as well as directly in our GitHub repos. +The first section focuses on key query operations, including text queries, 297s aggregations, range queries, date histograms, and sorting. These improvements were evaluated using the [OpenSearch Big5 workload](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/big5), which represents common use cases in both search and analytics applications. The benchmarks provide a repeatable framework for measuring real-world performance enhancements. The next section reports on vector search improvements. Finally, we present our roadmap for 2025, where you'll see that we're making qualitative improvements in many areas, in addition to important incremental changes. We are improving query speed by processing data in real time. We are building a query planner that uses resources more efficiently. We are speeding up intra-cluster communications. And we're adding efficient join operations to query domain-specific language (DSL), Piped Processing Language (PPL), and SQL. To follow our work in more detail, and to contribute comments or code, please participate on the [OpenSearch forum](https://forum.opensearch.org/) as well as directly in our GitHub repos.