Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 03:03 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.848 s 0.433245
2021-10-07 03:28 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.640 s -0.145079
2021-10-07 03:32 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.024 s 0.123469
2021-10-07 03:44 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.617 s -4.663210
2021-10-07 03:04 Python csv-read uncompressed, file, fanniemae_2016Q4 1.173 s 0.055406
2021-10-07 03:28 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.189 s 0.616400
2021-10-07 03:32 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.044 s -0.108034
2021-10-07 03:45 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.000 s 0.052648
2021-10-07 03:46 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.238 s 0.169253
2021-10-07 03:05 Python csv-read gzip, streaming, fanniemae_2016Q4 14.768 s 0.515325
2021-10-07 03:32 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.003 s 0.280722
2021-10-07 03:45 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.677 s 0.697308
2021-10-07 03:05 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.462012
2021-10-07 03:46 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.993 s 0.162745
2021-10-07 03:56 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.303 s -0.865083
2021-10-07 03:06 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.681 s -0.215856
2021-10-07 03:10 Python dataframe-to-table type_integers 0.011 s 1.003456
2021-10-07 03:10 Python dataframe-to-table type_nested 2.869 s 0.856112
2021-10-07 03:46 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.820 s 0.182236
2021-10-07 03:47 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.811 s -1.032951
2021-10-07 03:48 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.258 s -1.061031
2021-10-07 03:06 Python csv-read uncompressed, file, nyctaxi_2010-01 1.012 s 0.102186
2021-10-07 03:47 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.284 s 0.305973
2021-10-07 03:07 Python csv-read gzip, streaming, nyctaxi_2010-01 10.669 s -0.360817
2021-10-07 03:47 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.897 s -1.237662
2021-10-07 03:47 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.152 s -0.418836
2021-10-07 03:48 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 1.679413
2021-10-07 03:07 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.325898
2021-10-07 03:48 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.917 s -0.511527
2021-10-07 03:10 Python dataframe-to-table type_floats 0.011 s 1.368495
2021-10-07 03:48 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s 0.009319
2021-10-07 03:48 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.019 s 1.250876
2021-10-07 03:49 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.281 s -0.865780
2021-10-07 03:50 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.466 s -0.973153
2021-10-07 03:09 Python dataframe-to-table chi_traffic_2020_Q1 19.837 s -0.786308
2021-10-07 03:50 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.334 s -1.059446
2021-10-07 03:53 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.436 s 0.720357
2021-10-07 03:50 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.740961
2021-10-07 03:55 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.770 s -0.365908
2021-10-07 03:51 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.712161
2021-10-07 03:10 Python dataframe-to-table type_strings 0.375 s -0.615604
2021-10-07 03:51 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.947 s -0.896328
2021-10-07 03:52 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 0.755458
2021-10-07 03:10 Python dataframe-to-table type_dict 0.012 s -0.258191
2021-10-07 03:53 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.351 s -0.081420
2021-10-07 03:54 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.676 s 0.136072
2021-10-07 03:54 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.373 s -0.290196
2021-10-07 03:55 Python file-write lz4, feather, table, fanniemae_2016Q4 1.162 s 0.052107
2021-10-07 03:10 Python dataframe-to-table type_simple_features 0.915 s -0.215355
2021-10-07 03:56 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.780 s 0.961600
2021-10-07 03:57 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.767 s 0.726847
2021-10-07 03:11 Python dataset-filter nyctaxi_2010-01 4.350 s 0.740957
2021-10-07 03:58 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.846 s 0.909974
2021-10-07 04:00 Python wide-dataframe use_legacy_dataset=false 0.628 s -1.839382
2021-10-07 03:58 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.816 s 0.619817
2021-10-07 03:59 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.347 s 0.195940
2021-10-07 03:59 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.337 s 0.071629
2021-10-07 03:59 Python file-write lz4, feather, table, nyctaxi_2010-01 1.820 s -0.594255
2021-10-07 03:14 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.200 s -0.555127
2021-10-07 03:49 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.038 s -0.081901
2021-10-07 04:00 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.162489
2021-10-07 04:15 R dataframe-to-table type_strings, R 0.491 s 0.312137
2021-10-07 04:15 R dataframe-to-table type_dict, R 0.041 s 1.150771
2021-10-07 04:14 R dataframe-to-table chi_traffic_2020_Q1, R 5.478 s -0.388184
2021-10-07 04:16 R dataframe-to-table type_integers, R 0.085 s -0.233167
2021-10-07 04:16 R dataframe-to-table type_nested, R 0.542 s -1.483752
2021-10-07 04:16 R dataframe-to-table type_floats, R 0.111 s -0.290359
2021-10-07 04:26 R dataframe-to-table type_simple_features, R 3.313 s 4.405662
2021-10-07 04:26 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.240 s 0.292528
2021-10-07 04:27 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.903 s 0.349217
2021-10-07 04:28 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.118287
2021-10-07 04:28 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.118287
2021-10-07 04:32 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s 0.034202
2021-10-07 04:29 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.901 s 0.234283
2021-10-07 04:31 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.376728
2021-10-07 04:29 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.060962
2021-10-07 04:30 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.485539
2021-10-07 04:32 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.372 s 0.831206
2021-10-07 04:35 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.252 s -0.535009
2021-10-07 04:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.131 s -0.385199
2021-10-07 04:33 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.163 s 0.780258
2021-10-07 04:37 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.672 s 0.159371
2021-10-07 04:35 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.564830
2021-10-07 04:37 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.978 s 0.364397
2021-10-07 04:38 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.509 s 1.266055
2021-10-07 04:42 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 0.677858
2021-10-07 04:44 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.760 s 0.531995
2021-10-07 04:41 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.293 s 0.576526
2021-10-07 04:39 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.852 s 0.647928
2021-10-07 04:39 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.852 s 0.647928
2021-10-07 04:47 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.579 s 0.007700
2021-10-07 04:45 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.513045
2021-10-07 04:47 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.392 s 1.846880
2021-10-07 04:49 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.229 s -0.513652
2021-10-07 04:50 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.842 s 0.591255
2021-10-07 04:51 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.782 s 1.158690
2021-10-07 04:51 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.782 s 1.158690
2021-10-07 04:53 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.456 s 1.059587
2021-10-07 04:53 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.456 s 1.059587
2021-10-07 04:58 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.496 s -1.084737
2021-10-07 04:55 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.648 s 1.065080
2021-10-07 05:00 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.868 s 0.675206
2021-10-07 05:01 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.219021
2021-10-07 04:56 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 1.086568
2021-10-07 04:57 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.252 s 0.045016
2021-10-07 04:59 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.180 s 0.117724
2021-10-07 05:02 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.602 s 0.629248
2021-10-07 04:59 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.569539
2021-10-07 05:03 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.885 s 0.656148
2021-10-07 05:01 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 0.600070
2021-10-07 05:02 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.552760
2021-10-07 05:04 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s 0.147027
2021-10-07 05:02 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.749949
2021-10-07 05:17 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.112021
2021-10-07 05:03 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.960952
2021-10-07 05:03 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.530 s 0.977282
2021-10-07 05:13 JavaScript Parse Table.from, tracks 0.000 s 0.608366
2021-10-07 05:13 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.875986
2021-10-07 05:16 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.517103
2021-10-07 05:18 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.113362
2021-10-07 05:04 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -2.124640
2021-10-07 05:13 JavaScript Parse readBatches, tracks 0.000 s 0.809197
2021-10-07 05:04 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.480 s -0.691976
2021-10-07 05:13 JavaScript Parse serialize, tracks 0.005 s -0.740395
2021-10-07 05:05 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -2.014399
2021-10-07 05:13 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.023 s 0.624378
2021-10-07 05:14 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.656 s 0.538469
2021-10-07 05:05 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.184 s 0.636337
2021-10-07 05:13 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.641 s -0.383736
2021-10-07 05:05 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.512 s 0.053943
2021-10-07 05:13 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.587 s -0.292726
2021-10-07 05:15 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.883 s 0.493262
2021-10-07 05:13 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.889071
2021-10-07 05:14 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.613377
2021-10-07 05:13 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.921578
2021-10-07 05:14 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.717 s 0.169918
2021-10-07 05:16 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.064451
2021-10-07 05:17 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.206697
2021-10-07 05:14 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.368901
2021-10-07 05:15 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.859 s 0.507180
2021-10-07 05:15 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.854511
2021-10-07 05:15 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.608590
2021-10-07 05:15 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.517103
2021-10-07 05:16 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.075298
2021-10-07 05:17 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.134579
2021-10-07 05:17 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.676247
2021-10-07 05:17 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.326054
2021-10-07 05:17 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.815667
2021-10-07 05:17 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.571270
2021-10-07 05:17 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.114023
2021-10-07 05:18 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.537 s -0.499291
2021-10-07 03:19 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.916 s 0.633043
2021-10-07 04:00 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.818 s -0.080018