A propos de l'auteur
Jim Ferenczi is an Elasticsearch Engineer and a Lucene committer at The Apache Software Foundation. Before joining Elastic, Jim worked for Exalead on web search and for Rakuten on e-commerce search.
Articles de l'auteur
4 février 2026
Speed up vector ingestion using Base64-encoded strings
Introducing Base64-encoded strings to speed up vector ingestion in Elasticsearch.
26 août 2025
Lighter by default: Excluding vectors from source
Elasticsearch now excludes vectors from source by default, saving space and improving performance while keeping vectors accessible when needed.
12 juin 2024
Designing for large scale vector search with Elasticsearch
Explore the cost, performance and benchmarking for running large-scale vector search in Elasticsearch, with a focus on high-fidelity dense vector search.
26 avril 2024
Making Elasticsearch and Lucene the best vector database: up to 8x faster and 32x efficient
Discover the recent enhancements and optimizations that notably improve vector search performance in Elasticsearch & Lucene vector database.