Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 05:05 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.846 s 0.741092
2021-10-08 05:06 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.823 s 0.498057
2021-10-08 05:06 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s -0.128366
2021-10-08 05:06 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.346 s -0.268268
2021-10-08 05:06 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.255342
2021-10-08 05:07 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.821 s -0.528103
2021-10-08 05:07 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.923786
2021-10-08 05:07 Python wide-dataframe use_legacy_dataset=false 0.619 s 0.833462
2021-10-08 05:27 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.481 s 3.419380
2021-10-08 05:41 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.780 s 1.042529
2021-10-08 05:43 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.646 s 0.931605
2021-10-08 05:58 JavaScript Parse Table.from, tracks 0.000 s -0.166307
2021-10-08 05:58 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.649 s -0.439138
2021-10-08 05:58 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.661 s -0.505138
2021-10-08 05:58 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.741 s 0.035665
2021-10-08 05:58 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.750458
2021-10-08 05:58 JavaScript Parse serialize, tracks 0.005 s -0.685922
2021-10-08 05:58 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.539850
2021-10-08 05:58 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.985621
2021-10-08 05:58 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.960690
2021-10-08 05:58 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.962161
2021-10-08 05:58 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.504 s 0.210212
2021-10-08 05:20 R dataframe-to-table type_strings, R 0.488 s 0.200238
2021-10-08 05:27 R dataframe-to-table type_simple_features, R 3.383 s 2.409779
2021-10-08 04:45 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.040 s -0.140000
2021-10-08 04:55 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.932 s -0.245424
2021-10-08 04:56 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.990 s 0.279856
2021-10-08 05:03 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.289 s 0.324700
2021-10-08 04:18 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.984 s -0.500290
2021-10-08 04:18 Python csv-read uncompressed, file, fanniemae_2016Q4 1.161 s 0.798657
2021-10-08 04:20 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.495 s 1.089828
2021-10-08 04:23 Python dataframe-to-table type_strings 0.375 s -0.871883
2021-10-08 04:23 Python dataframe-to-table type_integers 0.011 s 0.679382
2021-10-08 04:23 Python dataframe-to-table type_floats 0.011 s 1.102479
2021-10-08 04:58 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.232 s -0.494555
2021-10-08 04:58 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.310 s -0.819697
2021-10-08 04:59 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.369 s -1.038269
2021-10-08 04:20 Python csv-read uncompressed, file, nyctaxi_2010-01 1.022 s -0.867449
2021-10-08 04:27 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.452 s 0.013569
2021-10-08 04:45 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.010 s 0.379686
2021-10-08 04:45 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.006 s 0.227814
2021-10-08 05:00 Python file-read lz4, feather, table, nyctaxi_2010-01 0.707 s -7.560624
2021-10-08 04:21 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.021583
2021-10-08 04:24 Python dataframe-to-table type_nested 2.890 s 0.201648
2021-10-08 04:24 Python dataframe-to-table type_simple_features 0.911 s 0.234868
2021-10-08 04:41 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.862 s -0.262713
2021-10-08 04:55 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.744 s 0.044744
2021-10-08 04:56 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.293 s -0.039278
2021-10-08 04:57 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.881 s -0.851476
2021-10-08 04:58 Python file-read lz4, feather, table, fanniemae_2016Q4 0.595 s 1.419514
2021-10-08 04:58 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.036 s 0.513271
2021-10-08 05:02 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 0.545812
2021-10-08 04:23 Python dataframe-to-table chi_traffic_2020_Q1 19.292 s 1.312977
2021-10-08 04:31 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.133 s 0.480405
2021-10-08 04:56 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.244 s 0.032200
2021-10-08 04:56 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.830 s -0.042824
2021-10-08 04:57 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.806 s -0.781313
2021-10-08 04:58 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.041 s -0.287780
2021-10-08 05:03 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.816 s -0.754099
2021-10-08 05:03 Python file-write lz4, feather, table, fanniemae_2016Q4 1.165 s -0.258634
2021-10-08 04:23 Python dataframe-to-table type_dict 0.011 s 1.104340
2021-10-08 04:24 Python dataset-filter nyctaxi_2010-01 4.356 s 0.553465
2021-10-08 04:41 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.281 s 0.177323
2021-10-08 04:55 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.013 s -0.062772
2021-10-08 04:57 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.148 s -0.072241
2021-10-08 05:04 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.780 s 0.802885
2021-10-08 04:21 Python csv-read gzip, streaming, nyctaxi_2010-01 10.489 s 1.061999
2021-10-08 04:57 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.065387
2021-10-08 05:00 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.075 s 0.607939
2021-10-08 05:04 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.282 s -0.694565
2021-10-08 05:05 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.782 s 0.526809
2021-10-08 04:57 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.926 s -0.499654
2021-10-08 04:59 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.187 s -2.146071
2021-10-08 05:00 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.992 s -0.919458
2021-10-08 04:20 Python csv-read gzip, file, fanniemae_2016Q4 6.036 s -1.150776
2021-10-08 04:59 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.469 s -0.816559
2021-10-08 04:19 Python csv-read gzip, streaming, fanniemae_2016Q4 14.915 s -0.477418
2021-10-08 05:01 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.339 s -0.158109
2021-10-08 05:02 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.662 s 0.088393
2021-10-08 05:21 R dataframe-to-table type_nested, R 0.540 s 0.199414
2021-10-08 05:58 JavaScript Parse readBatches, tracks 0.000 s -0.271239
2021-10-08 05:58 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.878 s -0.002881
2021-10-08 05:58 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-08 05:58 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-10-08 05:58 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.072294
2021-10-08 05:58 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.036064
2021-10-08 05:58 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.600442
2021-10-08 05:27 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.249 s 0.032316
2021-10-08 05:29 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.059 s -0.461810
2021-10-08 05:45 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.235 s 1.339036
2021-10-08 05:50 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -2.157097
2021-10-08 05:20 R dataframe-to-table chi_traffic_2020_Q1, R 3.409 s 0.251652
2021-10-08 05:20 R dataframe-to-table type_dict, R 0.051 s 0.058803
2021-10-08 05:39 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.186 s 1.312866
2021-10-08 05:58 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.841219
2021-10-08 05:58 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.326103
2021-10-08 05:29 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.139 s -1.064746
2021-10-08 05:48 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.526009
2021-10-08 05:49 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.915 s 0.495920
2021-10-08 05:50 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.192 s 0.449609
2021-10-08 05:47 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 0.499271
2021-10-08 05:47 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.516587
2021-10-08 05:58 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.219337
2021-10-08 05:34 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.260 s 0.665063
2021-10-08 05:40 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.828 s 0.694351
2021-10-08 05:44 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.288 s -2.710747
2021-10-08 05:49 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.366 s -0.166723
2021-10-08 05:28 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.931 s -0.580613
2021-10-08 05:29 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.402 s -1.057456
2021-10-08 05:34 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.310 s 0.446703
2021-10-08 05:20 R dataframe-to-table type_floats, R 0.013 s 3.654354
2021-10-08 05:29 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.244 s 3.408965
2021-10-08 05:30 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.789 s -48.241558
2021-10-08 05:30 R file-read lz4, feather, table, nyctaxi_2010-01, R 1.621 s -13.244496
2021-10-08 05:31 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 14.500 s -56.518923
2021-10-08 05:47 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s 0.590480
2021-10-08 05:47 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 0.536448
2021-10-08 05:48 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.517 s 0.014642
2021-10-08 05:28 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.549 s 2.812277
2021-10-08 05:48 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.623575
2021-10-08 05:27 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.480 s 3.213089
2021-10-08 05:27 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.746676
2021-10-08 05:29 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.411523
2021-10-08 05:36 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.715 s 0.688702
2021-10-08 05:50 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.486 s -1.634684
2021-10-08 05:51 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.505 s -0.803622
2021-10-08 05:42 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.456 s 0.854748
2021-10-08 05:58 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.655 s 0.625068
2021-10-08 05:58 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 05:38 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.560 s 0.510113
2021-10-08 05:46 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.479 s 2.122313
2021-10-08 05:49 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.721196
2021-10-08 05:36 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.838 s -1.307158
2021-10-08 05:47 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.911133
2021-10-08 05:20 R dataframe-to-table type_integers, R 0.010 s 3.679036
2021-10-08 05:27 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.240 s 0.294478
2021-10-08 05:58 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.229701
2021-10-08 05:58 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.901 s 0.105372
2021-10-08 05:58 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.626801
2021-10-08 05:58 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.655967
2021-10-08 05:58 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.076700
2021-10-08 05:58 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.019 s -3.137148
2021-10-08 05:32 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.872 s 0.351907
2021-10-08 05:38 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.361951
2021-10-08 05:47 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.519375
2021-10-08 05:58 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.864013
2021-10-08 05:58 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.649261
2021-10-08 05:29 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.167 s 3.411903
2021-10-08 05:49 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.530 s 0.944629