Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-04 16:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.487 s 1.390011
2021-10-04 16:23 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.362794
2021-10-04 17:01 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.935 s -1.074166
2021-10-04 17:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.023 s 0.820234
2021-10-04 17:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.768160
2021-10-04 17:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.794 s -0.192292
2021-10-04 16:20 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.371075
2021-10-04 16:26 Python dataset-filter nyctaxi_2010-01 4.365 s 0.042506
2021-10-04 16:46 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.004 s 0.481068
2021-10-04 16:25 Python dataframe-to-table type_dict 0.012 s -1.669242
2021-10-04 16:59 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.700 s 0.448498
2021-10-04 16:25 Python dataframe-to-table type_integers 0.011 s 1.001695
2021-10-04 17:06 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.276 s 0.445760
2021-10-04 17:24 R dataframe-to-table type_integers, R 0.085 s -0.177201
2021-10-04 17:25 R dataframe-to-table type_nested, R 0.540 s -0.754818
2021-10-04 16:46 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.011 s 0.159138
2021-10-04 17:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.041 s -0.324672
2021-10-04 17:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.186 s -2.085964
2021-10-04 16:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.102 s 0.822702
2021-10-04 16:59 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.822 s 0.410552
2021-10-04 17:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.488 s 0.589974
2021-10-04 17:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.223064
2021-10-04 16:20 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.940 s -0.262007
2021-10-04 17:00 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.246 s -0.114508
2021-10-04 17:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.971 s -1.253484
2021-10-04 17:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.770 s -0.222139
2021-10-04 17:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.361 s 0.084879
2021-10-04 16:25 Python dataframe-to-table chi_traffic_2020_Q1 19.375 s 1.209416
2021-10-04 16:25 Python dataframe-to-table type_floats 0.011 s 1.622875
2021-10-04 17:07 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.264 s -0.327550
2021-10-04 17:09 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.851 s 0.510712
2021-10-04 17:24 R dataframe-to-table chi_traffic_2020_Q1, R 5.335 s 0.077637
2021-10-04 16:22 Python csv-read uncompressed, file, nyctaxi_2010-01 1.007 s 0.533949
2021-10-04 16:22 Python csv-read gzip, streaming, nyctaxi_2010-01 10.470 s 1.462032
2021-10-04 16:25 Python dataframe-to-table type_nested 2.888 s 0.660444
2021-10-04 16:42 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.164 s -0.352274
2021-10-04 17:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.279 s -1.064888
2021-10-04 16:25 Python dataframe-to-table type_simple_features 0.919 s -0.727841
2021-10-04 16:46 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.018 s 0.225285
2021-10-04 16:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.600 s 0.688055
2021-10-04 16:42 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.212 s 0.537156
2021-10-04 17:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.842 s -0.460272
2021-10-04 17:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.439862
2021-10-04 17:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.119 s 0.680641
2021-10-04 16:59 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.007 s -0.262068
2021-10-04 17:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.652788
2021-10-04 17:11 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.487737
2021-10-04 16:21 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.737801
2021-10-04 17:00 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.291 s -0.137134
2021-10-04 17:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.331 s -1.295569
2021-10-04 16:25 Python dataframe-to-table type_strings 0.373 s -0.048085
2021-10-04 16:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.968 s 0.296584
2021-10-04 17:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.499 s -1.356120
2021-10-04 17:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.330 s 0.246124
2021-10-04 17:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.827 s 0.559454
2021-10-04 16:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.873 s -0.249319
2021-10-04 17:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.868 s -1.127936
2021-10-04 17:24 R dataframe-to-table type_dict, R 0.054 s -0.370307
2021-10-04 17:24 R dataframe-to-table type_floats, R 0.108 s -0.040880
2021-10-04 17:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.573981
2021-10-04 17:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.262 s -1.409019
2021-10-04 17:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.812 s -1.360725
2021-10-04 17:08 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.829 s 0.417086
2021-10-04 17:11 Python wide-dataframe use_legacy_dataset=false 0.616 s 1.129122
2021-10-04 17:24 R dataframe-to-table type_strings, R 0.490 s 0.859312
2021-10-04 18:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.371598
2021-10-04 18:21 JavaScript Parse serialize, tracks 0.005 s 0.408051
2021-10-04 18:21 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-04 17:01 Python file-read lz4, feather, table, fanniemae_2016Q4 0.622 s -3.538600
2021-10-04 17:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.864 s 0.939883
2021-10-04 17:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.347 s 0.177734
2021-10-04 17:10 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.832 s 0.085835
2021-10-04 17:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.852 s 0.877894
2021-10-04 17:57 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.335 s 0.573089
2021-10-04 18:04 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.772 s 1.732000
2021-10-04 18:10 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s 0.792844
2021-10-04 17:48 R dataframe-to-table type_simple_features, R 275.384 s -0.756317
2021-10-04 17:49 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.246 s 0.052870
2021-10-04 17:53 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.840196
2021-10-04 17:54 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.683 s 0.000119
2021-10-04 18:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.898 s 0.809356
2021-10-04 18:21 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.224490
2021-10-04 18:21 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.930934
2021-10-04 17:51 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.051 s 0.914841
2021-10-04 18:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.749709
2021-10-04 18:10 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.320675
2021-10-04 18:21 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.116559
2021-10-04 18:21 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.408802
2021-10-04 18:21 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.964274
2021-10-04 18:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.493 s 0.107464
2021-10-04 18:21 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.511 s -0.063497
2021-10-04 18:01 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.554 s 0.781632
2021-10-04 18:02 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.273 s -1.365439
2021-10-04 18:21 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.098567
2021-10-04 18:21 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.384632
2021-10-04 18:21 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.846444
2021-10-04 18:21 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-04 18:21 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.439843
2021-10-04 17:53 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.238 s 0.228345
2021-10-04 17:54 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.545 s -0.865848
2021-10-04 18:10 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.862 s 0.921408
2021-10-04 17:50 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.908 s 0.647107
2021-10-04 18:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.683 s -1.386177
2021-10-04 17:52 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.128 s 0.021343
2021-10-04 17:59 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.491156
2021-10-04 18:21 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.690356
2021-10-04 18:10 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.998410
2021-10-04 18:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -2.056718
2021-10-04 18:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -1.405517
2021-10-04 18:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.186 s 0.802142
2021-10-04 17:49 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.907 s 0.166107
2021-10-04 17:53 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.993 s -0.807496
2021-10-04 18:21 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.537150
2021-10-04 17:57 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.305 s 0.873943
2021-10-04 18:07 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.269189
2021-10-04 18:09 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.498 s -1.415241
2021-10-04 18:21 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -2.036804
2021-10-04 18:03 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 1.403099
2021-10-04 18:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.820226
2021-10-04 18:21 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.869 s 0.771495
2021-10-04 17:59 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.749 s 0.829980
2021-10-04 18:01 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.190844
2021-10-04 18:05 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.457 s 1.396168
2021-10-04 18:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -3.268198
2021-10-04 18:21 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.199802
2021-10-04 17:48 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.245 s 0.241706
2021-10-04 17:49 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.295938
2021-10-04 17:50 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.768272
2021-10-04 18:06 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.636 s 1.763730
2021-10-04 18:08 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.237 s 1.362026
2021-10-04 18:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s -3.243570
2021-10-04 18:21 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.537150
2021-10-04 18:21 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.350833
2021-10-04 17:49 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.906 s 0.306659
2021-10-04 17:51 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -0.717582
2021-10-04 17:52 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.166 s 0.555340
2021-10-04 18:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.128994
2021-10-04 18:21 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.136285
2021-10-04 18:10 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s 0.675289
2021-10-04 18:21 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.031783
2021-10-04 18:21 JavaScript Parse Table.from, tracks 0.000 s -0.451026
2021-10-04 18:21 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.517 s -0.080362
2021-10-04 18:21 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.678 s 0.409584
2021-10-04 18:21 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.128962
2021-10-04 18:21 JavaScript Parse readBatches, tracks 0.000 s -0.242628
2021-10-04 18:21 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.576 s -0.157045
2021-10-04 18:21 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.673 s 0.212590
2021-10-04 18:21 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.813 s 1.589447
2021-10-04 18:21 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.925780