Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 00:54 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.838 s 0.869243
2021-10-11 00:54 Python csv-read uncompressed, file, fanniemae_2016Q4 1.163 s 0.589934
2021-10-11 00:55 Python csv-read gzip, streaming, fanniemae_2016Q4 14.776 s 0.792356
2021-10-11 00:55 Python csv-read gzip, file, fanniemae_2016Q4 6.024 s 1.537428
2021-10-11 00:56 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.619 s -0.016805
2021-10-11 00:56 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s -0.753791
2021-10-11 00:56 Python csv-read gzip, streaming, nyctaxi_2010-01 10.606 s -0.098208
2021-10-11 00:59 Python dataframe-to-table chi_traffic_2020_Q1 19.638 s -0.166445
2021-10-11 00:59 Python dataframe-to-table type_strings 0.370 s 0.207866
2021-10-11 00:59 Python dataframe-to-table type_dict 0.012 s 0.415460
2021-10-11 00:59 Python dataframe-to-table type_integers 0.011 s -1.814573
2021-10-11 00:59 Python dataframe-to-table type_floats 0.011 s -0.298601
2021-10-11 00:59 Python dataframe-to-table type_simple_features 0.931 s -0.771810
2021-10-11 01:21 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.033 s -0.077765
2021-10-11 01:31 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.809 s 0.439472
2021-10-11 01:32 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.982 s 0.264861
2021-10-11 01:32 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.729 s 0.176982
2021-10-11 01:32 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.951 s 0.767328
2021-10-11 01:33 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.787 s 0.552314
2021-10-11 02:37 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.031997
2021-10-11 01:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.530 s 2.872528
2021-10-11 01:34 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.281 s 1.134958
2021-10-11 01:34 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.684 s 2.934890
2021-10-11 01:34 Python file-read lz4, feather, table, fanniemae_2016Q4 0.595 s 1.225524
2021-10-11 01:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.003 s 2.865089
2021-10-11 01:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.043 s 0.068659
2021-10-11 01:35 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.176 s 1.066287
2021-10-11 01:35 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.024 s 0.707299
2021-10-11 01:35 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.634751
2021-10-11 01:36 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.301 s 1.323281
2021-10-11 01:38 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.442 s 0.583535
2021-10-11 01:39 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.031 s -1.870605
2021-10-11 01:39 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.428 s -0.659716
2021-10-11 01:40 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.943 s -1.289601
2021-10-11 01:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.120 s 0.974788
2021-10-11 01:41 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.968 s -1.468880
2021-10-11 01:41 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.917 s -0.054317
2021-10-11 01:43 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.810 s 0.409578
2021-10-11 01:43 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.559909
2021-10-11 01:43 Python wide-dataframe use_legacy_dataset=false 0.612 s 2.010918
2021-10-11 01:57 R dataframe-to-table chi_traffic_2020_Q1, R 3.391 s 0.272256
2021-10-11 01:59 R dataframe-to-table type_dict, R 0.053 s -0.256519
2021-10-11 01:59 R dataframe-to-table type_integers, R 0.011 s 1.243495
2021-10-11 02:05 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.221 s 0.345020
2021-10-11 02:06 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.211 s 0.849068
2021-10-11 02:06 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.444 s 1.198051
2021-10-11 02:06 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -2.450444
2021-10-11 02:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.378871
2021-10-11 02:07 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.467 s -5.089822
2021-10-11 02:07 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.402624
2021-10-11 02:08 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.160 s 1.208405
2021-10-11 02:09 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.702 s -0.129239
2021-10-11 02:10 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.486 s 0.538701
2021-10-11 02:10 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.856 s 0.504667
2021-10-11 02:12 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.253 s 0.653128
2021-10-11 02:13 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.279 s 0.718846
2021-10-11 02:14 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.735 s 0.490917
2021-10-11 02:16 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.568 s -0.119663
2021-10-11 02:17 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.205 s -0.216686
2021-10-11 02:18 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.504056
2021-10-11 02:19 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.865 s -0.657084
2021-10-11 02:20 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.479820
2021-10-11 02:22 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.721 s -0.581365
2021-10-11 02:22 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 2.155315
2021-10-11 02:23 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.232 s 1.126792
2021-10-11 02:25 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.340137
2021-10-11 02:25 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.854 s 0.629403
2021-10-11 02:25 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s -0.273266
2021-10-11 02:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.250579
2021-10-11 02:26 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -1.302724
2021-10-11 02:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.621 s -1.118277
2021-10-11 02:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.270711
2021-10-11 02:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.842703
2021-10-11 02:28 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.490 s -1.230407
2021-10-11 02:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.087593
2021-10-11 02:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.502 s -0.162947
2021-10-11 02:36 JavaScript Parse Table.from, tracks 0.000 s 0.893750
2021-10-11 02:36 JavaScript Parse readBatches, tracks 0.000 s 1.379189
2021-10-11 02:36 JavaScript Parse serialize, tracks 0.005 s -0.553235
2021-10-11 02:36 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.838344
2021-10-11 02:36 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.815239
2021-10-11 02:36 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.661 s -0.472728
2021-10-11 02:36 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.300569
2021-10-11 02:36 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.255994
2021-10-11 02:36 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.761 s -1.472697
2021-10-11 02:36 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.845 s -0.534338
2021-10-11 02:37 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.925 s -1.223671
2021-10-11 02:37 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.569442
2021-10-11 02:37 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.613656
2021-10-11 02:37 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.451641
2021-10-11 02:37 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.196232
2021-10-11 02:37 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.815191
2021-10-11 02:37 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.253702
2021-10-11 02:37 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.918691
2021-10-11 02:37 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.362678
2021-10-11 02:37 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.585 s -0.946158
2021-10-11 01:33 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.298 s -0.483331
2021-10-11 01:58 R dataframe-to-table type_strings, R 0.492 s 0.232953
2021-10-11 01:59 R dataframe-to-table type_floats, R 0.013 s 1.263974
2021-10-11 02:16 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.383 s 2.074580
2021-10-11 02:25 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.591 s -0.556474
2021-10-11 02:25 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.838926
2021-10-11 02:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.166 s 0.661565
2021-10-11 01:16 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.372 s -0.012534
2021-10-11 01:36 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.789 s 1.447751
2021-10-11 01:37 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 0.632167
2021-10-11 02:06 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 1.220417
2021-10-11 02:37 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.327766
2021-10-11 02:37 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.014103
2021-10-11 02:37 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.361849
2021-10-11 01:00 Python dataset-filter nyctaxi_2010-01 4.314 s 1.803992
2021-10-11 01:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.636 s 2.556697
2021-10-11 01:59 R dataframe-to-table type_nested, R 0.532 s 0.236031
2021-10-11 02:15 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.810 s 2.400398
2021-10-11 02:24 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.478 s 1.044305
2021-10-11 00:57 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.540924
2021-10-11 00:59 Python dataframe-to-table type_nested 2.868 s 0.573139
2021-10-11 01:21 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.075 s -0.378539
2021-10-11 01:36 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.134437
2021-10-11 01:40 Python file-write lz4, feather, table, fanniemae_2016Q4 1.151 s 0.661470
2021-10-11 01:43 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.913856
2021-10-11 02:09 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.000 s -0.026566
2021-10-11 02:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.414362
2021-10-11 02:37 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.831128
2021-10-11 02:37 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.880907
2021-10-11 02:37 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.015440
2021-10-11 01:03 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.063 s 1.023701
2021-10-11 01:16 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.244 s 0.213367
2021-10-11 01:37 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.554 s -1.091129
2021-10-11 01:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.072 s -2.005253
2021-10-11 02:05 R dataframe-to-table type_simple_features, R 3.379 s 1.048144
2021-10-11 02:07 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.039 s -1.967576
2021-10-11 02:08 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.216 s 1.194461
2021-10-11 02:08 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -2.429643
2021-10-11 02:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.646 s -0.857680
2021-10-11 02:37 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.027593
2021-10-11 02:37 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.884 s 0.460934
2021-10-11 02:37 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.188113
2021-10-11 01:07 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.902 s -1.191829
2021-10-11 01:21 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.057 s -1.034008
2021-10-11 01:32 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.212 s 0.379771
2021-10-11 01:35 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.132 s 1.602531
2021-10-11 01:40 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.565 s -2.968224
2021-10-11 01:42 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.943 s -0.872760
2021-10-11 01:42 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.185558
2021-10-11 01:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.343 s 0.319642
2021-10-11 02:08 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.100 s 1.649800
2021-10-11 02:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.877 s 1.359955
2021-10-11 02:36 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.650 s -0.481415