Jim Ferenczi
Tech Lead - Enterprise Search
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.
The disk that never woke up: what actually decided our Qdrant vector search benchmark rematch
On the same hardware, Elasticsearch and Qdrant land in the same range at 56 QPS. The io_uring disk scorer and memory claims turned out to be the two things that mattered least.
Speed up vector ingestion using Base64-encoded strings
Introducing Base64-encoded strings to speed up vector ingestion in Elasticsearch.
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.
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.
