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Elastic, the company behind Elasticsearch, reports significant performance gains in its vector database with up to 8x speed and 32x efficiency. New optimization strategies include multi-threaded search capabilities, accelerated multi-graph vector search, Panama Vector API integration, memory efficiency enhancements, seamless compression improvements, and multi-vector integration. These advancements aim to make Elasticsearch and Apache Lucene the best vector database for generative AI use cases.
Elastic, l'azienda che sta dietro a Elasticsearch, riporta notevoli miglioramenti di prestazione nella sua base di dati vettoriale, con velocità fino a 8 volte superiore e un'efficienza migliorata di 32 volte. Le nuove strategie di ottimizzazione includono capacità di ricerca multi-thread, ricerca vettoriale multi-grafo accelerata, integrazione dell'API Vettoriale di Panama, miglioramenti dell'efficienza della memoria, perfezionamenti nella compressione senza soluzione di continuità e integrazione multi-vettoriale. Questi progressi mirano a rendere Elasticsearch e Apache Lucene la migliore base di dati vettoriale per casi d'uso di intelligenza artificiale generativa.
Elastic, la compañía detrás de Elasticsearch, informa importantes ganancias de rendimiento en su base de datos vectorial, con velocidades de hasta 8 veces más rápidas y una eficiencia 32 veces mayor. Las nuevas estrategias de optimización incluyen capacidades de búsqueda multi-hilo, búsqueda vectorial multi-gráfico acelerada, integración de la API Vector de Panamá, mejoras en la eficiencia de la memoria, mejoras continuas en la compresión y integración multi-vector. Estos avances tienen como objetivo hacer de Elasticsearch y Apache Lucene la mejor base de datos vectorial para casos de uso de IA generativa.
Elasticsearch를 개발한 기업인 Elastic은 벡터 데이터베이스에서 최대 8배의 속도 향상과 32배의 효율성 증가를 보고했습니다. 새로운 최적화 전략에는 멀티 스레드 검색 기능, 가속화된 멀티 그래프 벡터 검색, 파나마 벡터 API 통합, 메모리 효율성 향상, 매끄러운 압축 개선, 멀티 벡터 통합이 포함됩니다. 이러한 발전은 Elasticsearch와 Apache Lucene을 생성적 AI 사용 사례에 가장 적합한 벡터 데이터베이스로 만들기 위한 것입니다.
Elastic, la société derrière Elasticsearch, signale des gains de performance significatifs dans sa base de données vectorielle, avec une vitesse jusqu'à 8 fois supérieure et une efficacité 32 fois meilleure. Les nouvelles stratégies d'optimisation comprennent les capacités de recherche multi-thread, la recherche vectorielle multi-graphe accélérée, l'intégration de l'API Vector de Panama, les améliorations de l'efficacité de la mémoire, les améliorations de la compression sans couture et l'intégration multi-vectorielle. Ces avancées visent à faire d'Elasticsearch et Apache Lucene la meilleure base de données vectorielle pour les cas d'utilisation de l'IA générative.
Elastic, das Unternehmen hinter Elasticsearch, berichtet von erheblichen Leistungssteigerungen in seiner Vektordatenbank, mit bis zu 8-facher Geschwindigkeit und 32-facher Effizienz. Neue Optimierungsstrategien umfassen Multi-Thread-Suchfähigkeiten, beschleunigte Multi-Graph-Vektorsuche, Integration der Panama Vector API, Speichereffizienzverbesserungen, nahtlose Kompressionsverbesserungen und Multi-Vektor-Integration. Diese Fortschritte zielen darauf ab, Elasticsearch und Apache Lucene zur besten Vektordatenbank für generative AI-Anwendungsfälle zu machen.
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The world’s most downloaded vector database is used by customers to store and search billions of vector embeddings
SAN FRANCISCO--(BUSINESS WIRE)--
Elastic (NYSE: ESTC), the company behind Elasticsearch®, today announced new vector database performance gains with Elasticsearch and Apache Lucene, with up to 8x speed and 32x efficiency. These advancements provide developers with the most flexible and open tools needed to keep up with rapid generative AI innovation.
New optimization strategies and enhancements driving the increased gains in Elasticsearch and Apache Lucene include*:
Multi-threaded search capabilities run searches in independent segments, minimizing response times, ensuring users receive their search results as swiftly as possible
Accelerated multi-graph vector search and information exchange among segment searches, reducing query latencies up to 60%
Panama Vector API Integration enabling Java code to interface seamlessly with SIMD instructions, unlocking a new era of vector search performance optimization
Maximized memory efficiency with scalar quantization, slashing memory usage by approximately 75% without sacrificing search performance
Seamless compression improvements that keep search results accurate while making data smaller, laying the foundations for binary quantization that will deliver the full potential of vector search while maximizing resource utilization and scalability
Multi-vector integration in Lucene and Elasticsearch that enables searches across nested documents and joins, making document searches in Lucene more effective
“Our goal is to make Elasticsearch and Apache Lucene the best vector database and Elasticsearch the best retrieval platform for semantic search, RAG, and generative AI use cases,” said Shay Banon, founder and chief technology officer at Elastic. “Elastic’s contributions bring multifold performance gains in speed, scale, and efficiency, ensuring the Elasticsearch-Lucene integration continues to be a versatile vector database and search experience loved by developers and trusted by enterprises the world over.”
Customers are building the next generation of AI enabled search applications with Elastic’s vector database and vector search technology. For example, Roboflow is used by over 500,000 engineers to create datasets, train models, and deploy computer vision models to production. Roboflow uses Elastic vector database to store and search billions of vector embeddings.
Learn more about Lucene’s integration into Elasticsearch and how these investments deliver faster vector search performance and scale here.
*These features are available in Elasticsearch version 8.10 and above.
About Elastic
Elastic (NYSE: ESTC), the leading search analytics company, securely harnesses search powered AI to enable everyone to find the answers they need in real-time using all their data, at scale. Elastic’s solutions for security, observability and search are built on the Elasticsearch platform, the development platform used by thousands of companies, including more than 50% of the Fortune 500. Learn more at elastic.co.
What performance gains did Elastic report for its vector database?
Elastic reported up to 8x speed and 32x efficiency gains in its vector database.
What are some of the optimization strategies driving the increased gains in Elasticsearch and Apache Lucene?
Optimization strategies include multi-threaded search capabilities, accelerated multi-graph vector search, Panama Vector API integration, memory efficiency enhancements, seamless compression improvements, and multi-vector integration.
Who is the founder and chief technology officer at Elastic?
Shay Banon is the founder and chief technology officer at Elastic.
How is Elastic contributing to the performance gains in Elasticsearch and Apache Lucene?
Elastic's contributions bring multifold performance gains in speed, scale, and efficiency in Elasticsearch and Apache Lucene.
What is Roboflow and how is it using Elastic's vector database?
Roboflow is used by over 500,000 engineers to create datasets, train models, and deploy computer vision models. It uses Elastic's vector database to store and search billions of vector embeddings.
