Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-03 06:39 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.947131
2021-10-03 06:40 Python csv-read uncompressed, file, nyctaxi_2010-01 1.018 s -0.425806
2021-10-03 06:51 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.124 s 1.067517
2021-10-03 06:43 Python dataframe-to-table type_floats 0.011 s 0.655690
2021-10-03 06:43 Python dataframe-to-table type_simple_features 0.911 s 0.196093
2021-10-03 06:39 Python csv-read gzip, streaming, fanniemae_2016Q4 15.044 s -1.403395
2021-10-03 06:46 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.388 s -0.034491
2021-10-03 06:40 Python csv-read gzip, streaming, nyctaxi_2010-01 10.886 s -2.053453
2021-10-03 06:38 Python csv-read uncompressed, file, fanniemae_2016Q4 1.161 s 0.673827
2021-10-03 06:38 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.101 s -1.377626
2021-10-03 06:43 Python dataframe-to-table type_dict 0.012 s 1.073859
2021-10-03 06:43 Python dataframe-to-table type_strings 0.370 s 0.192090
2021-10-03 06:43 Python dataframe-to-table type_integers 0.011 s 1.109527
2021-10-03 06:43 Python dataframe-to-table type_nested 2.866 s 1.425871
2021-10-03 06:43 Python dataset-filter nyctaxi_2010-01 4.349 s 0.637769
2021-10-03 06:41 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.379178
2021-10-03 06:40 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.912 s -1.867412
2021-10-03 06:42 Python dataframe-to-table chi_traffic_2020_Q1 19.461 s 1.322499
2021-10-03 07:00 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.215 s 0.606545
2021-10-03 07:00 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.159 s 0.878151
2021-10-03 07:04 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.025 s 0.114491
2021-10-03 07:16 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.988 s 0.140092
2021-10-03 07:16 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.937 s 1.399688
2021-10-03 07:20 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.254381
2021-10-03 07:41 R dataframe-to-table type_dict, R 0.049 s 0.021214
2021-10-03 08:09 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -2.032582
2021-10-03 08:10 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.687 s -0.036980
2021-10-03 08:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -1.724566
2021-10-03 08:38 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.335311
2021-10-03 07:17 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.206 s 0.778172
2021-10-03 08:38 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.841658
2021-10-03 08:38 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.141542
2021-10-03 07:17 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.833 s -0.951550
2021-10-03 07:18 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.030 s 0.592223
2021-10-03 07:05 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.007 s 0.225271
2021-10-03 07:27 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.985857
2021-10-03 08:13 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.579 s -0.638686
2021-10-03 08:16 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.356201
2021-10-03 08:22 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.568 s -0.674889
2021-10-03 08:38 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.922 s -0.416058
2021-10-03 07:22 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.746 s -1.036149
2021-10-03 07:23 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.980 s -0.934895
2021-10-03 07:27 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.189750
2021-10-03 07:40 R dataframe-to-table chi_traffic_2020_Q1, R 5.336 s 1.135665
2021-10-03 07:41 R dataframe-to-table type_floats, R 0.107 s 0.873057
2021-10-03 08:14 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.612 s -0.981804
2021-10-03 07:04 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.027 s 0.138897
2021-10-03 07:17 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.261 s 1.083078
2021-10-03 07:17 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.116 s 1.086872
2021-10-03 07:18 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.261 s -1.839372
2021-10-03 07:26 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.964 s -0.182269
2021-10-03 08:07 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.908 s 0.783723
2021-10-03 08:07 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.066421
2021-10-03 08:17 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.604 s -0.219804
2021-10-03 08:25 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.248 s 0.759964
2021-10-03 08:38 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.969813
2021-10-03 07:20 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.466 s -1.452371
2021-10-03 07:21 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.988 s -1.542971
2021-10-03 07:18 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.864043
2021-10-03 07:25 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.935 s -0.208282
2021-10-03 07:21 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.396 s -1.074427
2021-10-03 07:24 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.336 s -1.011157
2021-10-03 08:18 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.393 s 1.502594
2021-10-03 08:28 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.934 s 0.937136
2021-10-03 08:38 JavaScript Parse readBatches, tracks 0.000 s
2021-10-03 08:38 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.014036
2021-10-03 07:18 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.753 s -0.773963
2021-10-03 07:19 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.009 s 1.785931
2021-10-03 08:21 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.890 s -0.469504
2021-10-03 08:38 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.544 s -0.148863
2021-10-03 08:38 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.580922
2021-10-03 08:38 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.553649
2021-10-03 08:38 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.091436
2021-10-03 08:38 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.503 s 0.018757
2021-10-03 07:24 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.816 s -0.574692
2021-10-03 07:27 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.491454
2021-10-03 08:10 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.012 s -2.107576
2021-10-03 08:15 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.032 s -0.655251
2021-10-03 08:20 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.930 s -0.677876
2021-10-03 08:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.929000
2021-10-03 07:24 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.321813
2021-10-03 08:07 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.374 s 0.571322
2021-10-03 08:08 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.180 s -0.323156
2021-10-03 08:27 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 1.141655
2021-10-03 08:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.600 s -0.049556
2021-10-03 07:18 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.939 s -1.507715
2021-10-03 07:19 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.323 s -1.427510
2021-10-03 08:27 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.141013
2021-10-03 08:30 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.515 s 0.053467
2021-10-03 08:38 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.718256
2021-10-03 07:26 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.991 s -0.897106
2021-10-03 07:41 R dataframe-to-table type_strings, R 0.492 s 0.017966
2021-10-03 08:04 R dataframe-to-table type_simple_features, R 275.973 s -2.046596
2021-10-03 08:06 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.291 s -2.002550
2021-10-03 08:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.604 s 0.911200
2021-10-03 08:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.172 s 0.902235
2021-10-03 08:38 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.102146
2021-10-03 08:38 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.174538
2021-10-03 08:38 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.024682
2021-10-03 07:16 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.888 s 0.101664
2021-10-03 07:20 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.130975
2021-10-03 07:22 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.634 s -1.015915
2021-10-03 07:23 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.359 s 0.006695
2021-10-03 07:25 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.991 s -1.896957
2021-10-03 08:05 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.202 s 0.577132
2021-10-03 08:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 1.049254
2021-10-03 08:29 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -2.491229
2021-10-03 08:38 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.154151
2021-10-03 07:19 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.313 s -1.395638
2021-10-03 07:41 R dataframe-to-table type_integers, R 0.085 s -0.567259
2021-10-03 08:08 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.137 s -0.469516
2021-10-03 08:09 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.226 s 0.994113
2021-10-03 08:25 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.483 s 1.381064
2021-10-03 08:38 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.727 s -0.796761
2021-10-03 07:17 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.761 s 1.482440
2021-10-03 07:18 Python file-read lz4, feather, table, fanniemae_2016Q4 0.606 s -0.769127
2021-10-03 07:27 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.816 s 0.240848
2021-10-03 07:16 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.723 s 0.266938
2021-10-03 08:12 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.161 s -0.989914
2021-10-03 08:26 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.007368
2021-10-03 08:38 JavaScript Parse serialize, tracks 0.005 s -0.716652
2021-10-03 08:38 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.735152
2021-10-03 07:26 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.239908
2021-10-03 08:05 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.207 s 0.482722
2021-10-03 08:06 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.877 s 0.489655
2021-10-03 08:11 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.568 s -2.126367
2021-10-03 08:24 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.156505
2021-10-03 08:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.393676
2021-10-03 08:38 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.553649
2021-10-03 08:38 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.317993
2021-10-03 07:27 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.398 s -0.215893
2021-10-03 08:05 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.872 s 0.564071
2021-10-03 08:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.619 s -1.722837
2021-10-03 08:38 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.694 s 0.303894
2021-10-03 07:41 R dataframe-to-table type_nested, R 0.534 s 1.053503
2021-10-03 08:38 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.859 s 0.511101
2021-10-03 08:19 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.205 s 0.694221
2021-10-03 08:38 JavaScript Parse Table.from, tracks 0.000 s 0.447214
2021-10-03 08:38 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.181254
2021-10-03 08:23 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.752 s -0.560472
2021-10-03 08:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.182 s -0.710201
2021-10-03 08:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 0.543587
2021-10-03 08:38 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.106495
2021-10-03 08:38 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.210102
2021-10-03 08:38 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.225855
2021-10-03 08:26 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.175 s 0.954162
2021-10-03 08:27 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s 1.007581
2021-10-03 08:38 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.649 s -0.324374
2021-10-03 08:38 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.339451