Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-07 16:02 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.955 s -0.214817
2021-10-07 16:02 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.955 s -0.214817
2021-10-07 16:03 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.331091
2021-10-07 16:04 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.445113
2021-10-07 16:04 Python csv-read gzip, streaming, fanniemae_2016Q4 14.899 s -0.301176
2021-10-07 16:05 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.482 s 1.132392
2021-10-07 16:05 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s 0.001511
2021-10-07 16:06 Python csv-read gzip, streaming, nyctaxi_2010-01 10.479 s 1.091066
2021-10-07 16:07 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.689344
2021-10-07 16:10 Python dataframe-to-table type_simple_features 0.916 s -0.300536
2021-10-07 16:08 Python dataframe-to-table chi_traffic_2020_Q1 19.667 s -0.271889
2021-10-07 16:09 Python dataframe-to-table type_floats 0.011 s 1.272196
2021-10-07 16:09 Python dataframe-to-table type_strings 0.371 s -0.121536
2021-10-07 16:10 Python dataframe-to-table type_nested 2.892 s 0.023190
2021-10-07 16:09 Python dataframe-to-table type_integers 0.011 s -0.819417
2021-10-07 16:09 Python dataframe-to-table type_dict 0.012 s 0.717700
2021-10-07 16:10 Python dataset-filter nyctaxi_2010-01 4.355 s 0.545455
2021-10-07 16:13 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.095 s 0.418412
2021-10-07 16:18 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 80.847 s 0.425466
2021-10-07 16:18 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 80.847 s 0.425466
2021-10-07 16:27 Python dataset-read async=True, nyctaxi_multi_ipc_s3 178.019 s 1.092709
2021-10-07 16:27 Python dataset-read async=True, nyctaxi_multi_ipc_s3 178.019 s 1.092709
2021-10-07 16:28 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.145 s 0.267574
2021-10-07 16:33 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.001 s 0.502921
2021-10-07 16:33 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.010 s 0.373170
2021-10-07 16:33 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.020 s 0.041932
2021-10-07 16:46 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.829 s 0.348505
2021-10-07 16:47 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.245 s 0.002788
2021-10-07 16:46 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.950 s 0.440059
2021-10-07 16:46 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.726 s 0.237110
2021-10-07 16:47 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.982 s 0.472176
2021-10-07 16:48 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.155 s -0.405067
2021-10-07 16:47 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.841 s -0.337024
2021-10-07 16:48 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.303 s -0.411536
2021-10-07 16:48 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.883 s -0.813328
2021-10-07 16:48 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.795 s -0.508718
2021-10-07 16:49 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.030 s 0.728808
2021-10-07 16:48 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.946330
2021-10-07 16:49 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.357905
2021-10-07 16:49 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.936 s -0.603808
2021-10-07 16:49 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.244 s -0.605960
2021-10-07 16:50 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.049 s -0.746733
2021-10-07 16:51 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.290 s -0.600920
2021-10-07 17:00 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.808 s -0.109034
2021-10-07 16:51 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.546793
2021-10-07 16:52 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.460 s -0.703411
2021-10-07 16:52 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.313921
2021-10-07 16:52 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.947 s -0.641035
2021-10-07 16:53 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.087 s 0.522256
2021-10-07 17:28 R dataframe-to-table chi_traffic_2020_Q1, R 301.899 s -4.421306
2021-10-07 16:54 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.391 s -0.414056
2021-10-07 16:56 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.807 s -0.624683
2021-10-07 16:54 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.453 s 0.445483
2021-10-07 17:30 R dataframe-to-table type_strings, R 17.221 s -4.915776
2021-10-07 16:55 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.651 s 0.202404
2021-10-07 16:55 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.322 s 0.075975
2021-10-07 16:59 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.137741
2021-10-07 16:56 Python file-write lz4, feather, table, fanniemae_2016Q4 1.157 s 0.321651
2021-10-07 16:56 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.257 s -0.333438
2021-10-07 17:30 R dataframe-to-table type_dict, R 0.062 s -1.388690
2021-10-07 17:43 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.507 s 2.589616
2021-10-07 16:57 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.786 s 0.672934
2021-10-07 16:58 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.782 s 0.514229
2021-10-07 16:58 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.869 s 0.348090
2021-10-07 16:59 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.358 s -0.447952
2021-10-07 17:31 R dataframe-to-table type_integers, R 0.082 s -0.245568
2021-10-07 16:50 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.304 s -0.719267
2021-10-07 16:59 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.827 s 0.427205
2021-10-07 17:00 Python file-write lz4, feather, table, nyctaxi_2010-01 1.864 s -3.093903
2021-10-07 17:31 R dataframe-to-table type_floats, R 0.107 s -0.264500
2021-10-07 17:32 R dataframe-to-table type_nested, R 17.353 s -4.881825
2021-10-07 17:00 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.479520
2021-10-07 17:45 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.409 s -1.457451
2021-10-07 17:45 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.736257
2021-10-07 17:46 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.230 s 2.711822
2021-10-07 17:01 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.004582
2021-10-07 17:42 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.481 s 2.719249
2021-10-07 17:46 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.112 s 1.182335
2021-10-07 17:52 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.276 s 0.548757
2021-10-07 17:55 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.750 s 0.434014
2021-10-07 17:56 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s 0.193674
2021-10-07 17:57 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.561 s 0.453477
2021-10-07 17:59 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.191 s 0.997370
2021-10-07 18:01 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.786 s 0.834766
2021-10-07 18:03 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.465 s 0.618441
2021-10-07 18:06 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s 0.423887
2021-10-07 18:09 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.175 s 0.413012
2021-10-07 18:09 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.372355
2021-10-07 18:10 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.236198
2021-10-07 17:42 R dataframe-to-table type_simple_features, R 3.228 s 2.080803
2021-10-07 17:53 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.306 s 0.488949
2021-10-07 17:58 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.406 s -0.804382
2021-10-07 18:00 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.826 s 0.686531
2021-10-07 18:03 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.465 s 0.618441
2021-10-07 18:08 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.605741
2021-10-07 18:09 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.857 s 0.496110
2021-10-07 18:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.610 s 0.341581
2021-10-07 17:42 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.267 s 0.088819
2021-10-07 18:04 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.641 s 0.957193
2021-10-07 18:09 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 0.400265
2021-10-07 18:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.912 s 0.421719
2021-10-07 17:43 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.232 s 0.239482
2021-10-07 18:05 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.407221
2021-10-07 18:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.526637
2021-10-07 17:43 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.848347
2021-10-07 18:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.533 s -2.169541
2021-10-07 17:44 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.938 s -1.054132
2021-10-07 17:44 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.564 s -0.164819
2021-10-07 18:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.618 s -0.980353
2021-10-07 17:46 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.173 s 2.708865
2021-10-07 18:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.701 s -1.747428
2021-10-07 18:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s -0.768290
2021-10-07 18:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -1.179940
2021-10-07 18:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.365 s -0.223431
2021-10-07 17:46 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.230 s 2.711822
2021-10-07 18:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -1.027568
2021-10-07 17:47 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.536715
2021-10-07 18:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.187 s 0.384925
2021-10-07 17:48 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.025 s -0.378860
2021-10-07 18:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.516 s -1.666130
2021-10-07 17:49 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.678 s 0.122844
2021-10-07 17:51 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.865 s 0.415698
2021-10-07 17:50 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.556 s -0.131845
2021-10-07 18:21 JavaScript Parse Table.from, tracks 0.000 s -1.231080
2021-10-07 18:22 JavaScript Parse readBatches, tracks 0.000 s -1.261475
2021-10-07 18:21 JavaScript Parse Table.from, tracks 0.000 s -1.231080
2021-10-07 18:22 JavaScript Parse readBatches, tracks 0.000 s -1.261475
2021-10-07 18:23 JavaScript Parse serialize, tracks 0.005 s -0.795513
2021-10-07 18:23 JavaScript Parse serialize, tracks 0.005 s -0.795513
2021-10-07 18:24 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.680 s -0.524068
2021-10-07 18:23 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.242392
2021-10-07 18:27 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.598719
2021-10-07 18:26 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.713 s 0.193908
2021-10-07 18:26 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.581313
2021-10-07 18:24 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.252085
2021-10-07 18:24 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.614 s -0.439712
2021-10-07 18:25 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.734190
2021-10-07 18:25 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.691498
2021-10-07 18:25 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.692 s -0.041159
2021-10-07 18:27 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-07 18:26 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.755063
2021-10-07 18:27 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.897 s 0.192898
2021-10-07 18:26 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.878 s 0.005825
2021-10-07 18:27 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.557557
2021-10-07 18:30 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.149999
2021-10-07 18:29 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.408949
2021-10-07 18:28 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.484069
2021-10-07 18:28 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.041741
2021-10-07 18:30 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.288353
2021-10-07 18:29 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.062851
2021-10-07 18:28 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.147271
2021-10-07 18:29 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.226739
2021-10-07 18:31 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.574 s -1.069872
2021-10-07 18:30 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.151773
2021-10-07 18:30 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.317441
2021-10-07 18:31 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.239213
2021-10-07 18:31 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.232191