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Scaling Package Queries to a Billion Tuples via Hierarchical Partitioning and Customized Optimization

Abstract

A package query returns a package — a multiset of tuples — that maximizes or minimizes a linear objective function subject to linear constraints, thereby enabling in-database decision support. Prior work has established the equivalence of package queries to Integer Linear Programs (ILPs) and developed the Sketch Refine algorithm for package query processing. While this algorithm was an important first step toward supporting prescriptive analytics scalably inside a relational database, it struggles when the data size grows beyond a few hundred million tuples or when the constraints become very tight. In this paper, we present Progressive Shading, a novel algorithm for processing package queries that can scale efficiently to billions of tuples and gracefully handle tight constraints. Progressive Shading solves a sequence of optimization problems over a hierarchy of relations, each resulting from an ever-finer partitioning of the original tuples into homogeneous groups until the original relation is obtained. This strategy avoids the premature discarding of high-quality tuples that can occur with Sketch Refine. Our novel partitioning scheme, Dynamic Low Variance, can handle very large relations with multiple attributes and can dynamically adapt to both concentrated and spread-out sets of attribute values, provably outperforming traditional partitioning schemes such as kd-tree. We further optimize our system by replacing our off-the-shelf optimization software with customized ILP and LP solvers, called Dual Reducer and Parallel Dual Simplex respectively, that are highly accurate and orders of magnitude faster.

Installation and Setup

OS Requirements

Ubuntu 20.04.4 LTS

Install Python packages

This code needs Python 3.8 or higher.

pip3 install -r requirements.txt

Setup Postgres server and hardware information

This code needs Postgres v14.7 or higher. Configure the file config.txt with the appropriate information

Parameters for Postgres: hostname, port, username, database, password, schema (to find these information run psql and then \conninfo)

Parameters for Hardware information:

  • physical_core: Number of cores in CPU

  • main_memory_size: Main memory allowed in GB

Data generation

cd resource
python3 ssds.py
python3 tpch.py

Setup C++ and Gurobi

Install CMake

Download CMake

wget https://github.com/Kitware/CMake/releases/download/v3.23.0-rc1/cmake-3.23.0-rc1-linux-x86_64.tar.gz
tar -xvf cmake-3.23.0-rc1-linux-x86_64.tar.gz

Add CMake path

vim .bashrc
insert into .bashrc the line "export PATH=$<CMakeFolder>/cmake-3.23.0-rc1-linux-x86_64/:$PATH"
source .bashrc

Install Conan

pip3 install conan
cd PackageQuery/build
conan install . --build=missing

Install Gurobi

This code needs Gurobiv9.5.2 or higher.

  1. Request an academic license from Gurobi.
  2. Follow the instructions to download and unzip Gurobi as well as set up the license.
  3. Edit the file cmake/FindGUROBI.cmake:
    • Change the below line
    set(GUROBI_HOME "/home/alm818/downloads/gurobi950/linux64")
    to the Gurobi's home in your system
    • Change the below line
    find_library(GUROBI_LIBRARY
 NAMES gurobi gurobi95
 HINTS ${GUROBI_DIR} ${GUROBI_HOME}
 PATH_SUFFIXES lib)
    to the correct Gurobi's version in your system

Result Reproduction

Build files (only need to do once)

  cd build
  cmake -DCMAKE_BUILD_TYPE=Release ..

Compile files and run

  cd build
  cmake --build . --config Release
  bin/main

This will create the following files: G1.txt, G2.txt, M1.txt, M2.txt, M3.txt, M4.txt, M5.txt, M6.txt, M8.txt, E1.txt, E2.txt where:

  • G1.txt is the result for Mini-Experiment 6. What is the optimal configuration of augmenting size α and downscale factor df?
  • G2.txt is the result for Mini-Experiment 7. What is the optimal sub-ILP size q for Dual Reducer?
  • M1.txt is the result for Mini-Experiment 1. Does replacing the LP solution with an ILP solution in SHADING improve overall optimality?
  • M2.txt is the result for Mini-Experiment 2. Does replacing Neighbor Sampling with a random sampling of representative tuples impact the overall performance of Progressive Shading?
  • M3.txt is the result for Mini-Experiment 3. How well does Parallel Dual Simplex scale with more cores?
  • M4.txt is the result for Mini-Experiment 4. Does replacing the Auxiliary LP with a random sampling of tuples from S to formulate a sub-ILP of size q impact the overall performance of Dual Reducer?
  • M5.txt is the result for Mini-Experiment 5. How efficient is DLV compared to KD-Tree when producing a large number of groups?
  • M6.txt is the result for Mini-Experiment 8. Is it worth using Dual Reducer for the last layer of Progressive Shading instead of Gurobi?
  • M8.txt is the result for What happens when we use Random Partitioning for Progressive Shading?
  • E1.txt is the result for Query performance as relation size increases
  • E2.txt is the result for False infeasibility as hardness increases

You can draw the related graph in the paper by running Plot.ipynb in the plots folder