Databases
Jack of all trades: query federation in modern OLAP databases
<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 its vectorized execution engine, native connectors, and deep Apache Iceberg integration that together deliver high-performance lakehouse querying. We examine common lakehouse challenges—schema evolution, file fragmentation, and object-storage latency—and show how federation and hot/cold data separation help address them.</p>
<p>Finally, we explore federating additional sources such as Elasticsearch, PostgreSQL, and Apache Paimon to build a unified analytical architecture.</p>
Weitere Infos
| Live Stream | https://live.fosdem.org/watch/ub2252a |
|---|---|
| Format | devroom |
| Sprache | Englisch |
Weitere Sessions
| 31.01.26 |
<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 ...
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| 31.01.26 |
<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 ...
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| 31.01.26 |
<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, ...
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| 31.01.26 |
<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 ...
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| 31.01.26 |
<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 ...
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| 31.01.26 |
<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), ...
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| 31.01.26 |
<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 ...
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