Benchmarks
Date Lang Batch Benchmark Mean Z-Score Error
2023-01-24 09:03 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.357 s -0.329
2023-01-24 08:44 Python dataframe-to-table type_integers 0.010 s 1.077
2023-01-24 09:04 Python dataset-selectivity 100%, chi_traffic_2020_Q1 1.136 s 0.269
2023-01-24 09:09 Python dataset-serialize feather, 100pc, nyctaxi_multi_parquet_s3 2.156 s -1.919
2023-01-24 09:17 Python dataset-serialize arrow, 10pc, nyctaxi_multi_ipc_s3 0.225 s 0.434
2023-01-24 08:39 Python csv-read gzip, arrow_table, file, fanniemae_2016Q4 5.775 s 0.730
2023-01-24 08:41 Python csv-read gzip, arrow_table, file, nyctaxi_2010-01 8.432 s 0.795
2023-01-24 08:44 Python dataframe-to-table type_dict 0.011 s 3.970
2023-01-24 09:03 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.233 s -0.140
2023-01-24 09:03 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.032 s 0.217
2023-01-24 09:04 Python dataset-serialize feather, 1pc, nyctaxi_multi_parquet_s3 0.023 s 0.177
2023-01-24 09:18 Python dataset-serialize csv, 10pc, nyctaxi_multi_ipc_s3 8.390 s 1.420
2023-01-24 08:39 Python csv-read uncompressed, arrow_table, file, fanniemae_2016Q4 1.253 s 0.085
2023-01-24 08:39 Python csv-read gzip, arrow_table, streaming, fanniemae_2016Q4 13.503 s 1.348
2023-01-24 08:40 Python csv-read uncompressed, arrow_table, streaming, fanniemae_2016Q4 13.535 s 1.119
2023-01-24 08:41 Python csv-read uncompressed, arrow_table, file, nyctaxi_2010-01 1.130 s 0.109
2023-01-24 08:41 Python csv-read gzip, arrow_table, streaming, nyctaxi_2010-01 11.392 s -1.575
2023-01-24 08:42 Python csv-read uncompressed, arrow_table, streaming, nyctaxi_2010-01 11.368 s -1.666
2023-01-24 08:44 Python dataframe-to-table chi_traffic_2020_Q1 20.890 s 0.856
2023-01-24 08:44 Python dataframe-to-table type_strings 0.426 s 0.472
2023-01-24 08:44 Python dataframe-to-table type_floats 0.010 s 0.041
2023-01-24 08:44 Python dataframe-to-table type_nested 2.949 s 1.001
2023-01-24 08:44 Python dataset-filter nyctaxi_2010-01 1.031 s -0.059
2023-01-24 08:48 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 68.701 s 1.530
2023-01-24 08:52 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.687 s 0.625
2023-01-24 09:03 Python dataset-read async=True, nyctaxi_multi_ipc_s3 214.898 s 0.939
2023-01-24 09:03 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.256 s 0.522
2023-01-24 09:03 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.264 s -0.156
2023-01-24 09:03 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.047 s 0.183
2023-01-24 09:03 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.663 s 0.232
2023-01-24 09:04 Python dataset-serialize csv, 1pc, nyctaxi_multi_parquet_s3 0.732 s 1.335
2023-01-24 09:04 Python dataset-serialize parquet, 10pc, nyctaxi_multi_parquet_s3 2.905 s 1.511
2023-01-24 09:05 Python dataset-serialize csv, 10pc, nyctaxi_multi_parquet_s3 7.279 s 1.327
2023-01-24 09:09 Python dataset-serialize arrow, 100pc, nyctaxi_multi_parquet_s3 2.151 s -0.719
2023-01-24 09:16 Python dataset-serialize feather, 1pc, nyctaxi_multi_ipc_s3 0.026 s -1.420
2023-01-24 09:17 Python dataset-serialize parquet, 10pc, nyctaxi_multi_ipc_s3 3.001 s 1.069
2023-01-24 09:04 Python dataset-selectivity 1%, chi_traffic_2020_Q1 1.183 s -0.510
2023-01-24 09:16 Python dataset-serialize csv, 100pc, nyctaxi_multi_parquet_s3 73.518 s 1.316
2023-01-24 09:16 Python dataset-serialize arrow, 1pc, nyctaxi_multi_ipc_s3 0.026 s 0.112
2023-01-24 09:22 Python dataset-serialize feather, 100pc, nyctaxi_multi_ipc_s3 2.409 s 0.208
2023-01-24 09:30 Python dataset-serialize csv, 100pc, nyctaxi_multi_ipc_s3 83.225 s 1.441
2023-01-24 09:04 Python dataset-selectivity 10%, chi_traffic_2020_Q1 1.212 s -0.078
2023-01-24 09:17 Python dataset-serialize feather, 10pc, nyctaxi_multi_ipc_s3 0.227 s -1.786
2023-01-24 09:21 Python dataset-serialize parquet, 100pc, nyctaxi_multi_ipc_s3 30.428 s 1.089
2023-01-24 09:04 Python dataset-serialize parquet, 1pc, nyctaxi_multi_parquet_s3 0.303 s 1.729
2023-01-24 09:17 Python dataset-serialize csv, 1pc, nyctaxi_multi_ipc_s3 0.835 s 1.437
2023-01-24 09:21 Python dataset-serialize arrow, 100pc, nyctaxi_multi_ipc_s3 2.404 s 0.775
2023-01-24 09:30 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.654 s -0.089
2023-01-24 09:04 Python dataset-serialize arrow, 1pc, nyctaxi_multi_parquet_s3 0.023 s -0.305
2023-01-24 09:16 Python dataset-serialize parquet, 1pc, nyctaxi_multi_ipc_s3 0.284 s 0.760
2023-01-24 09:30 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 1.639 s -0.043
2023-01-24 09:04 Python dataset-serialize arrow, 10pc, nyctaxi_multi_parquet_s3 0.199 s -0.402
2023-01-24 09:08 Python dataset-serialize parquet, 100pc, nyctaxi_multi_parquet_s3 30.029 s 1.564
2023-01-24 09:04 Python dataset-serialize feather, 10pc, nyctaxi_multi_parquet_s3 0.199 s 0.388
2023-01-24 09:31 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 1.529 s -1.096
2023-01-24 09:31 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.528 s -0.856
2023-01-24 09:31 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.340 s 0.124
2023-01-24 09:31 Python file-read lz4, feather, table, fanniemae_2016Q4 0.838 s -1.393
2023-01-24 09:31 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 4.319 s -2.291
2023-01-24 09:31 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 5.731 s -0.149
2023-01-24 09:32 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 1.578 s 0.281
2023-01-24 09:32 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s 0.259
2023-01-24 09:32 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 0.987 s -0.110
2023-01-24 09:32 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 1.319 s 0.430
2023-01-24 09:32 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.992 s -0.151
2023-01-24 09:32 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.943 s -0.112
2023-01-24 09:32 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 0.932 s 0.138
2023-01-24 09:32 Python file-read uncompressed, feather, table, nyctaxi_2010-01 0.937 s 0.185
2023-01-24 09:33 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 10.551 s 0.484
2023-01-24 09:34 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 20.096 s 0.500
2023-01-24 09:39 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 9.221 s 0.240
2023-01-24 09:34 Python file-write snappy, parquet, table, fanniemae_2016Q4 10.754 s 0.603
2023-01-24 09:35 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 20.235 s 0.707
2023-01-24 09:36 Python file-write uncompressed, feather, table, fanniemae_2016Q4 6.246 s -0.114
2023-01-24 09:37 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 15.237 s -0.197
2023-01-24 09:37 Python file-write lz4, feather, table, fanniemae_2016Q4 1.842 s -0.030
2023-01-24 09:37 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 10.968 s -0.744
2023-01-24 09:38 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.801 s 0.528
2023-01-24 09:38 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 7.324 s 0.272
2023-01-24 09:38 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.705 s 0.447
2023-01-24 09:39 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 4.093 s -0.020
2023-01-24 09:39 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.679 s 0.058
2023-01-24 09:39 Python file-write lz4, feather, table, nyctaxi_2010-01 1.762 s -0.013
2023-01-24 09:40 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 3.180 s -0.004
2023-01-24 09:40 Python wide-dataframe use_legacy_dataset=true 0.377 s 0.149
2023-01-24 09:40 Python wide-dataframe use_legacy_dataset=false 0.512 s 0.365
2023-01-24 09:50 R dataframe-to-table chi_traffic_2020_Q1, R 4.380 s -0.326
2023-01-24 09:51 R dataframe-to-table type_strings, R 0.535 s 0.046
2023-01-24 09:51 R dataframe-to-table type_dict, R 0.060 s -0.853
2023-01-24 09:51 R dataframe-to-table type_integers, R 0.010 s -0.143
2023-01-24 09:51 R dataframe-to-table type_floats, R 0.013 s -1.136
2023-01-24 09:51 R dataframe-to-table type_nested, R 0.574 s 0.256
2023-01-24 09:52 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.350 s -1.422
2023-01-24 09:52 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.351 s -1.435
2023-01-24 09:52 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.603 s -1.243
2023-01-24 09:53 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 0.572 s 0.120
2023-01-24 09:52 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.601 s -1.136
2023-01-24 09:53 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.312 s 0.219
2023-01-24 09:53 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.594 s 0.167
2023-01-24 09:53 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 0.856 s 0.185
2023-01-24 09:53 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 0.577 s -0.490
2023-01-24 09:53 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 0.920 s -0.211
2023-01-24 09:54 R file-read snappy, parquet, table, nyctaxi_2010-01, R 0.581 s -0.625
2023-01-24 09:54 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 0.924 s -0.563
2023-01-24 09:54 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -0.023
2023-01-24 09:54 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 0.811 s 0.191
2023-01-24 09:54 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.580 s 0.167
2023-01-24 09:55 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 0.891 s 0.259
2023-01-24 09:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 9.716 s 0.574
2023-01-24 09:57 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 16.673 s 0.587
2023-01-24 09:58 R file-write snappy, parquet, table, fanniemae_2016Q4, R 10.142 s 0.523
2023-01-24 10:00 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 17.104 s 0.714
2023-01-24 10:01 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.960 s 0.224
2023-01-24 10:02 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 8.993 s 0.892
2023-01-24 10:03 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.493 s 0.064
2023-01-24 10:04 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 7.476 s -0.032
2023-01-24 10:05 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.929 s 0.754
2023-01-24 10:06 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.780 s 0.412
2023-01-24 10:07 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.660 s 0.702
2023-01-24 10:09 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.732 s 0.463
2023-01-24 10:09 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.312 s -0.235
2023-01-24 10:10 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.194 s -0.885
2023-01-24 10:11 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.470 s 0.579
2023-01-24 10:12 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.088 s -0.521
2023-01-24 10:12 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.565 s -0.036
2023-01-24 10:12 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.605 s -0.527
2023-01-24 10:13 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.258 s -1.060
2023-01-24 10:13 R partitioned-dataset-filter dims, dataset-taxi-parquet, R 0.590 s 0.789
2023-01-24 10:23 JavaScript vectorFromArray booleans 0.017 s 0.509
2023-01-24 10:23 JavaScript Iterate Vector int8Array 0.002 s 0.835
2023-01-24 10:23 JavaScript toArray Vector uint16Array
2023-01-24 10:23 JavaScript Table Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.031 s 0.658
2023-01-24 10:23 JavaScript vectorFromArray numbers 0.016 s 0.382
2023-01-24 10:23 JavaScript Spread Vector numbers 0.008 s -0.929
2023-01-24 10:23 JavaScript Iterate Vector int32Array 0.002 s 0.533
2023-01-24 10:23 JavaScript toArray Vector int8Array
2023-01-24 10:23 JavaScript get Vector numbers 0.002 s 0.383
2023-01-24 10:23 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.030 s -0.531
2023-01-24 10:23 JavaScript vectorFromArray dictionary 0.017 s -1.044
2023-01-24 10:23 JavaScript Iterate Vector uint16Array 0.002 s -0.273
2023-01-24 10:23 JavaScript Iterate Vector numbers 0.002 s 0.884
2023-01-24 10:23 JavaScript Spread Vector uint32Array 0.007 s -0.979
2023-01-24 10:23 JavaScript Spread Vector int8Array 0.007 s -1.518
2023-01-24 10:23 JavaScript Spread Vector int16Array 0.007 s -0.668
2023-01-24 10:23 JavaScript Spread Vector int64Array 0.012 s -0.099
2023-01-24 10:23 JavaScript Spread Vector booleans 0.010 s 1.013
2023-01-24 10:23 JavaScript Spread Vector string 0.143 s 1.562
2023-01-24 10:23 JavaScript Iterate Vector uint8Array 0.002 s 2.347
2023-01-24 10:23 JavaScript Iterate Vector uint32Array 0.002 s 0.158
2023-01-24 10:23 JavaScript Iterate Vector float32Array 0.002 s 0.110
2023-01-24 10:23 JavaScript Iterate Vector dictionary 0.004 s 0.854
2023-01-24 10:23 JavaScript Iterate Vector uint64Array 0.004 s 0.620
2023-01-24 10:23 JavaScript Iterate Vector int16Array 0.002 s 0.874
2023-01-24 10:23 JavaScript Iterate Vector int64Array 0.004 s 0.056
2023-01-24 10:23 JavaScript toArray Vector int16Array
2023-01-24 10:23 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.030 s 0.233
2023-01-24 10:23 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s -0.907
2023-01-24 10:23 JavaScript Iterate Vector float64Array 0.002 s 0.578
2023-01-24 10:23 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.200
2023-01-24 10:23 JavaScript Spread vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.315
2023-01-24 10:23 JavaScript Iterate Vector booleans 0.004 s 0.496
2023-01-24 10:23 JavaScript Iterate Vector string 0.123 s 1.805
2023-01-24 10:23 JavaScript Spread Vector uint16Array 0.007 s -1.063
2023-01-24 10:23 JavaScript Spread Vector uint64Array 0.012 s 0.068
2023-01-24 10:23 JavaScript Spread Vector float64Array 0.008 s -1.334
2023-01-24 10:23 JavaScript toArray Vector uint64Array
2023-01-24 10:23 JavaScript Spread Vector uint8Array 0.007 s -0.522
2023-01-24 10:23 JavaScript Spread Vector int32Array 0.007 s -1.320
2023-01-24 10:23 JavaScript Spread Vector float32Array 0.008 s -0.834
2023-01-24 10:23 JavaScript get Vector int8Array 0.003 s 0.777
2023-01-24 10:23 JavaScript get Vector int32Array 0.003 s 0.570
2023-01-24 10:23 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 10:23 JavaScript Spread Vector dictionary 0.010 s -0.255
2023-01-24 10:23 JavaScript toArray Vector uint8Array
2023-01-24 10:23 JavaScript toArray Vector uint32Array
2023-01-24 10:23 JavaScript toArray Vector int32Array
2023-01-24 10:23 JavaScript toArray Vector numbers
2023-01-24 10:23 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s
2023-01-24 10:23 JavaScript Spread vectors lat, 1,000,000, Float32, tracks 0.190 s -0.971
2023-01-24 10:23 JavaScript Spread vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s -0.344
2023-01-24 10:23 JavaScript toArray Vector int64Array
2023-01-24 10:23 JavaScript toArray Vector float64Array
2023-01-24 10:23 JavaScript toArray Vector booleans 0.010 s 0.974
2023-01-24 10:23 JavaScript toArray Vector string 0.145 s 0.510
2023-01-24 10:23 JavaScript get Vector uint16Array 0.003 s 0.572
2023-01-24 10:23 JavaScript get Vector uint64Array 0.003 s 0.996
2023-01-24 10:23 JavaScript Spread vectors lng, 1,000,000, Float32, tracks 0.188 s 0.063
2023-01-24 10:23 JavaScript toArray Vector float32Array
2023-01-24 10:23 JavaScript toArray Vector dictionary 0.010 s 0.747
2023-01-24 10:23 JavaScript Parse read recordBatches, tracks 0.000 s -1.718
2023-01-24 10:23 JavaScript get Vector uint8Array 0.003 s -0.778
2023-01-24 10:23 JavaScript get Vector uint32Array 0.003 s 0.995
2023-01-24 10:23 JavaScript get Vector dictionary 0.002 s 0.987
2023-01-24 10:23 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.223
2023-01-24 10:23 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.023 s 0.174
2023-01-24 10:23 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.040 s -0.014
2023-01-24 10:23 JavaScript Table tracks, 1,000,000 0.270 s -0.051
2023-01-24 10:23 JavaScript get Vector int16Array 0.003 s 0.839
2023-01-24 10:23 JavaScript get Vector int64Array 0.003 s 0.595
2023-01-24 10:23 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.602
2023-01-24 10:23 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.023 s 0.162
2023-01-24 10:23 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.039 s 0.216
2023-01-24 10:23 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s
2023-01-24 10:23 JavaScript Table tracks, 1,000,000 0.095 s 0.110
2023-01-24 10:23 JavaScript get Vector float32Array 0.002 s -0.866
2023-01-24 10:23 JavaScript Table Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.032 s 0.355
2023-01-24 10:23 JavaScript get Vector float64Array 0.002 s 0.476
2023-01-24 10:23 JavaScript get Vector booleans 0.002 s 0.407
2023-01-24 10:23 JavaScript get Vector string 0.124 s 0.568
2023-01-24 10:23 JavaScript Parse write recordBatches, tracks 0.002 s -2.799
2023-01-24 10:23 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s -0.059
2023-01-24 10:23 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.108 s 0.219
2023-01-24 10:23 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s
2023-01-24 10:23 JavaScript Table tracks, 1,000,000 0.050 s 1.100
2023-01-24 10:23 JavaScript Table 1,000,000, tracks 0.260 s 0.623
2023-01-24 10:23 JavaScript Table Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.032 s 0.907