From 810621146f780e2a9f99a51eea41e825320fbe63 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Wed, 27 Nov 2024 14:29:18 -0500 Subject: [PATCH 1/3] Fix typo in performance blog Signed-off-by: Fanit Kolchina --- _posts/2024-11-26-opensearch-performance-2.17.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-11-26-opensearch-performance-2.17.md b/_posts/2024-11-26-opensearch-performance-2.17.md index cfaae8940..b46d298fe 100644 --- a/_posts/2024-11-26-opensearch-performance-2.17.md +++ b/_posts/2024-11-26-opensearch-performance-2.17.md @@ -26,7 +26,7 @@ Our commitment to enhancing OpenSearch's performance remains unwavering, and thi 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, 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. +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.