Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 07:52 Python csv-read uncompressed, file, nyctaxi_2010-01 1.006 s 0.629234
2021-10-08 07:53 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.015259
2021-10-08 07:55 Python dataframe-to-table chi_traffic_2020_Q1 19.718 s -0.454880
2021-10-08 07:56 Python dataset-filter nyctaxi_2010-01 4.349 s 0.851019
2021-10-08 08:29 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.311 s -3.296525
2021-10-08 08:30 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.325 s -0.854791
2021-10-08 08:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.464 s -0.758842
2021-10-08 08:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -1.179573
2021-10-08 08:37 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.839 s 0.836989
2021-10-08 08:37 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.815 s 0.603763
2021-10-08 08:38 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.254441
2021-10-08 08:38 Python wide-dataframe use_legacy_dataset=false 0.629 s -1.907107
2021-10-08 09:01 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.243 s 3.016780
2021-10-08 09:05 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.313 s 0.430750
2021-10-08 09:16 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 0.519125
2021-10-08 09:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.470550
2021-10-08 09:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.606 s -0.260739
2021-10-08 09:21 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.203 s 0.372166
2021-10-08 09:22 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s -0.967927
2021-10-08 09:29 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.146727
2021-10-08 09:29 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.747529
2021-10-08 09:29 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.523 s -0.167109
2021-10-08 08:35 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.814 s 0.261222
2021-10-08 08:58 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.247 s 0.238821
2021-10-08 07:51 Python csv-read gzip, streaming, fanniemae_2016Q4 14.919 s -0.500013
2021-10-08 08:27 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.062 s -0.435832
2021-10-08 08:35 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.264 s -0.461088
2021-10-08 07:50 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.970 s -0.385450
2021-10-08 07:51 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.924809
2021-10-08 08:27 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.000 s 0.033037
2021-10-08 08:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.091 s 0.488563
2021-10-08 08:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.645 s 0.205943
2021-10-08 08:27 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.844 s 0.262605
2021-10-08 08:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.748 s 0.005886
2021-10-08 08:33 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.437 s 0.569717
2021-10-08 08:35 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s 0.077991
2021-10-08 07:50 Python csv-read uncompressed, file, fanniemae_2016Q4 1.174 s -0.015012
2021-10-08 08:34 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.315 s 0.133079
2021-10-08 09:29 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.537150
2021-10-08 08:17 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.018 s 0.270095
2021-10-08 08:28 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.256 s -0.254725
2021-10-08 08:28 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.353580
2021-10-08 08:29 Python file-read lz4, feather, table, fanniemae_2016Q4 0.613 s -1.630967
2021-10-08 08:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.046 s -0.603337
2021-10-08 08:31 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.037073
2021-10-08 08:38 Python wide-dataframe use_legacy_dataset=true 0.396 s -0.891174
2021-10-08 08:29 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.280 s -1.284850
2021-10-08 08:38 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.347 s -0.267169
2021-10-08 07:55 Python dataframe-to-table type_strings 0.372 s -0.227741
2021-10-08 07:55 Python dataframe-to-table type_integers 0.011 s 0.489949
2021-10-08 07:55 Python dataframe-to-table type_simple_features 0.917 s -0.392444
2021-10-08 08:31 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.971 s -0.791404
2021-10-08 08:33 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.283 s 0.187683
2021-10-08 08:36 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.778 s 0.579975
2021-10-08 07:55 Python dataframe-to-table type_floats 0.011 s 0.345071
2021-10-08 07:55 Python dataframe-to-table type_nested 2.866 s 0.818004
2021-10-08 07:59 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.066 s 0.430169
2021-10-08 08:28 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.897 s -1.108372
2021-10-08 08:29 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.813 s -0.892974
2021-10-08 07:52 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.494 s 1.065262
2021-10-08 07:53 Python csv-read gzip, streaming, nyctaxi_2010-01 10.476 s 1.145957
2021-10-08 08:13 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.913 s -0.508143
2021-10-08 08:29 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.031 s 0.725733
2021-10-08 08:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.362 s -0.973965
2021-10-08 07:55 Python dataframe-to-table type_dict 0.012 s -0.268584
2021-10-08 08:03 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.241 s 0.449107
2021-10-08 08:17 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.028 s -0.079773
2021-10-08 08:28 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.162 s -0.749389
2021-10-08 08:29 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.965 s -1.142419
2021-10-08 08:13 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.324 s 0.148806
2021-10-08 08:37 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.359 s -0.508033
2021-10-08 08:38 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.840 s -1.081555
2021-10-08 08:17 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.059 s -0.445676
2021-10-08 08:28 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.829 s -0.029697
2021-10-08 08:35 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.746 s -0.136398
2021-10-08 08:51 R dataframe-to-table chi_traffic_2020_Q1, R 3.371 s 0.251594
2021-10-08 08:52 R dataframe-to-table type_integers, R 0.010 s 3.213981
2021-10-08 08:51 R dataframe-to-table type_strings, R 0.486 s 0.200711
2021-10-08 08:52 R dataframe-to-table type_nested, R 0.541 s 0.199215
2021-10-08 08:51 R dataframe-to-table type_dict, R 0.051 s -0.024863
2021-10-08 08:52 R dataframe-to-table type_floats, R 0.013 s 3.194978
2021-10-08 09:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 0.440385
2021-10-08 09:21 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.487 s -1.821099
2021-10-08 09:29 JavaScript Parse Table.from, tracks 0.000 s -0.183998
2021-10-08 09:29 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-10-08 09:29 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 09:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.516 s 0.273299
2021-10-08 08:59 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.920 s 0.004277
2021-10-08 09:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.243544
2021-10-08 09:17 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.482 s 1.478800
2021-10-08 09:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.393034
2021-10-08 09:20 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.917 s 0.464065
2021-10-08 09:29 JavaScript Parse readBatches, tracks 0.000 s -0.278591
2021-10-08 09:29 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.581261
2021-10-08 09:01 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.986 s 0.100791
2021-10-08 09:18 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.174 s 0.489560
2021-10-08 08:58 R dataframe-to-table type_simple_features, R 3.317 s 2.238422
2021-10-08 09:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.784 s 0.925399
2021-10-08 09:14 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.645 s 0.899689
2021-10-08 09:21 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.492536
2021-10-08 09:29 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.332518
2021-10-08 08:59 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s 0.281423
2021-10-08 09:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.319358
2021-10-08 09:20 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.571723
2021-10-08 09:29 JavaScript Parse serialize, tracks 0.004 s 0.517394
2021-10-08 09:29 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.899 s 0.154637
2021-10-08 09:29 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.677289
2021-10-08 09:18 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.421735
2021-10-08 09:18 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 0.504503
2021-10-08 09:20 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.054727
2021-10-08 09:29 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.236146
2021-10-08 09:29 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.599 s -0.385094
2021-10-08 09:29 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.759879
2021-10-08 09:29 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.737 s 0.057279
2021-10-08 09:29 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.305903
2021-10-08 09:29 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.300810
2021-10-08 09:01 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.139900
2021-10-08 09:29 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.934 s -1.410440
2021-10-08 09:29 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.610610
2021-10-08 09:29 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.519955
2021-10-08 09:29 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.509978
2021-10-08 09:29 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.784377
2021-10-08 09:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.821 s 0.822961
2021-10-08 09:29 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.609 s -0.357679
2021-10-08 09:29 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.769586
2021-10-08 09:29 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.774 s -1.632959
2021-10-08 09:29 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.774113
2021-10-08 09:29 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.767235
2021-10-08 08:58 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.135293
2021-10-08 09:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.860 s 0.442872
2021-10-08 08:59 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.762449
2021-10-08 09:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 0.800548
2021-10-08 09:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.512856
2021-10-08 08:58 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 3.027009
2021-10-08 09:00 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.172 s 3.017329
2021-10-08 09:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.517 s 0.037551
2021-10-08 08:59 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 2.874942
2021-10-08 09:05 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.255 s 0.700677
2021-10-08 09:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.706 s 0.752044
2021-10-08 09:01 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.441342
2021-10-08 09:00 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.408 s -1.399072
2021-10-08 09:07 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.370137
2021-10-08 09:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.214 s -0.192814
2021-10-08 09:09 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.565 s 0.319955
2021-10-08 09:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.403 s -0.101060
2021-10-08 09:00 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.084197
2021-10-08 09:00 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.131 s -0.401857
2021-10-08 09:29 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.227518
2021-10-08 09:29 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.148855