Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 14:44 Python csv-read gzip, streaming, fanniemae_2016Q4 14.711 s 0.802004
2021-10-06 14:43 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.802 s 0.650804
2021-10-06 14:43 Python csv-read uncompressed, file, fanniemae_2016Q4 1.154 s 1.067152
2021-10-06 14:44 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.348305
2021-10-06 14:45 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.669 s -0.096412
2021-10-06 14:45 Python csv-read uncompressed, file, nyctaxi_2010-01 1.039 s -2.470148
2021-10-06 14:46 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.302278
2021-10-06 14:46 Python csv-read gzip, streaming, nyctaxi_2010-01 10.666 s -0.293359
2021-10-06 14:48 Python dataframe-to-table type_dict 0.012 s 0.790585
2021-10-06 14:48 Python dataframe-to-table type_strings 0.373 s -0.392651
2021-10-06 14:48 Python dataframe-to-table type_nested 2.864 s 1.055772
2021-10-06 14:48 Python dataframe-to-table type_integers 0.011 s 1.363022
2021-10-06 14:49 Python dataset-filter nyctaxi_2010-01 4.360 s 0.310017
2021-10-06 14:48 Python dataframe-to-table chi_traffic_2020_Q1 19.549 s 0.511587
2021-10-06 14:48 Python dataframe-to-table type_floats 0.011 s 0.729896
2021-10-06 14:48 Python dataframe-to-table type_simple_features 0.909 s 0.448971
2021-10-06 15:22 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.805 s -1.062235
2021-10-06 15:26 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 0.853489
2021-10-06 15:28 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.395 s -0.435782
2021-10-06 15:20 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.732 s 0.148075
2021-10-06 15:21 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.243 s -0.005144
2021-10-06 15:22 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.489217
2021-10-06 15:45 R dataframe-to-table type_floats, R 0.112 s -1.218249
2021-10-06 16:14 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -0.998091
2021-10-06 15:23 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.235 s -0.820995
2021-10-06 15:29 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.255 s -0.284275
2021-10-06 16:20 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.752 s 0.677297
2021-10-06 16:22 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s 0.907090
2021-10-06 15:30 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.833 s 1.232891
2021-10-06 15:32 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.257030
2021-10-06 16:16 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 0.755995
2021-10-06 16:23 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.211 s 0.294415
2021-10-06 16:25 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.791 s 1.146488
2021-10-06 15:24 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.330 s -1.198267
2021-10-06 16:13 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.111 s 1.257103
2021-10-06 16:28 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.728861
2021-10-06 14:56 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.731 s 0.720008
2021-10-06 15:21 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.994 s 0.074731
2021-10-06 15:22 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.295 s -0.676338
2021-10-06 15:27 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.445 s 0.750995
2021-10-06 15:32 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.784 s 0.455756
2021-10-06 16:24 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.823 s 1.111041
2021-10-06 16:09 R dataframe-to-table type_simple_features, R 275.599 s -0.983750
2021-10-06 16:10 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.231643
2021-10-06 16:29 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.254 s 0.034758
2021-10-06 16:31 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.755057
2021-10-06 15:20 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.869 s 0.099420
2021-10-06 15:45 R dataframe-to-table type_integers, R 0.085 s -0.191493
2021-10-06 16:09 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.259448
2021-10-06 16:15 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.557 s -1.352737
2021-10-06 16:31 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s 0.644826
2021-10-06 15:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.232 s 0.296303
2021-10-06 15:10 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.059 s -0.433369
2021-10-06 15:23 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.295 s -1.084676
2021-10-06 15:26 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.309 s 0.226413
2021-10-06 15:31 Python file-write lz4, feather, table, nyctaxi_2010-01 1.795 s 0.810770
2021-10-06 15:45 R dataframe-to-table type_strings, R 0.494 s -0.924827
2021-10-06 16:11 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 0.982102
2021-10-06 16:14 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.008 s -1.495147
2021-10-06 16:26 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.456 s 1.216219
2021-10-06 15:20 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.948 s 0.436936
2021-10-06 15:22 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.578104
2021-10-06 15:24 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.042 s -0.319794
2021-10-06 14:52 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.172 s -0.109181
2021-10-06 15:22 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.870 s -0.960432
2021-10-06 16:12 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.844510
2021-10-06 16:13 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.254 s -0.712249
2021-10-06 15:23 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.066 s -0.752190
2021-10-06 15:24 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.364347
2021-10-06 15:25 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.470 s -1.142916
2021-10-06 15:30 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.756 s 0.839057
2021-10-06 16:10 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.893 s 0.320089
2021-10-06 15:29 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.760 s 1.388687
2021-10-06 15:31 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.798 s 0.806219
2021-10-06 15:31 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.309 s 0.476563
2021-10-06 15:05 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.311 s -0.136150
2021-10-06 15:10 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.005 s 0.251795
2021-10-06 15:25 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -1.234721
2021-10-06 15:27 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.697 s 0.124402
2021-10-06 15:28 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.670 s 0.431805
2021-10-06 15:31 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.343 s 0.384124
2021-10-06 15:46 R dataframe-to-table type_nested, R 0.539 s -0.257441
2021-10-06 15:10 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.031 s 0.064755
2021-10-06 16:31 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.273159
2021-10-06 15:21 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.823 s 0.054335
2021-10-06 16:11 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.893 s 1.542591
2021-10-06 16:13 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.164 s 0.686261
2021-10-06 16:30 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.342389
2021-10-06 15:23 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.942 s -1.016928
2021-10-06 16:10 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.135028
2021-10-06 15:28 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.968011
2021-10-06 15:45 R dataframe-to-table chi_traffic_2020_Q1, R 5.505 s -2.116692
2021-10-06 15:45 R dataframe-to-table type_dict, R 0.051 s -0.109561
2021-10-06 16:14 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.093526
2021-10-06 16:18 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 0.776347
2021-10-06 16:22 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.601 s -0.556004
2021-10-06 16:31 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.874 s 0.757020
2021-10-06 16:12 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.375 s 0.632545
2021-10-06 16:20 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.563431
2021-10-06 16:31 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.177 s 0.405963
2021-10-06 16:32 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.609 s 0.687381
2021-10-06 16:31 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.684339
2021-10-06 16:32 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.510 s 0.954854
2021-10-06 16:33 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.887 s 0.754032
2021-10-06 16:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.581 s 0.227997
2021-10-06 16:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.796194
2021-10-06 16:33 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -2.298599
2021-10-06 16:33 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.367 s 0.091840
2021-10-06 16:34 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -1.553028
2021-10-06 16:34 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.206 s 0.671030
2021-10-06 16:34 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -2.055423
2021-10-06 16:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.484 s 0.129217
2021-10-06 16:42 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.230884
2021-10-06 16:42 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.686661
2021-10-06 16:42 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.534 s -0.442327
2021-10-06 16:42 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.195754
2021-10-06 16:42 JavaScript Parse serialize, tracks 0.005 s -0.893135
2021-10-06 16:42 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.978 s -1.608995
2021-10-06 16:42 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.633827
2021-10-06 16:42 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.233192
2021-10-06 16:42 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.571 s -0.198203
2021-10-06 16:42 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.180644
2021-10-06 16:42 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.620 s 1.194399
2021-10-06 16:42 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.906 s -0.657898
2021-10-06 16:42 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.696 s 0.283960
2021-10-06 16:42 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497214
2021-10-06 16:42 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.669 s -0.465283
2021-10-06 16:42 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.044677
2021-10-06 16:42 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.042677
2021-10-06 16:42 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.201267
2021-10-06 16:42 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497214
2021-10-06 16:42 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.349555
2021-10-06 16:42 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.166831
2021-10-06 16:42 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565628
2021-10-06 16:42 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.123992
2021-10-06 16:42 JavaScript Parse readBatches, tracks 0.000 s 0.745103
2021-10-06 16:42 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.018100
2021-10-06 16:42 JavaScript Parse Table.from, tracks 0.000 s 0.578008
2021-10-06 16:42 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.173422
2021-10-06 16:42 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.934155
2021-10-06 16:42 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.533207
2021-10-06 16:42 JavaScript DataFrame Iterate 1,000,000, tracks 0.051 s 2.715802
2021-10-06 16:42 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.444089
2021-10-06 15:23 Python file-read lz4, feather, table, fanniemae_2016Q4 0.595 s 1.251560
2021-10-06 15:25 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.969 s -1.145417
2021-10-06 15:32 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.712431
2021-10-06 16:10 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.954 s -0.048683
2021-10-06 16:18 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.279 s 0.761867
2021-10-06 16:27 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.652 s 1.175457