Elastic Rolls Out Major Upgrades for Faster and More Efficient Search

Elastic, the company behind Elasticsearch, has introduced several key enhancements to its platform with the release of Elastic 8.17. The release focuses on performance enhancements, expanded capabilities, and greater operational efficiencies.

A key feature of the new release is the general availability of Elasticsearch logsdb index mode. The company claims the new feature reduces the storage footprint of log data by up to 65% compared to recent versions without logsdb index mode. Elastic aims to provide observability and security teams with enhanced visibility while keeping it readily available for analysis.

According to Elastic, the update enables organizations to store more data at a lower cost and provides quick access to logs for faster problem resolution and investigations. Logsdb allows teams to retain all log data without the need to prematurely filter or delete logs, improving operational efficiency. By keeping all logs accessible, it supports ongoing analysis and troubleshooting without compromising data retention.

“Logs are critical for detection and remediation, but the growing log volume generated by infrastructure and applications is driving up costs and forcing compromises that hinder analysis,” said Ken Exner, chief product officer at Elastic.“Logsdb index mode reduces the disk footprint and overall cost of storing log data with features including smart index sorting, synthetic source and advanced compression.”

Logsdb index is available to self-managed customers and Elastic Cloud. It also comes as default for Elastic Cloud Serverless.

The American-Dutch company also unveiled Elastic Rerank – a cross-encoder reranking model developed by Elastic to enhance search relevance without reindexing or altering their data schemas. The tool works by improving the order of search results based on a deeper semantic understanding of both queries and documents.

Elastic Rerank users can improve existing search applications including Hybrid semantic search and Retrieval Augmented Generation (RAG). When used for reranking BM25 search results, Elastic claims it delivers an average 40% improvement in ranking quality across a range of retrieval tasks, matching the performance of models 11 times larger.

“Reranking models provide a semantic boost to any search experience,” said Steve Kearns, general manager, Search at Elastic. “Building a reranking model into the Elasticsearch Open Inference API makes Elastic Rerank effortless to load and use in search pipelines. It allows users to quickly apply the accuracy benefits of semantic ranking to their Elasticsearch data just by adding a few parameters to existing queries.”

(Chor muang/Shutterstock)

Elastic makes it easy to integrate Elastic Rerank into existing search systems through the Elasticsearch Inference API. The model is available in technical preview across the company’s product suite.

Elastic Cloud has introduced SAML Single Sign-On (SSO), a feature designed to simplify enterprise access management. This new capability allows organizations to centralize authentication, reducing the complexity of managing user accounts across Elastic Cloud environments.

To make the search queries in Elasticsearch faster and more efficient, the company has released new full-text search capabilities through the MATCH function for ES|QL – an SQL-like query language designed specifically for Elasticsearch. The full-text search offers better search capabilities, especially when dealing with conditional logic or multiple terms. Additionally, the new QTSR function supports advanced filtering of log data by enabling Lucene query string queries.

Elastic 8.17 comes only about a month after its last release. The company wanted to fast track some key features to unlock storage savings and search performance benefits for its users. These updates are especially crucial as businesses seek more efficient ways to manage growing data volumes without compromising on performance or driving up costs.

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