Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 05:21 Python dataframe-to-table type_nested 2.951 s 0.258090
2021-09-28 05:25 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.869 s -1.517322
2021-09-28 05:18 Python csv-read gzip, streaming, nyctaxi_2010-01 10.750 s -0.876301
2021-09-28 05:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.460 s -0.857350
2021-09-28 05:52 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.025 s -0.164027
2021-09-28 05:19 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.654705
2021-09-28 05:21 Python dataframe-to-table type_strings 0.364 s 0.961750
2021-09-28 05:38 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 269.406 s 0.136509
2021-09-28 05:17 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.072874
2021-09-28 05:18 Python csv-read uncompressed, file, nyctaxi_2010-01 1.002 s 0.280594
2021-09-28 05:21 Python dataframe-to-table type_integers 0.011 s 0.827893
2021-09-28 05:21 Python dataset-filter nyctaxi_2010-01 4.375 s -0.675133
2021-09-28 05:52 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.023 s 0.119299
2021-09-28 05:16 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.055 s -0.960208
2021-09-28 05:21 Python dataframe-to-table type_dict 0.012 s 0.692504
2021-09-28 05:17 Python csv-read gzip, streaming, fanniemae_2016Q4 15.000 s -0.977283
2021-09-28 05:18 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.650 s -0.453593
2021-09-28 05:48 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.050 s 0.096579
2021-09-28 05:21 Python dataframe-to-table chi_traffic_2020_Q1 19.797 s -0.017572
2021-09-28 05:21 Python dataframe-to-table type_simple_features 0.910 s -0.182853
2021-09-28 05:16 Python csv-read uncompressed, file, fanniemae_2016Q4 1.172 s -0.045419
2021-09-28 05:52 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.003 s 0.455500
2021-09-28 05:21 Python dataframe-to-table type_floats 0.012 s -2.033433
2021-09-28 06:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.743 s -1.016489
2021-09-28 07:16 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.933969
2021-09-28 07:16 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.489 s 0.177076
2021-09-28 07:24 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.617 s -0.153584
2021-09-28 06:02 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.956 s 0.391745
2021-09-28 06:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.143 s -0.700170
2021-09-28 06:10 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.601 s 1.028478
2021-09-28 06:54 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.179 s -0.600776
2021-09-28 07:12 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.188 s 0.385053
2021-09-28 07:24 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.588805
2021-09-28 07:24 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.353995
2021-09-28 06:08 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.105 s 1.680287
2021-09-28 06:09 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.457 s 1.718291
2021-09-28 07:12 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.591 s 0.925539
2021-09-28 07:15 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.035284
2021-09-28 06:05 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.886 s 0.828361
2021-09-28 06:05 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.843 s 0.939048
2021-09-28 06:06 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.469 s 1.034944
2021-09-28 06:12 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.799 s 1.185753
2021-09-28 06:51 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.911 s 0.108253
2021-09-28 07:15 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.393 s -0.101196
2021-09-28 07:24 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.774 s -0.170202
2021-09-28 07:24 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.187710
2021-09-28 07:24 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.061218
2021-09-28 06:04 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.820 s -1.333982
2021-09-28 06:55 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.978 s -0.633274
2021-09-28 07:07 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.516 s 1.316549
2021-09-28 07:08 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.734 s 0.867808
2021-09-28 07:12 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.733 s 1.255327
2021-09-28 07:13 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.608 s -0.204802
2021-09-28 07:24 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.928921
2021-09-28 06:13 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.751 s 0.777288
2021-09-28 06:03 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.841 s -0.980960
2021-09-28 06:12 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.354 s -0.214656
2021-09-28 06:13 Python file-write lz4, feather, table, nyctaxi_2010-01 1.801 s 0.524478
2021-09-28 06:51 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.888 s 0.325151
2021-09-28 07:09 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.491144
2021-09-28 06:07 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.076 s 1.771239
2021-09-28 06:11 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.889 s 1.937571
2021-09-28 07:04 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.214 s 0.958513
2021-09-28 07:13 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.175 s 0.155913
2021-09-28 07:24 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.999 s -2.084525
2021-09-28 07:24 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.595 s -1.387671
2021-09-28 06:06 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.320771
2021-09-28 06:50 R dataframe-to-table type_simple_features, R 274.496 s 0.604675
2021-09-28 06:59 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.264 s 1.879296
2021-09-28 07:24 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.922596
2021-09-28 07:24 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.958 s -1.895550
2021-09-28 07:24 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-28 07:24 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.089964
2021-09-28 07:24 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.138720
2021-09-28 06:04 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.054 s -0.438363
2021-09-28 06:05 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.052 s -0.905423
2021-09-28 06:52 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.627608
2021-09-28 06:53 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -1.078346
2021-09-28 06:55 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.236 s 0.230369
2021-09-28 06:57 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.847 s 1.882426
2021-09-28 07:14 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.867 s 0.999670
2021-09-28 06:06 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.970 s 1.011200
2021-09-28 06:10 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.144693
2021-09-28 06:13 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.297 s 0.591808
2021-09-28 06:13 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.194928
2021-09-28 06:53 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.391 s -0.743480
2021-09-28 07:24 JavaScript Parse serialize, tracks 0.003 s 2.987687
2021-09-28 06:04 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.297010
2021-09-28 06:13 Python wide-dataframe use_legacy_dataset=false 0.624 s -1.381296
2021-09-28 07:06 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.869 s 1.174679
2021-09-28 07:11 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.252541
2021-09-28 06:02 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.822 s 0.440967
2021-09-28 06:08 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.432 s 1.744642
2021-09-28 06:09 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.381 s -0.405395
2021-09-28 06:26 R dataframe-to-table type_dict, R 0.055 s -0.375329
2021-09-28 06:51 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.211 s 0.474908
2021-09-28 06:52 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.259986
2021-09-28 06:59 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.282 s 2.025070
2021-09-28 07:12 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.696 s 0.658114
2021-09-28 07:13 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.520 s -0.619564
2021-09-28 07:24 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -3.964986
2021-09-28 06:02 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.753 s 0.244513
2021-09-28 06:04 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.749 s -4.105670
2021-09-28 06:10 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.832 s 1.655145
2021-09-28 06:03 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.255 s -0.791166
2021-09-28 06:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.110 s 0.535578
2021-09-28 06:56 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.517 s -0.169019
2021-09-28 07:14 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.681 s -1.268598
2021-09-28 07:24 JavaScript Parse Table.from, tracks 0.000 s -1.220165
2021-09-28 06:03 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.290 s -0.840614
2021-09-28 06:06 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.449872
2021-09-28 06:26 R dataframe-to-table type_integers, R 0.083 s 1.639164
2021-09-28 06:04 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.305 s -2.108594
2021-09-28 06:11 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.856 s 0.535421
2021-09-28 06:26 R dataframe-to-table type_strings, R 0.495 s -1.658213
2021-09-28 06:54 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.136 s -0.386599
2021-09-28 07:01 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.741 s 1.714013
2021-09-28 07:12 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.096497
2021-09-28 07:15 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.675485
2021-09-28 07:16 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.809 s 0.695661
2021-09-28 07:24 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.816 s -2.747669
2021-09-28 06:51 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.117227
2021-09-28 07:05 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.885 s 1.190112
2021-09-28 07:13 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.978 s 0.276355
2021-09-28 07:24 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.918145
2021-09-28 07:24 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.039200
2021-09-28 07:24 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-28 07:24 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.502038
2021-09-28 07:24 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.599650
2021-09-28 07:24 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.074897
2021-09-28 06:02 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.015 s -0.931122
2021-09-28 06:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.088 s 1.302417
2021-09-28 06:26 R dataframe-to-table chi_traffic_2020_Q1, R 5.379 s 0.509968
2021-09-28 06:56 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.679 s -0.349926
2021-09-28 07:24 JavaScript Parse readBatches, tracks 0.000 s -0.389545
2021-09-28 07:24 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.811800
2021-09-28 06:27 R dataframe-to-table type_floats, R 0.108 s 0.287721
2021-09-28 07:03 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.404 s -0.274487
2021-09-28 07:24 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.663 s -0.227430
2021-09-28 06:27 R dataframe-to-table type_nested, R 0.540 s -1.088306
2021-09-28 06:50 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.222 s 0.334162
2021-09-28 07:10 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.260 s 0.383089
2021-09-28 07:24 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.948543
2021-09-28 07:24 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-28 07:24 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.374767
2021-09-28 06:55 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.773175
2021-09-28 07:01 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.003989
2021-09-28 07:03 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.571 s 1.196708
2021-09-28 07:24 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267