Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-25 19:22 Python dataframe-to-table type_dict 0.012 s 0.319633
2021-09-25 19:18 Python csv-read gzip, streaming, fanniemae_2016Q4 14.553 s -0.606035
2021-09-25 19:22 Python dataframe-to-table chi_traffic_2020_Q1 19.721 s 0.422293
2021-09-25 19:17 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.618 s -0.594114
2021-09-25 19:22 Python dataframe-to-table type_floats 0.011 s 0.224781
2021-09-25 19:19 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.203508
2021-09-25 19:22 Python dataframe-to-table type_strings 0.367 s 0.695072
2021-09-25 19:39 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 269.337 s 0.080682
2021-09-25 19:23 Python dataframe-to-table type_simple_features 0.914 s -0.685710
2021-09-25 19:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.017 s 0.674906
2021-09-25 19:18 Python csv-read uncompressed, file, fanniemae_2016Q4 1.153 s 0.205472
2021-09-25 19:23 Python dataset-filter nyctaxi_2010-01 4.377 s -0.777072
2021-09-25 19:26 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 51.892 s 1.465798
2021-09-25 19:19 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s 0.091405
2021-09-25 19:20 Python csv-read gzip, streaming, nyctaxi_2010-01 10.535 s -0.122186
2021-09-25 19:22 Python dataframe-to-table type_integers 0.011 s -0.034383
2021-09-25 19:19 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.562 s -0.184207
2021-09-25 19:49 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.315 s -0.078899
2021-09-25 19:52 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.999 s 0.326090
2021-09-25 19:20 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.155211
2021-09-25 19:23 Python dataframe-to-table type_nested 2.951 s 0.275740
2021-09-25 19:48 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.551 s 0.884610
2021-09-25 19:52 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.032 s -0.019283
2021-09-25 20:10 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.102 s 2.312276
2021-09-25 20:13 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.085 s 1.584336
2021-09-25 21:15 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.737500
2021-09-25 20:17 Python wide-dataframe use_legacy_dataset=false 0.622 s -1.071335
2021-09-25 21:28 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.370578
2021-09-25 20:05 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.705 s 0.458145
2021-09-25 20:07 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.818 s -1.207968
2021-09-25 20:16 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.293556
2021-09-25 20:08 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.054 s -0.576050
2021-09-25 20:12 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 2.521188
2021-09-25 20:57 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.914 s 0.016414
2021-09-25 20:59 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.275 s -1.986628
2021-09-25 21:10 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.899 s 0.370771
2021-09-25 21:28 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.909 s -0.642829
2021-09-25 20:07 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.732 s -0.424209
2021-09-25 20:09 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.848 s 1.074825
2021-09-25 20:15 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.904 s 2.391081
2021-09-25 20:55 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.220 s 0.582944
2021-09-25 21:16 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.175 s 1.262808
2021-09-25 20:12 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.470 s 2.308986
2021-09-25 20:31 R dataframe-to-table type_strings, R 0.493 s -1.052686
2021-09-25 21:28 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.891691
2021-09-25 20:06 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.023 s -1.414776
2021-09-25 20:08 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -1.061124
2021-09-25 20:13 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.626 s 1.142535
2021-09-25 20:16 Python file-write lz4, feather, table, nyctaxi_2010-01 1.829 s -0.851622
2021-09-25 20:54 R dataframe-to-table type_simple_features, R 274.377 s 0.804639
2021-09-25 21:01 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.537 s -1.358583
2021-09-25 21:17 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.986 s -0.015462
2021-09-25 21:28 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.574335
2021-09-25 21:28 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.490931
2021-09-25 20:05 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.817 s 0.492470
2021-09-25 20:08 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.113 s 0.179220
2021-09-25 20:13 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.181992
2021-09-25 21:02 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.830 s 2.309806
2021-09-25 21:16 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.710 s 0.428280
2021-09-25 21:20 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.476 s -1.092929
2021-09-25 21:28 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.639337
2021-09-25 21:28 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.124329
2021-09-25 21:28 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.656455
2021-09-25 20:09 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.948 s 1.256795
2021-09-25 20:16 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.760 s 0.806555
2021-09-25 20:57 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.433830
2021-09-25 20:57 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.160933
2021-09-25 20:58 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.127 s 0.223527
2021-09-25 21:00 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s -0.688141
2021-09-25 21:20 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.198 s -0.132052
2021-09-25 21:28 JavaScript Parse Table.from, tracks 0.000 s 1.846805
2021-09-25 21:28 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.155175
2021-09-25 20:16 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.293 s 0.727386
2021-09-25 20:11 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.121 s 2.233929
2021-09-25 21:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.345430
2021-09-25 21:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.510 s 1.444884
2021-09-25 21:28 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.909 s -0.240854
2021-09-25 20:07 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.316 s -4.047215
2021-09-25 20:08 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.035 s 0.105526
2021-09-25 20:15 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.833 s 1.140495
2021-09-25 21:28 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.771646
2021-09-25 20:06 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.269 s -1.503016
2021-09-25 20:10 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.484 s 1.141165
2021-09-25 20:59 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.772731
2021-09-25 21:14 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.249 s 1.143432
2021-09-25 21:28 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.490 s 0.285155
2021-09-25 20:31 R dataframe-to-table type_nested, R 0.537 s -0.166122
2021-09-25 21:28 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.735 s 0.000703
2021-09-25 21:28 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-25 20:07 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.137 s -0.300583
2021-09-25 20:13 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.366 s -0.246726
2021-09-25 20:14 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.787 s 1.192852
2021-09-25 20:31 R dataframe-to-table type_floats, R 0.113 s -1.161629
2021-09-25 21:04 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.287 s 2.248084
2021-09-25 21:17 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.009293
2021-09-25 21:28 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.667 s 0.137810
2021-09-25 21:28 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.150307
2021-09-25 20:05 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.990 s 0.213217
2021-09-25 20:06 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.852 s -1.632913
2021-09-25 20:09 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s 0.022699
2021-09-25 21:07 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.596 s 0.714336
2021-09-25 20:06 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.287 s -0.619457
2021-09-25 20:09 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.186287
2021-09-25 20:30 R dataframe-to-table chi_traffic_2020_Q1, R 5.483 s -1.131857
2021-09-25 20:31 R dataframe-to-table type_dict, R 0.056 s -0.421146
2021-09-25 21:19 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.391 s 0.506496
2021-09-25 21:20 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.832 s -1.125893
2021-09-25 21:28 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.624 s -0.113407
2021-09-25 21:28 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.184242
2021-09-25 21:28 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.932440
2021-09-25 20:07 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.656 s 0.019108
2021-09-25 20:08 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.835 s 1.199439
2021-09-25 20:14 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.847 s 1.992439
2021-09-25 20:55 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.217 s 0.809316
2021-09-25 21:09 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.214 s 0.960889
2021-09-25 20:16 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.118373
2021-09-25 20:55 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.883 s 0.574103
2021-09-25 20:58 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.178 s -0.534293
2021-09-25 21:12 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.525 s 0.679963
2021-09-25 21:28 JavaScript Parse readBatches, tracks 0.000 s 1.378330
2021-09-25 21:28 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.879954
2021-09-25 20:31 R dataframe-to-table type_integers, R 0.086 s -0.764797
2021-09-25 21:08 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -0.844585
2021-09-25 21:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.905 s 0.915522
2021-09-25 21:28 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.360965
2021-09-25 21:28 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.279484
2021-09-25 20:56 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.871 s 1.167962
2021-09-25 20:57 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.374 s -0.025256
2021-09-25 21:00 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.994 s -1.629865
2021-09-25 21:03 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.253 s 2.395725
2021-09-25 21:05 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.728 s 2.242477
2021-09-25 21:06 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.841 s -2.461405
2021-09-25 21:11 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.841 s 1.998415
2021-09-25 21:13 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.706 s 1.497360
2021-09-25 21:14 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.203754
2021-09-25 21:16 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.609 s 1.044558
2021-09-25 21:28 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.663625
2021-09-25 21:28 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.071442
2021-09-25 20:56 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.217137
2021-09-25 21:17 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.763 s 0.662378
2021-09-25 21:17 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.396650
2021-09-25 21:21 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.495 s 0.197820
2021-09-25 21:28 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.639 s -0.242239
2021-09-25 21:28 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.879954
2021-09-25 21:28 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.240712
2021-09-25 21:17 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.505 s 1.411345
2021-09-25 21:19 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.100 s 0.761508
2021-09-25 21:28 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.171366
2021-09-25 21:28 JavaScript Parse serialize, tracks 0.005 s 0.450486