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SOSD is a benchmark to compare (learned) index structures on equality lookup performance over densely packed, sorted data. It comes with state-of-the-art baseline implementations to compare against and many datasets to compare on. Each dataset consists of 200 million to 800 million 32-bit or 64-bit unsigned integers.
We provide a number of scripts to automate things. Each is located in the scripts
directory, but should be executed from the repository root.
scripts/download.sh
downloads and stores required data from the Internetscripts/build_rmis.sh
compiles and builds the RMIs for each datasetscripts/download_rmis.sh
will download pre-built RMIs instead, which may be faster. You'll need to runbuild_rmis.sh
if you want to measure build times on your platform.
scripts/prepare.sh
constructs query workloads and compiles the benchmarkscripts/execute.sh
executes the benchmark on each workload, storing the results inresults
. You can use the-c
flag to output a .csv file of results rather than a .txt.
Build times can be long, as we make aggressive use of templates to ensure we do not accidentally measure vtable lookup time. For development, this can be annoying: you can set USE_FAST_MODE
in config.h
to disable some features and get a faster build time.
If you use this benchmark in your own work, please cite our paper:
@article{sosd,
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}
}