Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 18:17 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.089 s 0.556308
2021-10-11 18:19 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.235 s 0.446976
2021-10-11 18:20 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.533 s -2.081219
2021-10-11 18:23 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.671316
2021-10-11 19:04 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.171 s 0.046123
2021-10-11 17:34 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.931 s -0.168108
2021-10-11 17:34 Python csv-read uncompressed, file, fanniemae_2016Q4 1.153 s 1.161757
2021-10-11 17:39 Python dataframe-to-table type_integers 0.011 s -1.850542
2021-10-11 17:35 Python csv-read gzip, streaming, fanniemae_2016Q4 14.887 s -0.369439
2021-10-11 18:16 Python file-read lz4, feather, table, nyctaxi_2010-01 0.678 s -1.308022
2021-10-11 17:37 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.641031
2021-10-11 18:01 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.079 s -0.385370
2021-10-11 18:12 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.958 s 0.351692
2021-10-11 18:14 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.603 s 2.247254
2021-10-11 17:37 Python csv-read gzip, streaming, nyctaxi_2010-01 10.516 s 0.572550
2021-10-11 18:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 1.356664
2021-10-11 18:14 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.160066
2021-10-11 18:16 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.108 s 2.495307
2021-10-11 18:17 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.802 s 1.859103
2021-10-11 17:39 Python dataframe-to-table type_simple_features 0.929 s -0.706947
2021-10-11 18:47 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.228 s 1.037332
2021-10-11 18:47 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.779409
2021-10-11 19:06 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.069620
2021-10-11 19:08 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 0.977641
2021-10-11 17:56 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.181 s -0.758817
2021-10-11 18:21 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.850 s 0.068348
2021-10-11 18:23 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.357 s -0.155933
2021-10-11 17:39 Python dataframe-to-table chi_traffic_2020_Q1 19.538 s 0.129582
2021-10-11 17:43 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.692 s 0.778827
2021-10-11 18:15 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.018 s 1.996649
2021-10-11 18:15 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.031 s 0.289142
2021-10-11 18:21 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.890 s -0.371040
2021-10-11 18:22 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.917 s -0.003028
2021-10-11 18:22 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.928 s -0.579687
2021-10-11 18:23 Python file-write lz4, feather, table, nyctaxi_2010-01 1.792 s 0.777087
2021-10-11 17:35 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.200042
2021-10-11 17:39 Python dataframe-to-table type_strings 0.367 s 0.425369
2021-10-11 18:15 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.169 s 1.669872
2021-10-11 18:18 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.446 s 0.550203
2021-10-11 18:20 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.974 s -1.015375
2021-10-11 17:36 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.534 s 0.524434
2021-10-11 17:57 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.653 s -0.053214
2021-10-11 18:15 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.040 s 0.200261
2021-10-11 18:18 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.561 s -0.995556
2021-10-11 18:19 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.911 s -0.880532
2021-10-11 17:39 Python dataframe-to-table type_floats 0.011 s -0.583833
2021-10-11 18:20 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 0.895539
2021-10-11 17:36 Python csv-read uncompressed, file, nyctaxi_2010-01 1.008 s 0.300139
2021-10-11 18:46 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.675810
2021-10-11 18:51 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.235 s 0.755955
2021-10-11 19:06 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.869 s 1.751164
2021-10-11 17:40 Python dataset-filter nyctaxi_2010-01 4.357 s -0.816831
2021-10-11 18:14 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.131 s 0.339774
2021-10-11 18:46 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.162 s 1.049107
2021-10-11 17:39 Python dataframe-to-table type_nested 2.878 s 0.053930
2021-10-11 18:01 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.059 s -0.965195
2021-10-11 18:12 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.819 s 0.377907
2021-10-11 18:45 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 1.040511
2021-10-11 18:45 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -1.862570
2021-10-11 18:46 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.265636
2021-10-11 18:48 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.986 s 0.138988
2021-10-11 18:52 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.283 s 0.666442
2021-10-11 17:47 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.799 s -0.315587
2021-10-11 18:13 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.306 s -2.583187
2021-10-11 18:16 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.284 s 2.084222
2021-10-11 18:45 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.245 s -1.038275
2021-10-11 18:46 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.417 s -1.615397
2021-10-11 18:49 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.830 s 0.675289
2021-10-11 19:05 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.967444
2021-10-11 19:07 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -1.372184
2021-10-11 18:13 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.056 s -2.509025
2021-10-11 18:13 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.281 s 0.250833
2021-10-11 18:14 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.540 s 2.039899
2021-10-11 19:03 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.466 s 2.078783
2021-10-11 19:07 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.290755
2021-10-11 19:08 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.493 s 0.926490
2021-10-11 18:02 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.027 s -0.010112
2021-10-11 18:12 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.679 s 0.658915
2021-10-11 18:13 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.904 s -3.064008
2021-10-11 17:39 Python dataframe-to-table type_dict 0.012 s 0.342501
2021-10-11 18:14 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.685 s 2.190207
2021-10-11 18:16 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.164 s 2.139934
2021-10-11 18:23 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.463261
2021-10-11 18:23 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.934748
2021-10-11 18:23 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.822 s -0.033140
2021-10-11 18:44 R dataframe-to-table type_simple_features, R 3.392 s 0.914610
2021-10-11 19:05 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.145451
2021-10-11 19:07 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.610583
2021-10-11 18:45 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.075 s -2.102747
2021-10-11 18:48 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.698 s -0.079142
2021-10-11 18:48 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.515 s 0.243292
2021-10-11 18:55 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.541 s 0.731051
2021-10-11 19:06 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.593 s 0.055359
2021-10-11 18:44 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.214 s 0.417966
2021-10-11 18:47 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.125 s -0.164893
2021-10-11 18:55 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.394 s 0.518204
2021-10-11 19:05 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.619 s -0.683688
2021-10-11 19:15 JavaScript Parse Table.from, tracks 0.000 s 0.110637
2021-10-11 19:15 JavaScript Parse serialize, tracks 0.004 s 0.523454
2021-10-11 19:15 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.087090
2021-10-11 19:15 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.400044
2021-10-11 19:15 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.099483
2021-10-11 19:15 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.112689
2021-10-11 19:15 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.257562
2021-10-11 19:15 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.529 s -0.088580
2021-10-11 18:57 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.894 s -0.369223
2021-10-11 19:00 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.720 s -0.480402
2021-10-11 19:02 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s -0.335059
2021-10-11 19:15 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.193674
2021-10-11 19:15 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.123181
2021-10-11 18:38 R dataframe-to-table type_nested, R 0.537 s 0.233432
2021-10-11 18:54 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.817 s 1.140873
2021-10-11 18:56 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.181 s 1.016370
2021-10-11 19:15 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.195133
2021-10-11 19:15 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.623 s -0.400045
2021-10-11 19:15 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.138332
2021-10-11 18:37 R dataframe-to-table chi_traffic_2020_Q1, R 3.399 s 0.268843
2021-10-11 19:04 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.598 s -1.304188
2021-10-11 19:15 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 19:15 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500672
2021-10-11 19:15 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.167979
2021-10-11 19:15 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.815666
2021-10-11 18:38 R dataframe-to-table type_dict, R 0.062 s -1.973666
2021-10-11 19:04 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.882 s -0.088972
2021-10-11 19:04 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.122028
2021-10-11 19:15 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.718 s -0.640341
2021-10-11 19:05 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s 0.204005
2021-10-11 19:15 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.007 s -1.965944
2021-10-11 19:15 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.591460
2021-10-11 19:15 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.243449
2021-10-11 19:15 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.143718
2021-10-11 18:38 R dataframe-to-table type_integers, R 0.010 s 1.109873
2021-10-11 19:01 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.836632
2021-10-11 19:04 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s -0.354904
2021-10-11 19:15 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.162076
2021-10-11 18:38 R dataframe-to-table type_strings, R 0.488 s 0.232554
2021-10-11 18:38 R dataframe-to-table type_floats, R 0.013 s 1.090527
2021-10-11 18:53 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.692 s 0.763656
2021-10-11 18:58 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.858 s -0.430724
2021-10-11 18:59 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.414252
2021-10-11 19:15 JavaScript Parse readBatches, tracks 0.000 s 0.344691
2021-10-11 19:15 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.638 s -0.470911
2021-10-11 19:15 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.746 s 0.011725
2021-10-11 19:15 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.539236
2021-10-11 19:15 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.225789
2021-10-11 19:15 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.194097
2021-10-11 19:15 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.267075
2021-10-11 19:15 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.957 s -2.030316
2021-10-11 18:44 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 1.067397