pqg 0.2.3

A synthetic pandas query generation tool

Pandas Query Generator 🐼

Pandas Query Generator (pqg) is a tool designed to help users generate synthetic pandas queries for training machine learning models that estimate query execution costs or predict cardinality.

The binary is called pqg and has only been tested on a unix-based system.

Installation

You can install the query generator using pip, the Python package manager:

pip install pqg

Usage

Below is the standard output of pqg --help, which elaborates on the various command-line arguments the tool accepts:

usage: pqg [--filter] [--groupby-aggregation-probability] [--max-groupby-columns] [--max-merges] [--max-projection-columns] [--max-selection-conditions] [--multi-line] --num-queries [--output-file] [--projection-probability] --schema [--selection-probability] [--sort] [--verbose]

Pandas Query Generator CLI

options:
  -h --help Show this help message and exit
  --filter Filter generated queries by specific criteria
  --groupby-aggregation-probability Probability of including groupby aggregation operations (default: 0.5)
  --max-groupby-columns Maximum number of columns in group by operations (default: 5)
  --max-merges Maximum number of table merges allowed (default: 2)
  --max-projection-columns Maximum number of columns to project (default: 5)
  --max-selection-conditions Maximum number of conditions in selection operations (default: 5)
  --multi-line Format queries on multiple lines (default: False)
  --num-queries num_queries The number of queries to generate
  --output-file The name of the file to write the results to (default: queries.txt)
  --projection-probability Probability of including projection operations (default: 0.5)
  --schema schema Path to the relational schema JSON file
  --selection-probability Probability of including selection operations (default: 0.5)
  --sort Whether or not to sort the queries by complexity (default: False)
  --verbose Print extra generation information and statistics (default: False)

The required options, as shown, are --num-queries and --schema. The --num-queries option simply instructs the program to generate a certain amount of queries.

The --schema option is a pointer to a JSON file path that describes meta-information about the data we're generating queries for.

A sample schema looks like this:

{
  "entities": {
    "customer": {
      "primary_key": "C_CUSTKEY",
      "properties": {
        "C_CUSTKEY": { "type": "int", "min": 1, "max": 1000 },
        "C_NAME": { "type": "string", "starting_character": ["A", "B", "C"] },
        "C_STATUS": { "type": "enum", "values": ["active", "inactive"] }
      },
      "foreign_keys": {}
    },
    "order": {
      "primary_key": "O_ORDERKEY",
      "properties": {
        "O_ORDERKEY": { "type": "int", "min": 1, "max": 5000 },
        "O_CUSTKEY": { "type": "int", "min": 1, "max": 1000 },
        "O_TOTALPRICE": { "type": "float", "min": 10.0, "max": 1000.0 },
        "O_ORDERSTATUS": { "type": "enum", "values": ["pending", "completed", "cancelled"] }
      },
      "foreign_keys": {
        "O_CUSTKEY": ["C_CUSTKEY", "customer"]
      }
    }
  }
}

This file can be found in /examples/customer/schema.json, generate a few queries from this schema with pqg --num-queries 100 --schema examples/customer/schema.json --verbose.

Schemas for these files can be found in their respective directories within /examples.

How does it work?

Check out the paper in the /docs folder for more information!

Prior Art

This version of the Pandas Query Generator is based off of the thorough research work of previous students of COMP 400 at McGill University, namely Edge Satir, Hongxin Huo and Dailun Li.