Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 21:59 Python csv-read gzip, streaming, fanniemae_2016Q4 14.909 s -0.426871
2021-10-07 21:58 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.969 s -0.375326
2021-10-07 21:58 Python csv-read uncompressed, file, fanniemae_2016Q4 1.168 s 0.380053
2021-10-07 22:04 Python dataframe-to-table type_dict 0.011 s 1.208000
2021-10-07 22:01 Python csv-read gzip, streaming, nyctaxi_2010-01 10.486 s 1.144384
2021-10-07 22:00 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.492 s 1.159655
2021-10-07 21:59 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.575492
2021-10-07 22:00 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s -0.267524
2021-10-07 22:02 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.459428
2021-10-07 22:05 Python dataframe-to-table type_simple_features 0.914 s -0.115495
2021-10-07 22:03 Python dataframe-to-table chi_traffic_2020_Q1 19.684 s -0.250052
2021-10-07 22:13 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.761 s 0.522535
2021-10-07 22:22 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.868 s 0.380956
2021-10-07 22:27 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.013 s 0.297246
2021-10-07 22:37 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.868 s 0.133136
2021-10-07 22:04 Python dataframe-to-table type_strings 0.376 s -1.064397
2021-10-07 22:04 Python dataframe-to-table type_floats 0.011 s 1.228963
2021-10-07 22:23 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.350 s -0.290714
2021-10-07 22:27 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.017 s 0.277840
2021-10-07 22:37 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.999 s 0.054226
2021-10-07 22:04 Python dataframe-to-table type_integers 0.011 s 0.668725
2021-10-07 22:27 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.003 s 0.277427
2021-10-07 22:37 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.693 s 0.543825
2021-10-07 22:39 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.928 s -0.588983
2021-10-07 22:44 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.457 s 0.465318
2021-10-07 22:05 Python dataframe-to-table type_nested 2.885 s 0.381006
2021-10-07 22:37 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.001 s -0.016409
2021-10-07 22:05 Python dataset-filter nyctaxi_2010-01 4.349 s 0.821583
2021-10-07 22:38 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.250 s -0.127529
2021-10-07 22:38 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.870 s -0.716264
2021-10-07 22:08 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 55.855 s 1.330199
2021-10-07 22:38 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.824 s 0.094960
2021-10-07 22:39 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.323503
2021-10-07 22:40 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.249 s -0.808104
2021-10-07 22:49 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.381 s -1.023137
2021-10-07 22:38 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.307 s -0.636084
2021-10-07 22:38 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.160 s -0.752678
2021-10-07 22:41 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.312 s -0.844097
2021-10-07 22:42 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.962 s -0.840887
2021-10-07 22:39 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.802 s -0.785667
2021-10-07 22:43 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 0.602764
2021-10-07 23:13 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.405 s -1.184781
2021-10-07 23:13 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.166 s 4.386210
2021-10-07 22:39 Python file-read lz4, feather, table, fanniemae_2016Q4 0.610 s -1.170549
2021-10-07 22:46 Python file-write lz4, feather, table, fanniemae_2016Q4 1.153 s 0.651373
2021-10-07 22:49 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.118015
2021-10-07 22:40 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.982 s 2.799571
2021-10-07 22:46 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.783 s 0.808733
2021-10-07 22:41 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.330 s -0.970909
2021-10-07 22:41 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 1.238170
2021-10-07 22:41 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.035 s 0.087561
2021-10-07 22:42 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.487 s -0.946625
2021-10-07 22:46 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.748 s -0.204925
2021-10-07 22:42 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.409698
2021-10-07 22:44 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.270 s 0.255793
2021-10-07 22:45 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.630 s 0.285435
2021-10-07 22:45 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.398 s -0.487104
2021-10-07 22:48 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.814 s 0.667980
2021-10-07 22:47 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.786 s 0.520724
2021-10-07 22:49 Python wide-dataframe use_legacy_dataset=false 0.627 s -1.456991
2021-10-07 22:48 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.851 s 0.717617
2021-10-07 22:48 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s -0.057301
2021-10-07 22:49 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.811 s -0.266768
2021-10-07 22:49 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.186941
2021-10-07 23:21 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.733 s 0.603091
2021-10-07 23:23 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.406 s -0.789077
2021-10-07 23:27 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 0.872821
2021-10-07 23:30 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 0.589105
2021-10-07 23:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.907 s 0.545277
2021-10-07 23:04 R dataframe-to-table type_integers, R 0.010 s 4.944423
2021-10-07 23:14 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.998 s -0.710171
2021-10-07 23:03 R dataframe-to-table chi_traffic_2020_Q1, R 3.389 s 0.252934
2021-10-07 23:03 R dataframe-to-table type_strings, R 0.489 s 0.199954
2021-10-07 23:04 R dataframe-to-table type_dict, R 0.053 s -0.255333
2021-10-07 23:15 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.513 s 1.213018
2021-10-07 23:04 R dataframe-to-table type_floats, R 0.013 s 4.914417
2021-10-07 23:04 R dataframe-to-table type_nested, R 0.540 s 0.199591
2021-10-07 23:10 R dataframe-to-table type_simple_features, R 3.387 s 2.739558
2021-10-07 23:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.487 s 4.380192
2021-10-07 23:10 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.247 s 0.238372
2021-10-07 23:22 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.634 s -2.282369
2021-10-07 23:11 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.238 s 0.159889
2021-10-07 23:11 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 3.994454
2021-10-07 23:11 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.577918
2021-10-07 23:12 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.918 s 0.167487
2021-10-07 23:18 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.263 s 0.683404
2021-10-07 23:12 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.569 s -1.158212
2021-10-07 23:19 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.303 s 0.541262
2021-10-07 23:13 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.054 s 0.371703
2021-10-07 23:13 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.129 s -0.243483
2021-10-07 23:14 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.229 s 4.387669
2021-10-07 22:46 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.251 s -0.384786
2021-10-07 23:14 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.701459
2021-10-07 23:16 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.853 s 0.537867
2021-10-07 23:15 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.090571
2021-10-07 23:21 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.498472
2021-10-07 23:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 0.573403
2021-10-07 23:24 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.204 s 0.421485
2021-10-07 23:25 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.828 s 0.745742
2021-10-07 23:26 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.785 s 0.979773
2021-10-07 23:28 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.640 s 1.103893
2021-10-07 23:29 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.216444
2021-10-07 23:33 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.480123
2021-10-07 23:31 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.449044
2021-10-07 23:32 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.158 s 1.740018
2021-10-07 23:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s -0.129244
2021-10-07 23:46 JavaScript Parse Table.from, tracks 0.000 s -2.120192
2021-10-07 23:33 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.868 s 0.577326
2021-10-07 23:34 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.767315
2021-10-07 23:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.527 s -1.454767
2021-10-07 23:35 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.613 s -0.429392
2021-10-07 23:33 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 0.496217
2021-10-07 23:37 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.486 s -1.747702
2021-10-07 23:34 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.531512
2021-10-07 23:36 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.540 s 0.796382
2021-10-07 23:46 JavaScript Parse Table.from, tracks 0.000 s -2.120192
2021-10-07 23:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.862012
2021-10-07 23:47 JavaScript Parse readBatches, tracks 0.000 s -1.744782
2021-10-07 23:37 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.366 s -0.069976
2021-10-07 23:37 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.672915
2021-10-07 23:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.175 s 0.562329
2021-10-07 23:47 JavaScript Parse readBatches, tracks 0.000 s -1.744782
2021-10-07 23:47 JavaScript Parse readBatches, tracks 0.000 s -1.744782
2021-10-07 23:47 JavaScript Parse readBatches, tracks 0.000 s -1.744782
2021-10-07 23:51 JavaScript Parse serialize, tracks 0.005 s 0.362648
2021-10-07 23:47 JavaScript Parse readBatches, tracks 0.000 s -1.744782
2021-10-07 23:51 JavaScript Parse serialize, tracks 0.005 s 0.362648
2021-10-07 23:51 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.159502
2021-10-07 23:52 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.172305
2021-10-07 23:51 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.159502
2021-10-07 23:52 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.172305
2021-10-07 23:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.548 s -0.198911
2021-10-07 23:54 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.792422
2021-10-07 23:53 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.548 s -0.198911
2021-10-07 23:55 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.632 s 1.084933
2021-10-07 23:53 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.570 s -0.291863
2021-10-07 23:55 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.901 s -0.570221
2021-10-07 23:54 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.735588
2021-10-07 23:55 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.682 s 0.373888
2021-10-07 23:55 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.102384
2021-10-07 23:56 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.903 s 0.062141
2021-10-07 23:55 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.417977
2021-10-07 23:56 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.652464
2021-10-07 23:58 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.950928
2021-10-07 23:56 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.841658
2021-10-07 23:56 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.652464
2021-10-07 23:57 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-10-07 23:57 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-10-07 23:58 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-10-07 23:59 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.135204
2021-10-07 23:58 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.950928
2021-10-08 00:00 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.432695
2021-10-08 00:01 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.286831
2021-10-08 00:00 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.432695
2021-10-07 23:59 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.135204
2021-10-08 00:00 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.014157
2021-10-08 00:00 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.014157
2021-10-08 00:01 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.286831
2021-10-08 00:02 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.277243
2021-10-08 00:02 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.277243
2021-10-08 00:03 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.632722
2021-10-08 00:03 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.586784
2021-10-08 00:03 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.632722
2021-10-08 00:03 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.586784
2021-10-08 00:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.568914
2021-10-08 00:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.568914
2021-10-08 00:06 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.907177
2021-10-08 00:05 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.895526
2021-10-08 00:05 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.895526
2021-10-08 00:06 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.907177
2021-10-08 00:07 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.570 s -1.058473
2021-10-08 00:07 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.570 s -1.058473
2021-10-07 23:46 JavaScript Parse Table.from, tracks 0.000 s -2.120192