Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 02:25 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.752 s 0.593558
2021-10-07 02:30 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.218 s -0.074135
2021-10-07 02:32 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.784 s 1.138517
2021-10-07 02:34 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.654 s 0.966561
2021-10-07 02:37 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.484 s 1.193662
2021-10-07 02:38 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s -0.823323
2021-10-07 02:40 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.522 s -0.765425
2021-10-07 02:41 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.134467
2021-10-07 02:42 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.532 s 0.962420
2021-10-07 02:42 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -2.025164
2021-10-07 02:43 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.190 s 0.635288
2021-10-07 02:51 JavaScript Parse readBatches, tracks 0.000 s 0.819345
2021-10-07 02:52 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.896589
2021-10-07 02:53 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.533489
2021-10-07 02:26 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.833 s -0.264306
2021-10-07 02:31 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.823 s 0.991362
2021-10-07 02:33 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.455 s 1.100156
2021-10-07 02:35 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.321104
2021-10-07 02:39 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.573294
2021-10-07 02:39 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.647274
2021-10-07 02:40 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.589438
2021-10-07 02:42 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.365 s 0.078001
2021-10-07 02:51 JavaScript Parse Table.from, tracks 0.000 s -0.137373
2021-10-07 02:51 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.864612
2021-10-07 02:28 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.584 s -0.139045
2021-10-07 02:36 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.250 s 0.200508
2021-10-07 02:39 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.873 s 0.677676
2021-10-07 02:41 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.913 s 0.658933
2021-10-07 02:43 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -1.457407
2021-10-07 02:51 JavaScript Parse serialize, tracks 0.005 s -0.485584
2021-10-07 02:52 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.593 s -0.276212
2021-10-07 02:28 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.408 s -1.120244
2021-10-07 02:39 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.518748
2021-10-07 02:44 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.084452
2021-10-07 02:52 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.661 s -0.456655
2021-10-07 02:53 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.896 s 0.193685
2021-10-07 02:40 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 0.626089
2021-10-07 02:43 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -1.387305
2021-10-07 02:52 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.116532
2021-10-07 02:53 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.640 s 0.853843
2021-10-07 02:52 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.112795
2021-10-07 02:54 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.976202
2021-10-07 02:53 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.706 s 0.233368
2021-10-07 02:53 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.701639
2021-10-07 02:54 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.976202
2021-10-07 02:53 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.009341
2021-10-07 02:53 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.840 s 1.004424
2021-10-07 02:54 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.147945
2021-10-07 02:54 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.044026
2021-10-07 02:54 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.173749
2021-10-07 02:55 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.067302
2021-10-07 02:55 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.092470
2021-10-07 02:55 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.103498
2021-10-07 02:56 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.654759
2021-10-07 02:57 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.917741
2021-10-07 02:56 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -1.742949
2021-10-07 02:56 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -1.473873
2021-10-07 02:56 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.948772
2021-10-07 02:57 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.552 s -0.812107
2021-10-07 00:40 Python csv-read uncompressed, file, fanniemae_2016Q4 1.170 s 0.232164
2021-10-07 00:41 Python csv-read gzip, streaming, fanniemae_2016Q4 14.769 s 0.486478
2021-10-07 00:40 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.833 s 0.522744
2021-10-07 00:42 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.088390
2021-10-07 00:43 Python csv-read gzip, streaming, nyctaxi_2010-01 10.668 s -0.355596
2021-10-07 00:43 Python csv-read uncompressed, file, nyctaxi_2010-01 1.021 s -0.733661
2021-10-07 00:42 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.694 s -0.320665
2021-10-07 00:44 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.183267
2021-10-07 00:48 Python dataframe-to-table type_simple_features 0.914 s -0.145828
2021-10-07 00:48 Python dataset-filter nyctaxi_2010-01 4.357 s 0.452679
2021-10-07 00:47 Python dataframe-to-table type_integers 0.011 s 0.989446
2021-10-07 00:46 Python dataframe-to-table type_strings 0.370 s 0.064416
2021-10-07 00:46 Python dataframe-to-table chi_traffic_2020_Q1 19.674 s -0.116083
2021-10-07 00:47 Python dataframe-to-table type_floats 0.012 s -0.991224
2021-10-07 00:47 Python dataframe-to-table type_dict 0.012 s -0.095124
2021-10-07 00:47 Python dataframe-to-table type_nested 2.873 s 0.781030
2021-10-07 00:51 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.348 s -0.117351
2021-10-07 00:56 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.130 s 0.656801
2021-10-07 01:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 179.583 s 1.046217
2021-10-07 01:06 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.386 s -0.425102
2021-10-07 01:05 Python dataset-read async=True, nyctaxi_multi_ipc_s3 179.583 s 1.046217
2021-10-07 01:06 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.386 s -0.425102
2021-10-07 01:12 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.153139
2021-10-07 01:11 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.053 s -0.345393
2021-10-07 01:12 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.018 s 0.260091
2021-10-07 01:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.827 s 0.375724
2021-10-07 01:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.827 s 0.375724
2021-10-07 01:22 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.016 s -0.072102
2021-10-07 01:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.160 s -0.802629
2021-10-07 01:23 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.728 s 0.188601
2021-10-07 01:23 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.985 s 0.364367
2021-10-07 01:24 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.238 s 0.158533
2021-10-07 01:25 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.890 s -1.152489
2021-10-07 01:25 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.820 s 0.182173
2021-10-07 01:25 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.292 s -0.076672
2021-10-07 01:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.806 s -0.967871
2021-10-07 01:27 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.595463
2021-10-07 01:27 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.931 s -0.751239
2021-10-07 01:27 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.690205
2021-10-07 01:28 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -0.818405
2021-10-07 01:29 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.284 s -0.896899
2021-10-07 01:28 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.071 s -0.911875
2021-10-07 01:29 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.796164
2021-10-07 01:30 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.316 s -1.000671
2021-10-07 01:33 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.088 s 0.678374
2021-10-07 01:30 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.184 s -1.562831
2021-10-07 01:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.447 s -0.906841
2021-10-07 01:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.511653
2021-10-07 01:34 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.282 s 0.290419
2021-10-07 01:32 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.980 s -1.059729
2021-10-07 01:34 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.446 s 0.665235
2021-10-07 01:35 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.676 s 0.153482
2021-10-07 01:36 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.375 s -0.307904
2021-10-07 01:38 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.772 s 1.093385
2021-10-07 01:37 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.244 s -0.246677
2021-10-07 01:37 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.782 s -0.449907
2021-10-07 01:37 Python file-write lz4, feather, table, fanniemae_2016Q4 1.192 s -2.142505
2021-10-07 01:39 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.782 s 0.583271
2021-10-07 01:39 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.836 s 1.072953
2021-10-07 01:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.811 s 0.056652
2021-10-07 01:40 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.821 s 0.571354
2021-10-07 01:41 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.358014
2021-10-07 01:42 Python wide-dataframe use_legacy_dataset=false 0.629 s -2.066700
2021-10-07 01:41 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.394 s -2.531400
2021-10-07 01:41 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.343 s 0.029328
2021-10-07 01:42 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.486280
2021-10-07 01:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.499 s -0.453763
2021-10-07 01:56 R dataframe-to-table chi_traffic_2020_Q1, R 5.499 s -0.453763
2021-10-07 01:57 R dataframe-to-table type_strings, R 0.494 s -1.187052
2021-10-07 01:57 R dataframe-to-table type_strings, R 0.494 s -1.187052
2021-10-07 01:58 R dataframe-to-table type_dict, R 0.042 s 1.056558
2021-10-07 01:59 R dataframe-to-table type_floats, R 0.113 s -0.370773
2021-10-07 01:58 R dataframe-to-table type_integers, R 0.085 s -0.217695
2021-10-07 01:59 R dataframe-to-table type_nested, R 0.541 s -1.059894
2021-10-07 02:09 R dataframe-to-table type_simple_features, R 3.324 s 4.950492
2021-10-07 02:10 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.239 s 0.301057
2021-10-07 02:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.938 s 0.070336
2021-10-07 02:15 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.572413
2021-10-07 02:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.938 s 0.070336
2021-10-07 02:12 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.123630
2021-10-07 02:12 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.123630
2021-10-07 02:14 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.895 s 1.462485
2021-10-07 02:13 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.892 s 0.344845
2021-10-07 02:13 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.022725
2021-10-07 02:18 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.041285
2021-10-07 02:16 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.189 s -0.891494
2021-10-07 02:15 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.376 s 0.595568
2021-10-07 02:17 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.117 s 0.794606
2021-10-07 02:15 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.695079
2021-10-07 02:18 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.256 s -0.797752
2021-10-07 02:19 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.006 s -1.200531
2021-10-07 02:19 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.099985
2021-10-07 02:20 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.574 s -2.245431
2021-10-07 02:21 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 0.695440
2021-10-07 02:23 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.306 s 0.514934
2021-10-07 02:24 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.305 s 0.653683
2021-10-07 02:54 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.615049