Repository for reproducible benchmarking of database-like operations in single-node environment.
Benchmark report is available at h2oai.github.io/db-benchmark.
We focused mainly on portability and reproducibility. Benchmark is routinely re-run to present up-to-date timings. Most of solutions used are automatically upgraded to their stable or development versions.
This benchmark is meant to compare scalability both in data volume and data complexity.
Contribution and feedback are very welcome!

Tasks

• groupby
• join
• sort
• read

Solutions

• dask
• data.table
• dplyr
• DataFrames.jl
• pandas
• (py)datatable
• spark
• cuDF
• ClickHouse (join not yet added)

More solutions has been proposed. Some of them are not yet mature enough to address benchmark questions well enough (e.g. modin). Others haven't been yet evaluated or implemented. Status of all can be tracked in dedicated issues labelled as new solution in project repository.

Reproduce

Batch benchmark run

• edit path.env and set julia and java paths
• if solution uses python create new virtualenv as $solution/py-$solution, example for pandas use virtualenv pandas/py-pandas --python=/usr/bin/python3.6
• install every solution (if needed activate each virtualenv)
• edit run.conf to define solutions and tasks to benchmark
• generate data, for groupby use Rscript _data/groupby-datagen.R 1e7 1e2 0 0 to create G1_1e7_1e2_0_0.csv, re-save to binary data where needed, create data directory and keep all data files there
• edit _control/data.csv to define data sizes to benchmark using active flag
• start benchmark with ./run.sh

Single solution benchmark interactively

• generate data (see related point above)
• set data name env var, for example in groupby use something like export SRC_GRP_LOCAL=G1_1e7_1e2_0_0
• if solution uses python activate virtualenv of a solution
• enter interactive console and run lines of script interactively

Extra care needed

• cuDF
  • use conda instead of virtualenv
• ClickHouse
  • generate data having extra primary key column according to clickhouse/setup-clickhouse.sh
  • follow "reproduce interactive environment" section from clickhouse/setup-clickhouse.sh

Example environment

• setting up r3-8xlarge: 244GB RAM, 32 cores: Amazon EC2 for beginners
• (slightly outdated) full reproduce script on clean Ubuntu 16.04: _utils/repro.sh

Acknowledgment

Timings for some solutions might be missing for particular data sizes or questions. Some functions are not yet implemented in all solutions so we were unable to answer all questions in all solutions. Some solutions might also run out of memory when running benchmark script which results the process to be killed by OS. Lastly we also added timeout for single benchmark script to run, once timeout value is reached script is terminated. Please check issues labelled as exceptions in our repository for a list of issues/defects in solutions, that makes us unable to provide all timings.