Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-24 20:09 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.227 s 0.362
2023-01-24 20:09 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.768 s 1.198
2023-01-24 20:10 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.530 s 1.177
2023-01-24 20:12 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.399 s -1.580
2023-01-24 20:14 Python dataframe-to-table type_floats 0.010 s 0.205
2023-01-24 20:14 Python dataframe-to-table type_nested 2.962 s -0.003
2023-01-24 20:23 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.293 s 0.048
2023-01-24 20:10 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.595 s 1.102
2023-01-24 20:11 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.438 s -0.334
2023-01-24 20:14 Python dataframe-to-table chi_traffic_2020_Q1 20.692 s 2.009
2023-01-24 20:14 Python dataframe-to-table type_integers 0.010 s 0.121
2023-01-24 20:15 Python dataset-filter nyctaxi_2010-01 1.035 s -0.106
2023-01-24 20:11 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.127 s 0.135
2023-01-24 20:12 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.291 s -1.484
2023-01-24 20:14 Python dataframe-to-table type_dict 0.011 s 2.375
2023-01-24 20:14 Python dataframe-to-table type_strings 0.424 s 1.216
2023-01-24 20:18 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 74.040 s 0.818
2023-01-24 20:34 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.263 s -0.335
2023-01-24 20:35 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s 1.390
2023-01-24 20:47 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.282 s 1.275
2023-01-24 20:47 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.025 s 1.028
2023-01-24 20:34 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.369 s -0.416
2023-01-24 20:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.658 s 0.242
2023-01-24 20:34 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.031 s 0.231
2023-01-24 20:34 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.285 s 0.377
2023-01-24 20:35 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.730 s 1.346
2023-01-24 20:48 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.225 s -0.246
2023-01-24 20:34 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.276 s -0.217
2023-01-24 20:39 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.153 s -1.672
2023-01-24 20:52 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.421 s 1.151
2023-01-24 20:33 Python dataset-read async=True, nyctaxi_multi_ipc_s3 215.752 s 0.826
2023-01-24 20:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.141 s 0.201
2023-01-24 20:36 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.254 s 1.364
2023-01-24 20:52 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.409 s 0.184
2023-01-24 20:35 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -1.211
2023-01-24 20:39 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.034 s 1.449
2023-01-24 20:47 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.836 s 1.262
2023-01-24 20:34 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.039 s 0.191
2023-01-24 20:34 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.181 s -0.348
2023-01-24 20:35 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.541
2023-01-24 20:34 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.211 s 0.049
2023-01-24 20:35 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.303 s 1.758
2023-01-24 20:35 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.918 s 0.601
2023-01-24 20:40 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.152 s -0.431
2023-01-24 20:47 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.300 s 1.332
2023-01-24 20:47 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.007
2023-01-24 20:48 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.742
2023-01-24 20:49 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.391 s 1.264
2023-01-24 20:52 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.411 s -1.501
2023-01-24 20:35 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.011
2023-01-24 20:48 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.000 s 1.137
2023-01-24 21:01 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.251 s 1.272
2023-01-24 21:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.527 s -0.829
2023-01-24 21:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.641 s -0.054
2023-01-24 21:01 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.660 s -0.157
2023-01-24 21:02 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.324 s 0.180
2023-01-24 21:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.515 s -0.456
2023-01-24 21:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.304 s -1.640
2023-01-24 21:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.991 s -0.091
2023-01-24 21:02 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.725 s -0.108
2023-01-24 21:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.947 s -0.081
2023-01-24 21:02 Python file-read lz4, feather, table, fanniemae_2016Q4 0.824 s -0.428
2023-01-24 21:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.940 s -0.020
2023-01-24 21:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.987 s -0.072
2023-01-24 21:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.574 s 0.305
2023-01-24 21:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.933 s 0.214
2023-01-24 21:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.357
2023-01-24 21:03 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.314 s 0.497
2023-01-24 21:03 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.552 s 0.550
2023-01-24 21:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.090 s 0.626
2023-01-24 21:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.769 s 0.595
2023-01-24 21:07 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.353 s -0.307
2023-01-24 21:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.295 s 0.453
2023-01-24 21:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.329 s -0.337
2023-01-24 21:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.878 s -0.361
2023-01-24 21:08 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 11.056 s -1.499
2023-01-24 21:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.841 s 0.244
2023-01-24 21:09 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.373 s -0.069
2023-01-24 21:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.743 s 0.158
2023-01-24 21:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.738 s -0.194
2023-01-24 21:10 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.238 s 0.117
2023-01-24 21:10 Python wide-dataframe use_legacy_dataset=true 0.375 s 0.317
2023-01-24 21:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.815 s -0.472
2023-01-24 21:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.159 s -0.317
2023-01-24 21:10 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.222 s -0.325
2023-01-24 21:11 Python wide-dataframe use_legacy_dataset=false 0.509 s 0.733
2023-01-24 21:21 R dataframe-to-table type_dict, R 0.059 s -0.656
2023-01-24 21:21 R dataframe-to-table chi_traffic_2020_Q1, R 4.369 s -0.067
2023-01-24 21:21 R dataframe-to-table type_integers, R 0.010 s -1.754
2023-01-24 21:21 R dataframe-to-table type_strings, R 0.535 s -0.109
2023-01-24 21:21 R dataframe-to-table type_floats, R 0.013 s 0.824
2023-01-24 21:22 R dataframe-to-table type_nested, R 0.576 s -0.306
2023-01-24 21:22 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.349 s -1.236
2023-01-24 21:22 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.594 s -0.668
2023-01-24 21:22 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.343 s -0.955
2023-01-24 21:23 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.595 s -0.737
2023-01-24 21:23 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.312 s 0.220
2023-01-24 21:23 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.572 s 0.122
2023-01-24 21:23 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.592 s 0.195
2023-01-24 21:23 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.843 s 0.275
2023-01-24 21:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.574 s -0.312
2023-01-24 21:24 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.578 s -0.447
2023-01-24 21:24 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.920 s -0.287
2023-01-24 21:24 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.923 s -0.435
2023-01-24 21:24 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.817 s 0.143
2023-01-24 21:24 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s 0.177
2023-01-24 21:25 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.578 s 0.178
2023-01-24 21:25 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.899 s 0.202
2023-01-24 21:26 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.712 s 0.699
2023-01-24 21:28 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.676 s 0.658
2023-01-24 21:29 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.133 s 0.680
2023-01-24 21:30 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.116 s 0.734
2023-01-24 21:31 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.966 s -0.656
2023-01-24 21:33 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.051 s -0.777
2023-01-24 21:33 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.491 s 0.774
2023-01-24 21:35 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.518 s -1.486
2023-01-24 21:35 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.925 s 0.903
2023-01-24 21:37 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.771 s 0.601
2023-01-24 21:38 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.651 s 0.905
2023-01-24 21:39 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.740 s 0.402
2023-01-24 21:40 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.305 s 2.239
2023-01-24 21:41 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.187 s -0.477
2023-01-24 21:41 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.475 s -1.656
2023-01-24 21:42 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.071 s 0.865
2023-01-24 21:42 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.550 s 0.972
2023-01-24 21:43 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.255 s -0.443
2023-01-24 21:43 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.587 s 0.965
2023-01-24 21:53 JavaScript vectorFromArray booleans 0.017 s 0.437
2023-01-24 21:53 JavaScript Spread Vector float32Array 0.008 s -1.603
2023-01-24 21:54 JavaScript toArray Vector int32Array
2023-01-24 21:54 JavaScript toArray Vector float32Array
2023-01-24 21:53 JavaScript Spread Vector uint8Array 0.007 s -0.848
2023-01-24 21:53 JavaScript Spread Vector int32Array 0.007 s -1.050
2023-01-24 21:54 JavaScript get Vector uint8Array 0.003 s 1.610
2023-01-24 21:43 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.593 s 0.096
2023-01-24 21:53 JavaScript Iterate Vector uint32Array 0.002 s 0.723
2023-01-24 21:53 JavaScript Iterate Vector int8Array 0.002 s 0.739
2023-01-24 21:53 JavaScript Spread Vector float64Array 0.008 s -2.049
2023-01-24 21:53 JavaScript Spread Vector booleans 0.010 s 0.176
2023-01-24 21:54 JavaScript get Vector int64Array 0.003 s 1.210
2023-01-24 21:54 JavaScript Table tracks, 1,000,000 0.097 s -3.134
2023-01-24 21:53 JavaScript Iterate Vector uint8Array 0.002 s 0.115
2023-01-24 21:53 JavaScript Iterate Vector numbers 0.002 s 0.865
2023-01-24 21:53 JavaScript Iterate Vector dictionary 0.004 s -2.821
2023-01-24 21:53 JavaScript vectorFromArray numbers 0.016 s 0.333
2023-01-24 21:53 JavaScript Iterate Vector uint16Array 0.002 s 0.422
2023-01-24 21:53 JavaScript Iterate Vector uint64Array 0.004 s 0.597
2023-01-24 21:53 JavaScript Iterate Vector int16Array 0.002 s 0.433
2023-01-24 21:53 JavaScript Iterate Vector float64Array 0.002 s 0.925
2023-01-24 21:53 JavaScript vectorFromArray dictionary 0.017 s 0.498
2023-01-24 21:53 JavaScript Iterate Vector booleans 0.004 s -1.039
2023-01-24 21:53 JavaScript Iterate Vector string 0.127 s -0.955
2023-01-24 21:54 JavaScript toArray Vector int64Array
2023-01-24 21:54 JavaScript get Vector uint64Array 0.003 s 1.423
2023-01-24 21:54 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.363
2023-01-24 21:54 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-24 21:53 JavaScript Iterate Vector int32Array 0.002 s 0.717
2023-01-24 21:54 JavaScript get Vector dictionary 0.002 s -1.013
2023-01-24 21:53 JavaScript Iterate Vector int64Array 0.004 s 0.567
2023-01-24 21:54 JavaScript toArray Vector float64Array
2023-01-24 21:54 JavaScript toArray Vector string 0.145 s 0.163
2023-01-24 21:54 JavaScript get Vector uint16Array 0.003 s 1.354
2023-01-24 21:54 JavaScript get Vector booleans 0.002 s 0.476
2023-01-24 21:54 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.031 s 0.604
2023-01-24 21:53 JavaScript Iterate Vector float32Array 0.002 s 0.413
2023-01-24 21:53 JavaScript Spread Vector numbers 0.008 s -2.167
2023-01-24 21:53 JavaScript Spread Vector dictionary 0.010 s -0.171
2023-01-24 21:53 JavaScript toArray Vector uint8Array
2023-01-24 21:54 JavaScript toArray Vector dictionary 0.010 s 0.156
2023-01-24 21:54 JavaScript get Vector float32Array 0.002 s -0.190
2023-01-24 21:53 JavaScript Spread Vector uint16Array 0.007 s -0.999
2023-01-24 21:53 JavaScript Spread Vector int64Array 0.012 s -0.655
2023-01-24 21:53 JavaScript Spread Vector string 0.147 s -0.801
2023-01-24 21:53 JavaScript toArray Vector uint16Array
2023-01-24 21:54 JavaScript toArray Vector uint64Array
2023-01-24 21:54 JavaScript toArray Vector int16Array
2023-01-24 21:54 JavaScript toArray Vector booleans 0.010 s -0.134
2023-01-24 21:53 JavaScript Spread Vector uint32Array 0.007 s -0.903
2023-01-24 21:53 JavaScript Spread Vector int8Array 0.007 s -1.176
2023-01-24 21:54 JavaScript toArray Vector uint32Array
2023-01-24 21:54 JavaScript toArray Vector int8Array
2023-01-24 21:54 JavaScript Table tracks, 1,000,000 0.050 s -0.161
2023-01-24 21:54 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s -0.899
2023-01-24 21:53 JavaScript Spread Vector uint64Array 0.012 s -0.275
2023-01-24 21:53 JavaScript Spread Vector int16Array 0.007 s -0.917
2023-01-24 21:54 JavaScript Parse write recordBatches, tracks 0.002 s -1.182
2023-01-24 21:54 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.453
2023-01-24 21:54 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.859
2023-01-24 21:54 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -1.024
2023-01-24 21:54 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -1.062
2023-01-24 21:54 JavaScript toArray Vector numbers
2023-01-24 21:54 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.342
2023-01-24 21:54 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -0.958
2023-01-24 21:54 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -0.994
2023-01-24 21:54 JavaScript Table 1,000,000, tracks 0.242 s 1.392
2023-01-24 21:54 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 0.552
2023-01-24 21:54 JavaScript get Vector uint32Array 0.003 s 1.440
2023-01-24 21:54 JavaScript get Vector int8Array 0.003 s 0.796
2023-01-24 21:54 JavaScript get Vector int32Array 0.003 s 1.027
2023-01-24 21:54 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 21:54 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.188 s -0.101
2023-01-24 21:54 JavaScript get Vector int16Array 0.003 s 1.612
2023-01-24 21:54 JavaScript get Vector float64Array 0.002 s -0.158
2023-01-24 21:54 JavaScript get Vector string 0.125 s -0.023
2023-01-24 21:54 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -1.331
2023-01-24 21:54 JavaScript Table tracks, 1,000,000 0.250 s 1.329
2023-01-24 21:54 JavaScript get Vector numbers 0.002 s -0.153
2023-01-24 21:54 JavaScript Parse read recordBatches, tracks 0.000 s -1.568
2023-01-24 21:54 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.650
2023-01-24 21:54 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.611
2023-01-24 21:54 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.852
2023-01-24 21:54 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.724
2023-01-24 21:54 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.002
2023-01-24 21:54 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-24 21:54 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 21:54 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.187 s 0.775