Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 04:25 Python dataframe-to-table type_integers 0.011 s -0.493683
2021-10-12 04:26 Python dataframe-to-table type_nested 2.861 s 1.041457
2021-10-12 04:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.859 s -0.337212
2021-10-12 04:48 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.083 s -1.804952
2021-10-12 05:01 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.284 s 0.118697
2021-10-12 05:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.668655
2021-10-12 05:32 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.552 s 1.900376
2021-10-12 05:33 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.121648
2021-10-12 05:33 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.212 s 0.967291
2021-10-12 05:40 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.714 s 0.556834
2021-10-12 05:45 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.642031
2021-10-12 05:51 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.232652
2021-10-12 05:52 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.524 s -0.303909
2021-10-12 06:02 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.721 s 0.156390
2021-10-12 06:02 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.008223
2021-10-12 06:02 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.890 s -0.298258
2021-10-12 06:02 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.138087
2021-10-12 04:26 Python dataset-filter nyctaxi_2010-01 4.368 s -1.389093
2021-10-12 04:21 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.392135
2021-10-12 04:22 Python csv-read uncompressed, file, nyctaxi_2010-01 0.979 s 2.879352
2021-10-12 04:43 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.510 s -0.755277
2021-10-12 04:22 Python csv-read gzip, file, fanniemae_2016Q4 6.023 s 1.616607
2021-10-12 04:23 Python csv-read gzip, file, nyctaxi_2010-01 9.050 s -1.890364
2021-10-12 05:01 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.888 s -2.156186
2021-10-12 04:20 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.929 s -0.125978
2021-10-12 04:43 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.238 s 0.212138
2021-10-12 04:48 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.079 s -0.720601
2021-10-12 05:00 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.009 s 0.092397
2021-10-12 05:00 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.770 s -0.232286
2021-10-12 04:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.686 s -0.516124
2021-10-12 04:23 Python csv-read gzip, streaming, nyctaxi_2010-01 10.677 s -0.834179
2021-10-12 04:48 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.103 s -0.686681
2021-10-12 05:00 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.044 s -1.846485
2021-10-12 04:25 Python dataframe-to-table type_floats 0.011 s 0.399443
2021-10-12 06:02 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.524002
2021-10-12 04:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.810 s 0.484900
2021-10-12 05:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.009 s 1.845712
2021-10-12 05:07 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.249 s 0.346463
2021-10-12 04:25 Python dataframe-to-table type_dict 0.012 s 0.086440
2021-10-12 04:26 Python dataframe-to-table type_simple_features 0.936 s -1.044741
2021-10-12 04:59 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.512 s -3.824332
2021-10-12 04:25 Python dataframe-to-table chi_traffic_2020_Q1 19.531 s 0.142444
2021-10-12 04:25 Python dataframe-to-table type_strings 0.369 s 0.299393
2021-10-12 04:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.871 s 0.137138
2021-10-12 05:00 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.305 s -2.209864
2021-10-12 05:02 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.689 s 1.895516
2021-10-12 05:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.597 s 2.040874
2021-10-12 05:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.537 s 1.831506
2021-10-12 05:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.183504
2021-10-12 05:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.176 s 1.339412
2021-10-12 05:02 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.136922
2021-10-12 05:04 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.293 s 1.730712
2021-10-12 05:03 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.175 s 1.386407
2021-10-12 05:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.057 s -0.521445
2021-10-12 05:02 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.460674
2021-10-12 05:03 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.040 s -0.256400
2021-10-12 05:04 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s 0.046522
2021-10-12 05:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.576 s -1.085129
2021-10-12 05:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.991 s -1.076324
2021-10-12 05:05 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.078 s 0.588669
2021-10-12 05:06 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 0.561819
2021-10-12 05:08 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.440 s -1.013761
2021-10-12 05:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.807 s 1.585578
2021-10-12 05:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.856 s -0.052787
2021-10-12 05:08 Python file-write lz4, feather, table, fanniemae_2016Q4 1.144 s 0.984355
2021-10-12 05:07 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.863 s -0.556627
2021-10-12 05:35 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.528 s 0.101088
2021-10-12 05:42 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.390 s 0.932855
2021-10-12 06:02 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.669 s 0.308729
2021-10-12 05:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.325 s 0.713237
2021-10-12 05:50 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 1.752890
2021-10-12 05:52 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.318615
2021-10-12 06:02 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.882244
2021-10-12 06:02 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.488993
2021-10-12 06:02 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.939850
2021-10-12 05:31 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.213 s 0.673005
2021-10-12 06:02 JavaScript Parse serialize, tracks 0.004 s 0.562965
2021-10-12 06:02 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.675795
2021-10-12 06:02 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.916 s -0.161413
2021-10-12 05:25 R dataframe-to-table type_nested, R 0.537 s 0.231991
2021-10-12 05:31 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 0.992563
2021-10-12 05:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.875 s 1.338943
2021-10-12 06:02 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.139449
2021-10-12 06:02 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.672 s -0.506905
2021-10-12 06:02 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.604220
2021-10-12 06:02 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-12 05:49 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.233 s 1.061220
2021-10-12 06:02 JavaScript Parse readBatches, tracks 0.000 s -1.055684
2021-10-12 06:02 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.156008
2021-10-12 06:02 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578421
2021-10-12 06:02 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.060681
2021-10-12 05:31 R dataframe-to-table type_simple_features, R 3.357 s 0.851053
2021-10-12 05:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.925 s -0.153756
2021-10-12 05:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.341 s 0.492251
2021-10-12 05:24 R dataframe-to-table chi_traffic_2020_Q1, R 3.365 s 0.266936
2021-10-12 05:33 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.049 s 0.777901
2021-10-12 05:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -0.983807
2021-10-12 05:11 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.815 s 0.221552
2021-10-12 05:38 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.283 s 0.615147
2021-10-12 05:09 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.869 s -0.133161
2021-10-12 05:34 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.010 s -0.148908
2021-10-12 05:44 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.892 s -0.360622
2021-10-12 06:02 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.726613
2021-10-12 05:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.263 s -0.013946
2021-10-12 05:48 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 1.230629
2021-10-12 06:02 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.848398
2021-10-12 06:02 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.011469
2021-10-12 06:02 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.482646
2021-10-12 05:25 R dataframe-to-table type_floats, R 0.014 s 1.011901
2021-10-12 05:42 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.555 s 0.168048
2021-10-12 06:02 JavaScript Parse Table.from, tracks 0.000 s -0.999634
2021-10-12 06:02 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.721011
2021-10-12 06:02 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.850338
2021-10-12 06:02 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.505 s 0.306177
2021-10-12 05:10 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.893 s -0.075600
2021-10-12 05:34 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.703 s -0.122646
2021-10-12 05:37 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.255 s 0.564801
2021-10-12 05:43 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.181 s 0.937813
2021-10-12 05:52 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.626 s -2.098823
2021-10-12 06:02 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.610 s -0.330720
2021-10-12 06:02 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.482313
2021-10-12 06:02 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.822674
2021-10-12 05:40 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.809 s 1.961811
2021-10-12 05:11 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.690013
2021-10-12 05:33 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.162 s 0.974865
2021-10-12 05:46 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.528 s -0.349772
2021-10-12 05:50 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.171 s -0.002621
2021-10-12 05:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.592 s 0.105103
2021-10-12 05:53 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.966437
2021-10-12 05:24 R dataframe-to-table type_strings, R 0.487 s 0.231706
2021-10-12 05:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.283 s 1.009908
2021-10-12 05:51 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.875 s 0.040007
2021-10-12 05:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.167 s 0.593278
2021-10-12 05:11 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.255719
2021-10-12 05:31 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.497 s 0.953928
2021-10-12 05:33 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.414 s -1.358753
2021-10-12 05:47 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.730 s -0.708949
2021-10-12 05:51 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -0.326311
2021-10-12 05:11 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.762324
2021-10-12 05:25 R dataframe-to-table type_integers, R 0.010 s 1.030209
2021-10-12 05:36 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.840 s 0.552815
2021-10-12 05:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.498 s 0.357446
2021-10-12 05:24 R dataframe-to-table type_dict, R 0.053 s -0.121760
2021-10-12 05:32 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.072 s -1.781719
2021-10-12 05:34 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.214 s -1.348289
2021-10-12 05:51 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.599 s -1.318065
2021-10-12 05:51 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.127109
2021-10-12 05:53 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.010658
2021-10-12 05:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -0.036445