Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 15:38 Python csv-read gzip, streaming, fanniemae_2016Q4 14.903 s -0.561611
2021-10-12 15:39 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.694684
2021-10-12 15:39 Python csv-read uncompressed, file, nyctaxi_2010-01 0.987 s 2.086389
2021-10-12 15:40 Python csv-read gzip, streaming, nyctaxi_2010-01 10.983 s -3.278738
2021-10-12 15:40 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.681989
2021-10-12 15:42 Python dataframe-to-table chi_traffic_2020_Q1 19.450 s 0.358399
2021-10-12 15:42 Python dataframe-to-table type_strings 0.369 s 0.252377
2021-10-12 15:42 Python dataframe-to-table type_integers 0.011 s -0.329715
2021-10-12 15:42 Python dataframe-to-table type_floats 0.011 s 0.559475
2021-10-12 15:43 Python dataframe-to-table type_simple_features 0.933 s -0.819771
2021-10-12 16:00 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.481 s 0.060228
2021-10-12 16:04 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.046 s 0.082129
2021-10-12 16:04 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.052 s -0.318334
2021-10-12 16:16 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.715 s 0.319475
2021-10-12 16:50 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.158 s 0.916273
2021-10-12 16:50 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.104 s 1.180911
2021-10-12 16:50 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.212 s 0.909073
2021-10-12 16:50 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -1.657409
2021-10-12 17:04 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s -0.654510
2021-10-12 17:07 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.878 s -0.051261
2021-10-12 17:18 JavaScript Parse Table.from, tracks 0.000 s 0.821899
2021-10-12 17:18 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.849600
2021-10-12 17:19 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.956 s -0.934515
2021-10-12 17:19 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.587000
2021-10-12 17:18 JavaScript Parse readBatches, tracks 0.000 s 1.020456
2021-10-12 17:19 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.514424
2021-10-12 17:19 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.160580
2021-10-12 17:19 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.986438
2021-10-12 17:19 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.610943
2021-10-12 17:19 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.789528
2021-10-12 17:19 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.260922
2021-10-12 17:19 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.238765
2021-10-12 17:19 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.485 s 0.658993
2021-10-12 16:16 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.975 s 0.284093
2021-10-12 16:16 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.861 s 0.133114
2021-10-12 16:18 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.282 s 1.080607
2021-10-12 15:37 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.199445
2021-10-12 15:43 Python dataframe-to-table type_nested 2.887 s -0.471556
2021-10-12 15:43 Python dataset-filter nyctaxi_2010-01 4.371 s -1.468849
2021-10-12 16:04 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.083 s -1.691875
2021-10-12 15:42 Python dataframe-to-table type_dict 0.011 s 1.002130
2021-10-12 16:00 Python dataset-read async=True, nyctaxi_multi_ipc_s3 194.660 s -1.244490
2021-10-12 15:39 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.951 s -2.217293
2021-10-12 15:46 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.679 s 1.065130
2021-10-12 15:37 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.835 s 0.920641
2021-10-12 15:50 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.606 s 0.658723
2021-10-12 16:19 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.018 s 0.941392
2021-10-12 16:21 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 0.511557
2021-10-12 16:22 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.438 s 0.526791
2021-10-12 16:17 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.131 s 0.379392
2021-10-12 16:18 Python file-read lz4, feather, table, fanniemae_2016Q4 0.594 s 1.413707
2021-10-12 16:18 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.019 s 1.032943
2021-10-12 16:16 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.081 s -2.578977
2021-10-12 16:17 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.903 s -2.274549
2021-10-12 16:17 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.319 s -2.252514
2021-10-12 16:17 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.309 s -1.416771
2021-10-12 16:18 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.560 s 1.495364
2021-10-12 16:17 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.595 s 1.890619
2021-10-12 16:18 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.683 s 1.784191
2021-10-12 16:18 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.992 s 1.851353
2021-10-12 16:19 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.169 s 1.349692
2021-10-12 16:48 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.445 s 0.935568
2021-10-12 16:19 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.174 s 1.256337
2021-10-12 16:21 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.784 s 1.761601
2021-10-12 16:23 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.262 s 0.245863
2021-10-12 16:20 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 0.695677
2021-10-12 16:20 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.273 s 1.827001
2021-10-12 16:24 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.492 s -1.437544
2021-10-12 16:20 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -0.933491
2021-10-12 16:22 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.519 s -0.719012
2021-10-12 16:48 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.330 s -5.655871
2021-10-12 16:23 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.856 s -0.537995
2021-10-12 16:49 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.073 s -1.664890
2021-10-12 16:24 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.982 s -1.042490
2021-10-12 17:18 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.611 s -0.310411
2021-10-12 17:19 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.109764
2021-10-12 17:18 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.698212
2021-10-12 17:18 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.849 s -3.472076
2021-10-12 17:19 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.881 s -0.015630
2021-10-12 17:19 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.543209
2021-10-12 17:19 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.320116
2021-10-12 17:19 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.814808
2021-10-12 16:41 R dataframe-to-table type_strings, R 0.491 s 0.230547
2021-10-12 17:04 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.587952
2021-10-12 16:27 Python file-write lz4, feather, table, nyctaxi_2010-01 1.776 s 1.605778
2021-10-12 16:48 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.444 s 0.912352
2021-10-12 16:49 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -0.328547
2021-10-12 17:18 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.595 s -0.222122
2021-10-12 17:18 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.920873
2021-10-12 16:41 R dataframe-to-table chi_traffic_2020_Q1, R 3.386 s 0.265699
2021-10-12 17:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -0.337383
2021-10-12 17:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.867 s 1.625787
2021-10-12 17:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s 0.067268
2021-10-12 17:18 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.754314
2021-10-12 17:19 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.550893
2021-10-12 16:24 Python file-write lz4, feather, table, fanniemae_2016Q4 1.151 s 0.532651
2021-10-12 16:27 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.332 s 0.496097
2021-10-12 16:54 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.264 s 0.442771
2021-10-12 17:07 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.972111
2021-10-12 17:19 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.565873
2021-10-12 16:25 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.815 s 0.515690
2021-10-12 17:09 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s 0.283391
2021-10-12 17:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -1.403963
2021-10-12 17:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s -0.557462
2021-10-12 16:25 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.864 s -0.106987
2021-10-12 16:26 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.359 s -0.322895
2021-10-12 16:26 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.898 s -0.179188
2021-10-12 16:26 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.913 s -0.017307
2021-10-12 16:27 Python wide-dataframe use_legacy_dataset=false 0.619 s 0.215515
2021-10-12 17:02 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.524 s -0.350860
2021-10-12 16:51 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.988 s 0.133359
2021-10-12 17:08 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s 0.062224
2021-10-12 16:51 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.698 s -0.065170
2021-10-12 16:52 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.511 s 0.276859
2021-10-12 16:57 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.816 s 1.094344
2021-10-12 16:59 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.214 s -0.892004
2021-10-12 16:48 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -1.710295
2021-10-12 17:07 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.601 s -1.394888
2021-10-12 17:18 JavaScript Parse serialize, tracks 0.005 s -0.157915
2021-10-12 17:18 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.741 s 0.045150
2021-10-12 16:47 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.216 s 0.368901
2021-10-12 16:41 R dataframe-to-table type_dict, R 0.050 s 0.370916
2021-10-12 17:05 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s -0.000034
2021-10-12 16:27 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.865832
2021-10-12 16:49 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.719380
2021-10-12 17:07 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s -0.455144
2021-10-12 16:27 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.786 s 1.193754
2021-10-12 16:49 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.054 s 0.062398
2021-10-12 17:00 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.901 s -0.602220
2021-10-12 17:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.517 s 1.288915
2021-10-12 17:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -0.756501
2021-10-12 17:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.595941
2021-10-12 17:18 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.842701
2021-10-12 17:19 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.535349
2021-10-12 17:19 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.541796
2021-10-12 16:41 R dataframe-to-table type_integers, R 0.010 s 0.968312
2021-10-12 16:47 R dataframe-to-table type_simple_features, R 3.351 s 0.798258
2021-10-12 17:06 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.467 s 1.733521
2021-10-12 16:52 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.832 s 0.572639
2021-10-12 16:56 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.711 s 0.532780
2021-10-12 16:58 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.599 s -1.773240
2021-10-12 17:11 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.204 s -1.820654
2021-10-12 16:41 R dataframe-to-table type_floats, R 0.014 s 0.951164
2021-10-12 16:58 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.386 s 1.344221
2021-10-12 17:07 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.182 s -1.200786
2021-10-12 17:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.550237
2021-10-12 16:41 R dataframe-to-table type_nested, R 0.538 s 0.231715
2021-10-12 16:55 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 0.508020
2021-10-12 17:01 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.671260