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Readme for reproducibility submission of paper ID 63
SQLShare: Results from a Multi-Year SQL-as-a-Service Experiment

A) Source code info
Repository: https://github.com/uwescience/query-workload-analysis
Programming Language: Python
Additional Programming Language info:
Compiler Info: Python2.7
Packages/Libraries Needed: pip, virtualenv (rest of the requirements are installed by the script, sqlite, various python libraries, latex to build the final paper).

B) Datasets info
Repository: https://s3-us-west-2.amazonaws.com/shrquerylogs/sdssquerieswithplan.csv, https://s3-us-west-2.amazonaws.com/shrquerylogs/QueriesWithPlan.csv, https://s3-us-west-2.amazonaws.com/shrquerylogs/ViewsWithPlan.csv
Data generators: N/A
IMPORTANT NOTE: Original datasets used in the paper are not available since we have received more queries in our log since then. As a result, some of the figures
might look different, specially since dataset deletions by users renders all queries run on it impossible to analyze (as our SQLServer backend can't generate the plans for those queries anymore).
However, the trends and takeaways remain the same!

C) Hardware Info
OS Requirements: Requires a Debian based OS. Preferably Ubuntu 16.04 (on which this script has been tested.).
C1) Processor: N/A
C2) Caches: N/A
C3) Memory: 16GB
C4) Secondary Storage: About 25GB.
C5) Network: N/A, internet connectivity required for script to download dataset.

D) Experimentation Info

D1) Get the code: git clone https://github.com/uwescience/query-workload-analysis.git
D2) Software (install virtualenv): pip install virtualenv; virtualenv vqwla

source vqwla/bin/activate


Next, one script does everything, installs packages within virtualenv, downloads the datasets, creates sqlite databases, run experiments, creates graphs and updates the paper.pdf with new graphs.
D3) ./run-experiments.sh 
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