This is the repeatability page for our BTW 2021 conference paper on efficient theta-join processing within our actor database prototype A²DB.

1. Authors
2. Abstract
3. Algorithm Source Code
4. Evaluation Data

Julian Weise, Sebastian Schmidl, Thorsten Papenbrock

The Theta-Join is a powerful operation to connect tuples of different relational tables based on arbitrary conditions. The operation is a fundamental requirement for many data-driven use cases, such as data cleaning, consistency checking, and hypothesis testing. However, processing theta-joins without equality predicates is an expensive operation, because basically all database management systems (DBMSs) translate theta-joins into a Cartesian product with a post-filter for non-matching tuple pairs. This seems to be necessary, because most join optimization techniques, such as indexing, hashing, bloom-filters, or sorting, do not work for theta-joins with combinations of inequality predicates based on <,≤,≠,≥,>.

In this paper, we therefore study and evaluate optimization approaches for the efficient execution of theta-joins. More specifically, we propose a theta-join algorithm that exploits the high selectivity of theta-joins to prune most join candidates early; the algorithm also parallelizes and distributes the processing (over CPU cores and compute nodes, respectively) for scalable query processing. The algorithm is baked into our distributed in-memory database system prototype A²DB. Our evaluation on various real-world and synthetic datasets shows that A²DB significantly outperforms existing single-machine DBMSs including PostgreSQL and distributed data processing systems, such as Apache SparkSQL, in processing highly selective theta-join queries. [1]

The source code for A²DB can be found on Github.

For our experiments, we use synthetic and real-world datasets, which are differently sized subsets of four base datasets listed in the table below.

We link to the used SQL queries for each dataset in the column Queries.