Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 22:25 Python csv-read uncompressed, file, fanniemae_2016Q4 1.157 s 0.227371
2021-09-28 22:30 Python dataframe-to-table type_strings 0.369 s 0.249893
2021-09-28 23:10 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.255162
2021-09-28 22:30 Python dataframe-to-table type_floats 0.012 s -0.946519
2021-09-28 22:38 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.312 s 2.165483
2021-09-28 23:08 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.189 s 0.922040
2021-09-28 23:28 R dataframe-to-table type_nested, R 0.537 s 0.136732
2021-09-28 22:30 Python dataframe-to-table type_simple_features 0.936 s -3.371385
2021-09-28 22:34 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.175 s -0.737958
2021-09-28 23:04 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.300967
2021-09-28 23:05 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.007 s 1.284577
2021-09-28 22:25 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.816 s -0.539342
2021-09-28 22:27 Python csv-read gzip, streaming, nyctaxi_2010-01 10.814 s -1.094640
2021-09-28 22:47 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.874 s 0.045379
2021-09-28 22:26 Python csv-read gzip, streaming, fanniemae_2016Q4 14.783 s -0.581287
2021-09-28 22:52 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.025 s 0.140953
2021-09-28 22:27 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.820 s -1.071619
2021-09-28 22:30 Python dataframe-to-table chi_traffic_2020_Q1 19.371 s 2.415181
2021-09-28 23:13 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.361 s 0.004032
2021-09-28 23:13 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.854 s -0.123688
2021-09-28 23:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.164 s -1.327256
2021-09-28 23:06 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.123971
2021-09-28 23:09 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.561 s 0.877498
2021-09-28 23:05 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.954 s 0.378422
2021-09-28 22:26 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.976233
2021-09-28 23:03 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.857 s -1.071912
2021-09-28 23:27 R dataframe-to-table chi_traffic_2020_Q1, R 5.353 s 0.983135
2021-09-28 22:52 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.016 s 0.064742
2021-09-28 23:02 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.037 s -1.250451
2021-09-28 23:04 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.780 s -2.604747
2021-09-28 23:09 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.390 s -0.422187
2021-09-28 23:27 R dataframe-to-table type_dict, R 0.027 s 2.681538
2021-09-28 23:02 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.024 s -0.089855
2021-09-28 23:06 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.140 s 0.122144
2021-09-28 23:12 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.857 s 1.642171
2021-09-28 22:28 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.645582
2021-09-28 22:30 Python dataframe-to-table type_nested 2.913 s 1.527434
2021-09-28 23:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.852 s -3.276295
2021-09-28 23:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.205 s -3.784842
2021-09-28 23:12 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.366 s -0.933212
2021-09-28 23:05 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.979 s 0.244439
2021-09-28 23:07 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.634 s 0.169944
2021-09-28 23:10 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.788 s 1.716199
2021-09-28 23:13 Python file-write lz4, feather, table, nyctaxi_2010-01 1.850 s -1.949403
2021-09-28 23:27 R dataframe-to-table type_floats, R 0.108 s 0.123211
2021-09-28 22:30 Python dataframe-to-table type_integers 0.011 s -1.614885
2021-09-28 23:07 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.089 s 1.176517
2021-09-28 23:08 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 1.194349
2021-09-28 22:31 Python dataset-filter nyctaxi_2010-01 4.411 s -1.477080
2021-09-28 23:11 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.777 s 0.855699
2021-09-28 22:27 Python csv-read uncompressed, file, nyctaxi_2010-01 1.026 s -0.131756
2021-09-28 23:04 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.916 s -4.865984
2021-09-28 23:03 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.277 s -1.129168
2021-09-28 23:04 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.758313
2021-09-28 23:10 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.634 s 0.593249
2021-09-28 23:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.217 s -0.029820
2021-09-28 23:27 R dataframe-to-table type_integers, R 0.084 s 0.584006
2021-09-28 22:30 Python dataframe-to-table type_dict 0.012 s -1.693413
2021-09-28 23:02 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.821 s 0.441105
2021-09-28 23:03 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.308 s -1.286773
2021-09-28 23:13 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.089965
2021-09-28 23:27 R dataframe-to-table type_strings, R 0.491 s -0.125655
2021-09-28 23:06 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.363319
2021-09-28 23:12 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.843 s 0.665987
2021-09-28 23:02 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.738 s 0.267010
2021-09-28 23:05 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.023 s 0.870208
2021-09-28 23:54 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.422 s -2.275085
2021-09-29 00:06 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.832 s 1.772735
2021-09-29 00:13 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 2.101744
2021-09-28 23:51 R dataframe-to-table type_simple_features, R 276.073 s -2.467116
2021-09-28 23:51 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.239 s 0.137977
2021-09-28 23:53 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.598134
2021-09-29 00:04 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.594 s 0.545134
2021-09-28 23:53 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.952 s -1.727702
2021-09-28 23:57 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.530 s -0.542678
2021-09-28 23:58 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.860 s 1.135090
2021-09-29 00:05 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.240 s 0.354754
2021-09-29 00:13 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.199 s -0.354371
2021-09-29 00:24 JavaScript Parse Table.from, tracks 0.000 s -0.435385
2021-09-28 22:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.329 s -0.226810
2021-09-28 22:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.995 s 0.596162
2021-09-28 23:13 Python wide-dataframe use_legacy_dataset=false 0.618 s -0.081791
2021-09-28 23:52 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.921 s -0.008335
2021-09-28 23:56 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.214 s 1.388959
2021-09-29 00:24 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.506 s 0.097335
2021-09-29 00:25 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.623096
2021-09-28 23:55 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.123 s 0.506069
2021-09-29 00:02 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.773 s 0.986363
2021-09-29 00:07 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.801 s 1.999190
2021-09-29 00:17 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.149 s 1.639928
2021-09-29 00:24 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.675 s 0.154720
2021-09-29 00:24 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-29 00:13 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 1.820976
2021-09-29 00:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 1.251476
2021-09-29 00:11 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.261 s 0.442292
2021-09-29 00:16 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.346 s 1.682265
2021-09-29 00:24 JavaScript Parse serialize, tracks 0.005 s 0.375662
2021-09-29 00:24 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.522297
2021-09-29 00:24 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.667 s -0.289600
2021-09-29 00:25 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.771068
2021-09-28 23:52 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.263 s -0.132206
2021-09-29 00:10 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s -0.044687
2021-09-29 00:24 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.513673
2021-09-29 00:24 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.128982
2021-09-29 00:08 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 1.795343
2021-09-29 00:24 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.116242
2021-09-28 23:55 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.152 s 1.167740
2021-09-29 00:15 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.566 s 0.537717
2021-09-29 00:24 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.168735
2021-09-29 00:24 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -4.445040
2021-09-28 23:56 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.405410
2021-09-29 00:13 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.316458
2021-09-29 00:15 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.100 s 0.952749
2021-09-29 00:24 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.538454
2021-09-28 23:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.970 s -0.139306
2021-09-29 00:14 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.649062
2021-09-29 00:24 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.676 s 0.410986
2021-09-29 00:24 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.886 s 0.327392
2021-09-29 00:24 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.529902
2021-09-28 23:53 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.023541
2021-09-29 00:13 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.875 s 1.898340
2021-09-29 00:17 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.476 s 0.171947
2021-09-29 00:24 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.682269
2021-09-29 00:24 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.706714
2021-09-29 00:14 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.179 s -0.392619
2021-09-29 00:24 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.891 s -0.136494
2021-09-28 23:52 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.935 s -0.150457
2021-09-29 00:04 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -0.814263
2021-09-29 00:09 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.677 s 1.681659
2021-09-29 00:15 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.322535
2021-09-29 00:24 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.039326
2021-09-29 00:24 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.515435
2021-09-29 00:25 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.986217
2021-09-28 23:54 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.046 s 1.677251
2021-09-29 00:24 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.032115
2021-09-29 00:25 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.981944
2021-09-28 23:57 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 1.447095
2021-09-29 00:00 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.366 s 0.718248
2021-09-29 00:14 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.514 s 0.142786
2021-09-29 00:24 JavaScript Parse readBatches, tracks 0.000 s -0.290967
2021-09-29 00:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s -0.198485
2021-09-29 00:16 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -0.946843
2021-09-29 00:25 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.526 s -0.242474
2021-09-29 00:12 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.499270
2021-09-29 00:15 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.945 s 1.613701
2021-09-29 00:16 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.471 s 0.536921
2021-09-29 00:25 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.289983
2021-09-29 00:24 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.514015
2021-09-29 00:24 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461