Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-27 15:39 Python csv-read gzip, streaming, fanniemae_2016Q4 14.754 s -0.792158
2021-09-27 15:41 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.530765
2021-09-27 15:38 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.815 s -0.777382
2021-09-27 15:40 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.768 s -0.935416
2021-09-27 15:42 Python dataframe-to-table chi_traffic_2020_Q1 19.718 s 0.426044
2021-09-27 15:43 Python dataframe-to-table type_simple_features 0.906 s 0.550579
2021-09-27 15:43 Python dataframe-to-table type_integers 0.011 s -0.054788
2021-09-27 15:43 Python dataframe-to-table type_dict 0.012 s -1.777157
2021-09-27 15:43 Python dataset-filter nyctaxi_2010-01 4.360 s -0.210549
2021-09-27 15:39 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s -0.032934
2021-09-27 15:40 Python csv-read uncompressed, file, nyctaxi_2010-01 1.033 s -0.204655
2021-09-27 15:40 Python csv-read gzip, streaming, nyctaxi_2010-01 10.762 s -0.955403
2021-09-27 15:43 Python dataframe-to-table type_strings 0.370 s 0.339129
2021-09-27 15:46 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 55.209 s 0.755765
2021-09-27 15:43 Python dataframe-to-table type_floats 0.012 s -0.907594
2021-09-27 15:38 Python csv-read uncompressed, file, fanniemae_2016Q4 1.152 s 0.232089
2021-09-27 15:43 Python dataframe-to-table type_nested 2.941 s 0.770608
2021-09-27 16:09 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.103 s 0.697731
2021-09-27 16:13 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.043 s -0.215499
2021-09-27 16:24 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.743 s 0.298557
2021-09-27 16:26 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.797 s 0.896498
2021-09-27 16:32 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 1.052214
2021-09-27 17:13 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.882 s 0.572169
2021-09-27 17:17 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.222 s 0.986044
2021-09-27 17:33 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.258 s 0.547605
2021-09-27 17:35 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.757 s 0.751793
2021-09-27 16:25 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.830 s -0.747436
2021-09-27 16:26 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.726 s -0.018598
2021-09-27 16:29 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.508 s 0.957397
2021-09-27 16:09 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.272 s 0.149201
2021-09-27 16:27 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.010 s 1.681613
2021-09-27 16:30 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.139 s 1.782763
2021-09-27 16:35 Python wide-dataframe use_legacy_dataset=false 0.620 s -0.628720
2021-09-27 16:49 R dataframe-to-table chi_traffic_2020_Q1, R 5.465 s -0.839645
2021-09-27 17:34 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.696 s 0.739486
2021-09-27 16:24 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.776 s 0.657396
2021-09-27 17:15 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.427 s -2.856818
2021-09-27 17:26 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.404 s -0.255221
2021-09-27 16:25 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.289 s -0.798395
2021-09-27 16:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.145 s -0.826739
2021-09-27 16:33 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.815 s 2.544553
2021-09-27 17:29 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.843 s 1.935890
2021-09-27 16:13 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.999 s 0.309801
2021-09-27 16:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.651 s 0.225454
2021-09-27 16:26 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -1.505859
2021-09-27 16:28 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.989 s 1.006195
2021-09-27 16:33 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.764 s 1.261774
2021-09-27 16:35 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.113332
2021-09-27 17:20 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 1.992772
2021-09-27 16:24 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.914 s 0.660938
2021-09-27 16:25 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.005 s -0.715112
2021-09-27 16:28 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.859 s 0.959342
2021-09-27 16:26 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.486656
2021-09-27 16:28 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.345945
2021-09-27 16:34 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.312 s 2.887499
2021-09-27 17:16 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.120 s 0.705149
2021-09-27 16:00 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 272.672 s 0.014049
2021-09-27 16:30 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.430 s 2.158379
2021-09-27 16:29 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.073 s 2.193355
2021-09-27 16:35 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.737 s 0.940204
2021-09-27 17:13 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.265 s -1.511383
2021-09-27 17:24 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.840 s -2.040518
2021-09-27 17:30 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.526 s 0.752881
2021-09-27 16:27 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.824 s 1.166886
2021-09-27 17:14 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.867 s 1.252278
2021-09-27 16:13 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.033 s 0.039727
2021-09-27 16:27 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.050 s -0.373141
2021-09-27 16:34 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.811 s 1.200450
2021-09-27 16:35 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.224 s 1.197800
2021-09-27 16:49 R dataframe-to-table type_strings, R 0.492 s -0.761766
2021-09-27 16:49 R dataframe-to-table type_nested, R 0.539 s -0.837610
2021-09-27 17:27 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.194 s 1.359259
2021-09-27 16:25 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.264 s -1.213323
2021-09-27 16:27 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.135 s -2.216651
2021-09-27 16:31 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.199 s 1.085302
2021-09-27 16:35 Python file-write lz4, feather, table, nyctaxi_2010-01 1.791 s 1.137815
2021-09-27 16:49 R dataframe-to-table type_floats, R 0.113 s -1.006013
2021-09-27 17:31 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.709 s 1.483760
2021-09-27 16:28 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.028253
2021-09-27 16:34 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.894 s 2.258696
2021-09-27 17:13 R dataframe-to-table type_simple_features, R 274.530 s 0.549626
2021-09-27 17:34 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.177 s 1.084225
2021-09-27 16:31 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.499 s 1.824824
2021-09-27 16:32 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.499 s 2.052086
2021-09-27 16:32 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.114 s 1.184733
2021-09-27 17:22 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.298 s 2.024940
2021-09-27 17:21 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.262 s 2.119868
2021-09-27 17:23 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.728 s 2.033185
2021-09-27 17:25 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.578 s 1.030358
2021-09-27 17:36 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.607 s -0.046242
2021-09-27 16:49 R dataframe-to-table type_integers, R 0.086 s -0.717981
2021-09-27 17:36 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.515 s -0.052546
2021-09-27 17:13 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.209 s 1.232086
2021-09-27 17:15 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -1.299690
2021-09-27 17:16 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.159 s 0.641672
2021-09-27 17:15 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.893 s 0.923783
2021-09-27 17:18 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.418246
2021-09-27 17:35 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.593 s 1.024740
2021-09-27 17:28 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.892 s 0.687777
2021-09-27 17:14 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.100142
2021-09-27 17:17 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.117516
2021-09-27 17:19 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.572 s -2.896770
2021-09-27 17:32 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.337577
2021-09-27 17:33 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.483 s 1.452507
2021-09-27 17:35 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.983 s 0.082015
2021-09-27 17:35 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.361071
2021-09-27 17:35 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.281206
2021-09-27 17:37 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.884 s 0.973032
2021-09-27 17:37 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.395 s -0.616555
2021-09-27 17:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.361326
2021-09-27 17:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.594 s 0.107893
2021-09-27 17:37 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.100 s 0.941335
2021-09-27 17:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.749344
2021-09-27 17:39 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.181025
2021-09-27 17:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.816 s 0.132734
2021-09-27 17:46 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.186345
2021-09-27 17:46 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.647 s 0.531188
2021-09-27 17:46 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-27 17:46 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.178983
2021-09-27 17:46 JavaScript Parse serialize, tracks 0.004 s 0.730585
2021-09-27 17:46 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-27 17:46 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.194968
2021-09-27 17:46 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.260805
2021-09-27 17:46 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.518 s -0.146533
2021-09-27 17:46 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.486660
2021-09-27 17:46 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.638338
2021-09-27 17:46 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.231205
2021-09-27 17:46 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.937859
2021-09-27 17:46 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.926539
2021-09-27 17:46 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.596692
2021-09-27 17:46 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.580275
2021-09-27 17:46 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.629 s -0.208658
2021-09-27 17:46 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.885 s 0.241207
2021-09-27 17:46 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.694471
2021-09-27 17:46 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.226491
2021-09-27 17:46 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-27 17:46 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.066013
2021-09-27 17:46 JavaScript Parse readBatches, tracks 0.000 s 1.010829
2021-09-27 17:46 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.807 s 1.806196
2021-09-27 17:46 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.704 s 0.239423
2021-09-27 17:46 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.958386
2021-09-27 17:46 JavaScript Parse Table.from, tracks 0.000 s 0.464486
2021-09-27 17:46 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.935003
2021-09-27 17:46 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.570 s 0.081173
2021-09-27 17:46 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.173120
2021-09-27 17:46 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.966293
2021-09-27 16:49 R dataframe-to-table type_dict, R 0.053 s 0.005515
2021-09-27 17:16 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.530302
2021-09-27 17:18 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.993 s -1.497788