Streamling: Lightweight, Extensible Streaming on DataFusion
Apache DataFusion is moving beyond batch into streaming. We built Streamling, a Rust streaming engine that uses DataFusion planning and Arrow RecordBatch streams for real-time SQL/WASM transforms. This talk covers how we built it, highlights key features (FFI plugins, WASM transforms, and dynamic tables), and shares production lessons.
Apache Solr 10: What’s Coming up for Vector Search
With Apache Solr 10 out, there are plenty of goodies coming up for vector-search aficionados.
From scalar and binary quantization to speed up your search and reduce the memory footprint, to early termination and hybrid approaches to navigate the HNSW graph.
Join us if you want to learn about the big steps forward of Apache Solr vector search!
Zero downtime index upgrade in Apache Solr
In this talk we’ll explore how Apache Solr introduced the capability to upgrade an index in-place with zero downtime. This upgrade path helps prepare the index for a future Solr major version upgrade without needing to recreate the index from source as is the case with Lucene based search engines today.
From OLTP to OLAP: Is PostgreSQL Eating Analytics Too?
Can PostgreSQL become a serious analytics engine? With emerging columnar extensions, PostgreSQL is pushing beyond OLTP into OLAP territory. This talk explores the current columnar landscape, architectural trade-offs, and how far PostgreSQL can go compared to analytical engines like ClickHouse.
Context-Aware Segments: Solving the “Scatter-Read” Problem
Traditional OpenSearch segments are context-blind, scattering data across multiple segments. We introduce Context-Aware Segments (CAS), an architecture that brings “sharding” logic to the segment level. By enforcing document locality during indexing, we slashed query latency and minimized data footprint through superior pruning and compression.
Building a Local News RAG: The Quest for Trustworthiness
We will show you how we build a local newspaper rag and all the problems that came along the way. From trustworthiness to customer wishes, search optimization and generation problems. Local villages, that LLMs know nothing about, content that is semantically the same and outdated information are only a part of the journey we made.
One GPU, Four Retrieval Modes: Multi-Model Search Serving
Competitive search now needs dense embeddings, sparse vectors, ColBERT, and cross-encoder reranking. Most teams run four separate containers. This talk shows how to serve all four from one process, walks through building a hybrid retrieval pipeline with real benchmark data, and covers where each retrieval mode wins and where it wastes compute.
Text-to-Struct: Fine-tuning SLMs for Query Intent
Hybrid search fails on complex intent: vector search misses constraints, keywords miss nuance. This talk explores fine-tuning SLMs for ‘Query Understanding’—transforming vague inputs into structured requests. Learn to extract metadata, expand terms, and route intent to build a search engine that does the hard work for your users.
Circular Dependency Fixes when Bootstrapping a Golden Set
For a golden set, you need queries. Even if you have them, you can’t judge all docs for each query. Only the top N. How do we rank the top N? See the circular dependency? We’ll talk about ways to untangle it: lexical search, significant terms, training an embedder from scratch, etc. By iteratively refining data and queries, we’ll get there.
How to Survive the Vortex of LLM Change
The LLM ecosystem changes faster than most teams can adapt. This talk shares our experience and the practical lessons we’ve learned while building an intelligent search product in a world where models, tools, and best practices constantly evolve.