Poisson Sampling over Acyclic Joins
2026-03-11 • Databases
Databases
AI summaryⓘ
The authors tackle the problem of creating a sample from the result of a database join using Poisson sampling, which assigns a unique probability to each join item. They propose a fast algorithm for joins without cycles, relying on a special index that lets you jump directly to any join result without making the whole join first. Their method is efficient and works well in columnar databases. Experiments show it beats previous methods and can also efficiently handle regular join queries. This means their approach supports both sampling and normal joins efficiently using the same underlying tools.
Poisson samplingjoin queryacyclic joinBernoulli trialrandom-access indexcolumn storeYannakakis' algorithmquery enginesampling algorithmsjoin processing
Authors
Liese Bekkers, Frank Neven, Lorrens Pantelis, Stijn Vansummeren
Abstract
We introduce the problem of Poisson sampling over joins: compute a sample of the result of a join query by conceptually performing a Bernoulli trial for each join tuple, using a non-uniform and tuple-specific probability. We propose an algorithm for Poisson sampling over acyclic joins that is nearly instance-optimal, running in time O(N + k \log N) where N is the size of the input database, and k is the size of the resulting sample. Our algorithm hinges on two building blocks: (1) The construction of a random-access index that allows, given a number i, to randomly access the i-th join tuple without fully materializing the (possibly large) join result; (2) The probing of this index to construct the result sample. We study the engineering trade-offs required to make both components practical, focusing on their implementation in column stores, and identify best-performing alternatives for both. Our experiments on real-world data demonstrate that this pair of alternatives significantly outperforms the repeated-Bernoulli-trial algorithm for Poisson sampling while also demonstrating that the random-access index by itself can be used to competively implement Yannakakis' acyclic join processing algorithm when no sampling is required. This shows that, as far a query engine design is concerned, it is possible to adopt a uniform basis for both classical acyclic join processing and Poisson sampling, both without regret compared to classical join and sampling algorithms.