arXiv subjects

Claude Brisson

Publications and source records attributed to Claude Brisson.

3 recordsLinked to original sources

arXiv2026

Zero-Cost NDV Estimation from Columnar File Metadata

We present a method for estimating the number of distinct values (NDV) of a column in columnar file formats, using only existing file metadata--no extra storage, no data access. Two complementary signals are exploited: (1)~inverting the dictionary-encoded storage size equation yields accurate NDV estimates when distinct values are well-spread across row groups; (2)~counting distinct min/max values across row groups and inverting a coupon collector model provides robust estimates for sorted or partitioned data. A lightweight distribution detector routes between the two estimators. While demonstrated on Apache Parquet, the technique generalizes to any format with dictionary encoding and partition-level statistics, such as ORC and F3. Applications include cost-based query optimization, GPU memory allocation, and data profiling.

cs.DB↗

arXiv2026

Partial Partial Aggregates

We introduce partial partial aggregates (PPA), a query optimization technique for distributed engines that pushes only the local compute phase of an aggregate operation through joins. A query that aggregates after a join involves two logical operations, each requiring a network shuffle. Pushing a full aggregate (COMPUTE$\rightarrow$DISTRIBUTE$\rightarrow$MERGE) below the join introduces a third shuffle. In the specific case where the join key is included in the grouping key and the join is FK-PK, the full pushed aggregate can eliminate the top-level aggregate entirely, making it the preferred choice. In all other key configurations, the top aggregate must remain, and the extra shuffle is wasteful. A PPA pushes only COMPUTE, achieving data reduction before the join without the extra shuffle. The technique relies on the distributive property of aggregates and requires accurate NDV estimation for cost-based decisions.

cs.DB↗

arXiv2025

Theseus: A Distributed and Scalable GPU-Accelerated Query Processing Platform Optimized for Efficient Data Movement

Online analytical processing of queries on datasets in the many-terabyte range is only possible with costly distributed computing systems. To decrease the cost and increase the throughput, systems can leverage accelerators such as GPUs, which are now ubiquitous in the compute infrastructure. This introduces many challenges, the majority of which are related to when, where, and how to best move data around the system. We present Theseus -- a production-ready enterprise-scale distributed accelerator-native query engine designed to balance data movement, memory utilization, and computation in an accelerator-based system context. Specialized asynchronous control mechanisms are tightly coupled to the hardware resources for the purpose of network communication, data pre-loading, data spilling across memories and storage, and GPU compute tasks. The memory subsystem contains a mechanism for fixed-size page-locked host memory allocations to increase throughput and reduce memory fragmentation. For the TPC-H benchmarks at scale factors ranging from 1k to 30k on cloud infrastructure, Theseus outperforms Databricks Photon by up to $4\times$ at cost parity. Theseus is capable of processing all queries of the TPC-H and TPC-DS benchmarks at scale factor 100k (100 TB scale) with as few as 2 DGX A100 640GB nodes.

cs.DC↗