Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 07:09 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -1.320633
2021-10-13 07:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.107 s 0.865690
2021-10-13 07:10 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.214 s 0.766465
2021-10-13 07:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.810 s 1.509388
2021-10-13 07:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.386 s 1.132473
2021-10-13 07:39 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.806753
2021-10-13 06:11 Python csv-read gzip, streaming, nyctaxi_2010-01 10.680 s -0.532016
2021-10-13 06:44 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.333 s -2.159226
2021-10-13 06:47 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.328 s 0.840136
2021-10-13 06:50 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.036 s -1.896653
2021-10-13 06:51 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.356 s -0.063140
2021-10-13 06:54 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.332 s 0.392590
2021-10-13 07:07 R dataframe-to-table chi_traffic_2020_Q1, R 3.430 s 0.262667
2021-10-13 07:08 R dataframe-to-table type_floats, R 0.013 s 0.813105
2021-10-13 07:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.235 s -0.281952
2021-10-13 06:13 Python dataframe-to-table type_integers 0.011 s 0.116745
2021-10-13 06:14 Python dataframe-to-table type_nested 2.860 s 0.967695
2021-10-13 06:31 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.313 s 0.168959
2021-10-13 06:31 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.541 s -0.261453
2021-10-13 06:44 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.611 s 1.450112
2021-10-13 06:09 Python csv-read uncompressed, file, fanniemae_2016Q4 1.187 s -0.406173
2021-10-13 06:21 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.152 s -0.531973
2021-10-13 07:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.143 s -2.503048
2021-10-13 06:10 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.239905
2021-10-13 06:10 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.678 s -0.372004
2021-10-13 06:12 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.166720
2021-10-13 06:13 Python dataframe-to-table type_floats 0.011 s 0.574457
2021-10-13 06:35 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.030 s 0.028002
2021-10-13 06:43 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.997 s 0.193460
2021-10-13 06:43 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.101 s -2.414997
2021-10-13 06:45 Python file-read lz4, feather, table, fanniemae_2016Q4 0.616 s -1.170110
2021-10-13 06:52 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.878 s -0.424816
2021-10-13 06:44 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.918 s -2.107027
2021-10-13 06:48 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.841 s 0.860813
2021-10-13 07:08 R dataframe-to-table type_integers, R 0.009 s 0.822532
2021-10-13 07:08 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.787 s 0.685370
2021-10-13 06:49 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.698 s -2.088824
2021-10-13 06:53 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.996 s -1.507685
2021-10-13 07:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.047 s 0.931728
2021-10-13 07:11 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.693 s -0.008610
2021-10-13 07:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.693 s -3.370643
2021-10-13 06:43 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 2.333 s -5.680304
2021-10-13 06:45 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.046 s 1.077032
2021-10-13 06:46 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.168 s 1.109854
2021-10-13 07:08 R dataframe-to-table type_nested, R 0.544 s 0.230029
2021-10-13 06:09 Python csv-read gzip, streaming, fanniemae_2016Q4 14.848 s 0.039668
2021-10-13 06:13 Python dataframe-to-table type_dict 0.011 s 1.173939
2021-10-13 06:17 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.394 s 0.296528
2021-10-13 07:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.523 s 0.162657
2021-10-13 06:35 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.063 s -0.691654
2021-10-13 06:45 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.551 s 1.322540
2021-10-13 06:53 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.901 s -0.359894
2021-10-13 07:07 R dataframe-to-table type_dict, R 0.052 s -0.020637
2021-10-13 06:10 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s -0.900693
2021-10-13 06:43 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.863 s 0.122748
2021-10-13 06:51 Python file-write lz4, feather, table, fanniemae_2016Q4 1.135 s 1.586827
2021-10-13 07:07 R dataframe-to-table type_strings, R 0.498 s 0.228742
2021-10-13 06:13 Python dataframe-to-table type_strings 0.366 s 0.494573
2021-10-13 06:44 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.298 s -0.257111
2021-10-13 06:50 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.367 s -0.369024
2021-10-13 06:53 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.336 s 0.679302
2021-10-13 07:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.153085
2021-10-13 07:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.387167
2021-10-13 06:14 Python dataset-filter nyctaxi_2010-01 4.397 s -2.377802
2021-10-13 07:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.590 s -2.427526
2021-10-13 06:09 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.907 s 0.124173
2021-10-13 06:54 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.768 s 1.570585
2021-10-13 06:54 Python wide-dataframe use_legacy_dataset=false 0.618 s 0.241705
2021-10-13 07:09 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.480 s 0.759036
2021-10-13 07:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.661 s -3.979235
2021-10-13 06:13 Python dataframe-to-table chi_traffic_2020_Q1 19.450 s 0.328377
2021-10-13 06:45 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.707 s 0.917041
2021-10-13 06:46 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.123 s 1.589032
2021-10-13 06:52 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.908 s -1.066803
2021-10-13 06:46 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.036 s 0.053468
2021-10-13 06:47 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.152655
2021-10-13 06:48 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.352 s -2.343312
2021-10-13 07:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.973 s 0.320613
2021-10-13 07:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.143 s -3.292441
2021-10-13 06:49 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.710 s -2.394026
2021-10-13 06:51 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.881 s -0.186978
2021-10-13 07:08 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.514 s -3.361629
2021-10-13 06:45 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.114207
2021-10-13 06:45 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.060 s -0.283634
2021-10-13 06:47 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.168 s 0.187064
2021-10-13 07:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.037 s -0.903586
2021-10-13 07:10 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.393 s -0.051199
2021-10-13 07:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.160 s 0.771211
2021-10-13 06:35 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.087 s -0.411128
2021-10-13 07:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.918 s -1.125927
2021-10-13 06:44 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.136 s 0.245808
2021-10-13 06:54 Python file-write lz4, feather, table, nyctaxi_2010-01 1.794 s 0.559380
2021-10-13 07:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.297 s -4.371015
2021-10-13 06:54 Python wide-dataframe use_legacy_dataset=true 0.389 s 1.524710
2021-10-13 07:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.921 s -1.976522
2021-10-13 07:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.555 s -1.166824
2021-10-13 07:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.559 s 0.543007
2021-10-13 07:39 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.586 s -0.245087
2021-10-13 07:39 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.704 s 0.245103
2021-10-13 07:39 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.887404
2021-10-13 07:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.470 s 1.252005
2021-10-13 07:39 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.621479
2021-10-13 07:39 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.426806
2021-10-13 07:39 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.079188
2021-10-13 07:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.227226
2021-10-13 07:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.782 s -1.985176
2021-10-13 07:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.551 s 2.776120
2021-10-13 07:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.786 s -4.117450
2021-10-13 07:39 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.853297
2021-10-13 07:39 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.653 s 0.666738
2021-10-13 07:39 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.407119
2021-10-13 07:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.192 s -1.182760
2021-10-13 07:39 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.888 s 0.421332
2021-10-13 07:39 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.254370
2021-10-13 07:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.980 s -2.787100
2021-10-13 07:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.214 s -2.494468
2021-10-13 07:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.569 s 0.871440
2021-10-13 07:39 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 1.539347
2021-10-13 07:39 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.346613
2021-10-13 07:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.238042
2021-10-13 07:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.537 s -1.918515
2021-10-13 07:39 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.324590
2021-10-13 07:39 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.155941
2021-10-13 07:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.108 s 4.158389
2021-10-13 07:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.516 s -1.591164
2021-10-13 07:39 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.596024
2021-10-13 07:39 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.216662
2021-10-13 07:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.276 s 0.755532
2021-10-13 07:39 JavaScript Parse Table.from, tracks 0.000 s 0.423407
2021-10-13 07:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.488 s 0.609499
2021-10-13 07:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.427122
2021-10-13 07:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.116 s -2.416013
2021-10-13 07:39 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.447689
2021-10-13 07:27 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.194 s -2.641409
2021-10-13 07:39 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.572 s -0.168986
2021-10-13 07:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.267 s -2.859804
2021-10-13 07:28 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s -0.326472
2021-10-13 07:39 JavaScript Parse readBatches, tracks 0.000 s 0.614722
2021-10-13 07:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.848 s 0.784041
2021-10-13 07:39 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.833266
2021-10-13 07:39 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.880 s 0.028997
2021-10-13 07:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.357 s 1.956022
2021-10-13 07:39 JavaScript Parse serialize, tracks 0.004 s 0.749450
2021-10-13 07:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.492 s -1.250261
2021-10-13 07:39 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.234687
2021-10-13 07:39 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.021 s 1.566915
2021-10-13 07:39 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.648442