Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 15:48 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.620 s -0.034145
2021-10-11 15:51 Python dataframe-to-table type_simple_features 0.927 s -0.564083
2021-10-11 16:28 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.039 s -0.180869
2021-10-11 16:28 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.135 s 1.970649
2021-10-11 16:28 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.460923
2021-10-11 16:30 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.105 s 0.427393
2021-10-11 16:30 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.539 s -0.853437
2021-10-11 17:17 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.590 s 1.495143
2021-10-11 17:18 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.635 s -0.662064
2021-10-11 17:28 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.627 s -0.447311
2021-10-11 17:28 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.707177
2021-10-11 17:28 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.724 s 0.131837
2021-10-11 17:28 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.556661
2021-10-11 17:28 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.038516
2021-10-11 17:28 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.020751
2021-10-11 17:28 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.202711
2021-10-11 17:28 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.238315
2021-10-11 17:28 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.585 s -1.014702
2021-10-11 15:46 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.825 s 0.995827
2021-10-11 15:51 Python dataframe-to-table type_floats 0.011 s -0.440400
2021-10-11 16:12 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.057 s -0.873646
2021-10-11 16:32 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.002 s -1.256040
2021-10-11 15:47 Python csv-read gzip, streaming, fanniemae_2016Q4 14.770 s 0.840246
2021-10-11 15:48 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s -0.294406
2021-10-11 15:50 Python dataframe-to-table chi_traffic_2020_Q1 19.483 s 0.284024
2021-10-11 16:08 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.885 s -0.891578
2021-10-11 16:34 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.894 s -0.442329
2021-10-11 16:36 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.832 s -0.349458
2021-10-11 16:36 Python wide-dataframe use_legacy_dataset=true 0.389 s 1.721611
2021-10-11 15:46 Python csv-read uncompressed, file, fanniemae_2016Q4 1.156 s 0.991195
2021-10-11 15:47 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.390809
2021-10-11 16:12 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.083 s -0.451935
2021-10-11 16:12 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.040 s -0.189421
2021-10-11 16:31 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.909 s -0.880369
2021-10-11 16:32 Python file-write lz4, feather, table, fanniemae_2016Q4 1.145 s 0.952325
2021-10-11 15:51 Python dataframe-to-table type_strings 0.370 s 0.181143
2021-10-11 15:51 Python dataframe-to-table type_nested 2.878 s 0.072984
2021-10-11 15:54 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.450 s 0.869249
2021-10-11 16:26 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.837 s -1.027012
2021-10-11 16:32 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.258 s 0.329599
2021-10-11 16:27 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.060 s -0.596545
2021-10-11 16:27 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.128 s 2.117901
2021-10-11 15:51 Python dataset-filter nyctaxi_2010-01 4.319 s 1.482369
2021-10-11 16:27 Python file-read lz4, feather, table, fanniemae_2016Q4 0.598 s 0.722179
2021-10-11 16:27 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.994 s 2.338593
2021-10-11 16:31 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.452 s 0.502908
2021-10-11 15:51 Python dataframe-to-table type_dict 0.012 s 0.655287
2021-10-11 15:51 Python dataframe-to-table type_integers 0.011 s -1.679163
2021-10-11 16:27 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.686 s 2.257773
2021-10-11 16:08 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.078 s 0.314147
2021-10-11 15:58 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.692 s 0.557580
2021-10-11 16:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.665590
2021-10-11 16:25 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.242 s -0.611718
2021-10-11 16:26 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.619 s 2.148707
2021-10-11 17:28 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 16:26 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.301 s -0.904687
2021-10-11 16:26 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.519388
2021-10-11 16:29 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.154888
2021-10-11 16:24 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.802 s 0.484558
2021-10-11 16:24 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.002 s 0.128543
2021-10-11 15:48 Python csv-read gzip, streaming, nyctaxi_2010-01 10.613 s -0.237704
2021-10-11 15:49 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.558702
2021-10-11 16:25 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.000 s -0.768865
2021-10-11 16:29 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.782 s 2.023194
2021-10-11 16:25 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.674 s 0.711814
2021-10-11 16:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.531 s 2.205064
2021-10-11 16:29 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.266 s 2.181282
2021-10-11 16:33 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.853 s 0.012764
2021-10-11 16:35 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.457229
2021-10-11 17:00 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.698 s -0.078800
2021-10-11 17:11 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.882 s -0.949874
2021-10-11 16:33 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.501 s -1.814430
2021-10-11 16:34 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.918 s -0.028992
2021-10-11 17:03 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.263 s 0.566529
2021-10-11 17:10 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s -0.363912
2021-10-11 17:16 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.168 s 0.408623
2021-10-11 17:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.628 s -2.384540
2021-10-11 17:18 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.878 s 1.306491
2021-10-11 16:35 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.927 s -0.568721
2021-10-11 16:35 Python file-write lz4, feather, table, nyctaxi_2010-01 1.789 s 0.960395
2021-10-11 16:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.456 s 1.061320
2021-10-11 17:08 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.392 s 0.797212
2021-10-11 16:35 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.358 s -0.158119
2021-10-11 16:36 Python wide-dataframe use_legacy_dataset=false 0.614 s 1.210392
2021-10-11 17:00 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.974 s 0.288459
2021-10-11 17:17 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.194 s -1.504224
2021-10-11 17:19 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.277133
2021-10-11 16:57 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.215 s 0.406612
2021-10-11 17:09 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.189 s 0.575072
2021-10-11 17:17 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.275176
2021-10-11 16:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.211 s 0.776188
2021-10-11 16:57 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -1.947764
2021-10-11 16:50 R dataframe-to-table type_integers, R 0.010 s 1.133021
2021-10-11 17:15 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.474 s 1.252162
2021-10-11 17:20 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s 0.081557
2021-10-11 17:28 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.623 s -0.399667
2021-10-11 17:28 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.658437
2021-10-11 17:28 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.703 s -0.362876
2021-10-11 16:50 R dataframe-to-table type_floats, R 0.013 s 1.113344
2021-10-11 17:28 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.186835
2021-10-11 17:28 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.362978
2021-10-11 17:28 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.745136
2021-10-11 16:59 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.164 s 1.071198
2021-10-11 17:02 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.829 s 0.686590
2021-10-11 17:05 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.722 s 0.565757
2021-10-11 17:13 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.726 s -0.614707
2021-10-11 17:20 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 0.923361
2021-10-11 17:28 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.916 s -0.987265
2021-10-11 17:28 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.349205
2021-10-11 16:56 R dataframe-to-table type_simple_features, R 3.323 s 0.934485
2021-10-11 16:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.406 s -1.012705
2021-10-11 17:07 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.542 s 0.699614
2021-10-11 17:28 JavaScript Parse Table.from, tracks 0.000 s -0.609614
2021-10-11 17:28 JavaScript Parse readBatches, tracks 0.000 s 0.360775
2021-10-11 17:28 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.590069
2021-10-11 17:28 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.591460
2021-10-11 17:28 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.879345
2021-10-11 16:50 R dataframe-to-table type_dict, R 0.061 s -1.780798
2021-10-11 16:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.075 s -2.174814
2021-10-11 17:17 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.364141
2021-10-11 17:19 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.490 s -1.140206
2021-10-11 17:20 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.492 s 0.987276
2021-10-11 17:28 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.468974
2021-10-11 17:28 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.348708
2021-10-11 17:28 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.695113
2021-10-11 16:50 R dataframe-to-table chi_traffic_2020_Q1, R 3.376 s 0.269482
2021-10-11 16:59 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.119 s 0.267749
2021-10-11 16:59 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.924235
2021-10-11 16:59 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.360916
2021-10-11 17:06 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 1.714867
2021-10-11 17:16 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.852 s 0.634413
2021-10-11 16:50 R dataframe-to-table type_nested, R 0.536 s 0.233700
2021-10-11 16:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -1.216633
2021-10-11 17:28 JavaScript Parse serialize, tracks 0.005 s -0.717137
2021-10-11 17:28 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.882 s 0.487810
2021-10-11 17:28 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-11 16:59 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.227 s 1.059352
2021-10-11 17:04 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 0.612218
2021-10-11 17:14 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.989769
2021-10-11 17:14 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.238 s 0.527288
2021-10-11 17:16 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.595 s -0.911863
2021-10-11 17:28 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.693925
2021-10-11 16:50 R dataframe-to-table type_strings, R 0.488 s 0.232673
2021-10-11 17:12 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.533 s -0.432365
2021-10-11 17:17 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s -0.309022
2021-10-11 17:28 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.189855
2021-10-11 16:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.465 s 1.085776
2021-10-11 17:01 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.517 s 0.223306
2021-10-11 17:19 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.134133