An implementation of ReJOIN: a learned join ordering optimizer, as described in the following papers:

• Rejoin: Hands-Free Query Optimizer through Deep Learning - Ryan Marcus & Olga Papaemmanouil
• Deep Reinforcement Learning for Joining Enumaration - Ryan Marcus & Olga Papaemmanouil

• https://drive.google.com/open?id=1bOBtplkxfGXGRWmib4WYMotjJ47fGz2C

• Train target group 4 for 200 episodes sudo python3 main.py -e 200 -g 1 -tg 4 -se 100 -s ./saved_model/group4-200/

Now the plots are in ./outputs folder (default) and the model in ./saved_model/

• Restore saved model and test group 4 sudo python3 main.py -e 3 -g 1 -tg 4 -r ./saved_model/group4-200/ --testing -o ./outputs/testing/

• Restore saved model and keep training on group 5 for 500 episodes sudo python3 main.py -e 200 -g 1 -tg 5 -se 100 -r ./saved_model/group4-200/ -s ./saved_model/group5-500/

• Execute a single query python main.py --query 3a --episodes 150

• Agent configuration file
  "-a", "--agent-config"
  default="config/ppo.json"

• Network specification file
  "-n", "--network-spec"
  default="config/complex-network.json"

• Number of episodes
  "-e", "--episodes"
  default=800

• Total groups of different number of relations
  "-g", "--groups"
  default=1

• Run specific group
  "-tg", "--target_group"
  default=5

• Incremental Mode
  "-m", "--mode"
  default="round"

• Maximum number of timesteps per episode
  "-ti", "--max-timesteps"
  default=20

• Run specific query
  "-q", "--query"
  default=""

• Save agent to this dir
  "-s", "--save_agent"

• Restore Agent from this dir
  "-r", "--restore_agent"

• Test agent without learning (use deterministic) "-t", "--testing"
  action="store_true"
  default=False

• Order queries by relations_num
  "-all", "--run_all"
  default=False

• Save agent every x episodes
  "-se", "--save-episodes"
  default=100

• Select phase (1 or 2)
  "-p", "--phase"
  default=1