PSqueeze

Implementation and dataset for Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems, which extends our previous conference version

Requirements

At least python>=3.6 is required.

pip install -r requirements.txt

A virtual environment is strongly recommended.

Datasets

Datasets A, B1, B2, B3, B4, D are on Tsinghua Cloud. The ground truth root cause sets are in injection_info.csv in each subfolder.

Usage

For convenience, run_exp.sh provide a script to run all experiments.

Alternatively, you can run each experiment by yourself.

python run_algorithm.py --help

Usage: run_algorithm.py [OPTIONS]

  :param name: :param input_path: :param output_path: :param num_workers:
  :param kwargs: :return:

Options:
  --name TEXT            name of this setting
  --input-path TEXT      will read data from {input_path}/{name}
  --output-path TEXT     if {output_path} is a dir, save to
                         {output_path}/{name}.json; otherwise save to
                         {output_path}
  --num-workers INTEGER  num of processes
  --derived              means we should read {timestamp}.a.csv and
                         {timestamp}.b.csv
  --help                 Show this message and exit.

python run_evaluation.py --help

Usage: run_evaluation.py [OPTIONS]

Options:
  -i, --injection-info TEXT  injection_info.csv file
  -p, --predict TEXT         output json file
  -c, --config TEXT          config json file
  -o, --output-path TEXT     output path
  --help                     Show this message and exit.

The config json file should contain the attribute names, e.g.:

{
  "columns": [
    "a", "b", "c", "d"
  ]
}