Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-04 14:09 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.923 s -0.228635
2021-10-04 14:09 Python csv-read uncompressed, file, fanniemae_2016Q4 1.192 s -1.092139
2021-10-04 14:14 Python dataframe-to-table type_dict 0.012 s 1.035622
2021-10-04 15:35 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.880 s 0.443748
2021-10-04 14:10 Python csv-read gzip, streaming, fanniemae_2016Q4 14.872 s -0.316522
2021-10-04 14:55 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.996 s -0.954146
2021-10-04 15:10 R dataframe-to-table type_integers, R 0.084 s 0.637608
2021-10-04 15:10 R dataframe-to-table type_nested, R 0.542 s -1.995138
2021-10-04 14:45 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.919 s 0.648287
2021-10-04 14:47 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.292210
2021-10-04 14:10 Python csv-read gzip, file, fanniemae_2016Q4 6.022 s 1.854396
2021-10-04 14:14 Python dataframe-to-table type_integers 0.011 s -0.460424
2021-10-04 15:35 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.219946
2021-10-04 15:37 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.059 s -0.559550
2021-10-04 15:38 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.140 s -0.762592
2021-10-04 14:14 Python dataframe-to-table chi_traffic_2020_Q1 19.275 s 2.085308
2021-10-04 14:56 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.329 s 1.362944
2021-10-04 14:46 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.259 s 1.119418
2021-10-04 14:11 Python csv-read uncompressed, file, nyctaxi_2010-01 0.997 s 1.472597
2021-10-04 14:31 Python dataset-read async=True, nyctaxi_multi_ipc_s3 176.911 s 1.302962
2021-10-04 14:45 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.807 s -0.169792
2021-10-04 14:48 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.336 s -1.477146
2021-10-04 14:53 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.306383
2021-10-04 15:36 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.922 s -0.106703
2021-10-04 14:47 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.930 s -1.249831
2021-10-04 14:48 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.246 s -1.425298
2021-10-04 15:34 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.869 s 0.583767
2021-10-04 15:36 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.383 s 0.064112
2021-10-04 15:38 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.249 s -0.344724
2021-10-04 15:45 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.115 s -1.097083
2021-10-04 15:47 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.393 s 1.572845
2021-10-04 14:12 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.190668
2021-10-04 14:18 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.132 s -0.617627
2021-10-04 14:47 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.760 s -0.817519
2021-10-04 14:54 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.938 s -0.964535
2021-10-04 14:14 Python dataframe-to-table type_simple_features 0.909 s 0.352811
2021-10-04 14:49 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.634526
2021-10-04 14:52 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.932 s -0.699020
2021-10-04 14:56 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.315 s 0.502008
2021-10-04 14:46 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.168 s 1.672034
2021-10-04 14:50 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.383 s -0.944308
2021-10-04 15:49 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.937 s -0.857042
2021-10-04 14:11 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.516 s 1.467663
2021-10-04 14:47 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.426899
2021-10-04 14:51 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.595 s -0.815547
2021-10-04 15:46 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.549 s 1.020646
2021-10-04 14:22 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.515 s 0.965308
2021-10-04 14:45 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.864 s 0.216757
2021-10-04 14:49 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.577047
2021-10-04 14:52 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.770 s 4.419132
2021-10-04 14:54 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.944 s -0.264259
2021-10-04 15:10 R dataframe-to-table type_floats, R 0.107 s 0.891270
2021-10-04 14:14 Python dataframe-to-table type_floats 0.012 s -0.244181
2021-10-04 14:14 Python dataframe-to-table type_nested 2.877 s 1.064103
2021-10-04 14:49 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.465 s -1.409297
2021-10-04 15:10 R dataframe-to-table chi_traffic_2020_Q1, R 5.343 s 0.990247
2021-10-04 15:10 R dataframe-to-table type_dict, R 0.050 s -0.030768
2021-10-04 15:39 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.982 s -0.266143
2021-10-04 14:15 Python dataset-filter nyctaxi_2010-01 4.349 s 0.656012
2021-10-04 14:45 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.940 s 1.314736
2021-10-04 14:53 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.806 s -0.499015
2021-10-04 14:53 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.270 s -0.406846
2021-10-04 14:56 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.565570
2021-10-04 14:11 Python csv-read gzip, streaming, nyctaxi_2010-01 10.470 s 1.839453
2021-10-04 14:31 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.236 s 0.363561
2021-10-04 14:46 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.759 s 1.488522
2021-10-04 14:48 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.057 s -0.444278
2021-10-04 14:49 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.302 s -1.327982
2021-10-04 14:56 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.779 s 0.563905
2021-10-04 15:48 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.190 s 1.042688
2021-10-04 15:41 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.162 s -0.959366
2021-10-04 15:51 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.571 s -0.777851
2021-10-04 15:53 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.485703
2021-10-04 14:14 Python dataframe-to-table type_strings 0.372 s -0.043838
2021-10-04 14:35 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.037 s 0.004588
2021-10-04 14:47 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.122 s 0.811467
2021-10-04 14:35 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.994 s 0.417412
2021-10-04 14:56 Python file-write lz4, feather, table, nyctaxi_2010-01 1.782 s 1.661567
2021-10-04 14:56 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.328967
2021-10-04 15:34 R dataframe-to-table type_simple_features, R 275.379 s -0.736887
2021-10-04 15:50 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.892 s -0.541877
2021-10-04 14:35 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.040 s -0.117550
2021-10-04 14:46 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.834 s -0.903917
2021-10-04 14:50 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.946 s -1.306210
2021-10-04 14:51 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.738 s -0.943589
2021-10-04 15:37 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.166 s 0.547145
2021-10-04 15:40 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.549 s -1.115034
2021-10-04 15:42 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.598 s -0.727886
2021-10-04 15:10 R dataframe-to-table type_strings, R 0.490 s 0.803149
2021-10-04 15:34 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.218 s 0.446129
2021-10-04 15:36 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.579 s -3.284791
2021-10-04 15:38 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.383284
2021-10-04 15:39 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.680 s 0.056248
2021-10-04 15:52 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.746 s -0.426317
2021-10-04 15:55 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.566625
2021-10-04 15:54 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s 0.895668
2021-10-04 16:07 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.042556
2021-10-04 15:56 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 0.909115
2021-10-04 15:55 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.165 s 1.549160
2021-10-04 15:56 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 1.066188
2021-10-04 15:56 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s 0.916871
2021-10-04 15:56 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.358682
2021-10-04 15:57 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.606 s 0.864088
2021-10-04 15:56 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.889733
2021-10-04 16:07 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.087914
2021-10-04 15:59 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.479 s -0.990779
2021-10-04 16:00 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.482 s 0.139859
2021-10-04 16:07 JavaScript Parse readBatches, tracks 0.000 s 0.721236
2021-10-04 15:57 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.938814
2021-10-04 16:07 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.106923
2021-10-04 16:07 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.171350
2021-10-04 15:57 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.512 s 0.570883
2021-10-04 15:59 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.199 s 0.781690
2021-10-04 15:59 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.204 s -2.964698
2021-10-04 15:58 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.929 s 0.897776
2021-10-04 16:07 JavaScript Parse serialize, tracks 0.005 s 0.443785
2021-10-04 16:07 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.134963
2021-10-04 16:07 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -1.762068
2021-10-04 16:07 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.582417
2021-10-04 16:07 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.216746
2021-10-04 16:07 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.645 s 0.717873
2021-10-04 16:07 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.854 s 0.613604
2021-10-04 16:07 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 16:07 JavaScript Parse Table.from, tracks 0.000 s 0.916530
2021-10-04 16:07 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.259997
2021-10-04 16:07 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.124633
2021-10-04 16:07 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.741 s 0.041115
2021-10-04 16:07 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.907 s -0.078561
2021-10-04 16:07 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.549823
2021-10-04 15:58 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.107 s -2.659202
2021-10-04 16:07 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.686 s -0.470964
2021-10-04 16:07 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.452307
2021-10-04 16:07 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.549823
2021-10-04 15:58 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.524 s 1.140670
2021-10-04 16:07 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.138303
2021-10-04 15:58 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 0.492594
2021-10-04 16:07 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.976487
2021-10-04 16:07 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.629 s -0.270104
2021-10-04 16:07 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.386471
2021-10-04 16:07 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.299706
2021-10-04 16:07 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.958296
2021-10-04 16:07 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.000585
2021-10-04 16:07 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.504 s 0.043724
2021-10-04 16:07 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.931332
2021-10-04 14:48 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.038 s -0.074610
2021-10-04 14:55 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.983 s -0.300308
2021-10-04 15:35 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.202 s 0.537102
2021-10-04 15:43 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.616 s -0.959595
2021-10-04 15:45 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.124996