Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 09:27 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.465 s 1.114492
2021-10-09 09:27 Python csv-read uncompressed, file, nyctaxi_2010-01 1.006 s 0.614807
2021-10-09 09:52 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.026 s -0.026121
2021-10-09 10:06 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.053 s -1.026727
2021-10-09 10:07 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.444 s -0.377679
2021-10-09 10:07 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.353942
2021-10-09 10:09 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.330 s 0.148016
2021-10-09 10:09 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.447 s 0.530206
2021-10-09 10:10 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.297 s 0.322501
2021-10-09 10:12 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.849 s -0.253084
2021-10-09 10:13 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.905 s -0.559501
2021-10-09 10:14 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.345414
2021-10-09 10:27 R dataframe-to-table chi_traffic_2020_Q1, R 3.372 s 0.276210
2021-10-09 10:28 R dataframe-to-table type_floats, R 0.013 s 1.942354
2021-10-09 10:28 R dataframe-to-table type_nested, R 0.537 s 0.233532
2021-10-09 10:34 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.208 s 0.532055
2021-10-09 10:35 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.436 s 1.834511
2021-10-09 10:35 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.432 s 1.787014
2021-10-09 10:36 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.407 s -1.318732
2021-10-09 10:36 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.559915
2021-10-09 10:37 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.161 s 1.813574
2021-10-09 10:37 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.239 s 1.771741
2021-10-09 10:42 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.288 s 0.654011
2021-10-09 10:44 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.556059
2021-10-09 10:46 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.187 s 1.024889
2021-10-09 09:25 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.996 s -0.586515
2021-10-09 09:28 Python csv-read gzip, streaming, nyctaxi_2010-01 10.452 s 1.188613
2021-10-09 10:03 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.673 s 0.779328
2021-10-09 10:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.981 s 0.284204
2021-10-09 10:11 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.787 s -0.180208
2021-10-09 10:47 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.727654
2021-10-09 10:05 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.296 s -0.912264
2021-10-09 09:31 Python dataframe-to-table type_nested 2.886 s -0.073392
2021-10-09 10:06 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.013 s 1.376030
2021-10-09 10:03 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.018 s 0.031436
2021-10-09 10:05 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.941 s -0.470325
2021-10-09 10:10 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.687 s 0.194598
2021-10-09 09:30 Python dataframe-to-table type_dict 0.012 s -0.618231
2021-10-09 10:05 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 1.137121
2021-10-09 10:06 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.249 s -0.511344
2021-10-09 10:49 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.681823
2021-10-09 09:25 Python csv-read uncompressed, file, fanniemae_2016Q4 1.172 s 0.151557
2021-10-09 09:28 Python csv-read gzip, file, nyctaxi_2010-01 9.037 s 2.887825
2021-10-09 10:08 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.092 s 0.514205
2021-10-09 09:30 Python dataframe-to-table type_strings 0.373 s -0.031768
2021-10-09 10:05 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.131 s 0.790251
2021-10-09 10:39 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.531 s 0.121172
2021-10-09 10:39 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.846 s 0.585223
2021-10-09 10:51 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.715 s -0.661278
2021-10-09 11:05 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-09 09:30 Python dataframe-to-table type_floats 0.011 s 1.156676
2021-10-09 09:31 Python dataframe-to-table type_simple_features 0.908 s 0.445202
2021-10-09 10:38 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.160166
2021-10-09 10:48 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.854 s -0.653917
2021-10-09 09:31 Python dataset-filter nyctaxi_2010-01 4.333 s 1.329658
2021-10-09 09:34 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.597 s 0.991527
2021-10-09 09:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.256 s 0.204020
2021-10-09 09:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.999 s 1.671570
2021-10-09 10:12 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.896 s -0.887890
2021-10-09 10:13 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.972 s -1.151124
2021-10-09 10:14 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.070174
2021-10-09 10:36 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s 0.090285
2021-10-09 10:37 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.802281
2021-10-09 10:41 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.241 s 0.794218
2021-10-09 09:27 Python csv-read gzip, file, fanniemae_2016Q4 6.022 s 2.007740
2021-10-09 09:48 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.986 s -0.703580
2021-10-09 10:07 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.318 s -0.504162
2021-10-09 10:11 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.332 s -1.181415
2021-10-09 10:15 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.255363
2021-10-09 10:28 R dataframe-to-table type_dict, R 0.050 s 0.116587
2021-10-09 09:26 Python csv-read gzip, streaming, fanniemae_2016Q4 14.878 s -0.080636
2021-10-09 10:35 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.656637
2021-10-09 10:07 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.169264
2021-10-09 10:14 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.364 s -0.428996
2021-10-09 10:15 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.717373
2021-10-09 09:30 Python dataframe-to-table chi_traffic_2020_Q1 19.496 s 0.266453
2021-10-09 09:30 Python dataframe-to-table type_integers 0.011 s 0.672791
2021-10-09 10:04 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.769 s 1.249926
2021-10-09 10:05 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.853 s 0.043223
2021-10-09 10:05 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.766 s 0.557253
2021-10-09 10:45 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.393 s 1.577502
2021-10-09 10:28 R dataframe-to-table type_strings, R 0.488 s 0.232690
2021-10-09 09:38 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.701 s -0.039791
2021-10-09 09:52 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.048 s -0.136392
2021-10-09 10:06 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.259 s -0.234132
2021-10-09 10:08 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.954 s -0.440547
2021-10-09 10:14 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s 0.379312
2021-10-09 10:35 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.206 s 1.368212
2021-10-09 10:37 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.119 s 0.685784
2021-10-09 10:43 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.725 s 0.609756
2021-10-09 10:03 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.796 s 0.547046
2021-10-09 10:04 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.192 s 1.083815
2021-10-09 10:04 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.292 s 0.058563
2021-10-09 10:11 Python file-write lz4, feather, table, fanniemae_2016Q4 1.198 s -2.649329
2021-10-09 10:28 R dataframe-to-table type_integers, R 0.010 s 1.939236
2021-10-09 10:34 R dataframe-to-table type_simple_features, R 3.307 s 1.512179
2021-10-09 10:54 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s -0.397807
2021-10-09 10:38 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.982 s 0.161324
2021-10-09 10:45 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.552 s 0.632215
2021-10-09 10:51 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.851692
2021-10-09 11:06 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.960790
2021-10-09 10:56 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.413033
2021-10-09 10:54 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.586 s -1.876045
2021-10-09 10:54 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.311442
2021-10-09 10:55 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.294542
2021-10-09 10:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.538676
2021-10-09 10:52 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.237 s 0.896303
2021-10-09 10:55 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 1.080957
2021-10-09 10:57 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.985010
2021-10-09 10:54 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.168 s 0.775056
2021-10-09 10:57 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.087060
2021-10-09 10:56 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.894 s 0.964466
2021-10-09 10:56 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.524 s 1.071818
2021-10-09 10:57 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s -0.278648
2021-10-09 10:57 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -0.813955
2021-10-09 10:53 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.160362
2021-10-09 10:54 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.858 s 0.586660
2021-10-09 11:05 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.893739
2021-10-09 11:06 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-09 11:06 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.337555
2021-10-09 11:05 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.896632
2021-10-09 11:05 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.737 s 0.002305
2021-10-09 11:06 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.808669
2021-10-09 11:06 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.579 s -1.089700
2021-10-09 11:05 JavaScript Parse Table.from, tracks 0.000 s 0.221761
2021-10-09 11:05 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.886761
2021-10-09 11:05 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.073070
2021-10-09 11:05 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.074154
2021-10-09 11:06 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.743172
2021-10-09 11:05 JavaScript Parse readBatches, tracks 0.000 s 0.799887
2021-10-09 11:06 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.046389
2021-10-09 11:06 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.209770
2021-10-09 11:06 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.066241
2021-10-09 11:06 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.736386
2021-10-09 10:58 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.183 s -0.456900
2021-10-09 10:58 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.492 s 0.601490
2021-10-09 11:06 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.094963
2021-10-09 11:05 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.646 s -0.510829
2021-10-09 11:06 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.319064
2021-10-09 11:05 JavaScript Parse serialize, tracks 0.005 s -0.710593
2021-10-09 11:05 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.660 s -0.500987
2021-10-09 11:05 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.725 s -0.843844
2021-10-09 11:05 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.949 s -0.846941
2021-10-09 11:06 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.499244
2021-10-09 11:05 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.054551
2021-10-09 11:05 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.892 s -0.347314
2021-10-09 11:06 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.109679
2021-10-09 11:06 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.473105
2021-10-09 10:36 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -0.957131