Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 19:20 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.808 s -0.485451
2021-09-29 19:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.884 s -1.269457
2021-09-29 19:29 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 55.822 s 0.813457
2021-09-29 19:47 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.018 s 0.053829
2021-09-29 20:00 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.877 s 0.165399
2021-09-29 20:04 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.813243
2021-09-29 20:04 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.041 s -0.273782
2021-09-29 20:26 R dataframe-to-table type_floats, R 0.110 s -0.302483
2021-09-29 19:25 Python dataframe-to-table type_strings 0.361 s 1.197617
2021-09-29 20:03 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.892 s -2.197925
2021-09-29 20:00 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.973 s 0.259240
2021-09-29 20:02 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.872 s -2.676427
2021-09-29 20:10 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.776 s 0.824960
2021-09-29 20:26 R dataframe-to-table type_integers, R 0.083 s 1.321735
2021-09-29 19:25 Python dataframe-to-table chi_traffic_2020_Q1 19.406 s 1.961375
2021-09-29 19:25 Python dataframe-to-table type_floats 0.011 s 0.487513
2021-09-29 19:25 Python dataframe-to-table type_simple_features 0.931 s -1.931621
2021-09-29 20:02 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.172 s -1.592968
2021-09-29 20:02 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.283 s 1.179471
2021-09-29 20:03 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.218 s -2.687773
2021-09-29 20:07 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.532 s 0.959812
2021-09-29 19:21 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.570891
2021-09-29 19:22 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s 0.049278
2021-09-29 20:01 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.319 s -2.242660
2021-09-29 20:08 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.700 s 0.078425
2021-09-29 19:25 Python dataframe-to-table type_integers 0.011 s -1.196452
2021-09-29 20:03 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.549059
2021-09-29 20:04 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.029 s 0.017383
2021-09-29 20:09 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.791 s 1.491808
2021-09-29 20:11 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.389 s -2.343746
2021-09-29 19:25 Python dataframe-to-table type_dict 0.012 s 0.210544
2021-09-29 20:02 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.885 s -1.783287
2021-09-29 20:02 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.386 s -4.357901
2021-09-29 19:42 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.233 s 0.700007
2021-09-29 20:12 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.037400
2021-09-29 19:23 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.163385
2021-09-29 19:25 Python dataframe-to-table type_nested 2.895 s 1.482129
2021-09-29 19:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.244 s 1.781821
2021-09-29 19:47 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.052 s -0.248894
2021-09-29 20:02 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.774 s -1.848160
2021-09-29 20:05 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.629 s 0.176753
2021-09-29 20:06 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.193 s 0.866442
2021-09-29 19:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.759 s -0.508432
2021-09-29 19:42 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.322 s -0.114327
2021-09-29 19:47 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.020 s 0.222037
2021-09-29 20:01 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.735 s 0.260893
2021-09-29 20:06 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.091 s 1.050102
2021-09-29 20:07 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.437 s 1.113390
2021-09-29 20:08 Python file-write lz4, feather, table, fanniemae_2016Q4 1.164 s -0.351640
2021-09-29 20:11 Python file-write lz4, feather, table, nyctaxi_2010-01 1.874 s -3.218567
2021-09-29 20:09 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.165 s 0.433949
2021-09-29 20:11 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.798 s 0.939699
2021-09-29 20:26 R dataframe-to-table chi_traffic_2020_Q1, R 5.410 s -0.158388
2021-09-29 20:03 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.028 s 0.532846
2021-09-29 19:23 Python csv-read gzip, streaming, nyctaxi_2010-01 10.873 s -1.277180
2021-09-29 19:20 Python csv-read uncompressed, file, fanniemae_2016Q4 1.196 s -0.418097
2021-09-29 20:05 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.108 s 0.254724
2021-09-29 19:26 Python dataset-filter nyctaxi_2010-01 4.396 s -0.909640
2021-09-29 20:03 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.064 s -0.093506
2021-09-29 20:05 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.155182
2021-09-29 20:08 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.398 s -0.490914
2021-09-29 20:26 R dataframe-to-table type_nested, R 0.537 s 0.056675
2021-09-29 20:01 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.037 s -1.148988
2021-09-29 20:10 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.871 s 1.157214
2021-09-29 20:12 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.626832
2021-09-29 20:12 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.843 s -0.034257
2021-09-29 20:26 R dataframe-to-table type_dict, R 0.043 s 0.610235
2021-09-29 20:53 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.127 s 0.247051
2021-09-29 20:58 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.270 s 1.100290
2021-09-29 21:00 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.744 s 1.000694
2021-09-29 20:49 R dataframe-to-table type_simple_features, R 274.727 s 0.222280
2021-09-29 20:52 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.594926
2021-09-29 21:06 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.804 s 1.584483
2021-09-29 21:08 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.659 s 1.758317
2021-09-29 21:12 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.592 s 1.356206
2021-09-29 21:23 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.076066
2021-09-29 20:55 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.971 s -0.088270
2021-09-29 21:04 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.224 s 0.600894
2021-09-29 21:14 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 1.357877
2021-09-29 21:23 JavaScript Parse Table.from, tracks 0.000 s 0.180923
2021-09-29 21:23 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.636 s -0.286380
2021-09-29 21:23 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.734 s 0.073399
2021-09-29 21:23 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.898 s 0.066069
2021-09-29 20:50 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.246 s 0.070486
2021-09-29 21:07 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.468 s 1.555231
2021-09-29 21:12 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.367975
2021-09-29 21:15 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.123278
2021-09-29 21:23 JavaScript Parse serialize, tracks 0.005 s -0.443562
2021-09-29 20:52 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.925 s -0.394031
2021-09-29 21:12 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.207734
2021-09-29 21:12 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.505 s 1.457612
2021-09-29 21:15 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.200 s -1.432537
2021-09-29 21:23 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-09-29 21:02 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.606 s 0.199605
2021-09-29 21:11 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 1.539935
2021-09-29 21:13 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.628 s -3.237621
2021-09-29 21:14 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.573 s 0.369194
2021-09-29 21:23 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.147573
2021-09-29 21:14 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.118761
2021-09-29 20:52 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.064 s -1.380260
2021-09-29 20:54 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.305614
2021-09-29 21:03 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.251240
2021-09-29 21:23 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.684 s -0.046163
2021-09-29 20:52 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.388 s -0.431846
2021-09-29 20:53 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.171 s 0.089642
2021-09-29 21:23 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.977304
2021-09-29 21:23 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.757072
2021-09-29 20:51 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.035936
2021-09-29 21:09 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.262 s 0.337394
2021-09-29 21:23 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-29 20:50 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.235 s 0.193311
2021-09-29 20:56 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.527 s -0.314068
2021-09-29 21:23 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.226927
2021-09-29 20:51 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.913 s 0.107664
2021-09-29 21:23 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.321840
2021-09-29 21:09 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.286 s -1.704519
2021-09-29 21:12 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.571 s 1.756506
2021-09-29 21:23 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.924094
2021-09-29 20:54 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.279 s -1.909657
2021-09-29 21:01 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s -0.062048
2021-09-29 21:23 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.104416
2021-09-29 21:23 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.191094
2021-09-29 21:23 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.437611
2021-09-29 21:23 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.355051
2021-09-29 21:23 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.523 s -0.204441
2021-09-29 20:50 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.934 s -0.121423
2021-09-29 20:55 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.680 s 0.055068
2021-09-29 20:57 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.848 s 1.072647
2021-09-29 21:05 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.826 s 1.618492
2021-09-29 21:11 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.877 s 1.598359
2021-09-29 21:15 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.165 s 1.292671
2021-09-29 21:23 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.747 s -0.511774
2021-09-29 21:23 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-29 20:59 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.297 s 1.080088
2021-09-29 21:11 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s 0.024496
2021-09-29 21:23 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.146243
2021-09-29 21:23 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.057364
2021-09-29 21:23 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.151727
2021-09-29 21:23 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.965499
2021-09-29 21:10 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.487 s 0.536242
2021-09-29 21:13 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.956 s 1.316379
2021-09-29 21:14 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.468 s 1.622143
2021-09-29 21:23 JavaScript Parse readBatches, tracks 0.000 s -0.025313
2021-09-29 21:23 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.885 s 0.004669
2021-09-29 21:23 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.285230
2021-09-29 21:23 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.540337
2021-09-29 21:23 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.146245
2021-09-29 20:11 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.427 s -0.544999
2021-09-29 20:26 R dataframe-to-table type_strings, R 0.493 s -1.030732