Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 01:55 Python csv-read uncompressed, file, fanniemae_2016Q4 1.162 s 0.744853
2021-10-10 01:59 Python dataframe-to-table type_strings 0.367 s 0.485464
2021-10-10 02:22 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.060 s -0.762116
2021-10-10 02:32 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.828 s 0.327552
2021-10-10 02:33 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.804 s 0.341090
2021-10-10 02:33 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.875 s -0.501438
2021-10-10 02:34 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.909 s 0.461184
2021-10-10 02:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -0.243900
2021-10-10 02:35 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.012 s 1.469865
2021-10-10 02:35 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.259 s -0.161476
2021-10-10 02:36 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.488 s -0.595656
2021-10-10 02:37 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.929 s -0.227292
2021-10-10 02:40 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.807 s -0.330409
2021-10-10 02:43 Python file-write lz4, feather, table, nyctaxi_2010-01 1.862 s -2.886164
2021-10-10 02:58 R dataframe-to-table type_floats, R 0.013 s 1.775056
2021-10-10 02:58 R dataframe-to-table type_nested, R 0.532 s 0.235070
2021-10-10 03:04 R dataframe-to-table type_simple_features, R 3.334 s 1.406746
2021-10-10 03:05 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.445 s 1.641313
2021-10-10 03:06 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.063 s -5.846032
2021-10-10 03:06 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.406 s -1.255072
2021-10-10 03:06 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.549246
2021-10-10 03:07 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.211 s 1.636790
2021-10-10 03:09 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.504 s 0.386490
2021-10-10 03:12 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.286 s 0.677831
2021-10-10 03:13 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.692 s 0.833954
2021-10-10 03:15 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.543 s 0.880275
2021-10-10 03:15 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.384 s 3.147941
2021-10-10 03:17 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.897 s -0.698535
2021-10-10 03:18 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.853 s -0.607536
2021-10-10 03:19 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.538 s -0.757310
2021-10-10 03:20 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.709 s -0.499687
2021-10-10 03:21 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.277 s 2.721586
2021-10-10 03:24 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.173 s 0.308839
2021-10-10 03:24 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.988 s -2.974648
2021-10-10 03:24 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 0.627372
2021-10-10 03:25 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.651 s -6.595471
2021-10-10 03:27 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.358 s -0.116834
2021-10-10 03:27 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -0.610692
2021-10-10 03:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.160 s 0.876809
2021-10-10 03:28 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.504 s -0.400392
2021-10-10 03:35 JavaScript Parse serialize, tracks 0.005 s 0.371744
2021-10-10 03:35 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.881051
2021-10-10 03:35 JavaScript Parse Table.from, tracks 0.000 s 1.354014
2021-10-10 03:35 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.669 s -0.503489
2021-10-10 03:35 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.624 s -0.449912
2021-10-10 03:35 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.125119
2021-10-10 03:35 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.714 s -0.650793
2021-10-10 03:35 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.216814
2021-10-10 03:35 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.110488
2021-10-10 03:35 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.123967
2021-10-10 03:35 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.031997
2021-10-10 03:35 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.723785
2021-10-10 03:35 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.215934
2021-10-10 03:35 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.299075
2021-10-10 03:35 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.452079
2021-10-10 03:35 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.385288
2021-10-10 03:35 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.091024
2021-10-10 03:35 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.327083
2021-10-10 03:35 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.362718
2021-10-10 01:57 Python csv-read gzip, streaming, nyctaxi_2010-01 10.767 s -1.322852
2021-10-10 02:34 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.129 s 0.811780
2021-10-10 02:42 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.924 s -0.408015
2021-10-10 02:57 R dataframe-to-table chi_traffic_2020_Q1, R 3.373 s 0.275283
2021-10-10 02:33 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.205 s 0.715183
2021-10-10 02:40 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.914390
2021-10-10 02:40 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.281 s -0.312690
2021-10-10 02:43 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.790 s 0.979399
2021-10-10 02:58 R dataframe-to-table type_strings, R 0.487 s 0.232924
2021-10-10 02:36 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 1.582882
2021-10-10 02:39 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.607 s 0.785303
2021-10-10 02:41 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.914 s -1.098032
2021-10-10 02:17 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.790 s -0.660898
2021-10-10 02:34 Python file-read lz4, feather, table, fanniemae_2016Q4 0.603 s -0.019566
2021-10-10 03:07 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -8.750734
2021-10-10 03:11 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.253 s 0.694443
2021-10-10 03:26 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.601 s -0.176062
2021-10-10 01:55 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.871 s 0.625561
2021-10-10 02:21 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.078 s -2.965865
2021-10-10 02:21 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.075 s -0.784355
2021-10-10 02:34 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.281 s 1.377999
2021-10-10 02:32 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.686 s -3.529062
2021-10-10 02:36 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.089761
2021-10-10 02:38 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.429 s 0.674672
2021-10-10 02:03 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.012 s 1.436744
2021-10-10 02:37 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.104 s 0.426527
2021-10-10 02:41 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.892 s -0.769026
2021-10-10 03:04 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.214 s 0.459441
2021-10-10 03:04 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.453 s 1.679387
2021-10-10 03:14 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.811 s 4.002628
2021-10-10 03:23 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.467 s 4.219015
2021-10-10 01:58 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.070296
2021-10-10 02:17 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.267 s 0.197294
2021-10-10 02:32 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.957 s 0.802120
2021-10-10 02:33 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.295 s -0.137050
2021-10-10 02:42 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.389 s -2.146747
2021-10-10 02:43 Python wide-dataframe use_legacy_dataset=true 0.393 s 1.062026
2021-10-10 02:58 R dataframe-to-table type_dict, R 0.049 s 0.224255
2021-10-10 03:05 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.215 s 0.870231
2021-10-10 03:05 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -9.703046
2021-10-10 03:07 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.101 s 2.113020
2021-10-10 01:56 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.268760
2021-10-10 02:00 Python dataframe-to-table type_integers 0.011 s 0.889929
2021-10-10 02:38 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.243 s 0.762090
2021-10-10 03:08 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.985 s 0.122847
2021-10-10 03:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.923 s -0.117238
2021-10-10 02:32 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.747 s 0.038174
2021-10-10 02:39 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.434 s -0.767128
2021-10-10 02:43 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.425 s -1.919679
2021-10-10 02:58 R dataframe-to-table type_integers, R 0.010 s 1.772231
2021-10-10 01:55 Python csv-read gzip, streaming, fanniemae_2016Q4 14.797 s 0.659594
2021-10-10 02:00 Python dataframe-to-table type_simple_features 0.907 s 0.510777
2021-10-10 02:35 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.043 s -0.377027
2021-10-10 02:36 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.317 s -0.449691
2021-10-10 02:43 Python wide-dataframe use_legacy_dataset=false 0.621 s 0.355100
2021-10-10 03:25 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.521 s -0.217889
2021-10-10 03:35 JavaScript Parse readBatches, tracks 0.000 s 1.481244
2021-10-10 03:35 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.886127
2021-10-10 03:35 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.099222
2021-10-10 03:35 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-10 01:56 Python csv-read gzip, file, fanniemae_2016Q4 6.022 s 2.010791
2021-10-10 03:09 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.824 s 0.748498
2021-10-10 03:22 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.238 s 0.824415
2021-10-10 03:24 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.645371
2021-10-10 03:35 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.935 s -0.560532
2021-10-10 03:35 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.712794
2021-10-10 03:35 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.530 s -0.234904
2021-10-10 01:56 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.767 s -0.985791
2021-10-10 01:59 Python dataframe-to-table type_dict 0.011 s 1.208779
2021-10-10 02:00 Python dataframe-to-table type_nested 2.876 s 0.229454
2021-10-10 03:25 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 1.281997
2021-10-10 03:27 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.896221
2021-10-10 03:35 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.741 s -0.030750
2021-10-10 03:35 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-10 03:35 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.341551
2021-10-10 03:35 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.723649
2021-10-10 02:34 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.770 s 0.546273
2021-10-10 03:06 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.554 s 1.678074
2021-10-10 03:06 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.156 s 1.666834
2021-10-10 03:26 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.115 s -1.532563
2021-10-10 03:35 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.894 s -0.371060
2021-10-10 02:00 Python dataset-filter nyctaxi_2010-01 4.318 s 1.850085
2021-10-10 02:07 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.248 s -0.288029
2021-10-10 02:42 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.897 s -0.399462
2021-10-10 03:08 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s 0.024022
2021-10-10 01:59 Python dataframe-to-table chi_traffic_2020_Q1 19.503 s 0.232145
2021-10-10 02:00 Python dataframe-to-table type_floats 0.011 s 1.275283
2021-10-10 03:16 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.175 s 1.553244
2021-10-10 03:24 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.589 s -0.486771
2021-10-10 03:25 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.247012