Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-24 10:58 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 75.130 s 0.755
2023-01-24 10:49 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.225 s 0.380
2023-01-24 10:54 Python dataframe-to-table type_strings 0.426 s 0.512
2023-01-24 10:49 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.555 s 1.186
2023-01-24 10:49 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.771 s 1.054
2023-01-24 10:50 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.495 s 1.245
2023-01-24 10:54 Python dataframe-to-table type_dict 0.011 s 3.151
2023-01-24 11:15 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.184 s -0.548
2023-01-24 11:15 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.140 s 0.134
2023-01-24 11:20 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.150 s 0.561
2023-01-24 10:54 Python dataframe-to-table type_integers 0.010 s 0.449
2023-01-24 10:54 Python dataset-filter nyctaxi_2010-01 1.036 s -0.163
2023-01-24 10:51 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.435 s 0.214
2023-01-24 10:54 Python dataframe-to-table type_nested 2.979 s -1.209
2023-01-24 11:14 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.239 s 0.649
2023-01-24 11:14 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.301 s -0.430
2023-01-24 11:20 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.150 s -0.281
2023-01-24 10:51 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.122 s 0.181
2023-01-24 10:54 Python dataframe-to-table chi_traffic_2020_Q1 21.020 s 0.075
2023-01-24 10:54 Python dataframe-to-table type_floats 0.010 s 0.236
2023-01-24 11:16 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.495
2023-01-24 11:16 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.261 s 1.425
2023-01-24 10:51 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.444 s -1.695
2023-01-24 11:02 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.271 s 0.046
2023-01-24 10:52 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.294 s -1.492
2023-01-24 11:15 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.300
2023-01-24 11:15 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.731 s 1.403
2023-01-24 11:19 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.019 s 1.614
2023-01-24 11:15 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.303 s 1.620
2023-01-24 11:14 Python dataset-read async=True, nyctaxi_multi_ipc_s3 230.872 s -0.678
2023-01-24 11:15 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.030 s 0.234
2023-01-24 11:14 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.266 s -0.383
2023-01-24 11:15 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.034 s 0.210
2023-01-24 11:16 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.904 s 1.578
2023-01-24 11:14 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.342 s -0.184
2023-01-24 11:15 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.634 s 0.296
2023-01-24 11:15 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.226 s -0.620
2023-01-24 11:15 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s -1.241
2023-01-24 11:16 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.656
2023-01-24 11:27 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.354 s 1.406
2023-01-24 11:28 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.427
2023-01-24 11:28 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.837 s 1.312
2023-01-24 11:28 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.283 s 1.067
2023-01-24 11:28 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.448
2023-01-24 11:28 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.966
2023-01-24 11:28 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 2.998 s 1.200
2023-01-24 11:28 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.226 s -0.431
2023-01-24 11:29 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.396 s 1.334
2023-01-24 11:32 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.431 s 1.098
2023-01-24 11:33 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.407 s -0.066
2023-01-24 11:33 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.410 s -0.208
2023-01-24 11:42 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.522 s -0.771
2023-01-24 11:43 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.313 s 0.512
2023-01-24 11:49 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.781 s 0.682
2023-01-24 11:51 Python wide-dataframe use_legacy_dataset=false 0.515 s 0.096
2023-01-24 12:02 R dataframe-to-table chi_traffic_2020_Q1, R 4.383 s -0.382
2023-01-24 12:19 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.660 s 0.737
2023-01-24 12:22 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.478 s -3.394
2023-01-24 11:43 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.945 s -0.083
2023-01-24 11:51 Python file-write lz4, feather, table, nyctaxi_2010-01 1.764 s -0.038
2023-01-24 12:04 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.600 s -1.059
2023-01-24 12:04 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.575 s 0.086
2023-01-24 12:07 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.717 s 0.604
2023-01-24 12:14 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.025 s -0.033
2023-01-24 11:42 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.632 s 0.068
2023-01-24 12:02 R dataframe-to-table type_dict, R 0.060 s -0.831
2023-01-24 12:03 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.607 s -1.407
2023-01-24 11:42 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.648 s 0.002
2023-01-24 11:51 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.096 s -0.040
2023-01-24 12:02 R dataframe-to-table type_strings, R 0.535 s 0.061
2023-01-24 12:02 R dataframe-to-table type_integers, R 0.010 s -0.221
2023-01-24 12:04 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.576 s -0.412
2023-01-24 12:06 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.901 s 0.186
2023-01-24 12:14 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.493 s -0.075
2023-01-24 12:15 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.481 s -0.224
2023-01-24 12:16 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.925 s 0.849
2023-01-24 11:48 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.253 s -0.232
2023-01-24 11:50 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.690 s 0.572
2023-01-24 12:03 R dataframe-to-table type_nested, R 0.573 s 0.380
2023-01-24 12:03 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.347 s -1.245
2023-01-24 11:42 Python file-read lz4, feather, table, fanniemae_2016Q4 0.823 s -0.377
2023-01-24 11:43 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.990 s -0.086
2023-01-24 11:42 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.724 s -0.117
2023-01-24 11:43 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.312 s -2.008
2023-01-24 11:43 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.982 s -0.005
2023-01-24 11:45 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.110 s 0.456
2023-01-24 11:48 Python file-write lz4, feather, table, fanniemae_2016Q4 1.841 s -0.021
2023-01-24 11:50 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.686 s 0.022
2023-01-24 12:06 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.581 s 0.160
2023-01-24 12:21 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.172 s 0.369
2023-01-24 11:42 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.523 s -0.652
2023-01-24 11:43 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.938 s 0.013
2023-01-24 11:43 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.945 s 0.121
2023-01-24 11:43 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s 0.289
2023-01-24 11:44 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.560 s 0.443
2023-01-24 11:46 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.730 s 0.790
2023-01-24 12:05 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.580 s -0.543
2023-01-24 12:11 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.108 s 0.723
2023-01-24 12:12 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.969 s -1.299
2023-01-24 11:42 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.321 s 0.192
2023-01-24 11:50 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.201 s 0.409
2023-01-24 12:05 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s 0.226
2023-01-24 12:23 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.562 s 0.177
2023-01-24 11:41 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.311 s 1.341
2023-01-24 11:43 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.574 s 0.310
2023-01-24 11:47 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.241 s -0.109
2023-01-24 12:02 R dataframe-to-table type_floats, R 0.013 s -1.145
2023-01-24 12:05 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.919 s -0.147
2023-01-24 12:20 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.735 s 0.441
2023-01-24 12:24 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.607 s -0.591
2023-01-24 11:47 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.240 s 0.717
2023-01-24 11:49 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.875 s 0.070
2023-01-24 11:49 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.302 s 0.451
2023-01-24 11:51 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.183 s -0.031
2023-01-24 11:51 Python wide-dataframe use_legacy_dataset=true 0.375 s 0.334
2023-01-24 12:04 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s 0.162
2023-01-24 12:04 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.597 s 0.142
2023-01-24 12:10 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.137 s 0.586
2023-01-24 12:24 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.593 s 0.629
2023-01-24 12:03 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.351 s -1.395
2023-01-24 12:05 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.817 s 0.144
2023-01-24 12:21 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.309 s 0.822
2023-01-24 12:24 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.259 s -1.202
2023-01-24 12:04 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.854 s 0.197
2023-01-24 12:05 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.926 s -0.639
2023-01-24 12:09 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.647 s 0.778
2023-01-24 12:18 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.784 s 0.380
2023-01-24 12:23 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.081 s 0.012
2023-01-24 12:34 JavaScript Iterate Vector uint16Array 0.002 s 0.631
2023-01-24 12:34 JavaScript Iterate Vector uint32Array 0.002 s 0.581
2023-01-24 12:34 JavaScript vectorFromArray numbers 0.016 s 0.372
2023-01-24 12:34 JavaScript Iterate Vector uint8Array 0.002 s 0.018
2023-01-24 12:34 JavaScript vectorFromArray booleans 0.018 s 0.157
2023-01-24 12:34 JavaScript Iterate Vector int32Array 0.002 s 0.578
2023-01-24 12:34 JavaScript vectorFromArray dictionary 0.017 s 0.529
2023-01-24 12:34 JavaScript Iterate Vector uint64Array 0.004 s 0.344
2023-01-24 12:34 JavaScript Spread Vector uint32Array 0.007 s 0.741
2023-01-24 12:34 JavaScript Spread Vector float32Array 0.008 s 0.325
2023-01-24 12:35 JavaScript Spread Vector numbers 0.008 s 0.314
2023-01-24 12:35 JavaScript Spread Vector booleans 0.010 s -0.351
2023-01-24 12:35 JavaScript toArray Vector uint8Array
2023-01-24 12:35 JavaScript toArray Vector uint16Array
2023-01-24 12:34 JavaScript Iterate Vector int8Array 0.002 s 0.529
2023-01-24 12:34 JavaScript Iterate Vector int16Array 0.002 s 0.409
2023-01-24 12:34 JavaScript Iterate Vector booleans 0.004 s -1.107
2023-01-24 12:34 JavaScript Spread Vector int32Array 0.006 s 0.224
2023-01-24 12:34 JavaScript Iterate Vector int64Array 0.004 s -0.436
2023-01-24 12:34 JavaScript Iterate Vector float64Array 0.002 s 0.934
2023-01-24 12:35 JavaScript Spread Vector dictionary 0.010 s 0.612
2023-01-24 12:34 JavaScript Iterate Vector float32Array 0.002 s 0.757
2023-01-24 12:34 JavaScript Iterate Vector string 0.131 s -3.663
2023-01-24 12:34 JavaScript Spread Vector int16Array 0.007 s -0.041
2023-01-24 12:35 JavaScript toArray Vector string 0.151 s -3.227
2023-01-24 12:35 JavaScript get Vector uint8Array 0.003 s 0.456
2023-01-24 12:35 JavaScript toArray Vector int16Array
2023-01-24 12:34 JavaScript Iterate Vector numbers 0.002 s 0.823
2023-01-24 12:34 JavaScript Iterate Vector dictionary 0.004 s 0.926
2023-01-24 12:34 JavaScript Spread Vector uint8Array 0.006 s 0.501
2023-01-24 12:34 JavaScript Spread Vector uint16Array 0.006 s 0.230
2023-01-24 12:34 JavaScript Spread Vector uint64Array 0.012 s -1.350
2023-01-24 12:34 JavaScript Spread Vector int8Array 0.006 s 0.360
2023-01-24 12:34 JavaScript Spread Vector int64Array 0.012 s -0.798
2023-01-24 12:35 JavaScript toArray Vector int32Array
2023-01-24 12:35 JavaScript toArray Vector int64Array
2023-01-24 12:35 JavaScript toArray Vector float32Array
2023-01-24 12:35 JavaScript Spread Vector float64Array 0.008 s 0.313
2023-01-24 12:35 JavaScript get Vector float64Array 0.002 s -0.328
2023-01-24 12:35 JavaScript get Vector numbers 0.002 s -0.168
2023-01-24 12:35 JavaScript get Vector booleans 0.002 s -0.834
2023-01-24 12:35 JavaScript get Vector uint32Array 0.003 s 0.948
2023-01-24 12:35 JavaScript get Vector float32Array 0.002 s -0.306
2023-01-24 12:35 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.395
2023-01-24 12:35 JavaScript Spread Vector string 0.150 s -2.216
2023-01-24 12:35 JavaScript toArray Vector int8Array
2023-01-24 12:35 JavaScript Parse write recordBatches, tracks 0.002 s -1.328
2023-01-24 12:35 JavaScript toArray Vector uint32Array
2023-01-24 12:35 JavaScript toArray Vector uint64Array
2023-01-24 12:35 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.705
2023-01-24 12:35 JavaScript toArray Vector float64Array
2023-01-24 12:35 JavaScript toArray Vector numbers
2023-01-24 12:35 JavaScript toArray Vector booleans 0.010 s -0.653
2023-01-24 12:35 JavaScript toArray Vector dictionary 0.010 s 0.894
2023-01-24 12:35 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.710
2023-01-24 12:35 JavaScript get Vector uint16Array 0.003 s 0.560
2023-01-24 12:35 JavaScript get Vector int64Array 0.003 s 0.414
2023-01-24 12:35 JavaScript get Vector dictionary 0.002 s 1.125
2023-01-24 12:35 JavaScript Parse read recordBatches, tracks 0.000 s -1.806
2023-01-24 12:35 JavaScript get Vector uint64Array 0.003 s 1.335
2023-01-24 12:35 JavaScript get Vector int8Array 0.003 s 0.861
2023-01-24 12:35 JavaScript get Vector int16Array 0.003 s 0.647
2023-01-24 12:35 JavaScript get Vector int32Array 0.003 s 0.901
2023-01-24 12:35 JavaScript get Vector string 0.129 s -3.542
2023-01-24 12:35 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.767
2023-01-24 12:35 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.117
2023-01-24 12:35 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.003
2023-01-24 12:35 JavaScript Table tracks, 1,000,000 0.050 s 1.173
2023-01-24 12:35 JavaScript Table tracks, 1,000,000 0.288 s -1.275
2023-01-24 12:35 JavaScript Table 1,000,000, tracks 0.285 s -0.426
2023-01-24 12:35 JavaScript Table tracks, 1,000,000 0.095 s -0.494
2023-01-24 12:35 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.379
2023-01-24 12:35 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s 0.903
2023-01-24 12:35 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.517
2023-01-24 12:35 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-24 12:35 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 12:35 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.555
2023-01-24 12:35 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.584
2023-01-24 12:35 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.109 s -0.836
2023-01-24 12:35 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 1.018
2023-01-24 12:35 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-24 12:35 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.993
2023-01-24 12:35 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.555
2023-01-24 12:35 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 12:35 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.188 s -0.143
2023-01-24 12:35 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.193 s -1.952
2023-01-24 12:35 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s -0.336
2023-01-24 12:35 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s -0.438