Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 16:04 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.617 s 3.807428
2021-10-13 16:09 Python dataframe-to-table type_strings 0.362 s 0.765041
2021-10-13 16:09 Python dataframe-to-table type_dict 0.011 s 1.246518
2021-10-13 16:10 Python dataset-filter nyctaxi_2010-01 4.401 s -1.987741
2021-10-13 16:43 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.939 s 0.441478
2021-10-13 16:45 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.599 s 1.370367
2021-10-13 16:45 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.688 s 0.923223
2021-10-13 16:46 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.058 s -0.576098
2021-10-13 16:47 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.305 s 0.930698
2021-10-13 17:08 R dataframe-to-table type_dict, R 0.060 s -1.618801
2021-10-13 17:09 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.456 s 0.668655
2021-10-13 17:11 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.160 s 0.671260
2021-10-13 17:16 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.551 s -2.561563
2021-10-13 17:22 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.888 s -0.514493
2021-10-13 17:23 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.897 s -1.400903
2021-10-13 17:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.195 s -2.251160
2021-10-13 17:30 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.791 s -2.800718
2021-10-13 17:29 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.407531
2021-10-13 17:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.533 s -1.338508
2021-10-13 17:40 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.568328
2021-10-13 16:48 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.780 s 1.418314
2021-10-13 16:52 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.851 s -0.152437
2021-10-13 17:13 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.541 s -0.023947
2021-10-13 16:05 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.482751
2021-10-13 16:44 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.009 s -0.137263
2021-10-13 17:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.195 s -1.458605
2021-10-13 16:09 Python dataframe-to-table type_nested 2.847 s 1.617370
2021-10-13 16:06 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.476 s 0.999427
2021-10-13 16:31 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.076 s -0.968501
2021-10-13 17:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.241380
2021-10-13 17:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -0.792122
2021-10-13 16:51 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.606398
2021-10-13 16:53 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.941 s -0.686066
2021-10-13 17:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.215 s 0.667577
2021-10-13 17:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.677 s -3.265158
2021-10-13 16:09 Python dataframe-to-table chi_traffic_2020_Q1 19.063 s 1.358929
2021-10-13 16:09 Python dataframe-to-table type_integers 0.011 s -0.011962
2021-10-13 16:44 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.274 s 0.717447
2021-10-13 16:13 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.148 s -0.463040
2021-10-13 16:31 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.045 s -0.160264
2021-10-13 16:50 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.694 s -2.765486
2021-10-13 16:54 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.892 s -0.344187
2021-10-13 16:54 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.317 s 0.782922
2021-10-13 16:55 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.769219
2021-10-13 17:09 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.528 s -2.519467
2021-10-13 17:09 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.733 s 0.607492
2021-10-13 17:10 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.555 s 1.362010
2021-10-13 17:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.128 s -3.272802
2021-10-13 16:44 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.842 s -0.196638
2021-10-13 16:45 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.290 s 0.090697
2021-10-13 16:46 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.217 s 0.396844
2021-10-13 17:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.680 s -1.374729
2021-10-13 17:32 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -1.077157
2021-10-13 16:04 Python csv-read uncompressed, file, fanniemae_2016Q4 1.160 s 0.400710
2021-10-13 16:07 Python csv-read gzip, streaming, nyctaxi_2010-01 10.463 s 1.017336
2021-10-13 16:17 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.534 s -0.801652
2021-10-13 16:54 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.764 s 1.456508
2021-10-13 17:25 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.766 s -1.585834
2021-10-13 17:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.556 s 1.558698
2021-10-13 17:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.929 s -0.895603
2021-10-13 16:05 Python csv-read gzip, streaming, fanniemae_2016Q4 14.556 s 3.408971
2021-10-13 16:43 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.872 s 0.062975
2021-10-13 16:49 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.655 s -1.852896
2021-10-13 16:51 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.898 s -0.328075
2021-10-13 16:51 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.355 s -0.020157
2021-10-13 17:09 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.248 s -0.835739
2021-10-13 17:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.002 s -0.023798
2021-10-13 17:21 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.299 s -2.849010
2021-10-13 17:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.489 s 1.392370
2021-10-13 16:06 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s -0.745341
2021-10-13 16:43 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.724 s 0.130340
2021-10-13 16:45 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -0.651853
2021-10-13 16:47 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 0.152515
2021-10-13 17:20 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.397 s -0.247502
2021-10-13 17:26 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 0.984289
2021-10-13 16:09 Python dataframe-to-table type_floats 0.011 s 0.342172
2021-10-13 16:45 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.523 s 1.385646
2021-10-13 17:32 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.123 s 2.255127
2021-10-13 16:27 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.131 s 0.288503
2021-10-13 17:08 R dataframe-to-table type_nested, R 0.542 s 0.230720
2021-10-13 17:11 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.103 s 1.085788
2021-10-13 16:46 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.074 s -0.584034
2021-10-13 17:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s 0.466024
2021-10-13 16:47 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.134 s 1.351620
2021-10-13 16:07 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.118732
2021-10-13 16:46 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.001 s 1.302960
2021-10-13 16:47 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -0.077952
2021-10-13 17:08 R dataframe-to-table type_floats, R 0.013 s 0.711660
2021-10-13 17:11 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.017786
2021-10-13 17:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.213526
2021-10-13 17:18 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 0.975603
2021-10-13 16:44 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.263 s -0.291512
2021-10-13 16:27 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.404 s -0.507807
2021-10-13 16:52 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.882 s -0.816618
2021-10-13 16:55 Python wide-dataframe use_legacy_dataset=false 0.615 s 0.829491
2021-10-13 17:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.689 s 0.051433
2021-10-13 17:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.727 s -3.741928
2021-10-13 17:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.498 s -2.252335
2021-10-13 17:14 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.109 s -2.706091
2021-10-13 16:31 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.067 s -0.098038
2021-10-13 17:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.767705
2021-10-13 16:48 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.337 s -2.790558
2021-10-13 16:45 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.121 s 1.144250
2021-10-13 16:51 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.478 s -0.970947
2021-10-13 17:08 R dataframe-to-table type_strings, R 0.494 s 0.229661
2021-10-13 17:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.269 s -2.421681
2021-10-13 16:54 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.345 s 0.245372
2021-10-13 17:10 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.089 s -1.528100
2021-10-13 17:08 R dataframe-to-table type_integers, R 0.009 s 0.721404
2021-10-13 17:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.474 s 0.710191
2021-10-13 17:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.801 s 1.956516
2021-10-13 17:10 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.376 s 0.976523
2021-10-13 16:50 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.114 s -2.547899
2021-10-13 16:54 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.598507
2021-10-13 17:08 R dataframe-to-table chi_traffic_2020_Q1, R 3.472 s 0.260358
2021-10-13 17:09 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.322 s -1.319598
2021-10-13 17:24 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.523 s -0.512648
2021-10-13 17:40 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.662075
2021-10-13 17:40 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.569797
2021-10-13 17:40 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.065838
2021-10-13 17:40 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s 0.081302
2021-10-13 17:40 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578421
2021-10-13 17:40 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.748299
2021-10-13 17:40 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.635 s -0.323894
2021-10-13 17:40 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.842 s 1.309955
2021-10-13 17:40 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.612 s 1.390954
2021-10-13 17:40 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.231191
2021-10-13 17:40 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.862819
2021-10-13 17:40 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.549208
2021-10-13 17:40 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.061848
2021-10-13 17:40 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.879771
2021-10-13 17:40 JavaScript Parse serialize, tracks 0.005 s -0.923840
2021-10-13 17:40 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.296659
2021-10-13 17:40 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.465527
2021-10-13 17:39 JavaScript Parse Table.from, tracks 0.000 s -1.964063
2021-10-13 17:40 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.592 s -0.254222
2021-10-13 17:40 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.455 s 1.144478
2021-10-13 17:40 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.889 s -0.208936
2021-10-13 17:40 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.031639
2021-10-13 17:40 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.682 s 0.363913
2021-10-13 17:40 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.177935
2021-10-13 17:40 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.912877
2021-10-13 17:40 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.444722
2021-10-13 17:40 JavaScript Parse readBatches, tracks 0.000 s -1.414850
2021-10-13 17:40 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.672555
2021-10-13 17:40 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.843811
2021-10-13 17:40 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.281521