Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 06:19 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.760582
2021-10-11 06:19 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.630 s -0.053856
2021-10-11 06:19 Python csv-read uncompressed, file, nyctaxi_2010-01 1.018 s -0.641784
2021-10-11 06:20 Python csv-read gzip, streaming, nyctaxi_2010-01 10.605 s -0.104390
2021-10-11 06:20 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.171999
2021-10-11 06:22 Python dataframe-to-table chi_traffic_2020_Q1 19.558 s 0.060544
2021-10-11 06:22 Python dataframe-to-table type_dict 0.012 s 0.383570
2021-10-11 06:22 Python dataframe-to-table type_integers 0.011 s -2.035512
2021-10-11 06:22 Python dataframe-to-table type_floats 0.011 s -0.457997
2021-10-11 06:23 Python dataframe-to-table type_nested 2.879 s -0.077571
2021-10-11 06:30 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.660 s -0.983443
2021-10-11 06:40 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.236 s 0.215475
2021-10-11 06:43 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.073 s -1.603837
2021-10-11 06:43 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.039 s -0.174951
2021-10-11 06:53 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.996 s 0.191863
2021-10-11 06:53 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.728 s 0.185426
2021-10-11 06:53 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.970 s 0.227268
2021-10-11 06:54 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.225 s -0.039976
2021-10-11 06:54 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.797 s 0.263710
2021-10-11 06:54 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.273 s 0.728690
2021-10-11 06:54 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.590 s 2.786455
2021-10-11 06:55 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.126 s 0.648924
2021-10-11 06:55 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.529 s 2.523266
2021-10-11 06:55 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.445370
2021-10-11 06:55 Python file-read lz4, feather, table, fanniemae_2016Q4 0.608 s -0.833548
2021-10-11 06:55 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.013 s 2.395889
2021-10-11 06:56 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.032 s 0.557698
2021-10-11 06:56 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.151 s 1.436664
2021-10-11 06:56 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.466139
2021-10-11 06:57 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.138 s 1.648516
2021-10-11 06:57 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.751355
2021-10-11 06:57 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.273 s 1.743250
2021-10-11 06:57 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -0.994682
2021-10-11 06:58 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.783 s 1.650326
2021-10-11 06:58 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 0.645146
2021-10-11 06:59 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.535 s -0.889796
2021-10-11 06:59 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.453 s 0.496176
2021-10-11 07:01 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.908 s -0.556441
2021-10-11 07:01 Python file-write lz4, feather, table, fanniemae_2016Q4 1.135 s 1.737373
2021-10-11 07:01 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.493 s -1.909429
2021-10-11 07:02 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.843 s 0.144869
2021-10-11 07:02 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.854 s 0.048619
2021-10-11 07:03 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.914 s 0.016799
2021-10-11 07:03 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.925 s -0.567968
2021-10-11 07:03 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.309 s 2.322650
2021-10-11 07:04 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.304 s 1.359021
2021-10-11 07:04 Python file-write lz4, feather, table, nyctaxi_2010-01 1.773 s 1.959291
2021-10-11 07:04 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.804 s 0.629586
2021-10-11 07:04 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.510867
2021-10-11 07:17 R dataframe-to-table type_strings, R 0.497 s 0.230288
2021-10-11 07:18 R dataframe-to-table type_dict, R 0.040 s 2.550535
2021-10-11 07:18 R dataframe-to-table type_integers, R 0.011 s 1.172011
2021-10-11 07:18 R dataframe-to-table type_floats, R 0.013 s 1.189730
2021-10-11 07:18 R dataframe-to-table type_nested, R 0.536 s 0.233755
2021-10-11 07:24 R dataframe-to-table type_simple_features, R 3.359 s 0.994559
2021-10-11 07:24 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.672 s -3.804310
2021-10-11 07:24 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.913 s 1.014544
2021-10-11 07:25 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.322 s -2.356338
2021-10-11 07:25 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.556 s 1.165463
2021-10-11 07:26 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.403 s -0.851514
2021-10-11 07:26 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.045 s 1.381900
2021-10-11 07:26 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.199868
2021-10-11 06:17 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.838 s 0.854880
2021-10-11 06:18 Python csv-read uncompressed, file, fanniemae_2016Q4 1.157 s 0.947695
2021-10-11 06:18 Python csv-read gzip, streaming, fanniemae_2016Q4 14.783 s 0.702521
2021-10-11 07:35 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.389 s 1.248301
2021-10-11 07:36 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.200 s 0.059540
2021-10-11 07:38 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.867 s -0.672864
2021-10-11 07:39 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.536 s -0.527985
2021-10-11 07:40 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s -0.619815
2021-10-11 07:41 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 1.703083
2021-10-11 07:42 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.246 s -0.292108
2021-10-11 07:43 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.474 s 1.330640
2021-10-11 07:44 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.165 s 0.697884
2021-10-11 07:44 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s -0.436701
2021-10-11 07:44 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.853 s 0.615232
2021-10-11 07:44 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 0.723124
2021-10-11 07:44 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.076649
2021-10-11 07:44 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -0.430397
2021-10-11 07:45 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.035279
2021-10-11 07:45 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -0.561492
2021-10-11 07:45 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s 0.075256
2021-10-11 07:46 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.146290
2021-10-11 07:46 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.544933
2021-10-11 07:47 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.487 s -0.651360
2021-10-11 07:47 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.163 s 0.866444
2021-10-11 07:55 JavaScript Parse Table.from, tracks 0.000 s 1.265098
2021-10-11 07:55 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.183832
2021-10-11 07:55 JavaScript Parse serialize, tracks 0.005 s -0.812179
2021-10-11 07:55 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.638 s -0.474028
2021-10-11 07:55 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 1.280953
2021-10-11 07:55 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 1.357460
2021-10-11 07:55 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.615 s 1.287165
2021-10-11 07:55 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.675 s 0.406511
2021-10-11 07:55 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.674711
2021-10-11 07:55 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.877 s 0.004234
2021-10-11 07:55 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 07:55 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.974873
2021-10-11 07:55 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.833402
2021-10-11 07:55 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.662871
2021-10-11 07:55 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.186795
2021-10-11 07:55 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.480171
2021-10-11 07:55 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.374432
2021-10-11 07:55 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s 0.025391
2021-10-11 07:55 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.202122
2021-10-11 07:55 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.779602
2021-10-11 07:55 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.313259
2021-10-11 07:55 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.327101
2021-10-11 07:27 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -2.250966
2021-10-11 07:27 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.016 s -0.221040
2021-10-11 07:28 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.684 s 0.056346
2021-10-11 07:28 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.509 s 0.303105
2021-10-11 07:29 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.831 s 0.677428
2021-10-11 07:31 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.292 s 0.374117
2021-10-11 07:31 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 0.622585
2021-10-11 07:33 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.737 s 0.472571
2021-10-11 07:33 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.816 s 1.424688
2021-10-11 07:55 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.608883
2021-10-11 07:00 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.678 s 4.516117
2021-10-11 07:46 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.881 s 1.147290
2021-10-11 07:46 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.546 s 0.778692
2021-10-11 06:23 Python dataframe-to-table type_simple_features 0.927 s -0.586247
2021-10-11 07:25 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.490 s 1.119491
2021-10-11 07:26 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.184 s 1.138447
2021-10-11 07:55 JavaScript Parse readBatches, tracks 0.000 s 0.691781
2021-10-11 06:26 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.466 s 0.900290
2021-10-11 06:43 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.042 s 0.059861
2021-10-11 07:04 Python wide-dataframe use_legacy_dataset=false 0.618 s 0.681113
2021-10-11 06:22 Python dataframe-to-table type_strings 0.366 s 0.552931
2021-10-11 06:23 Python dataset-filter nyctaxi_2010-01 4.312 s 1.816299
2021-10-11 06:53 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.841 s 0.249608
2021-10-11 07:17 R dataframe-to-table chi_traffic_2020_Q1, R 3.410 s 0.269911
2021-10-11 07:35 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.558 s 0.177751
2021-10-11 07:47 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -0.219773
2021-10-11 07:48 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.517 s -1.596053
2021-10-11 06:40 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.903 s -0.581068
2021-10-11 06:55 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.682 s 2.572837
2021-10-11 07:00 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.921 s -1.036039
2021-10-11 07:24 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.205 s 1.135212
2021-10-11 07:25 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.101 s -2.966223
2021-10-11 07:27 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.215 s 1.130385
2021-10-11 07:37 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.899 s -0.522048
2021-10-11 07:55 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.896 s 0.214803
2021-10-11 07:55 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.853542
2021-10-11 07:55 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.482 s 0.623392
2021-10-11 07:55 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.185891
2021-10-11 07:55 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.674 s -0.513892
2021-10-11 07:55 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.573280
2021-10-11 07:55 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.174820