Cypher on Velox Abdullah Ozturk and Vishal Gandhi, Meta
Meta’s graph data — social graphs, knowledge graphs, data lineage — is spread across multiple storage backends with no unified query interface. Teams building RAG pipelines, knowledge graph applications, and data lineage tools write bespoke integration code against each backend. We built a Cypher/ISO GQL Query Layer that lets developers write a single declarative graph query and execute it across all of these backends, powered by Velox as the execution engine. To our knowledge, this is the first system to repurpose Velox’s connector and plan infrastructure for federated graph query execution. The key insight is that graph pattern matching maps naturally to relational operations: table scans, hash joins, and filters. By targeting Velox’s logical plan representation, we inherit its vectorized columnar execution, memory management, and connector abstraction without building a custom graph engine. Velox’s extensible connector interface lets us federate queries across TAO (Meta’s distributed graph store), ZippyDB, relational databases, search infrastructure, and Hive warehouse tables through a single execution framework. The same connector abstraction that federates queries across storage backends also provides a natural path to AI-native retrieval. The architecture supports pushing specialized operations — BM25 text search, vector similarity and recency — down to dedicated indexes before Velox handles post-processing joins and filters. This enables hybrid search patterns for RAG: find semantically similar entities via vector search, then traverse the knowledge graph to retrieve source documents, all expressed in a single Cypher query. We’ll discuss the architecture of this multi-retrieval path with plug-in backends. Whether you’re building query systems on top of Velox, working on RAG pipelines, or exploring graph query capabilities on columnar engines, you’ll walk away with a practical understanding of how to layer graph semantics onto Velox’s execution model and connector framework.

Atachen Gluten Architecture, Ecosystem Yuan Zhou, IBM

Demystifying CXL Memory Computation Yongil Jung, XCENA

Velox IO Nimble Index & Open Table Format Support Xiaoxuan Meng, Meta

Accelerating Oracle AI & Data Platform with Velox Koushik Kumar Mondal, Oracle

AI powered Java UDF Veloxification MJ Deng and Sandeep Thandassery, Meta

Zig 2026: No-AI Policy, $670K Foundation, Left GitHub & Why Zig Isn’t 1.0 - Andrew Kelley Explains

Why The Russian Accent Terrifies Everyone

Introducing the American AI Sovereign Wealth Fund Act

Knife Expert: Real Knife Defense Is TERRIFYING

LIVE: Conan O’Brien speaks at Harvard graduation ceremony (full)

Backend web development - a complete overview

Conan O’Brien Delivers the Commencement Address | Harvard Commencement 2026

Turing Award Winner: Disagreeing with Google, Postgres, Future Problems | Mike Stonebraker

The Insane Genius of a Formula 1 Gearbox

Building the PERFECT Linux PC with Linus Torvalds

Magnus Invents A New Opening So OUTRAGEOUS, You'll Question EVERY Chess Principle!

Veloxifying Spark workloads at Meta Ankur Pathela, Meta

The Skill That 10x’d My Claude Code Projects

How to Get and Evaluate Startup Ideas | Startup School

