This site provides the replication package for the ICDE 2021 tutorial Nullius in Verba. It does not contain (real) scientific results as such, but is intended to serve as a blueprint on how to structure replication pacakges based on an artificial research effort that addresses the important question of how to make interactions with database engined more polite.

NOTE: An archival version of the pre-built docker image, together with a copy of the git repository and the measured data, are available at the DOI 10.5281/zenodo.4730023. Using this version does not require any external ressources. The instructions below are for re-building the image from scratch, and do rely on the availability of external ressources on the internet. They are not supposed to work in the long run.

• Clone the repository

  git clone https://github.com/lfd/icde2021_tutorial

• Build the Docker image from scratch

  cd icde2021_tutorial

  docker build -t icde2021 .

• Run the measurements as described below.

• To execute a measurement sequence on an external target host (e.g., in the cloud), copy measure.tar.bz2 from the docker container to target, and untar the content:

  docker run --rm --entrypoint cat icde2021 /home/repro/deliverable.tar.bz2 > /path/to/deliverable.tar.bz2

  scp /path/to/deliverable.tar.bz2 host.domain.tld:

  ssh host.domain.tld

  host> tar xjf deliverable.tar.bz2

  host> cd measure

• The remaining steps are identical to the steps performed in the container. Once the measurement is complete, copy the folders res_* populated by the measurement script into folder /home/repro/results in the container. Then, proceed with data visualisation and paper generation as decribed below.

• For the sake of the tutorial, the measurement can be performed directly inside the container (this is usually not a recommended scenario for research measurements). Access the container interactively with

  docker run -it icde2021

  The following steps are identical for container- and host based runs.

  The measurement is based on the TPC-H dataset, which is produced by a deterministic generator included in the tarball. Use the following step to bild the generator from source code, and generate two sample datasets:

  ./prepare_data.sh

• Then, execute the measurements proper with the doall.sh script. A label can be chosen to uniquely identify the measurements. For our example scenario in the container, we choose docker:

  ./doall.sh docker

• In real scenarios, doall.sh (and probably also dispatch.sh) would need to be adapted to control which measurement is performed.

• Finally, we need to visualise the results and generate the paper. While the paper is, as usual, written in LaTeX, we use knitr to produce plots from the data, and integrate them into the LaTeX sources.

  Rscript -e "require ('knitr'); knit ('paper.Rnw')"

  pdflatex paper; biber paper; pdflatex paper; pdflatex paper

  (note that we did deliberately not use a build system like latexmk to avoid hiding the actual call sequence; knitr and the pdf build can be transparently integrated this way in production environments)

To perform our exemplary research, we had to modify SQLite in a number of ways. The repository at https://github.com/lfd/sqlite contains two branches:

• A clean, manually tended history in branch master (the default branch), which contains properly structured commits including explanations on why changes were performed, and includes a trail of responsibility using developer certificates of origin (Signed-off-by etc. lines).

• Branch devel_process keeps the history as it arose during development, including partial and incomplete commits. We show during the tutorial how to re-organise and clean up such a series to make it readable by others years or decades after research has been performed, or when interaction with the original authors is not possible anymore.

For convenience, the DOI archived artefacts are also available at non-archival locations. The pre-built Docker image (including a stable copy of all required sources) is available at https://cdn.lfdr.de/icde2021/icde2021-docker.tar.bz2 (0.6 GiB compressed/1.9 GiB uncompressed)