jennyjiang98 / noisepage-pilot

Because "pilot" was a better name than "brain"

NoisePage Pilot

This repository contains the pilot components for the NoisePage DBMS.

Quickstart

1. Install necessary packages.
   • pip3 install --upgrade -r requirements.txt
2. List all the tasks.
   • doit list
3. Select and run a doit task from the task list, e.g. doit action_recommendation. Task dependencies are executed automatically.

Sample Doit Tasks

The following is a list of some relevant doit tasks and their parameters:

• Action Recommendation
  • The following task will begin picking indexes: doit action_recommendation.
• Behavior Modeling
  • doit behavior_datagen runs behavior model training data generation using TScout, Benchbase and Postgres. Requires sudo permissions for TScout. Also performs training data differencing (subtracting child-plan costs).
  • doit behavior_train trains, evaluates, and serializes models along with their evaluations and predictions. It depends on behavior_datagen having been run first.
  • Configure data generation, model training, Benchbase, and Postgres in noisepage-pilot/config/behavior.
  • Training data differencing and model training default to using the most recent experiment data.
  • Additional behavior modeling documentation is available at noisepage-pilot/behavior/README.md.

Background

• Self-Driving DBMS = Workload Forecasting + Behavior Modeling + Action Planning.
  • Workload Forecasting: forecast folder.
  • Modeling: WIP.
  • Action Planning: action folder.
  • See ¹, ² for more details.

References

Footnotes

1. Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation.

   @article{pavlo21,
   author = {Pavlo, Andrew and Butrovich, Matthew and Ma, Lin and Lim, Wan Shen and Menon, Prashanth and Van Aken, Dana and Zhang, William},
   title = {Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation},
   journal = {Proc. {VLDB} Endow.},
   volume = {14},
   number = {12},
   pages = {3211--3221},
   year = {2021},
   url = {https://db.cs.cmu.edu/papers/2021/p3211-pavlo.pdf},
   }
   

   ↩

2. https://15799.courses.cs.cmu.edu/spring2022/ ↩