TL;DR Uber Freight supports search across 40+ microservices and nearly 100 data sources by composing one shared index from independently owned fragments. Title: Freight Search: Composing a Shared Search Index from Independently Owned Fragments URL: Key points ・🧩 Each index field comes from exactly one fragment, so ownership never conflicts ・🧱 Fragments merge into one denormalized document, so queries need no joins ・⚙️ Spark handles bootstrap; Flink with CDC handles live ingestion, with changes searchable in seconds ・🏷 Mappings are generated from Java annotations, so the class is the single source of truth ・🔗 Declare @/LinkedEntity or a fold function and the platform handles parent-child denormalization and 1:N aggregation ・🔄 The SIA-to-OpenSearch migration touched only a few places and applied to every entity ・📈 About 300 fragments across 20+ indices, thousands of queries per second, p99 in hundreds of milliseconds Separating business definitions from platform internals is what let the design survive an engine migration. #Uber# #SearchInfrastructure#

A search engine has many moving parts, and they constantly change with scale of index and traffic. In this blog post, we dive into the code-level abstractions we've built to develop search infrastructure that is both performant and a joy to work with.

✨ Employee Spotlight: Meet Richard Lu! Richard Lu is a New York-based Senior Software Engineer on the Search Infrastructure team at AlphaSense, where no two days look quite the same. Mornings are anchored by syncs with a global team, and afternoons open up into flexible, focused build time. He splits his week between home and the New York office, finding a rhythm that works for him. What drives him is impact, and the ability to see it quickly. Right now that impact is showing up on SuperAnalyst, our always-on AI agent. "Not only do I get to see it being developed from 0 to 1, but I also see the changes that it goes through. I'm probably more excited about this product than any other product I've developed over the last handful of years." Thinking about joining us? Explore current openings and learn more about our company culture on our #careers# page.

We're excited to welcome @asadovsky as our Chief Research Officer. Prior to Harvey, Adam co-led post-training at Microsoft AI and Google DeepMind. As CVP at Microsoft AI, he led post-training of MAI-Thinking-1. As a Distinguished Engineer at Google DeepMind, he led teams working on Gemini 1.0 through Gemini 2.5. Earlier at Google, he led teams working on Assistant, Search Quality, and Search Infrastructure.