Databases

Real-Time AI Powered by RonDB

UB2.252A (Lameere)
Mikael Ronström
<p>RonDB is a high-performance, MySQL-compatible distributed database engineered for real-time, latency-critical workloads. Built on decades of development in the MySQL NDB Cluster—led by the original founder of the NDB product—RonDB extends the NDB storage engine with new capabilities, cloud-native automation, and modern APIs tailored for large-scale AI and online services.</p> <p>This talk will describe how RonDB consistently delivers 1–4 ms latency even for large batched operations involving hundreds of rows and multi-megabyte payloads, and will explain the architectural techniques that make such performance possible. We will highlight RonDB’s role as the online feature store powering the Hopsworks Real-Time AI platform, deployed in production at companies such as Zalando for personalized recommendations and other low-latency machine-learning applications.</p> <p>The session will also introduce key components of the RonDB ecosystem:</p> <p>rondb-helm – Kubernetes and Helm tooling for deploying, managing, and scaling RonDB clusters in cloud environments.</p> <p>rondb-tools – Scripts and automation utilities for quickly setting up local or distributed RonDB testbeds.</p> <p>New API layers, including: • A REST API server offering batch key operations, batch scans, and aggregated SQL queries. • An experimental Redis-compatible interface, enabling RonDB to act as a durable, high-throughput backend behind standard Redis commands.</p> <p>We will outline the active collaboration between the RonDB team and Oracle’s MySQL NDB Cluster engineers, and how RonDB extends and complements the upstream NDB ecosystem. In addition, we will present ongoing cooperation with Datagraph to build a SPARQL interface to RonDB, leveraging Datagraph’s Common Lisp NDB API.</p> <p>Attendees will come away with a clear understanding of how RonDB achieves its performance characteristics, how it integrates with modern real-time AI pipelines, and how to deploy, operate, and experiment with RonDB using the available open-source tools.</p> <p>GitHub repositories: https://github.com/logicalclocks/rondb https://github.com/logicalclocks/rond-helm https://github.com/logicalclocks/rondb-tools https://github.com/datagraph/cl-ndbapi/</p> <p>Web sites of note: https://rondb.com https://docs.rondb.com https://hopsworks.ai https://blog.dydra.com/@datagenous/blog-catalog</p>

Weitere Infos

Live Stream https://live.fosdem.org/watch/ub2252a
Format devroom
Sprache Englisch

Weitere Sessions

31.01.26
Databases
UB2.252A (Lameere)
<p>In this session, four seasoned database administrators with sound knowledge of both PostgreSQL and MySQL present an unbiased comparison of the two technologies. Attendees will learn about the architectural and DX differences between the world's two most popular databases.</p> <p>Pep Pla, with his peculiar sense of humour, will open the session with a deep dive into the MVCC architectures between the two. The audience will learn why we need MVCC. Postgres and MySQL take very different ...
31.01.26
Databases
UB2.252A (Lameere)
<p>The success of open source databases like PostgreSQL and MySQL/MariaDB has created an ecosystem of derivatives claiming "drop-in compatibility." But as the distance between upstream and these derivatives grows, user confusion and brand dilution can follow.</p> <p>To address this, we explore the challenge of compatibility with de facto standards from two distinct angles: a governance perspective on defining the compatibility criteria, and a systems engineering case study on implementing ...
31.01.26
Databases
Nicoleta Lazar
UB2.252A (Lameere)
<p>As analytics ecosystems grow more diverse, organisations increasingly need to query data across warehouses, data lakes, and operational systems without excessive movement or duplication. Query federation has become essential by enabling unified SQL access and intelligent pushdown into heterogeneous sources. This talk introduces the core principles of federation and why it matters for modern OLAP workloads and how it is different to Trino.</p> <p>Using StarRocks as a model system, we highlight ...
31.01.26
Databases
Charly Batista
UB2.252A (Lameere)
<p>We all write SQL, but how many of us have looked under the hood of a relational database like PostgreSQL? This talk is a deep dive into the guts of the database engine, tracking a simple SELECT statement from the moment you hit "Enter" to the final result set.</p> <p>We'll lift the veil on the core components: the parser, the planner (and the optimizer's black magic!), and the executor, and see how they transform a text string into a low-level, high-performance operation. Using a live, ...
31.01.26
Databases
Vitor Oliveira
UB2.252A (Lameere)
<p>While optimizing a new heap storage engine across both MySQL and a PostgreSQL-based database we encountered a puzzling result: while on MySQL the throughput stalled below 500k tpmC, on the other database it achieved over 1 million tpmC. The mystery deepened when three different TPC-C benchmarks each told a conflicting story about MySQL’s speed.</p> <p>This talk details the systematic investigation to resolve these contradictions and reclaim the lost performance. We’ll walk through the ...
31.01.26
Databases
UB2.252A (Lameere)
<p>DuckDB has traditionally been seen as a last-mile analytics powerhouse, the fastest way to run a SQL query on your laptop. But DuckDB offers more than just fast SQL, of course; it supports full database semantics and ACID transactions, behaving like a fully fledged, in-process OLAP database. The in-process component has sometimes been viewed as a limitation when considering DuckDB as a data warehouse.</p> <p>However, DuckDB now supports reading and writing to most Open Table Formats (OTFs), ...
31.01.26
Databases
Sam Jewell
UB2.252A (Lameere)
<p>Observability data isn’t typically blended with the data that your Analysts are working with. These data types are typically stored in entirely separate databases, and interrogated through different tools.</p> <p>But that needn’t be the case. At Grafana Labs we’ve started blending this data together, to answer questions that we or our customers have, such as: - How much revenue did that downtime cost me? - How did latency impact on sales last Black Friday? - Which customers were ...