Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-25 01:37 Python dataframe-to-table type_dict 0.011 s 2.128
2023-01-25 01:37 Python dataset-filter nyctaxi_2010-01 1.033 s -0.041
2023-01-25 01:35 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.343 s -1.602
2023-01-25 01:58 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.292 s -0.326
2023-01-25 01:58 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.053
2023-01-25 01:59 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.905 s 1.523
2023-01-25 02:11 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.284 s 0.898
2023-01-25 02:11 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -0.626
2023-01-25 01:57 Python dataset-read async=True, nyctaxi_multi_ipc_s3 231.221 s -0.730
2023-01-25 01:58 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.183 s -0.404
2023-01-25 01:59 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.311
2023-01-25 02:12 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.389 s 1.205
2023-01-25 02:16 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.410 s -1.026
2023-01-25 01:58 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.034 s 0.221
2023-01-25 01:58 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.658 s 0.241
2023-01-25 01:33 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.111 s 0.281
2023-01-25 01:37 Python dataframe-to-table type_strings 0.426 s 0.542
2023-01-25 01:46 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.938 s 0.536
2023-01-25 01:58 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.209 s 0.141
2023-01-25 02:11 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.000 s 1.188
2023-01-25 01:57 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.238 s 0.698
2023-01-25 01:58 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.236 s -0.121
2023-01-25 01:58 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.355 s -0.266
2023-01-25 02:11 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s 0.954
2023-01-25 01:31 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.759 s 1.871
2023-01-25 01:31 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.247 s 0.154
2023-01-25 01:32 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.549 s 1.271
2023-01-25 01:33 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.448 s 1.464
2023-01-25 01:33 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.412 s 4.062
2023-01-25 01:34 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.333 s -1.432
2023-01-25 01:36 Python dataframe-to-table chi_traffic_2020_Q1 20.933 s 0.512
2023-01-25 01:37 Python dataframe-to-table type_integers 0.010 s -0.547
2023-01-25 01:37 Python dataframe-to-table type_floats 0.010 s 0.052
2023-01-25 01:37 Python dataframe-to-table type_nested 2.953 s 0.654
2023-01-25 01:58 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.040 s 0.190
2023-01-25 01:58 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.303 s 1.574
2023-01-25 01:59 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.450
2023-01-25 02:00 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.261 s 1.233
2023-01-25 02:03 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.046 s 1.368
2023-01-25 01:41 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 87.520 s -0.720
2023-01-25 01:58 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.139 s 0.247
2023-01-25 01:58 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.038
2023-01-25 01:59 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.730 s 1.247
2023-01-25 02:03 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.151 s -0.625
2023-01-25 02:11 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.352 s 1.221
2023-01-25 02:03 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.158 s -2.710
2023-01-25 02:11 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.836 s 1.196
2023-01-25 02:11 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.226 s -0.811
2023-01-25 02:12 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.590
2023-01-25 02:16 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.438 s 1.148
2023-01-25 02:16 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.413 s -1.020
2023-01-25 02:25 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.219 s 1.225
2023-01-25 02:25 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.518 s -0.558
2023-01-25 02:25 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.647 s 0.049
2023-01-25 02:25 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.517 s -0.413
2023-01-25 02:25 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.638 s 0.043
2023-01-25 02:26 Python file-read lz4, feather, table, fanniemae_2016Q4 0.824 s -0.451
2023-01-25 02:25 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.323 s 0.185
2023-01-25 02:26 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.718 s -0.067
2023-01-25 02:26 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.984 s 0.112
2023-01-25 02:26 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.982 s 0.051
2023-01-25 02:26 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.944 s -0.019
2023-01-25 02:26 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.306 s -1.562
2023-01-25 02:27 Python file-read lz4, feather, table, nyctaxi_2010-01 0.686 s 0.050
2023-01-25 02:27 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.572 s 0.317
2023-01-25 02:26 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.966 s -0.556
2023-01-25 02:26 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.926 s 0.271
2023-01-25 02:27 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.318 s 0.430
2023-01-25 02:27 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.560 s 0.540
2023-01-25 02:28 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.132 s 0.439
2023-01-25 02:29 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.740 s 0.875
2023-01-25 02:30 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.240 s -0.096
2023-01-25 02:30 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.266 s 0.696
2023-01-25 02:31 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.189 s -0.065
2023-01-25 02:31 Python file-write lz4, feather, table, fanniemae_2016Q4 1.829 s 0.090
2023-01-25 02:32 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.868 s 0.166
2023-01-25 02:32 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.790 s 0.657
2023-01-25 02:33 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.292 s 0.578
2023-01-25 02:33 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.684 s 0.662
2023-01-25 02:34 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.689 s 0.018
2023-01-25 02:34 Python file-write lz4, feather, table, nyctaxi_2010-01 1.775 s -0.117
2023-01-25 02:34 Python wide-dataframe use_legacy_dataset=false 0.511 s 0.525
2023-01-25 02:33 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.199 s 0.471
2023-01-25 02:34 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.108 s -0.079
2023-01-25 02:34 Python wide-dataframe use_legacy_dataset=true 0.377 s 0.127
2023-01-25 02:34 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.172 s 0.071
2023-01-25 02:45 R dataframe-to-table type_dict, R 0.057 s -0.294
2023-01-25 02:44 R dataframe-to-table chi_traffic_2020_Q1, R 4.350 s 0.360
2023-01-25 02:45 R dataframe-to-table type_strings, R 0.535 s -0.218
2023-01-25 02:45 R dataframe-to-table type_floats, R 0.013 s 0.488
2023-01-25 02:45 R dataframe-to-table type_integers, R 0.010 s -1.162
2023-01-25 02:45 R dataframe-to-table type_nested, R 0.578 s -1.259
2023-01-25 02:46 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.335 s -0.477
2023-01-25 02:46 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.591 s -0.520
2023-01-25 02:46 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.343 s -0.863
2023-01-25 02:46 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.592 s -0.520
2023-01-25 02:47 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.314 s 0.199
2023-01-25 02:47 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.568 s 0.174
2023-01-25 02:47 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.573 s -0.219
2023-01-25 02:47 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.590 s 0.224
2023-01-25 02:47 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.845 s 0.262
2023-01-25 02:47 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.916 s -0.086
2023-01-25 02:48 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.574 s -0.149
2023-01-25 02:48 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.920 s -0.231
2023-01-25 02:48 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s 0.156
2023-01-25 02:48 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.812 s 0.188
2023-01-25 02:48 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.584 s 0.138
2023-01-25 02:49 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.898 s 0.210
2023-01-25 02:50 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.708 s 0.825
2023-01-25 02:51 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.703 s 0.581
2023-01-25 02:52 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.129 s 0.806
2023-01-25 02:54 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.117 s 0.834
2023-01-25 02:55 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.969 s -1.101
2023-01-25 02:56 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.076 s -1.480
2023-01-25 02:57 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.490 s 1.134
2023-01-25 02:58 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.474 s 0.078
2023-01-25 02:59 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.927 s 0.956
2023-01-25 03:00 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.767 s 0.750
2023-01-25 03:01 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.652 s 0.969
2023-01-25 03:06 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.567 s -0.074
2023-01-25 03:03 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.718 s 0.805
2023-01-25 03:06 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.597 s -0.047
2023-01-25 03:07 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.587 s 0.933
2023-01-25 03:03 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.310 s 0.334
2023-01-25 03:04 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.602
2023-01-25 03:17 JavaScript Iterate Vector string 0.126 s 0.009
2023-01-25 03:17 JavaScript Spread Vector uint32Array 0.007 s 0.095
2023-01-25 03:17 JavaScript Spread Vector float32Array 0.008 s -1.062
2023-01-25 03:05 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.470 s 0.527
2023-01-25 03:07 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.258 s -1.068
2023-01-25 03:06 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.068 s 1.201
2023-01-25 03:17 JavaScript Iterate Vector int16Array 0.002 s 0.764
2023-01-25 03:17 JavaScript Iterate Vector float32Array 0.002 s 0.829
2023-01-25 03:17 JavaScript Parse read recordBatches, tracks 0.000 s -1.374
2023-01-25 03:18 JavaScript Table tracks, 1,000,000 0.094 s 0.865
2023-01-25 03:17 JavaScript Spread Vector booleans 0.010 s 1.250
2023-01-25 03:17 JavaScript Spread Vector dictionary 0.010 s 0.953
2023-01-25 03:17 JavaScript Spread Vector uint8Array 0.007 s 0.106
2023-01-25 03:17 JavaScript get Vector int8Array 0.003 s 0.394
2023-01-25 03:17 JavaScript get Vector float32Array 0.002 s 0.554
2023-01-25 03:17 JavaScript vectorFromArray numbers 0.016 s 0.300
2023-01-25 03:17 JavaScript Iterate Vector uint32Array 0.002 s 0.632
2023-01-25 03:17 JavaScript Iterate Vector float64Array 0.002 s 0.533
2023-01-25 03:17 JavaScript Iterate Vector dictionary 0.004 s -0.589
2023-01-25 03:17 JavaScript toArray Vector uint32Array
2023-01-25 03:17 JavaScript vectorFromArray booleans 0.018 s 0.388
2023-01-25 03:17 JavaScript vectorFromArray dictionary 0.017 s -1.398
2023-01-25 03:17 JavaScript Iterate Vector uint8Array 0.002 s -0.228
2023-01-25 03:17 JavaScript Iterate Vector uint16Array 0.002 s 0.755
2023-01-25 03:17 JavaScript Iterate Vector int32Array 0.002 s 0.257
2023-01-25 03:17 JavaScript Iterate Vector uint64Array 0.004 s 0.414
2023-01-25 03:17 JavaScript Iterate Vector int8Array 0.002 s 1.242
2023-01-25 03:17 JavaScript Spread Vector numbers 0.008 s -0.002
2023-01-25 03:17 JavaScript toArray Vector int8Array
2023-01-25 03:17 JavaScript toArray Vector float64Array
2023-01-25 03:17 JavaScript Iterate Vector int64Array 0.004 s 0.649
2023-01-25 03:17 JavaScript Spread Vector uint16Array 0.006 s 0.409
2023-01-25 03:17 JavaScript toArray Vector dictionary 0.010 s 0.709
2023-01-25 03:18 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-25 03:17 JavaScript Iterate Vector numbers 0.002 s 0.157
2023-01-25 03:17 JavaScript Iterate Vector booleans 0.004 s 0.165
2023-01-25 03:17 JavaScript Spread Vector uint64Array 0.012 s 0.592
2023-01-25 03:17 JavaScript Spread Vector int8Array 0.007 s -0.403
2023-01-25 03:17 JavaScript Spread Vector int16Array 0.006 s 0.447
2023-01-25 03:17 JavaScript Spread Vector string 0.144 s 0.812
2023-01-25 03:17 JavaScript Spread Vector int32Array 0.007 s 0.062
2023-01-25 03:17 JavaScript toArray Vector string 0.145 s 0.120
2023-01-25 03:17 JavaScript get Vector uint16Array 0.003 s 0.264
2023-01-25 03:18 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.476
2023-01-25 03:17 JavaScript Spread Vector int64Array 0.012 s 0.064
2023-01-25 03:17 JavaScript Spread Vector float64Array 0.008 s 0.109
2023-01-25 03:17 JavaScript toArray Vector uint8Array
2023-01-25 03:17 JavaScript toArray Vector uint16Array
2023-01-25 03:17 JavaScript toArray Vector int16Array
2023-01-25 03:17 JavaScript toArray Vector float32Array
2023-01-25 03:17 JavaScript toArray Vector booleans 0.010 s 0.852
2023-01-25 03:17 JavaScript get Vector uint32Array 0.003 s 0.759
2023-01-25 03:17 JavaScript get Vector uint64Array 0.003 s 0.776
2023-01-25 03:17 JavaScript get Vector dictionary 0.002 s 0.146
2023-01-25 03:17 JavaScript get Vector string 0.123 s 0.870
2023-01-25 03:17 JavaScript toArray Vector uint64Array
2023-01-25 03:17 JavaScript get Vector int32Array 0.003 s 0.096
2023-01-25 03:17 JavaScript get Vector int64Array 0.003 s 0.422
2023-01-25 03:17 JavaScript get Vector numbers 0.002 s -2.067
2023-01-25 03:18 JavaScript Parse write recordBatches, tracks 0.002 s -0.033
2023-01-25 03:17 JavaScript toArray Vector int32Array
2023-01-25 03:17 JavaScript toArray Vector int64Array
2023-01-25 03:18 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-25 03:18 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.107 s 0.761
2023-01-25 03:17 JavaScript toArray Vector numbers
2023-01-25 03:17 JavaScript get Vector uint8Array 0.003 s 0.149
2023-01-25 03:17 JavaScript get Vector int16Array 0.003 s 0.364
2023-01-25 03:17 JavaScript get Vector float64Array 0.002 s -0.324
2023-01-25 03:18 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s -0.460
2023-01-25 03:18 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.825
2023-01-25 03:18 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.233
2023-01-25 03:18 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.546
2023-01-25 03:18 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-25 03:18 JavaScript Table tracks, 1,000,000 0.050 s 0.334
2023-01-25 03:17 JavaScript get Vector booleans 0.002 s 0.414
2023-01-25 03:18 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s -0.961
2023-01-25 03:18 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.605
2023-01-25 03:18 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.189 s -0.424
2023-01-25 03:18 JavaScript Table 1,000,000, tracks 0.248 s 1.137
2023-01-25 03:18 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s -0.132
2023-01-25 03:18 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s -1.018
2023-01-25 03:18 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.976
2023-01-25 03:18 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.824
2023-01-25 03:18 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 1.030
2023-01-25 03:18 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.195 s -2.705
2023-01-25 03:18 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.882
2023-01-25 03:18 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-25 03:18 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.020
2023-01-25 03:18 JavaScript Table tracks, 1,000,000 0.248 s 1.440
2023-01-25 03:18 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.335
2023-01-25 03:18 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 0.311