Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 09:35 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.974 s -0.384631
2021-10-08 09:36 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.787823
2021-10-08 09:37 Python csv-read gzip, streaming, nyctaxi_2010-01 10.491 s 1.005033
2021-10-08 09:40 Python dataframe-to-table type_simple_features 0.911 s 0.289944
2021-10-08 10:01 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.025 s -0.039944
2021-10-08 10:12 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.744 s 0.055437
2021-10-08 10:12 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.254 s -0.216731
2021-10-08 10:14 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.805 s -0.720425
2021-10-08 10:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 1.098968
2021-10-08 10:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.928 s -0.480202
2021-10-08 10:14 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.025 s 0.945059
2021-10-08 10:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.444 s -0.644953
2021-10-08 10:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.086 s 0.529490
2021-10-08 10:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.297 s 0.124373
2021-10-08 10:20 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.791 s -0.497918
2021-10-08 10:21 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.793 s 0.375769
2021-10-08 10:21 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 0.690676
2021-10-08 10:23 Python wide-dataframe use_legacy_dataset=true 0.399 s -2.895902
2021-10-08 10:36 R dataframe-to-table type_dict, R 0.050 s 0.086449
2021-10-08 10:36 R dataframe-to-table type_floats, R 0.013 s 3.001673
2021-10-08 10:37 R dataframe-to-table type_nested, R 0.538 s 0.201146
2021-10-08 11:03 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 0.336127
2021-10-08 11:03 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.233196
2021-10-08 11:06 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -1.223364
2021-10-08 11:14 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.278 s 2.451826
2021-10-08 11:14 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.256 s 2.401418
2021-10-08 11:14 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.894 s 0.272872
2021-10-08 11:14 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.360344
2021-10-08 11:14 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.493 s 0.378076
2021-10-08 10:22 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.346 s 0.168353
2021-10-08 09:38 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.297896
2021-10-08 10:01 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.016 s 0.266772
2021-10-08 10:22 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.808 s 0.702151
2021-10-08 10:23 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.028366
2021-10-08 10:52 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.723 s 0.629925
2021-10-08 09:40 Python dataframe-to-table type_integers 0.011 s 0.738726
2021-10-08 09:44 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.954 s 0.467297
2021-10-08 09:37 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s -0.004319
2021-10-08 09:40 Python dataframe-to-table type_nested 2.869 s 0.706450
2021-10-08 10:20 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.063627
2021-10-08 10:23 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.331 s 0.093580
2021-10-08 09:35 Python csv-read uncompressed, file, fanniemae_2016Q4 1.199 s -1.440899
2021-10-08 09:40 Python dataframe-to-table type_dict 0.012 s 0.983309
2021-10-08 09:40 Python dataframe-to-table type_floats 0.011 s 1.254486
2021-10-08 10:01 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.031 s 0.076437
2021-10-08 10:20 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.241 s -0.154261
2021-10-08 10:20 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.793 s 0.577013
2021-10-08 09:40 Python dataframe-to-table chi_traffic_2020_Q1 19.477 s 0.519951
2021-10-08 10:14 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.727693
2021-10-08 10:18 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.432 s 0.611434
2021-10-08 10:23 Python file-write lz4, feather, table, nyctaxi_2010-01 1.813 s -0.228913
2021-10-08 10:23 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s 0.030601
2021-10-08 10:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.046 s -0.589654
2021-10-08 10:15 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.302 s -0.674301
2021-10-08 10:16 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.193142
2021-10-08 09:41 Python dataset-filter nyctaxi_2010-01 4.359 s 0.395305
2021-10-08 09:48 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.559 s 0.416530
2021-10-08 09:57 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.984 s -0.280821
2021-10-08 10:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.313 s -0.888084
2021-10-08 10:13 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.161 s -0.714904
2021-10-08 10:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.296 s -0.696268
2021-10-08 10:19 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.621 s 0.362919
2021-10-08 09:36 Python csv-read gzip, streaming, fanniemae_2016Q4 14.917 s -0.462563
2021-10-08 09:37 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.500 s 1.005747
2021-10-08 10:13 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.832 s -0.103897
2021-10-08 09:57 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.360 s 0.126350
2021-10-08 10:12 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.065 s -0.444456
2021-10-08 10:12 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.005 s -0.098845
2021-10-08 10:13 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.889 s -0.937232
2021-10-08 10:36 R dataframe-to-table chi_traffic_2020_Q1, R 3.410 s 0.251594
2021-10-08 10:16 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.965 s -0.738692
2021-10-08 10:43 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.476 s 2.858275
2021-10-08 10:54 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 1.157842
2021-10-08 10:36 R dataframe-to-table type_integers, R 0.010 s 3.016293
2021-10-08 10:12 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.877 s 0.069886
2021-10-08 10:15 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.569280
2021-10-08 10:36 R dataframe-to-table type_strings, R 0.491 s 0.200119
2021-10-08 10:19 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.312 s 0.149815
2021-10-08 10:56 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.833 s 0.543861
2021-10-08 10:43 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.484 s 2.721440
2021-10-08 10:43 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.250 s 0.218535
2021-10-08 10:44 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.446054
2021-10-08 10:43 R dataframe-to-table type_simple_features, R 3.305 s 2.149921
2021-10-08 10:43 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.242 s 0.129274
2021-10-08 09:40 Python dataframe-to-table type_strings 0.367 s 0.719715
2021-10-08 10:14 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.242 s -0.592933
2021-10-08 10:44 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.932 s -0.729527
2021-10-08 10:45 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.172 s 2.845543
2021-10-08 10:45 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.542721
2021-10-08 10:44 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.641262
2021-10-08 10:45 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.087096
2021-10-08 10:45 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.231 s 2.847962
2021-10-08 10:46 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.998 s -0.045002
2021-10-08 10:45 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.401 s -0.927456
2021-10-08 10:45 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.131 s -0.421315
2021-10-08 10:46 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.671 s 0.181969
2021-10-08 10:48 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.853 s 0.501592
2021-10-08 10:47 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.542 s 0.008333
2021-10-08 10:49 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.262 s 0.653550
2021-10-08 11:03 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.733474
2021-10-08 11:06 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.181 s 0.429856
2021-10-08 10:50 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.317 s 0.409091
2021-10-08 11:14 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.845863
2021-10-08 11:14 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.665 s 0.458716
2021-10-08 11:14 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.763840
2021-10-08 10:53 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.543 s 1.127967
2021-10-08 11:02 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.883283
2021-10-08 11:03 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.564 s 0.586141
2021-10-08 11:04 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.908 s 0.439845
2021-10-08 11:07 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.516 s -1.691810
2021-10-08 11:04 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.541179
2021-10-08 11:14 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.050346
2021-10-08 10:52 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.826 s 1.086534
2021-10-08 11:01 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.247 s 0.325062
2021-10-08 11:02 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.491890
2021-10-08 11:05 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.121105
2021-10-08 10:59 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.654 s 0.704482
2021-10-08 11:13 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.458540
2021-10-08 11:14 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.334032
2021-10-08 11:05 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.625915
2021-10-08 11:14 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.893 s -0.378381
2021-10-08 11:04 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.474923
2021-10-08 11:05 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.527 s 0.985301
2021-10-08 11:13 JavaScript Parse readBatches, tracks 0.000 s 0.078983
2021-10-08 10:55 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.179 s 1.650883
2021-10-08 11:14 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.828716
2021-10-08 11:13 JavaScript Parse serialize, tracks 0.005 s -0.760974
2021-10-08 11:14 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.103998
2021-10-08 11:14 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.040086
2021-10-08 10:58 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.471 s 0.519531
2021-10-08 11:03 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 0.398853
2021-10-08 11:00 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.549913
2021-10-08 11:13 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.508989
2021-10-08 11:14 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.615049
2021-10-08 11:14 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.484069
2021-10-08 11:14 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.105562
2021-10-08 11:14 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.065374
2021-10-08 11:14 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.824414
2021-10-08 11:14 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.605740
2021-10-08 11:14 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.773829
2021-10-08 11:05 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.487 s -1.672355
2021-10-08 11:14 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.505821
2021-10-08 11:13 JavaScript Parse Table.from, tracks 0.000 s -0.159638
2021-10-08 11:14 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 11:14 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.505521
2021-10-08 11:14 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.482385
2021-10-08 10:57 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.790 s 0.761698
2021-10-08 11:03 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.871 s 0.463692
2021-10-08 11:14 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.705 s 0.241833