Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-02 21:52 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.881 s -1.515096
2021-10-02 21:52 Python csv-read gzip, streaming, nyctaxi_2010-01 10.866 s -1.724360
2021-10-02 21:55 Python dataframe-to-table type_floats 0.012 s -0.169652
2021-10-02 21:55 Python dataframe-to-table type_simple_features 0.910 s 0.275671
2021-10-02 21:52 Python csv-read uncompressed, file, nyctaxi_2010-01 1.025 s -1.125678
2021-10-02 22:30 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.924 s 1.748983
2021-10-02 21:51 Python csv-read gzip, streaming, fanniemae_2016Q4 15.141 s -1.328557
2021-10-02 22:29 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.824 s 0.419873
2021-10-02 21:55 Python dataframe-to-table type_nested 2.927 s 0.030078
2021-10-02 22:29 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.030 s -0.164436
2021-10-02 22:17 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.009 s 0.195621
2021-10-02 21:55 Python dataframe-to-table chi_traffic_2020_Q1 19.840 s -0.507451
2021-10-02 21:55 Python dataset-filter nyctaxi_2010-01 4.349 s 0.646713
2021-10-02 22:13 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.939 s 0.185668
2021-10-02 22:31 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.832 s -0.955788
2021-10-02 21:51 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.240775
2021-10-02 21:53 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.398168
2021-10-02 21:55 Python dataframe-to-table type_strings 0.371 s 0.100371
2021-10-02 22:03 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.246 s 1.092771
2021-10-02 21:50 Python csv-read uncompressed, file, fanniemae_2016Q4 1.215 s -2.496615
2021-10-02 22:13 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.271 s 0.152229
2021-10-02 22:30 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.260 s 1.096004
2021-10-02 21:55 Python dataframe-to-table type_integers 0.011 s 1.281036
2021-10-02 21:59 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.875 s -1.187132
2021-10-02 22:17 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.015 s 0.302101
2021-10-02 21:55 Python dataframe-to-table type_dict 0.012 s 0.119752
2021-10-02 21:50 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.161 s -1.170413
2021-10-02 22:17 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.013 s 0.303043
2021-10-02 22:29 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.762 s 0.033207
2021-10-02 22:30 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.175 s 1.568116
2021-10-02 22:31 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.930 s -1.407566
2021-10-02 22:32 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.352 s -1.592601
2021-10-02 22:31 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.123 s 0.809789
2021-10-02 22:31 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.769 s -1.073476
2021-10-02 22:32 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.999 s 1.761174
2021-10-02 22:33 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.388935
2021-10-02 22:31 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.678204
2021-10-02 22:32 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.242 s -1.581601
2021-10-02 22:33 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.332 s -1.515949
2021-10-02 22:34 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.984 s -1.564680
2021-10-02 22:34 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.380 s -0.996124
2021-10-02 22:32 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.847902
2021-10-02 22:33 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.469 s -1.494302
2021-10-02 22:33 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.272182
2021-10-02 22:41 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.804 s 0.338351
2021-10-02 23:25 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.533 s -0.445510
2021-10-02 23:26 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.165 s -1.038879
2021-10-02 22:36 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.741 s -1.025456
2021-10-02 22:55 R dataframe-to-table type_floats, R 0.106 s 1.198593
2021-10-02 22:35 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.598 s -0.879680
2021-10-02 23:21 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.176440
2021-10-02 23:23 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.219 s 1.335430
2021-10-02 23:37 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.759 s -0.702909
2021-10-02 23:52 JavaScript Parse Table.from, tracks 0.000 s 0.777115
2021-10-02 23:52 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.094790
2021-10-02 23:52 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.551898
2021-10-02 22:40 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.970 s -0.225736
2021-10-02 23:41 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.265736
2021-10-02 23:52 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.497936
2021-10-02 23:52 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574204
2021-10-02 23:52 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.264527
2021-10-02 22:30 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.751 s 1.780814
2021-10-02 22:32 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.459286
2021-10-02 22:55 R dataframe-to-table type_nested, R 0.541 s -1.619713
2021-10-02 23:19 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.867 s 0.605889
2021-10-02 23:21 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.911 s 0.628212
2021-10-02 22:55 R dataframe-to-table chi_traffic_2020_Q1, R 5.335 s 1.177506
2021-10-02 22:36 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.988 s -0.990831
2021-10-02 22:41 Python wide-dataframe use_legacy_dataset=true 0.396 s -1.077499
2021-10-02 23:19 R dataframe-to-table type_simple_features, R 274.668 s 0.520462
2021-10-02 23:28 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.618 s -1.041207
2021-10-02 23:41 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 1.091217
2021-10-02 23:44 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.102393
2021-10-02 23:52 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.670 s -0.376740
2021-10-02 23:19 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.214 s 0.482951
2021-10-02 23:22 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.158 s 1.021910
2021-10-02 23:43 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -3.112139
2021-10-02 23:52 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.889290
2021-10-02 22:38 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.275 s -0.492130
2021-10-02 23:24 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.672 s 0.171732
2021-10-02 23:36 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.577 s -0.858950
2021-10-02 23:52 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.796468
2021-10-02 22:39 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.019 s -1.384910
2021-10-02 23:22 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.064 s -1.396542
2021-10-02 23:22 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.158 s -2.122893
2021-10-02 23:30 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.837 s -1.044538
2021-10-02 23:52 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.990747
2021-10-02 23:32 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.235613
2021-10-02 22:37 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.474913
2021-10-02 22:39 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.936 s -0.223502
2021-10-02 22:40 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.369 s 0.017239
2021-10-02 23:20 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.870 s 0.564347
2021-10-02 23:21 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.362 s 1.272855
2021-10-02 23:35 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.897 s -0.607803
2021-10-02 23:41 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.606 s 0.923930
2021-10-02 23:52 JavaScript Parse readBatches, tracks 0.000 s 0.618132
2021-10-02 23:52 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.560841
2021-10-02 23:52 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.323924
2021-10-02 23:52 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.255393
2021-10-02 22:40 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.453405
2021-10-02 23:23 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.205521
2021-10-02 23:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.535 s 0.992768
2021-10-02 23:52 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s 0.128264
2021-10-02 22:37 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.839 s -0.746805
2021-10-02 23:40 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 1.015556
2021-10-02 23:33 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.176 s 1.557621
2021-10-02 23:42 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.691608
2021-10-02 23:52 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.997084
2021-10-02 23:52 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.547348
2021-10-02 22:37 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.322 s 0.404831
2021-10-02 23:41 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.870 s 1.174189
2021-10-02 23:43 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -2.863556
2021-10-02 23:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.858072
2021-10-02 23:52 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.687 s 0.346685
2021-10-02 22:38 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.931 s -0.937008
2021-10-02 22:40 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.356 s -0.241013
2021-10-02 22:55 R dataframe-to-table type_strings, R 0.491 s 0.029782
2021-10-02 23:20 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.137484
2021-10-02 23:39 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.535966
2021-10-02 23:43 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s 0.471909
2021-10-02 23:30 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.039 s -0.712941
2021-10-02 23:31 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.551 s 1.044273
2021-10-02 22:41 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.467671
2021-10-02 23:24 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.031 s -3.445965
2021-10-02 22:55 R dataframe-to-table type_dict, R 0.049 s 0.035485
2021-10-02 23:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.551850
2021-10-02 23:42 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.937 s 0.958849
2021-10-02 23:52 JavaScript Parse serialize, tracks 0.005 s -0.748549
2021-10-02 22:55 R dataframe-to-table type_integers, R 0.085 s -1.058953
2021-10-02 23:27 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.616 s -0.852295
2021-10-02 23:34 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.946 s -1.029867
2021-10-02 23:38 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.769915
2021-10-02 23:52 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.540888
2021-10-02 23:40 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.165 s 1.771874
2021-10-02 23:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.172 s 0.925443
2021-10-02 23:52 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.617 s -0.321837
2021-10-02 23:19 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.215 s 0.403609
2021-10-02 23:52 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.887 s 0.337460
2021-10-02 23:52 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 23:52 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.833795
2021-10-02 23:41 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.842098
2021-10-02 23:52 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.658 s 0.421484
2021-10-02 23:52 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.907 s -0.609828
2021-10-02 23:52 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 23:52 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.209686
2021-10-02 23:39 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.246 s 0.963927
2021-10-02 23:52 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.218181
2021-10-02 23:52 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.550828
2021-10-02 23:52 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.557 s -0.791042