globosco / a-modified-sosd-platform-for-benchmarking-branchy-vs-branch-free-search-algorithms

Modified Search on Sorted Data Benchmark for Branchy vs Branchfree study

Modified Search on Sorted Data Benchmark for Branchy vs Branchfree study

SOSD is a benchmark to compare (learned) index structures on equality lookup performance over densely packed, sorted data. The original version of SOSD can be downloaded at Github. This modified version is designed to replicate the results presented in our paper at ArXiv and at Software, Practice and Experience concerning the comparison between Branchy and Banchfree algorithms using the best Learned Indexes in the Literature.

Principal modifications

We added some search routines to the ones provided by the platform. In particular, we provide the following methods.

1. Uniform Binary,
2. Uniform and Standard k-ary Search
3. Search layouts other than sorted, i.e. Eytzinger Layout

We also limited the execution on only three Learned Indexes, i.e., RMI, PGM-Index and RS. Finally, we provide the scripts to execute all of them on datasets linked in the dedicated Section.

Dependencies

Dependencies are the same of the original software. We repeat them here for completness.

On vanilla Ubuntu 20.04 LTS:

sudo apt -y update
sudo apt -y install zstd python3-pip m4 cmake clang libboost-all-dev
pip3 install --user numpy scipy
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

Datasets

Datasets used in this work can be downloaded at link. All downloaded files must be placed in the data folder located in the repository root before running the benchmark scripts.

Usage instructions

We provide a number of scripts to automate things. Each is located in the scripts directory, but should be executed from the repository root.

Running the benchmark

• ./scripts/build_rmis.sh compiles and builds the RMIs for each dataset
• ./scripts/prepare.sh constructs query workloads and compiles the benchmark
• ./scripts/execute_all.sh executes the benchmark on each workload and with all the search methods used in the work, storing the results in results. This script returns the results as csv files.

Cite

If you use this benchmark in your own work, please cite us:

@article{https://doi.org/10.1002/spe.3150,
      author = {Amato, Domenico and Lo Bosco, Giosué and Giancarlo, Raffaele},
      title = {Standard versus uniform binary search and their variants in learned static indexing: The case of the searching on sorted data benchmarking software platform},
      journal = {Software: Practice and Experience},
      volume = {53},
      number = {2},
      pages = {318-346},
      keywords = {algorithms with prediction, binary search variants, learned index structures, search on sorted data platform},
      doi = {https://doi.org/10.1002/spe.3150},
      url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.3150},
      eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.3150},
      year = {2023}
}

@misc{amato2022standard,
      title={Standard Vs Uniform Binary Search and Their Variants in Learned Static Indexing: The Case of the Searching on Sorted Data Benchmarking Software Platform}, 
      author={Domenico Amato and Giosuè Lo Bosco and Raffaele Giancarlo},
      year={2022},
      eprint={2201.01554},
      archivePrefix={arXiv},
      primaryClass={cs.DS}
}

and the original works:

@article{sosd-vldb,
  author    = {Ryan Marcus and
               Andreas Kipf and
               Alexander van Renen and
               Mihail Stoian and
               Sanchit Misra and
               Alfons Kemper and
               Thomas Neumann and
               Tim Kraska},
  title     = {Benchmarking Learned Indexes},
  journal   = {Proc. {VLDB} Endow.},
  volume    = {14},
  number    = {1},
  pages     = {1--13},
  year      = {2020}
}

@article{sosd-neurips,
  title={SOSD: A Benchmark for Learned Indexes},
  author={Kipf, Andreas and Marcus, Ryan and van Renen, Alexander and Stoian, Mihail and Kemper, Alfons and Kraska, Tim and Neumann, Thomas},
  journal={NeurIPS Workshop on Machine Learning for Systems},
  year={2019}
}