Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 18:16 Python csv-read uncompressed, file, fanniemae_2016Q4 1.191 s -0.324452
2021-09-28 18:16 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.084 s -0.922497
2021-09-28 18:17 Python csv-read gzip, file, fanniemae_2016Q4 6.037 s -1.837844
2021-09-28 18:16 Python csv-read gzip, streaming, fanniemae_2016Q4 15.030 s -0.939691
2021-09-28 18:17 Python csv-read uncompressed, file, nyctaxi_2010-01 1.015 s 0.072832
2021-09-28 18:19 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.261378
2021-09-28 18:17 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.633 s -0.359897
2021-09-28 18:18 Python csv-read gzip, streaming, nyctaxi_2010-01 10.617 s -0.339625
2021-09-28 18:51 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.031 s -1.323211
2021-09-28 19:02 Python wide-dataframe use_legacy_dataset=false 0.617 s -0.052054
2021-09-28 19:52 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.576 s 0.988804
2021-09-28 18:55 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.463 s 0.994988
2021-09-28 18:56 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.074 s 1.550252
2021-09-28 19:40 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.921 s -0.026080
2021-09-28 19:45 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.989 s -1.121426
2021-09-28 19:51 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.743 s 1.458965
2021-09-28 18:20 Python dataframe-to-table chi_traffic_2020_Q1 19.754 s 0.230695
2021-09-28 18:20 Python dataframe-to-table type_strings 0.371 s 0.060564
2021-09-28 18:24 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.735 s -0.805379
2021-09-28 18:52 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.318 s -2.630286
2021-09-28 18:59 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.033684
2021-09-28 19:43 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.048 s 1.440013
2021-09-28 20:00 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.271 s -0.280359
2021-09-28 18:29 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.413 s 4.134004
2021-09-28 18:38 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.215 s 0.429101
2021-09-28 18:59 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.793 s 2.149245
2021-09-28 19:43 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.177 s -0.506017
2021-09-28 19:57 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 2.593198
2021-09-28 18:41 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.990 s 0.426932
2021-09-28 18:53 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.007 s 1.256115
2021-09-28 19:44 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.601574
2021-09-28 19:59 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.028565
2021-09-28 20:01 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.496 s -1.051433
2021-09-28 18:20 Python dataframe-to-table type_dict 0.011 s 1.681054
2021-09-28 18:58 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.293 s 0.387969
2021-09-28 19:00 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.859 s 2.248438
2021-09-28 19:16 R dataframe-to-table type_floats, R 0.108 s 0.235297
2021-09-28 19:41 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.267712
2021-09-28 19:49 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 1.567589
2021-09-28 18:55 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.242531
2021-09-28 19:02 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.750 s 0.746878
2021-09-28 19:16 R dataframe-to-table type_nested, R 0.539 s -0.748300
2021-09-28 19:45 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.224260
2021-09-28 18:21 Python dataframe-to-table type_integers 0.011 s 0.010225
2021-09-28 18:51 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.925 s 0.017751
2021-09-28 19:00 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.723 s 1.345831
2021-09-28 18:59 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.097 s 1.076129
2021-09-28 19:40 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.235 s 0.186782
2021-09-28 19:44 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.126 s 0.292249
2021-09-28 19:46 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.533 s -0.895645
2021-09-28 19:48 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.283 s 1.501032
2021-09-28 18:51 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.991 s 0.157404
2021-09-28 18:53 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.795 s 1.159665
2021-09-28 18:51 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.767 s 0.162654
2021-09-28 18:55 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s -0.007610
2021-09-28 18:57 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.433 s 1.497155
2021-09-28 19:02 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.235467
2021-09-28 19:42 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.239949
2021-09-28 18:52 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.752 s -1.168306
2021-09-28 18:53 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -1.388968
2021-09-28 18:58 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.453 s 1.529617
2021-09-28 19:51 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.833 s -0.426710
2021-09-28 18:21 Python dataframe-to-table type_floats 0.012 s -0.992832
2021-09-28 18:21 Python dataset-filter nyctaxi_2010-01 4.404 s -1.583763
2021-09-28 18:52 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.872 s -1.830461
2021-09-28 18:54 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.008 s 1.792498
2021-09-28 18:55 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.956 s 1.000496
2021-09-28 18:59 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.627 s 0.701676
2021-09-28 19:01 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.791 s 1.139517
2021-09-28 19:44 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.243 s -0.153428
2021-09-28 18:57 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.077 s 1.617125
2021-09-28 19:43 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -1.001165
2021-09-28 18:21 Python dataframe-to-table type_simple_features 0.906 s 0.450878
2021-09-28 18:53 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.716 s -2.005554
2021-09-28 18:54 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.812 s 0.991250
2021-09-28 19:16 R dataframe-to-table type_integers, R 0.083 s 0.982047
2021-09-28 19:41 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.234 s 0.198386
2021-09-28 19:41 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.911 s 0.097803
2021-09-28 19:42 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.950 s -1.643585
2021-09-28 19:55 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.829 s 2.849837
2021-09-28 18:21 Python dataframe-to-table type_nested 2.940 s 0.863734
2021-09-28 18:38 Python dataset-read async=True, nyctaxi_multi_ipc_s3 178.849 s 1.066291
2021-09-28 18:53 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.114 s 0.070849
2021-09-28 19:16 R dataframe-to-table type_dict, R 0.054 s -0.237361
2021-09-28 19:40 R dataframe-to-table type_simple_features, R 274.490 s 0.564461
2021-09-28 19:58 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.676 s 2.472821
2021-09-28 18:41 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.027 s 0.105851
2021-09-28 19:02 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.274 s 0.732714
2021-09-28 19:16 R dataframe-to-table type_strings, R 0.492 s -0.548892
2021-09-28 19:53 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s 0.867907
2021-09-28 19:56 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.799 s 3.071479
2021-09-28 18:41 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.009 s 0.349129
2021-09-28 18:52 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.288 s -1.761190
2021-09-28 18:52 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.169 s -2.173232
2021-09-28 18:53 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 1.190764
2021-09-28 18:54 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.755 s 1.262213
2021-09-28 19:01 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.231401
2021-09-28 19:02 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.112208
2021-09-28 19:47 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.857 s 1.503482
2021-09-28 20:04 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.937 s 3.479715
2021-09-28 20:02 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.690702
2021-09-28 20:02 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.421960
2021-09-28 20:01 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s 0.066249
2021-09-28 20:02 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 2.689644
2021-09-28 20:02 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.878 s 2.780860
2021-09-28 20:02 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 3.514762
2021-09-28 20:02 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 3.701879
2021-09-28 20:03 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.529 s -1.802115
2021-09-28 20:04 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 3.309711
2021-09-28 20:05 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.505868
2021-09-28 20:03 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.042427
2021-09-28 20:04 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.192251
2021-09-28 20:04 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.688 s -1.241606
2021-09-28 20:05 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.472 s 0.352047
2021-09-28 20:05 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.156 s 3.480601
2021-09-28 20:06 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.493 s 0.156160
2021-09-28 20:13 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-28 20:13 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.131987
2021-09-28 20:13 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.115522
2021-09-28 20:13 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.432051
2021-09-28 20:13 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.692 s -0.290399
2021-09-28 20:13 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.879 s 0.117209
2021-09-28 20:13 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.267 s 4.169302
2021-09-28 20:13 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.550803
2021-09-28 20:13 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.634818
2021-09-28 20:13 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.532 s -0.337745
2021-09-28 20:13 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -3.601814
2021-09-28 20:13 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.733 s 0.068496
2021-09-28 20:13 JavaScript Parse serialize, tracks 0.005 s -0.656556
2021-09-28 20:13 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.724127
2021-09-28 20:13 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.766913
2021-09-28 20:13 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.070796
2021-09-28 20:13 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.322752
2021-09-28 20:13 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.667180
2021-09-28 20:13 JavaScript Parse readBatches, tracks 0.000 s -0.362382
2021-09-28 20:13 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.315 s 3.879175
2021-09-28 20:13 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.742557
2021-09-28 20:13 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.002602
2021-09-28 20:13 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.211550
2021-09-28 20:12 JavaScript Parse Table.from, tracks 0.000 s -0.165436
2021-09-28 20:13 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.035030
2021-09-28 20:13 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.894 s 0.095387
2021-09-28 20:13 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-28 20:13 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.208521
2021-09-28 20:13 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.505657
2021-09-28 20:13 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.549854
2021-09-28 20:13 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.025083
2021-09-28 19:16 R dataframe-to-table chi_traffic_2020_Q1, R 5.398 s 0.125139
2021-09-28 19:54 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.232 s 0.575572