Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 21:32 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.685 s -0.410303
2021-09-29 22:08 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.816 s 0.455970
2021-09-29 22:09 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.263 s 1.122266
2021-09-29 22:10 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.943 s -3.272207
2021-09-29 22:14 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.508 s -0.620787
2021-09-29 22:09 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.168 s 2.144072
2021-09-29 21:35 Python dataframe-to-table type_floats 0.011 s 2.054698
2021-09-29 21:52 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.414 s -0.009559
2021-09-29 22:12 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.307 s -1.258686
2021-09-29 22:13 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.380 s -1.224208
2021-09-29 22:19 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.956 s -0.185041
2021-09-29 21:31 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.493672
2021-09-29 22:08 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.979 s 0.240110
2021-09-29 22:10 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.838 s -1.633481
2021-09-29 22:18 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.918 s -0.168698
2021-09-29 22:10 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.122 s 0.940210
2021-09-29 21:35 Python dataframe-to-table type_dict 0.012 s 0.438383
2021-09-29 22:09 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.940 s 1.599108
2021-09-29 22:13 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.914 s -1.241916
2021-09-29 22:10 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.109828
2021-09-29 21:35 Python dataframe-to-table type_strings 0.371 s 0.038453
2021-09-29 21:30 Python csv-read uncompressed, file, fanniemae_2016Q4 1.169 s 0.040385
2021-09-29 22:11 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.044 s -0.028950
2021-09-29 22:20 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.789 s 0.435591
2021-09-29 21:32 Python csv-read uncompressed, file, nyctaxi_2010-01 1.006 s 0.221317
2021-09-29 21:33 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -0.962050
2021-09-29 21:35 Python dataset-filter nyctaxi_2010-01 4.349 s 0.550004
2021-09-29 21:56 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.140908
2021-09-29 22:12 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.443 s -1.414951
2021-09-29 22:15 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.927 s -0.865649
2021-09-29 22:19 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.483870
2021-09-29 22:09 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.757 s 1.864322
2021-09-29 22:10 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.763 s -1.466084
2021-09-29 22:20 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.044337
2021-09-29 21:35 Python dataframe-to-table type_simple_features 0.916 s -0.324518
2021-09-29 21:56 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.035 s -0.003996
2021-09-29 22:16 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.724 s -0.108635
2021-09-29 22:20 Python wide-dataframe use_legacy_dataset=false 0.619 s -0.094949
2021-09-29 21:30 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.742 s -0.347880
2021-09-29 21:32 Python csv-read gzip, streaming, nyctaxi_2010-01 10.678 s -0.437305
2021-09-29 22:19 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s -0.030496
2021-09-29 21:34 Python dataframe-to-table chi_traffic_2020_Q1 19.499 s 1.339776
2021-09-29 21:35 Python dataframe-to-table type_nested 2.905 s 0.994131
2021-09-29 22:11 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.292 s -1.133865
2021-09-29 22:12 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.182 s -1.219206
2021-09-29 22:16 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.095744
2021-09-29 21:52 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.256 s 0.259902
2021-09-29 21:56 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.043 s -0.120577
2021-09-29 22:08 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.720 s 0.313695
2021-09-29 22:11 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.243 s -3.016553
2021-09-29 22:17 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.956 s -1.580988
2021-09-29 21:31 Python csv-read gzip, streaming, fanniemae_2016Q4 14.668 s -0.344163
2021-09-29 21:35 Python dataframe-to-table type_integers 0.011 s 1.848039
2021-09-29 21:43 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.668 s 1.642074
2021-09-29 22:12 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.005628
2021-09-29 22:18 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.999 s -1.320065
2021-09-29 22:11 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.269358
2021-09-29 22:17 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.206 s 0.053432
2021-09-29 22:19 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.305 s 0.485297
2021-09-29 22:11 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.042 s -0.289584
2021-09-29 22:15 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.740 s -1.232133
2021-09-29 22:16 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.311 s 0.298566
2021-09-29 23:20 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.869 s 1.527259
2021-09-29 23:31 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.582 s -0.048397
2021-09-29 23:31 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.142123
2021-09-29 21:38 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.938 s -0.229483
2021-09-29 23:11 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.403 s -0.228652
2021-09-29 23:19 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.175 s 1.394578
2021-09-29 23:21 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.194263
2021-09-29 23:03 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.973 s -0.128234
2021-09-29 23:17 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.350517
2021-09-29 23:21 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 1.264149
2021-09-29 23:31 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.582417
2021-09-29 23:31 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.913279
2021-09-29 23:31 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.790801
2021-09-29 23:31 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.502 s 0.054109
2021-09-29 22:34 R dataframe-to-table chi_traffic_2020_Q1, R 5.432 s -0.578206
2021-09-29 22:59 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.239 s 0.154134
2021-09-29 23:22 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.892232
2021-09-29 23:31 JavaScript Parse Table.from, tracks 0.000 s -0.478293
2021-09-29 22:34 R dataframe-to-table type_strings, R 0.489 s 0.725321
2021-09-29 22:34 R dataframe-to-table type_integers, R 0.084 s 0.302278
2021-09-29 22:59 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.861 s 0.662762
2021-09-29 22:59 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.073669
2021-09-29 23:01 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.182 s -0.479195
2021-09-29 22:58 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.850 s 0.733206
2021-09-29 23:03 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.683 s 0.017584
2021-09-29 23:06 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.599 s -0.791535
2021-09-29 23:13 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.939 s -0.832081
2021-09-29 23:02 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.619795
2021-09-29 23:31 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.045668
2021-09-29 23:31 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.659 s 0.385325
2021-09-29 23:31 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.893 s -0.271751
2021-09-29 23:31 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.168641
2021-09-29 23:01 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s -0.129044
2021-09-29 23:09 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.695004
2021-09-29 23:20 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.418166
2021-09-29 23:31 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.976202
2021-09-29 23:19 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.092119
2021-09-29 23:22 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.920 s 1.247212
2021-09-29 23:23 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.179 s 1.167949
2021-09-29 22:34 R dataframe-to-table type_nested, R 0.533 s 1.299570
2021-09-29 23:02 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.241 s 0.101289
2021-09-29 23:31 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.089314
2021-09-29 23:31 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.835436
2021-09-29 22:34 R dataframe-to-table type_dict, R 0.052 s -0.232346
2021-09-29 22:58 R dataframe-to-table type_simple_features, R 275.168 s -0.607395
2021-09-29 23:01 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.058 s -0.401632
2021-09-29 23:07 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.625 s -1.344987
2021-09-29 23:20 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 1.579715
2021-09-29 23:31 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.162016
2021-09-29 23:05 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.170 s -1.330204
2021-09-29 22:34 R dataframe-to-table type_floats, R 0.113 s -1.789906
2021-09-29 23:02 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.132 s -0.172375
2021-09-29 23:31 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.021 s 2.476999
2021-09-29 23:31 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.641453
2021-09-29 23:31 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.566125
2021-09-29 23:23 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.471 s 0.678508
2021-09-29 23:31 JavaScript Parse serialize, tracks 0.004 s 0.706746
2021-09-29 23:31 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.890 s 0.201842
2021-09-29 23:04 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.532 s -0.569963
2021-09-29 23:10 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.586 s 0.576038
2021-09-29 23:12 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.210 s 0.923137
2021-09-29 23:31 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.163974
2021-09-29 23:31 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.671523
2021-09-29 23:31 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.973034
2021-09-29 23:00 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s -0.044807
2021-09-29 23:15 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.570 s -0.723706
2021-09-29 23:20 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 1.336558
2021-09-29 23:31 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 2.493913
2021-09-29 23:14 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.898 s -0.478298
2021-09-29 23:24 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.493 s 0.122729
2021-09-29 22:58 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.199 s 0.567934
2021-09-29 23:16 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.772 s -0.779631
2021-09-29 23:31 JavaScript Parse readBatches, tracks 0.000 s -0.359765
2021-09-29 23:31 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.242564
2021-09-29 23:00 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.920 s -0.122476
2021-09-29 23:18 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 1.975967
2021-09-29 23:20 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.419804
2021-09-29 23:21 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.524 s -1.096105
2021-09-29 23:22 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 1.202946
2021-09-29 23:31 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.363700
2021-09-29 23:31 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.977034
2021-09-29 23:09 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.053 s -0.866755
2021-09-29 23:22 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.605 s -0.134848
2021-09-29 23:23 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.745917
2021-09-29 23:31 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.570 s -0.099330
2021-09-29 23:31 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.685 s 0.356978
2021-09-29 23:31 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.886036