/graindb-experiments

Experiment evaluations for "Making RDBMSs Efficient on Graph Workloads Through Predefined Joins"

Primary LanguageC++

Experiment evaluations for paper "Making RDBMSs Efficient on Graph Workloads Through Predefined Joins".

Please see graindb/, graphflowdb/, and neo4j/ directories for detailed instructions to run experiments related to each system.

Plotting

Boxplot

End to end benchmarks: JOB, SNB-M, TPC-H.

  • Prepare input csv files:
    • Merge graindb/evaluations/job_duckdb_avg.out and graindb/evaluations/job_graindb_avg.out into result/end2end_job.csv.
    • Merge graindb/evaluations/snb_duckdb_avg.out, graindb/evaluations/mv_duckdb_avg.out, and graindb/evaluations/snb_graindb_avg.out into result/end2end_snb.csv.
    • Merge graindb/evaluations/tpch_duckdb_avg.out and graindb/evaluations/tpch_graindb_avg.out into result/end2end_tpch.csv.
  • Plot the graphs:
> python3 scripts/plot_boxplot_job.py result/end2end_job.csv
> python3 scripts/plot_boxplot_snb.py result/end2end_snb.csv
> python3 scripts/plot_boxplot_tpch.py result/end2end_tpch.csv

Ablation

  • Merge all perfromance of the ablation study into a single csv file result/ablation.csv. Each configuration takes a column in the final csv file as the following order: 'DUCKDB', 'GR-JM-RSJ', 'GR-JM', 'GR-FULL'.
  • Plot the graph:
> python3 scripts/plot_boxplot_ablation.py result/ablation.csv

Selectivity

  • Prepare input csv files:

    • Create an empty file result/micro_p.csv. Append "Selectivity" as the first column in the csv file.
    • Append performance columns from graindb/evaluations/micro_p_duckdb_avg.out, graindb/evaluations/micro_p_graindb_avg.out, graphflowdb/evaluations/micro_p_gfdb_avg.out, and neo4j/micro_p_neo_results.csv into result/micro_p.csv.
    • The final csv file's header is Selectivity,DuckDB,GRainDB,GFDB,Neo4j.
    • result/micro_k.csv is prepared in a similar way.

  • MICRO-P

> python3 scripts/plot_selectivity.py result/micro_p.csv
  • MICRO-K
> python3 scripts/plot_selectivity.py result/micro_k.csv

Spectrum

  • Prepare input csv files: For each query, organize the performance of DuckDB and GRainDB under different plans in a single csv file. The header of the csv file is DuckDB, GRainDB. Each row in the csv file corresponds to the performance number of DuckDB and GRainDB under the same join order.
> python3 scripts/plot_spectrum.py graindb/evaluations/spectrum_q1.csv -t q1a
> python3 scripts/plot_spectrum.py graindb/evaluations/spectrum_q2.csv -t q2a
> python3 scripts/plot_spectrum.py graindb/evaluations/spectrum_q3.csv -t q3a
> python3 scripts/plot_spectrum.py graindb/evaluations/spectrum_q4.csv -t q4a
> python3 scripts/plot_spectrum.py graindb/evaluations/spectrum_q5.csv -t q5a
> python3 scripts/plot_spectrum.py graindb/evaluations/spectrum_q6.csv -t q6a

Tips

screen or tmux are recommemded when running these experiments as some might take very long time.