Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 02:44 Python csv-read gzip, streaming, fanniemae_2016Q4 14.771 s 0.848281
2021-10-11 02:44 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.530201
2021-10-11 02:45 Python csv-read uncompressed, file, nyctaxi_2010-01 1.015 s -0.367335
2021-10-11 02:45 Python csv-read gzip, streaming, nyctaxi_2010-01 10.607 s -0.120146
2021-10-11 02:46 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.437122
2021-10-11 02:48 Python dataframe-to-table type_strings 0.366 s 0.516552
2021-10-11 02:48 Python dataframe-to-table type_dict 0.012 s 0.323717
2021-10-11 02:48 Python dataframe-to-table type_floats 0.011 s -0.333848
2021-10-11 02:48 Python dataframe-to-table type_simple_features 0.927 s -0.583979
2021-10-11 02:48 Python dataset-filter nyctaxi_2010-01 4.315 s 1.721422
2021-10-11 03:05 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.462 s 0.070761
2021-10-11 03:10 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.090 s -2.377230
2021-10-11 03:10 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.021 s 0.090719
2021-10-11 03:21 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.933 s 0.502604
2021-10-11 03:22 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.696 s 0.502286
2021-10-11 03:22 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.957 s 0.596391
2021-10-11 03:22 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.197 s 0.740352
2021-10-11 03:23 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.786 s 0.587553
2021-10-11 03:23 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.614 s 2.725368
2021-10-11 03:23 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.345073
2021-10-11 03:24 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.687 s 2.754321
2021-10-11 03:24 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.064 s -0.763401
2021-10-11 03:25 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.035 s 0.056475
2021-10-11 03:26 Python file-read lz4, feather, table, nyctaxi_2010-01 0.681 s -1.869711
2021-10-11 03:26 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.796 s 1.452115
2021-10-11 03:27 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.098 s 0.481632
2021-10-11 03:27 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.597 s -1.364203
2021-10-11 03:28 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.894 s -0.868971
2021-10-11 03:29 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.436 s -0.706753
2021-10-11 03:29 Python file-write lz4, feather, table, fanniemae_2016Q4 1.176 s -1.021896
2021-10-11 03:30 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.524 s -2.361033
2021-10-11 03:30 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.858 s -0.078255
2021-10-11 03:31 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.995 s -1.162903
2021-10-11 03:56 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.034 s 3.204879
2021-10-11 03:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 1.243020
2021-10-11 03:56 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 1.168654
2021-10-11 03:56 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -2.127645
2021-10-11 03:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.973 s 0.288357
2021-10-11 03:57 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.691 s -0.019480
2021-10-11 03:58 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.515 s 0.249691
2021-10-11 04:00 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.266 s 0.561471
2021-10-11 04:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.284 s 0.675158
2021-10-11 04:06 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.228 s -1.421223
2021-10-11 04:07 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.897 s -0.494329
2021-10-11 04:09 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.459744
2021-10-11 04:11 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.272 s 2.552861
2021-10-11 04:12 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 0.161213
2021-10-11 04:12 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.464 s 2.633636
2021-10-11 04:13 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.166 s 0.644126
2021-10-11 04:13 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.594 s -0.837971
2021-10-11 04:14 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.854 s 0.601574
2021-10-11 04:14 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s -0.138928
2021-10-11 04:14 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 1.441866
2021-10-11 04:14 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.524 s -0.390364
2021-10-11 04:15 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s 0.116415
2021-10-11 04:16 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.572 s 0.355662
2021-10-11 04:16 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.312222
2021-10-11 04:16 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.635218
2021-10-11 04:17 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.093994
2021-10-11 04:17 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.198 s -1.407018
2021-10-11 04:25 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.569442
2021-10-11 04:25 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.031997
2021-10-11 04:14 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.342672
2021-10-11 03:32 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.914 s -0.421118
2021-10-11 03:32 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.349 s 0.152388
2021-10-11 03:32 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.886156
2021-10-11 03:33 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.864 s -1.348208
2021-10-11 03:33 Python wide-dataframe use_legacy_dataset=false 0.612 s 1.877615
2021-10-11 03:47 R dataframe-to-table chi_traffic_2020_Q1, R 3.416 s 0.271612
2021-10-11 03:47 R dataframe-to-table type_strings, R 0.489 s 0.233636
2021-10-11 03:47 R dataframe-to-table type_dict, R 0.052 s 0.039220
2021-10-11 03:47 R dataframe-to-table type_integers, R 0.011 s 1.214454
2021-10-11 03:47 R dataframe-to-table type_floats, R 0.013 s 1.231557
2021-10-11 03:48 R dataframe-to-table type_nested, R 0.533 s 0.235767
2021-10-11 03:54 R dataframe-to-table type_simple_features, R 3.355 s 1.026442
2021-10-11 03:54 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.223 s 0.313121
2021-10-11 03:54 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.483 s 1.184252
2021-10-11 03:54 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.233 s -0.331254
2021-10-11 03:54 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.466 s 1.165472
2021-10-11 03:55 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.052 s -2.146945
2021-10-11 03:55 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.569 s -1.314765
2021-10-11 03:56 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.389 s -0.032652
2021-10-11 04:17 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.336722
2021-10-11 04:25 JavaScript Parse readBatches, tracks 0.000 s 0.021073
2021-10-11 04:25 JavaScript Parse serialize, tracks 0.004 s 0.646865
2021-10-11 04:25 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.822153
2021-10-11 04:25 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.083584
2021-10-11 04:25 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.672 s 0.219431
2021-10-11 04:25 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.699 s 0.278521
2021-10-11 04:25 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.086698
2021-10-11 04:25 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.866 s 0.288423
2021-10-11 04:25 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.880907
2021-10-11 04:25 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.749032
2021-10-11 04:25 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.125090
2021-10-11 04:25 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.655934
2021-10-11 04:25 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.075614
2021-10-11 04:25 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -2.492445
2021-10-11 04:25 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.399086
2021-10-11 02:45 Python csv-read uncompressed, streaming, nyctaxi_2010-01 11.246 s -4.371383
2021-10-11 03:23 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.524 s 2.799031
2021-10-11 03:56 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.163 s 1.181687
2021-10-11 02:43 Python csv-read uncompressed, file, fanniemae_2016Q4 1.148 s 1.572869
2021-10-11 03:23 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.700876
2021-10-11 03:24 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -0.921147
2021-10-11 03:24 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.006 s 2.685430
2021-10-11 03:28 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.445 s 0.557661
2021-10-11 03:29 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.175 s -2.772042
2021-10-11 02:48 Python dataframe-to-table type_integers 0.011 s -1.782237
2021-10-11 03:25 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.174 s 1.215338
2021-10-11 04:15 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.881 s 1.185518
2021-10-11 04:25 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.559 s -0.290316
2021-10-11 04:25 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.112156
2021-10-11 02:48 Python dataframe-to-table type_nested 2.864 s 0.777455
2021-10-11 02:56 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.821 s -0.327919
2021-10-11 04:02 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.717 s 0.614769
2021-10-11 04:04 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.548 s 0.522476
2021-10-11 04:25 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.589 s -0.317923
2021-10-11 04:25 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.613656
2021-10-11 04:25 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.568212
2021-10-11 04:25 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.915917
2021-10-11 02:43 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.829 s 0.968646
2021-10-11 02:48 Python dataframe-to-table chi_traffic_2020_Q1 19.764 s -0.510481
2021-10-11 03:25 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 1.051931
2021-10-11 03:26 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.276 s 1.652820
2021-10-11 03:31 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.994 s -1.771944
2021-10-11 03:33 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.564577
2021-10-11 03:54 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -2.338265
2021-10-11 03:59 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.838 s 0.631390
2021-10-11 04:05 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.394 s 0.564527
2021-10-11 04:08 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.876 s -0.886425
2021-10-11 04:14 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s 0.170205
2021-10-11 04:25 JavaScript Parse Table.from, tracks 0.000 s 0.134676
2021-10-11 04:25 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.809266
2021-10-11 04:25 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.508105
2021-10-11 02:51 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.578 s 0.555646
2021-10-11 03:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.525 s -4.242020
2021-10-11 03:23 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.304 s -0.942318
2021-10-11 03:32 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s 0.054284
2021-10-11 04:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.815 s 1.609405
2021-10-11 04:25 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.096516
2021-10-11 04:25 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.464365
2021-10-11 04:25 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.896 s 0.221077
2021-10-11 04:25 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.409725
2021-10-11 04:25 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.510 s 0.198638
2021-10-11 03:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.167 s -0.788551
2021-10-11 03:10 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.080 s -0.431128
2021-10-11 03:24 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.107 s 1.931193
2021-10-11 04:10 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.723 s -0.612071
2021-10-11 04:16 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -1.191751