Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 09:49 Python dataset-filter nyctaxi_2010-01 4.371 s -0.529782
2021-09-28 10:14 Python dataset-read async=True, nyctaxi_multi_ipc_s3 181.158 s 0.777379
2021-09-28 10:33 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.361932
2021-09-28 09:45 Python csv-read gzip, streaming, fanniemae_2016Q4 14.969 s -0.919355
2021-09-28 09:49 Python dataframe-to-table type_dict 0.012 s 1.444573
2021-09-28 10:33 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.103 s 1.180047
2021-09-28 10:33 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.842 s 0.977675
2021-09-28 10:42 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.852467
2021-09-28 09:44 Python csv-read uncompressed, file, fanniemae_2016Q4 1.190 s -0.308098
2021-09-28 10:32 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.285930
2021-09-28 10:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -1.378825
2021-09-28 10:41 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.306 s 3.038104
2021-09-28 10:42 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.343067
2021-09-28 10:34 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.826 s 0.979821
2021-09-28 09:49 Python dataframe-to-table type_integers 0.011 s 1.037925
2021-09-28 09:52 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 53.398 s 1.167148
2021-09-28 09:49 Python dataframe-to-table type_strings 0.366 s 0.746216
2021-09-28 10:38 Python file-write lz4, feather, table, fanniemae_2016Q4 1.176 s -1.451336
2021-09-28 10:19 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.049 s -0.334566
2021-09-28 10:39 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.812 s 2.060701
2021-09-28 10:55 R dataframe-to-table type_floats, R 0.108 s 0.356723
2021-09-28 10:32 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.137 s -0.237542
2021-09-28 10:55 R dataframe-to-table type_dict, R 0.056 s -0.566013
2021-09-28 10:40 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.777 s 1.052916
2021-09-28 10:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.767 s 0.640498
2021-09-28 09:47 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.201828
2021-09-28 09:49 Python dataframe-to-table type_nested 2.948 s 0.365017
2021-09-28 09:48 Python dataframe-to-table chi_traffic_2020_Q1 19.886 s -0.526945
2021-09-28 10:15 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.183 s 0.608399
2021-09-28 10:35 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 1.627394
2021-09-28 10:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 1.570987
2021-09-28 10:55 R dataframe-to-table type_integers, R 0.083 s 1.466075
2021-09-28 09:44 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.036 s -0.915358
2021-09-28 09:49 Python dataframe-to-table type_floats 0.012 s -0.703348
2021-09-28 10:38 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.634 s 0.743133
2021-09-28 10:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.813 s 1.057620
2021-09-28 10:55 R dataframe-to-table type_nested, R 0.538 s -0.345468
2021-09-28 09:45 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.647 s -0.435446
2021-09-28 10:30 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.852 s 0.319688
2021-09-28 09:46 Python csv-read gzip, streaming, nyctaxi_2010-01 10.640 s -0.444874
2021-09-28 10:19 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.026 s 0.127542
2021-09-28 10:32 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.735 s -0.527270
2021-09-28 10:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.983 s 0.927136
2021-09-28 09:49 Python dataframe-to-table type_simple_features 0.905 s 0.670799
2021-09-28 10:39 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.058 s 1.514339
2021-09-28 10:41 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.248 s 0.961466
2021-09-28 10:30 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.989 s 0.180416
2021-09-28 10:37 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.475 s 1.552945
2021-09-28 10:19 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 0.992 s 0.453446
2021-09-28 10:31 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.740 s 0.288328
2021-09-28 10:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.815 s -0.818092
2021-09-28 10:35 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.509 s 0.841090
2021-09-28 09:45 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.905363
2021-09-28 09:46 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s 0.141170
2021-09-28 10:34 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.166255
2021-09-28 10:36 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.128 s 1.492775
2021-09-28 10:05 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 265.527 s 0.292232
2021-09-28 10:32 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.285 s -0.397183
2021-09-28 10:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.036 s 0.042997
2021-09-28 10:40 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.887 s 1.884342
2021-09-28 10:31 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.008 s -0.708080
2021-09-28 10:32 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.836 s -0.806168
2021-09-28 10:55 R dataframe-to-table chi_traffic_2020_Q1, R 5.400 s 0.129888
2021-09-28 10:31 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.259 s -0.925370
2021-09-28 10:32 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.653 s 0.199487
2021-09-28 10:33 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.058 s -0.558886
2021-09-28 10:41 Python file-write lz4, feather, table, nyctaxi_2010-01 1.783 s 1.449092
2021-09-28 11:20 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.213 s 0.444392
2021-09-28 11:19 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.221 s 0.343509
2021-09-28 11:22 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.065 s -1.590402
2021-09-28 11:38 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.426273
2021-09-28 11:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.902 s 0.553956
2021-09-28 11:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.832874
2021-09-28 11:23 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.129 s 0.069431
2021-09-28 11:34 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.886 s 1.138176
2021-09-28 11:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.636057
2021-09-28 11:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.804 s 1.098970
2021-09-28 11:52 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.092922
2021-09-28 11:20 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.905 s 0.166626
2021-09-28 11:31 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.578 s 1.025730
2021-09-28 11:41 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.599 s 0.864701
2021-09-28 11:52 JavaScript Parse readBatches, tracks 0.000 s 0.325690
2021-09-28 11:19 R dataframe-to-table type_simple_features, R 274.797 s -0.029667
2021-09-28 11:27 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.281 s 1.666749
2021-09-28 11:41 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.075480
2021-09-28 11:52 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.954206
2021-09-28 11:52 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-28 11:19 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.938 s -0.188034
2021-09-28 11:21 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.924 s -0.387952
2021-09-28 11:26 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.827 s 1.947667
2021-09-28 11:32 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.393 s 1.434991
2021-09-28 11:35 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.867 s 1.243085
2021-09-28 11:52 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.661 s 0.486876
2021-09-28 11:40 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.696 s 0.638452
2021-09-28 11:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.710 s -1.684987
2021-09-28 11:52 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.538244
2021-09-28 11:52 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.942948
2021-09-28 11:23 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -2.106800
2021-09-28 11:24 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.105375
2021-09-28 11:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.393 s 0.135006
2021-09-28 11:20 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.056710
2021-09-28 11:40 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.188 s 0.402882
2021-09-28 11:52 JavaScript Parse serialize, tracks 0.005 s -0.660484
2021-09-28 11:52 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.316520
2021-09-28 11:52 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.496 s 0.264941
2021-09-28 11:28 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.267 s 2.039329
2021-09-28 11:52 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.467869
2021-09-28 11:23 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.237 s 0.152765
2021-09-28 11:30 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.399101
2021-09-28 11:44 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.471 s 0.488191
2021-09-28 11:52 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.553954
2021-09-28 11:30 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.708 s 1.829944
2021-09-28 11:52 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.558 s 0.053896
2021-09-28 11:36 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.525 s 0.989725
2021-09-28 11:42 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.519 s -0.511304
2021-09-28 11:43 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.113885
2021-09-28 11:52 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.643 s -0.161330
2021-09-28 11:37 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.723 s 1.234390
2021-09-28 11:41 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.751 s 0.773276
2021-09-28 11:52 JavaScript Parse Table.from, tracks 0.000 s 0.838283
2021-09-28 11:52 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.818 s 1.635319
2021-09-28 11:22 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.180 s -0.612448
2021-09-28 11:25 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.511 s 0.102738
2021-09-28 11:33 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.228 s 0.676338
2021-09-28 11:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.477 s 0.194094
2021-09-28 11:52 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-28 11:24 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.964 s 0.100730
2021-09-28 11:41 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.981 s 0.145091
2021-09-28 11:52 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.875 s 0.194575
2021-09-28 11:21 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.571 s -1.766781
2021-09-28 11:39 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.521778
2021-09-28 11:52 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.672 s 0.066517
2021-09-28 11:52 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-28 11:52 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.154602
2021-09-28 11:52 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.793732
2021-09-28 11:52 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.950810
2021-09-28 11:21 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.391 s -0.744541
2021-09-28 11:39 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.276 s -0.683772
2021-09-28 11:41 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.176 s 0.032088
2021-09-28 11:52 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-28 11:52 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.057058
2021-09-28 11:52 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.607722
2021-09-28 11:52 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.146889
2021-09-28 11:52 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.928120
2021-09-28 11:52 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.796820
2021-09-28 11:52 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.234554
2021-09-28 11:52 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.167062
2021-09-28 10:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.379 s -0.390770
2021-09-28 10:55 R dataframe-to-table type_strings, R 0.491 s -0.269385