Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 08:57 Python dataframe-to-table type_strings 0.368 s 0.429482
2021-09-29 09:30 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.873 s -1.681023
2021-09-29 09:00 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.112 s -0.002674
2021-09-29 08:55 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.235735
2021-09-29 09:33 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.976 s 0.861444
2021-09-29 09:36 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.337 s 0.016849
2021-09-29 09:32 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.806 s 0.987323
2021-09-29 09:39 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.862 s 1.810971
2021-09-29 09:32 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.863473
2021-09-29 09:37 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.127 s 0.754133
2021-09-29 09:39 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.790 s 1.064725
2021-09-29 08:52 Python csv-read uncompressed, file, fanniemae_2016Q4 1.159 s 0.190320
2021-09-29 08:54 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s 0.134245
2021-09-29 08:57 Python dataframe-to-table type_nested 2.934 s 1.130966
2021-09-29 09:18 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.019 s 0.214812
2021-09-29 09:34 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.093 s 1.267004
2021-09-29 09:39 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.351 s -0.073483
2021-09-29 09:40 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.201903
2021-09-29 09:30 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.326 s -2.686798
2021-09-29 09:37 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.575 s 1.050462
2021-09-29 09:53 R dataframe-to-table type_strings, R 0.493 s -1.005347
2021-09-29 08:57 Python dataframe-to-table type_simple_features 0.909 s -0.077598
2021-09-29 09:32 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.062 s -0.688396
2021-09-29 09:40 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.322059
2021-09-29 08:57 Python dataframe-to-table type_integers 0.011 s -0.417757
2021-09-29 09:29 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.042 s -1.542433
2021-09-29 09:30 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.753 s -1.046988
2021-09-29 09:33 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.183 s -1.143771
2021-09-29 08:53 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.403362
2021-09-29 09:14 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.192 s -0.082963
2021-09-29 09:31 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.817 s -0.984048
2021-09-29 09:37 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.197469
2021-09-29 09:53 R dataframe-to-table type_dict, R 0.052 s -0.028445
2021-09-29 09:14 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.279 s 0.056620
2021-09-29 09:29 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.986 s 0.178668
2021-09-29 09:53 R dataframe-to-table chi_traffic_2020_Q1, R 5.382 s 0.446118
2021-09-29 08:57 Python dataframe-to-table type_dict 0.011 s 1.451341
2021-09-29 09:31 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.103 s 1.163201
2021-09-29 09:34 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.496 s 0.793993
2021-09-29 09:29 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.696 s 0.446658
2021-09-29 09:31 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.175 s -2.199612
2021-09-29 09:33 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -1.477853
2021-09-29 09:40 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.766 s 0.593555
2021-09-29 08:57 Python dataset-filter nyctaxi_2010-01 4.394 s -1.094804
2021-09-29 09:18 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.348939
2021-09-29 09:18 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.013 s 0.120070
2021-09-29 09:30 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.274 s -1.136070
2021-09-29 09:35 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.151 s 1.153868
2021-09-29 09:38 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.743 s 1.126059
2021-09-29 09:40 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.288 s 0.598826
2021-09-29 08:54 Python csv-read gzip, streaming, nyctaxi_2010-01 10.609 s -0.296347
2021-09-29 08:57 Python dataframe-to-table type_floats 0.011 s 0.176050
2021-09-29 09:31 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.422393
2021-09-29 09:54 R dataframe-to-table type_integers, R 0.083 s 1.642717
2021-09-29 08:53 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.615 s -0.270089
2021-09-29 08:56 Python dataframe-to-table chi_traffic_2020_Q1 19.894 s -0.598634
2021-09-29 09:04 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.842 s 2.768916
2021-09-29 09:29 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.835 s 0.393825
2021-09-29 09:54 R dataframe-to-table type_nested, R 0.538 s -0.338242
2021-09-29 08:52 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.071 s -0.858193
2021-09-29 08:53 Python csv-read gzip, streaming, fanniemae_2016Q4 14.991 s -0.847063
2021-09-29 09:31 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.299 s -1.141103
2021-09-29 09:32 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.761 s 1.147302
2021-09-29 09:36 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.710 s 0.257695
2021-09-29 10:26 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.291 s 1.287056
2021-09-29 09:31 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.732 s -2.506112
2021-09-29 09:35 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.466 s 1.105758
2021-09-29 09:37 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.805 s 1.613948
2021-09-29 09:40 Python file-write lz4, feather, table, nyctaxi_2010-01 1.847 s -1.934203
2021-09-29 09:54 R dataframe-to-table type_floats, R 0.107 s 0.796153
2021-09-29 10:37 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s 0.138534
2021-09-29 10:43 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.158 s 2.146387
2021-09-29 10:17 R dataframe-to-table type_simple_features, R 275.154 s -0.717131
2021-09-29 10:18 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.958 s -0.397857
2021-09-29 10:19 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.715067
2021-09-29 10:21 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.123 s 0.466737
2021-09-29 10:17 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.094214
2021-09-29 10:29 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.815275
2021-09-29 10:51 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-29 10:51 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.546391
2021-09-29 10:51 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.182992
2021-09-29 10:51 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.519 s -0.127828
2021-09-29 10:51 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.922 s -0.894424
2021-09-29 10:18 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.264 s -0.137400
2021-09-29 10:39 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s 0.202661
2021-09-29 10:40 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.371599
2021-09-29 10:42 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 2.072038
2021-09-29 10:42 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.826807
2021-09-29 10:50 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.617 s -0.111671
2021-09-29 10:51 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.083172
2021-09-29 10:23 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.559 s -2.060791
2021-09-29 10:27 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.325 s 1.185654
2021-09-29 10:41 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.612 s -0.037185
2021-09-29 10:50 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.086824
2021-09-29 10:51 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -2.161329
2021-09-29 10:51 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-29 10:51 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.348724
2021-09-29 10:19 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.708294
2021-09-29 10:41 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.607 s 0.081160
2021-09-29 10:51 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.794683
2021-09-29 10:23 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.983 s -0.848794
2021-09-29 10:31 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.212 s 0.976246
2021-09-29 10:42 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -1.274654
2021-09-29 10:43 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.482 s 0.168145
2021-09-29 10:51 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.058220
2021-09-29 10:51 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.023677
2021-09-29 10:19 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.921 s -0.010992
2021-09-29 10:22 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.235 s 0.267184
2021-09-29 10:30 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.576 s 0.959341
2021-09-29 10:36 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s -0.077151
2021-09-29 10:50 JavaScript Parse serialize, tracks 0.004 s 0.492467
2021-09-29 10:19 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.912 s 0.065300
2021-09-29 10:34 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.466 s 2.228429
2021-09-29 10:51 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-29 10:20 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -1.029940
2021-09-29 10:39 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 2.748462
2021-09-29 10:40 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.611 s 2.045929
2021-09-29 10:50 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.087230
2021-09-29 10:50 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.398269
2021-09-29 10:51 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.002 s -3.081254
2021-09-29 10:51 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.145167
2021-09-29 10:21 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.190 s -1.223622
2021-09-29 10:24 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 1.413931
2021-09-29 10:50 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.523 s 0.105131
2021-09-29 10:50 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.747 s -0.004330
2021-09-29 10:51 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.966 s -1.359613
2021-09-29 10:40 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.512 s 0.564121
2021-09-29 10:50 JavaScript Parse Table.from, tracks 0.000 s 0.187871
2021-09-29 10:22 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.702918
2021-09-29 10:23 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.419568
2021-09-29 10:30 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.408 s -1.029385
2021-09-29 10:32 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.823 s 2.464556
2021-09-29 10:39 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.578 s 2.269451
2021-09-29 10:50 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.734 s -1.030042
2021-09-29 10:20 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.401 s -1.242246
2021-09-29 10:36 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.672 s 2.146733
2021-09-29 10:42 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s 0.019303
2021-09-29 10:39 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.355641
2021-09-29 10:41 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.963 s 2.122226
2021-09-29 10:50 JavaScript Parse readBatches, tracks 0.000 s 0.346208
2021-09-29 10:50 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.560268
2021-09-29 10:51 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-29 10:51 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.556702
2021-09-29 10:51 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.150684
2021-09-29 10:51 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.025660
2021-09-29 10:33 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.803 s 2.360136
2021-09-29 10:28 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.764 s 1.199570
2021-09-29 10:38 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.724077
2021-09-29 10:39 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.876 s 2.284927