Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 04:09 Python csv-read gzip, streaming, fanniemae_2016Q4 14.702 s 1.642229
2021-10-09 04:10 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s -0.581488
2021-10-09 04:13 Python dataframe-to-table chi_traffic_2020_Q1 19.294 s 0.841533
2021-10-09 05:16 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.215 s 0.460683
2021-10-09 05:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.245523
2021-10-09 05:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.611527
2021-10-09 05:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -0.611351
2021-10-09 05:47 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.009793
2021-10-09 05:47 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.491300
2021-10-09 04:09 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.606019
2021-10-09 04:13 Python dataframe-to-table type_dict 0.012 s -1.650075
2021-10-09 04:14 Python dataset-filter nyctaxi_2010-01 4.334 s 1.349809
2021-10-09 05:09 R dataframe-to-table type_integers, R 0.010 s 2.144173
2021-10-09 04:21 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.700 s 0.098820
2021-10-09 04:50 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.317 s 0.189166
2021-10-09 04:51 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.455 s 0.465256
2021-10-09 05:09 R dataframe-to-table chi_traffic_2020_Q1, R 3.426 s 0.276351
2021-10-09 04:10 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.668 s -0.262396
2021-10-09 04:13 Python dataframe-to-table type_strings 0.372 s 0.009479
2021-10-09 04:30 Python dataset-read async=True, nyctaxi_multi_ipc_s3 185.120 s 0.212506
2021-10-09 04:35 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.039 s -0.008772
2021-10-09 04:35 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.032 s -0.113864
2021-10-09 04:13 Python dataframe-to-table type_integers 0.011 s 0.677190
2021-10-09 04:54 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.876 s -0.638418
2021-10-09 04:48 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.317 s -0.609099
2021-10-09 04:49 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.489 s -0.679267
2021-10-09 04:55 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s -0.004299
2021-10-09 05:20 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.542 s 0.002465
2021-10-09 04:13 Python dataframe-to-table type_nested 2.880 s 0.185410
2021-10-09 04:46 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.275 s 0.768329
2021-10-09 04:47 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.050 s -0.096322
2021-10-09 04:46 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.808 s -0.697724
2021-10-09 04:48 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.324 s -0.586215
2021-10-09 05:19 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.995 s -0.005942
2021-10-09 04:49 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.884423
2021-10-09 04:44 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.884 s 0.048031
2021-10-09 04:45 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.957 s 0.923891
2021-10-09 04:45 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.194 s 1.099888
2021-10-09 04:53 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.279 s -0.427999
2021-10-09 04:56 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.481951
2021-10-09 05:09 R dataframe-to-table type_dict, R 0.049 s 0.235376
2021-10-09 04:08 Python csv-read uncompressed, file, fanniemae_2016Q4 1.155 s 1.205114
2021-10-09 04:47 Python file-read lz4, feather, table, fanniemae_2016Q4 0.602 s 0.230403
2021-10-09 04:47 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.240 s -0.343366
2021-10-09 05:16 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.210 s 1.223546
2021-10-09 05:18 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.167 s 1.990003
2021-10-09 04:55 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.900 s -0.526626
2021-10-09 04:11 Python csv-read gzip, streaming, nyctaxi_2010-01 10.657 s -0.422159
2021-10-09 04:54 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.927 s -0.517439
2021-10-09 04:11 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.187358
2021-10-09 04:46 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.836 s 0.379315
2021-10-09 04:46 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.130 s 0.839834
2021-10-09 04:46 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.282 s 1.185066
2021-10-09 04:47 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.944 s -0.584080
2021-10-09 04:48 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.037 s -0.024623
2021-10-09 04:48 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.125343
2021-10-09 05:17 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.554 s 1.617451
2021-10-09 05:18 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.044 s 2.178589
2021-10-09 04:08 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.805 s 1.346956
2021-10-09 04:13 Python dataframe-to-table type_simple_features 0.909 s 0.423425
2021-10-09 04:30 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.306 s 0.169763
2021-10-09 04:49 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.991 s -0.689908
2021-10-09 04:55 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.378436
2021-10-09 05:16 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.456 s 2.006617
2021-10-09 04:13 Python dataframe-to-table type_floats 0.011 s 1.184710
2021-10-09 04:55 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.334 s 0.228917
2021-10-09 05:16 R dataframe-to-table type_simple_features, R 3.315 s 1.632332
2021-10-09 05:18 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.137 s -0.881697
2021-10-09 04:17 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.758 s 0.621085
2021-10-09 04:50 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.103 s 0.425940
2021-10-09 04:52 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.781 s -0.197404
2021-10-09 04:53 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.883 s -0.741223
2021-10-09 05:09 R dataframe-to-table type_strings, R 0.492 s 0.231613
2021-10-09 04:44 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.999 s 0.134464
2021-10-09 05:10 R dataframe-to-table type_nested, R 0.537 s 0.233626
2021-10-09 05:16 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.449 s 1.950078
2021-10-09 04:35 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.047 s -1.309206
2021-10-09 04:56 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.815 s -0.113732
2021-10-09 05:09 R dataframe-to-table type_floats, R 0.013 s 2.145965
2021-10-09 04:46 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.773 s 1.232370
2021-10-09 05:18 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.393 s -0.367031
2021-10-09 04:45 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.722 s 0.296306
2021-10-09 04:52 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.278 s 0.491156
2021-10-09 04:52 Python file-write lz4, feather, table, fanniemae_2016Q4 1.164 s -0.132626
2021-10-09 04:56 Python wide-dataframe use_legacy_dataset=false 0.622 s 0.070755
2021-10-09 05:16 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.491244
2021-10-09 05:17 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.966 s -2.463461
2021-10-09 05:18 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.238131
2021-10-09 05:18 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.250 s 1.936565
2021-10-09 05:25 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.700 s 0.797132
2021-10-09 05:22 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.233 s 0.868888
2021-10-09 05:21 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.841 s 0.626351
2021-10-09 05:29 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.751501
2021-10-09 05:23 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.279 s 0.718092
2021-10-09 05:35 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.592 s -0.261761
2021-10-09 05:47 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.216049
2021-10-09 05:47 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.881 s -0.075069
2021-10-09 05:47 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.498854
2021-10-09 05:47 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.757479
2021-10-09 05:36 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s -0.128670
2021-10-09 05:47 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.604363
2021-10-09 05:47 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.841 s 1.357233
2021-10-09 05:47 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.746506
2021-10-09 05:25 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.464403
2021-10-09 05:36 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.568 s 0.356619
2021-10-09 05:47 JavaScript Parse Table.from, tracks 0.000 s -0.905060
2021-10-09 05:47 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.230246
2021-10-09 05:47 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.595 s -0.387855
2021-10-09 05:47 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.647 s 0.810114
2021-10-09 05:47 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.591460
2021-10-09 05:47 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.569 s -0.942302
2021-10-09 05:39 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.207 s -0.927216
2021-10-09 05:47 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-09 05:47 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.066713
2021-10-09 05:47 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.206348
2021-10-09 05:36 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.858 s 0.341919
2021-10-09 05:47 JavaScript Parse serialize, tracks 0.004 s 0.668215
2021-10-09 05:47 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.274290
2021-10-09 05:47 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.681 s -0.597301
2021-10-09 05:47 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.147137
2021-10-09 05:47 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.722 s 0.100671
2021-10-09 05:31 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.735693
2021-10-09 05:39 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.183 s 0.059808
2021-10-09 05:40 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s -0.261974
2021-10-09 05:47 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.547272
2021-10-09 05:47 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.486534
2021-10-09 05:47 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.067898
2021-10-09 05:47 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.537132
2021-10-09 05:47 JavaScript Parse readBatches, tracks 0.000 s -0.580268
2021-10-09 05:28 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.200 s 0.410285
2021-10-09 05:33 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s 0.051989
2021-10-09 05:30 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.869 s -1.002929
2021-10-09 05:26 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.540 s 1.137563
2021-10-09 05:37 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.246523
2021-10-09 05:27 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 1.119376
2021-10-09 05:32 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.723 s -0.831149
2021-10-09 05:38 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.590 s 0.039661
2021-10-09 05:35 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.165 s 1.027721
2021-10-09 05:37 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.916 s 0.154873
2021-10-09 05:34 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 0.449911
2021-10-09 05:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.168665
2021-10-09 05:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.513 s 0.877738
2021-10-09 05:36 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.714452
2021-10-09 05:34 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.488 s 0.238704
2021-10-09 05:47 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.381148
2021-10-09 05:47 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.315327
2021-10-09 05:47 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.592931
2021-10-09 04:52 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.679 s 0.210843
2021-10-09 05:19 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.139668