Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 00:34 Python csv-read gzip, streaming, nyctaxi_2010-01 10.480 s 1.014641
2021-10-09 00:36 Python dataframe-to-table type_integers 0.011 s -0.808456
2021-10-09 00:36 Python dataframe-to-table type_floats 0.011 s 0.328982
2021-10-09 00:37 Python dataframe-to-table type_simple_features 0.912 s 0.217198
2021-10-09 01:08 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.978 s 0.247333
2021-10-09 01:09 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.789 s 0.887364
2021-10-09 01:16 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.346837
2021-10-09 01:17 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.854 s -0.327429
2021-10-09 01:18 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.960 s -1.046172
2021-10-09 01:19 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.293 s 1.130220
2021-10-09 01:20 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.791 s 0.603026
2021-10-09 01:34 R dataframe-to-table type_nested, R 0.539 s 0.230615
2021-10-09 01:51 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.572 s -0.015641
2021-10-09 02:00 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.128331
2021-10-09 02:01 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.359336
2021-10-09 02:12 JavaScript Parse readBatches, tracks 0.000 s -0.586327
2021-10-09 02:12 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.754591
2021-10-09 02:12 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.228036
2021-10-09 02:12 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.697256
2021-10-09 02:12 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.561692
2021-10-09 02:12 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.750796
2021-10-09 00:37 Python dataframe-to-table type_nested 2.881 s 0.212248
2021-10-09 01:11 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.253 s -0.676664
2021-10-09 01:19 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.895 s -0.499751
2021-10-09 00:33 Python csv-read uncompressed, file, nyctaxi_2010-01 1.005 s 0.714036
2021-10-09 01:13 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.496 s -0.762327
2021-10-09 00:31 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.971 s -0.228217
2021-10-09 00:31 Python csv-read uncompressed, file, fanniemae_2016Q4 1.188 s -0.706227
2021-10-09 00:36 Python dataframe-to-table type_dict 0.012 s 0.563910
2021-10-09 00:32 Python csv-read gzip, streaming, fanniemae_2016Q4 14.916 s -0.333315
2021-10-09 00:37 Python dataset-filter nyctaxi_2010-01 4.335 s 1.366772
2021-10-09 00:54 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.313 s 0.165494
2021-10-09 01:11 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.062 s -0.560451
2021-10-09 01:19 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.281575
2021-10-09 00:36 Python dataframe-to-table type_strings 0.371 s -0.038298
2021-10-09 01:10 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.313077
2021-10-09 01:11 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.293 s -0.536604
2021-10-09 01:12 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.769010
2021-10-09 01:12 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.366 s -0.851514
2021-10-09 01:12 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.677081
2021-10-09 01:20 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.309486
2021-10-09 01:20 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.321978
2021-10-09 01:34 R dataframe-to-table type_floats, R 0.014 s 2.307167
2021-10-09 01:10 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.281 s 0.540908
2021-10-09 01:14 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.310 s 0.181600
2021-10-09 01:17 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.281 s -0.507129
2021-10-09 01:34 R dataframe-to-table type_strings, R 0.492 s 0.229291
2021-10-09 01:10 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.782 s -0.109159
2021-10-09 01:14 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.100 s 0.440278
2021-10-09 01:34 R dataframe-to-table type_integers, R 0.010 s 2.337619
2021-10-09 01:16 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.789 s -0.334811
2021-10-09 00:58 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.022 s 0.232935
2021-10-09 01:08 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.875 s 0.094070
2021-10-09 01:09 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.733 s 0.188421
2021-10-09 00:34 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.810801
2021-10-09 00:44 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.763 s 0.267510
2021-10-09 00:40 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.002 s -0.666770
2021-10-09 01:09 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.955 s 1.017253
2021-10-09 01:10 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.862 s -0.310485
2021-10-09 01:16 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.437 s -0.756597
2021-10-09 01:15 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.442 s 0.552450
2021-10-09 01:16 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.659 s 0.266057
2021-10-09 01:09 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.212 s 0.703300
2021-10-09 01:10 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.123 s 1.222700
2021-10-09 01:13 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.992 s -0.741379
2021-10-09 01:18 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.830 s -0.069974
2021-10-09 01:11 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.941 s -0.572050
2021-10-09 00:59 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.014 s 0.132827
2021-10-09 01:11 Python file-read lz4, feather, table, fanniemae_2016Q4 0.610 s -1.181251
2021-10-09 01:34 R dataframe-to-table chi_traffic_2020_Q1, R 3.384 s 0.275177
2021-10-09 01:13 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.095298
2021-10-09 01:19 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.399 s -2.732451
2021-10-09 01:34 R dataframe-to-table type_dict, R 0.049 s 0.249785
2021-10-09 00:33 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.619063
2021-10-09 00:53 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.022 s 0.625448
2021-10-09 00:33 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.485 s 1.022561
2021-10-09 00:36 Python dataframe-to-table chi_traffic_2020_Q1 19.792 s -0.896981
2021-10-09 01:42 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.400 s -0.868368
2021-10-09 01:43 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.163 s 2.159535
2021-10-09 00:58 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.038 s -0.012199
2021-10-09 01:43 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.122 s 0.452954
2021-10-09 02:03 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s -0.063268
2021-10-09 02:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -0.803149
2021-10-09 02:12 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.712627
2021-10-09 02:12 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.704 s -0.358704
2021-10-09 02:12 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.779940
2021-10-09 02:12 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.467597
2021-10-09 02:04 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.171 s 0.289016
2021-10-09 02:12 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.011 s -2.278127
2021-10-09 02:12 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.447675
2021-10-09 01:42 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.940 s -0.986538
2021-10-09 01:42 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.677550
2021-10-09 01:41 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.445 s 2.180236
2021-10-09 01:58 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 0.772343
2021-10-09 02:12 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.793 s -0.391354
2021-10-09 02:12 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.476660
2021-10-09 01:44 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.682 s 0.098092
2021-10-09 01:53 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.743495
2021-10-09 02:03 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.343486
2021-10-09 02:03 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.144496
2021-10-09 02:12 JavaScript Parse Table.from, tracks 0.000 s -0.893153
2021-10-09 02:12 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 6.063 s -1.352902
2021-10-09 01:41 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.209 s 0.536460
2021-10-09 01:40 R dataframe-to-table type_simple_features, R 3.300 s 1.747183
2021-10-09 01:40 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.212 s 0.511153
2021-10-09 01:41 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.459 s 2.104817
2021-10-09 01:41 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.141553
2021-10-09 01:42 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.083618
2021-10-09 01:43 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.247 s 2.095302
2021-10-09 01:43 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.153970
2021-10-09 01:45 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.844 s 0.588433
2021-10-09 01:44 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.007 s -0.158064
2021-10-09 01:45 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.555 s -0.130289
2021-10-09 01:50 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.839 s -1.585615
2021-10-09 01:49 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.699 s 0.810112
2021-10-09 02:12 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.888504
2021-10-09 02:12 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.582378
2021-10-09 02:12 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.718032
2021-10-09 02:12 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.888604
2021-10-09 02:12 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.092470
2021-10-09 02:12 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.088646
2021-10-09 01:51 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.405 s -0.634149
2021-10-09 01:53 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s 0.574351
2021-10-09 02:02 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.598 s -0.070553
2021-10-09 02:12 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.946 s -1.677621
2021-10-09 02:12 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.833402
2021-10-09 02:12 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.166454
2021-10-09 01:59 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.368955
2021-10-09 02:12 JavaScript Parse serialize, tracks 0.005 s -0.049495
2021-10-09 02:12 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 6.370 s -1.950017
2021-10-09 02:12 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.479885
2021-10-09 02:12 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.107190
2021-10-09 02:12 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.568486
2021-10-09 02:12 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.666 s -2.739832
2021-10-09 01:55 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.536 s -0.770677
2021-10-09 01:57 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.390093
2021-10-09 02:00 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s 0.536898
2021-10-09 02:00 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s 0.046220
2021-10-09 02:02 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.496813
2021-10-09 02:04 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.513 s -1.356114
2021-10-09 01:54 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.852 s -0.608843
2021-10-09 02:02 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.912 s 0.264300
2021-10-09 01:57 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.730 s -0.949935
2021-10-09 02:00 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.583 s 0.061361
2021-10-09 01:47 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.239 s 0.832217
2021-10-09 01:48 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 0.589303
2021-10-09 02:01 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.270479
2021-10-09 02:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.453015
2021-10-09 02:00 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.865 s 0.329462