Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 11:13 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.474 s 1.032758
2021-10-09 11:13 Python csv-read uncompressed, file, nyctaxi_2010-01 0.995 s 1.554991
2021-10-09 11:13 Python csv-read gzip, streaming, nyctaxi_2010-01 10.462 s 1.083772
2021-10-09 11:33 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.454 s 0.078527
2021-10-09 11:46 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.734 s 0.185265
2021-10-09 11:47 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.792 s 0.682451
2021-10-09 11:47 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.128 s 0.874769
2021-10-09 11:48 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.920 s 0.085061
2021-10-09 11:49 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.080 s -1.358403
2021-10-09 11:54 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.342 s -1.330410
2021-10-09 11:55 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.900 s -0.932216
2021-10-09 11:56 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.923 s -0.408510
2021-10-09 11:56 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.902 s -0.494399
2021-10-09 11:57 Python wide-dataframe use_legacy_dataset=false 0.628 s -1.409184
2021-10-09 12:19 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.157 s 1.762237
2021-10-09 12:20 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.237 s 1.723006
2021-10-09 12:21 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.159741
2021-10-09 12:21 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.523 s 0.208324
2021-10-09 12:28 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 0.982709
2021-10-09 12:30 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.898 s -0.747085
2021-10-09 12:32 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.531 s -0.654590
2021-10-09 12:34 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.284690
2021-10-09 12:37 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.858 s 0.590511
2021-10-09 12:39 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.930 s -0.374393
2021-10-09 12:40 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.749629
2021-10-09 12:17 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.235 s 0.241875
2021-10-09 12:20 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.810231
2021-10-09 12:24 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.408 s -0.221182
2021-10-09 12:38 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.316195
2021-10-09 12:40 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -1.659351
2021-10-09 11:16 Python dataframe-to-table chi_traffic_2020_Q1 19.656 s -0.179647
2021-10-09 11:37 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.038 s -0.744368
2021-10-09 11:52 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.299 s 0.365639
2021-10-09 12:37 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.040803
2021-10-09 12:40 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -0.624612
2021-10-09 11:48 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.932753
2021-10-09 12:17 R dataframe-to-table type_simple_features, R 3.344 s 1.475331
2021-10-09 11:16 Python dataframe-to-table type_strings 0.370 s 0.231025
2021-10-09 11:16 Python dataframe-to-table type_integers 0.011 s 0.510303
2021-10-09 11:16 Python dataframe-to-table type_nested 2.892 s -0.333194
2021-10-09 12:17 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.447 s 1.777539
2021-10-09 11:37 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.055 s -0.233721
2021-10-09 11:37 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.025 s -0.016945
2021-10-09 11:48 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.775 s 0.312140
2021-10-09 12:21 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.998 s -0.032658
2021-10-09 12:37 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s -0.073585
2021-10-09 12:37 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.272385
2021-10-09 11:51 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.951 s -0.406660
2021-10-09 12:18 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.451 s 1.730098
2021-10-09 12:26 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.718 s 0.660476
2021-10-09 12:27 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.212200
2021-10-09 12:41 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.516 s -1.578839
2021-10-09 12:19 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -0.589972
2021-10-09 12:20 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.136 s -0.716732
2021-10-09 12:22 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.835 s 0.666913
2021-10-09 12:39 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.096178
2021-10-09 12:40 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.184 s -0.498348
2021-10-09 11:11 Python csv-read uncompressed, file, fanniemae_2016Q4 1.149 s 1.554413
2021-10-09 11:16 Python dataframe-to-table type_simple_features 0.909 s 0.432792
2021-10-09 11:20 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.300 s 0.761409
2021-10-09 11:47 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.867 s -0.294744
2021-10-09 11:49 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.042 s -0.314518
2021-10-09 11:50 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.406027
2021-10-09 11:53 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.371 s -0.265153
2021-10-09 11:54 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.021400
2021-10-09 11:16 Python dataframe-to-table type_dict 0.012 s -0.407312
2021-10-09 11:16 Python dataframe-to-table type_floats 0.012 s -0.741501
2021-10-09 11:17 Python dataset-filter nyctaxi_2010-01 4.339 s 1.027064
2021-10-09 11:51 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.092 s 0.518871
2021-10-09 11:52 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.438 s 0.604305
2021-10-09 12:18 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.213 s 0.955962
2021-10-09 12:19 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.047 s 1.592259
2021-10-09 12:24 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.232 s 0.849556
2021-10-09 12:28 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.541 s 1.013697
2021-10-09 12:31 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.854 s -0.653817
2021-10-09 12:39 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.526 s 1.012990
2021-10-09 11:14 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.695737
2021-10-09 11:47 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.286 s 0.275954
2021-10-09 11:11 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.939 s -0.033646
2021-10-09 11:33 Python dataset-read async=True, nyctaxi_multi_ipc_s3 178.999 s 1.055112
2021-10-09 11:46 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.874 s 0.030061
2021-10-09 11:48 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.254 s -0.613208
2021-10-09 11:55 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.863 s -0.427255
2021-10-09 12:10 R dataframe-to-table chi_traffic_2020_Q1, R 3.409 s 0.275396
2021-10-09 11:46 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.044 s -0.101872
2021-10-09 11:50 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.365 s -0.771170
2021-10-09 11:46 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.955 s 0.899518
2021-10-09 11:12 Python csv-read gzip, streaming, fanniemae_2016Q4 14.886 s -0.178704
2021-10-09 11:47 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.216 s 0.494912
2021-10-09 11:48 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -1.348930
2021-10-09 11:50 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.792121
2021-10-09 11:24 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.388 s 0.107746
2021-10-09 11:50 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.469 s -0.504074
2021-10-09 11:53 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.701 s 0.127460
2021-10-09 11:54 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.837 s -0.681315
2021-10-09 11:56 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.355 s -0.158352
2021-10-09 11:57 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.828 s -0.496432
2021-10-09 12:18 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.941 s -0.947036
2021-10-09 12:19 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.323928
2021-10-09 11:12 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.838948
2021-10-09 11:49 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.341 s -0.682675
2021-10-09 11:57 Python wide-dataframe use_legacy_dataset=true 0.393 s 1.003500
2021-10-09 12:11 R dataframe-to-table type_strings, R 0.490 s 0.232354
2021-10-09 12:11 R dataframe-to-table type_integers, R 0.010 s 1.881061
2021-10-09 12:11 R dataframe-to-table type_floats, R 0.013 s 1.872150
2021-10-09 12:18 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.992769
2021-10-09 12:37 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.731106
2021-10-09 12:38 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.515 s 0.556356
2021-10-09 12:35 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.250 s -0.125003
2021-10-09 11:57 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.350 s -0.074966
2021-10-09 12:29 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.196 s 0.571409
2021-10-09 12:33 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.718 s -0.707083
2021-10-09 12:48 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.131634
2021-10-09 11:57 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.215867
2021-10-09 12:36 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.303292
2021-10-09 12:37 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.177 s 0.041715
2021-10-09 12:38 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.741724
2021-10-09 12:48 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.629 s 1.188445
2021-10-09 12:48 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.932317
2021-10-09 12:48 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500672
2021-10-09 12:48 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.776013
2021-10-09 12:48 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.336224
2021-10-09 12:48 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.784870
2021-10-09 12:48 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.521760
2021-10-09 12:48 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.049179
2021-10-09 12:48 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s -0.022602
2021-10-09 12:48 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.804516
2021-10-09 12:48 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.512 s 0.088850
2021-10-09 12:48 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.704 s 0.225848
2021-10-09 12:48 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.093766
2021-10-09 12:48 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.587253
2021-10-09 12:48 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.822744
2021-10-09 12:48 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.804014
2021-10-09 12:48 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.776674
2021-10-09 12:48 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.066948
2021-10-09 12:48 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.814270
2021-10-09 12:48 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.914 s -0.134885
2021-10-09 12:48 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.925 s -1.148689
2021-10-09 12:48 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.700875
2021-10-09 12:48 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.705573
2021-10-09 12:48 JavaScript Parse Table.from, tracks 0.000 s 0.597892
2021-10-09 12:48 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.239401
2021-10-09 12:48 JavaScript Parse serialize, tracks 0.005 s 0.449854
2021-10-09 12:48 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.609 s -0.395616
2021-10-09 12:48 JavaScript Parse readBatches, tracks 0.000 s 0.790114
2021-10-09 12:48 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.606 s -0.431410
2021-10-09 12:48 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.256653
2021-10-09 12:11 R dataframe-to-table type_dict, R 0.051 s -0.020925
2021-10-09 12:11 R dataframe-to-table type_nested, R 0.538 s 0.233372