SQL query engines can act as efficient runtime environments for the execution of imperative programs over database-resident tabular data. To make this point, we lay out the details of a compilation strategy that maps the basic blocks of arbitrarily branching and looping control flow graphs into plain---possibly recursive---SQL:1999 common table expressions. The compiler does not stumble when faced with imperative programs of several hundred lines and emits SQL code that can execute such programs over entire batches of input arguments. These batches create opportunities for parallel program evaluation which contemporary query decorrelation techniques exploit automatically. SQL engines that already support UDFs may find the present program execution approach to outperform their native implementation---SQL engines without such support may gain UDF capabilities without the need to build a dedicated interpreter.

Reproducibility is a key aspect of the scientific method, and it is essential for building trust in the results of research. This tutorial aims to provide concrete guidance on how to leverage containerized reproducibility using Docker for database systems research. In this tutorial, we present a step-by-step guide on how to prepare a Docker-based artifact for an experiment. We will cover topics such as Dockerfiles, Docker images, Docker Compose, automation using Python, Bash, and Make, and also artifact documentation and packaging best practices. The tutorial itself is a reproducible artifact, and we provide a public GitHub repository with all the code and examples used in the tutorial. This repository can serve as a starting point to prepare artifacts for experiments and publications.

"Move your computation close to the data" is decades-old advice that is hard to follow if your code exhibits complex control flow. The runtime of such applications suffers from a continual back and forth between database-external code execution and plan-based SQL evaluation. We demonstrate the ByePy compiler which translates entire Python functions with arbitrary control flow-including deeply nested iteration-into plain recursive SQL:1999 queries. The invocation of a ByePy-compiled function enters the database engine once to execute the plan of a single query. Computation does not get much closer to the data than this. The system rewards this translation effort from Python to SQL with runtime improvements of up to an order of magnitude.