Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 19:30 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.979 s 0.360944
2021-10-08 19:49 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.756 s -0.065232
2021-10-08 19:52 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.363 s -0.958063
2021-10-08 19:52 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.446826
2021-10-08 19:53 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.359 s -0.884641
2021-10-08 19:53 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.467 s -0.696493
2021-10-08 19:57 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.262 s 0.504785
2021-10-08 19:57 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.806 s -0.569758
2021-10-08 19:58 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.844 s -0.310699
2021-10-08 19:59 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.923 s -0.492011
2021-10-08 20:00 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.329 s 0.155878
2021-10-08 20:00 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.201141
2021-10-08 20:00 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.802 s 0.124078
2021-10-08 20:01 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.247902
2021-10-08 20:33 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.189 s 1.072078
2021-10-08 20:39 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.245 s 0.454930
2021-10-08 20:40 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.490 s -0.128616
2021-10-08 20:41 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.182 s -0.345238
2021-10-08 20:43 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.671 s -1.243230
2021-10-08 20:44 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.169 s 0.414470
2021-10-08 20:45 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s -0.870727
2021-10-08 20:52 JavaScript Parse readBatches, tracks 0.000 s -0.567208
2021-10-08 20:52 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.470403
2021-10-08 20:52 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.543560
2021-10-08 20:52 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.103162
2021-10-08 20:52 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.441184
2021-10-08 20:52 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.077823
2021-10-08 20:52 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.109069
2021-10-08 20:52 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.259282
2021-10-08 20:52 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.260198
2021-10-08 19:18 Python csv-read gzip, streaming, fanniemae_2016Q4 14.926 s -0.478685
2021-10-08 19:51 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.961 s -1.033927
2021-10-08 19:52 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.047 s 0.068424
2021-10-08 19:19 Python csv-read gzip, streaming, nyctaxi_2010-01 10.504 s 0.870144
2021-10-08 19:50 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.939 s 1.515217
2021-10-08 19:51 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.870 s -0.544319
2021-10-08 19:19 Python csv-read uncompressed, file, nyctaxi_2010-01 0.998 s 1.503999
2021-10-08 20:00 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.204771
2021-10-08 19:26 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.755 s 0.575471
2021-10-08 19:51 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.128 s 0.983346
2021-10-08 19:51 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.447542
2021-10-08 19:52 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.241 s -0.515516
2021-10-08 19:20 Python csv-read gzip, file, nyctaxi_2010-01 9.038 s 2.353043
2021-10-08 19:50 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.272 s 0.911164
2021-10-08 19:51 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.312 s -3.474988
2021-10-08 20:01 Python wide-dataframe use_legacy_dataset=false 0.630 s -2.059913
2021-10-08 19:18 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.789795
2021-10-08 19:38 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.120 s 0.284939
2021-10-08 19:54 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.996 s -0.832546
2021-10-08 19:56 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.629 s 0.368909
2021-10-08 19:59 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.888 s -0.457113
2021-10-08 19:19 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.510 s 0.900745
2021-10-08 19:38 Python dataset-read async=True, nyctaxi_multi_ipc_s3 158.186 s 3.336173
2021-10-08 19:49 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.486 s -3.668665
2021-10-08 19:49 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 3.311 s -9.889289
2021-10-08 19:53 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.269965
2021-10-08 19:51 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.782 s -0.218931
2021-10-08 19:53 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.337630
2021-10-08 19:56 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 0.551517
2021-10-08 19:22 Python dataframe-to-table type_nested 2.881 s 0.321190
2021-10-08 19:55 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.299 s 0.161588
2021-10-08 19:22 Python dataframe-to-table type_floats 0.011 s 1.293792
2021-10-08 19:41 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.005 s 0.451954
2021-10-08 19:17 Python csv-read uncompressed, file, fanniemae_2016Q4 1.170 s 0.314469
2021-10-08 19:54 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.098 s 0.439209
2021-10-08 19:22 Python dataframe-to-table type_strings 0.378 s -1.275467
2021-10-08 19:57 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.278 s -0.549939
2021-10-08 19:22 Python dataframe-to-table chi_traffic_2020_Q1 19.267 s 1.360933
2021-10-08 19:41 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.004 s 0.275187
2021-10-08 19:57 Python file-write lz4, feather, table, fanniemae_2016Q4 1.198 s -2.798857
2021-10-08 19:22 Python dataframe-to-table type_dict 0.012 s 0.870955
2021-10-08 19:50 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.186 s 1.439910
2021-10-08 19:17 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.985 s -0.420366
2021-10-08 19:22 Python dataset-filter nyctaxi_2010-01 4.335 s 1.467352
2021-10-08 19:22 Python dataframe-to-table type_integers 0.011 s 0.384060
2021-10-08 19:22 Python dataframe-to-table type_simple_features 0.911 s 0.385028
2021-10-08 19:41 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.994 s 0.611587
2021-10-08 19:50 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.765 s 1.580977
2021-10-08 19:58 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.854 s -0.349990
2021-10-08 20:32 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.614 s -1.547939
2021-10-08 20:35 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.854 s -0.638320
2021-10-08 20:30 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.691 s 0.862649
2021-10-08 20:43 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.090619
2021-10-08 20:52 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.652 s 0.702164
2021-10-08 20:52 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.467365
2021-10-08 20:23 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.142 s -1.322955
2021-10-08 20:21 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.211 s 0.546386
2021-10-08 20:52 JavaScript Parse Table.from, tracks 0.000 s -0.130925
2021-10-08 20:52 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-08 20:52 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.574183
2021-10-08 20:24 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.238 s 2.382264
2021-10-08 20:36 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.533 s -0.731330
2021-10-08 20:52 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.711 s 0.197860
2021-10-08 20:52 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.433779
2021-10-08 20:21 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 2.489188
2021-10-08 20:21 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.212 s 0.484340
2021-10-08 20:34 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.898 s -0.777484
2021-10-08 20:37 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.714 s -0.611656
2021-10-08 20:41 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 0.992586
2021-10-08 20:52 JavaScript Parse serialize, tracks 0.005 s -0.301788
2021-10-08 20:52 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.748541
2021-10-08 20:22 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.668154
2021-10-08 20:26 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.839 s 0.613859
2021-10-08 20:41 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.856 s 0.459631
2021-10-08 20:41 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.127162
2021-10-08 20:43 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.914 s 0.372713
2021-10-08 20:52 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.909366
2021-10-08 20:52 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.624 s -0.487365
2021-10-08 20:52 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.719062
2021-10-08 20:30 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.824 s 1.487053
2021-10-08 20:32 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.280419
2021-10-08 20:52 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.906630
2021-10-08 20:52 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.585 s -0.354644
2021-10-08 20:52 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.634065
2021-10-08 20:22 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.147087
2021-10-08 20:23 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.394 s -0.453483
2021-10-08 20:38 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.036299
2021-10-08 20:41 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s 0.213780
2021-10-08 20:41 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s 0.335301
2021-10-08 20:52 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.839 s 0.936519
2021-10-08 20:52 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.544705
2021-10-08 20:52 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.382472
2021-10-08 20:22 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.946 s -1.422809
2021-10-08 20:42 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -1.799102
2021-10-08 20:42 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.461191
2021-10-08 20:52 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.565346
2021-10-08 20:52 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.847 s 1.255844
2021-10-08 20:52 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.086574
2021-10-08 20:52 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.525 s -0.175673
2021-10-08 20:14 R dataframe-to-table type_strings, R 0.489 s 0.226636
2021-10-08 20:28 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 0.574319
2021-10-08 20:15 R dataframe-to-table type_floats, R 0.013 s 2.709778
2021-10-08 20:23 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s 0.115862
2021-10-08 20:15 R dataframe-to-table type_integers, R 0.010 s 2.709704
2021-10-08 20:15 R dataframe-to-table type_nested, R 0.537 s 0.227634
2021-10-08 20:25 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.527 s 0.155768
2021-10-08 20:43 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.332724
2021-10-08 20:14 R dataframe-to-table chi_traffic_2020_Q1, R 3.414 s 0.272280
2021-10-08 20:24 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.540394
2021-10-08 20:28 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.240 s 0.819320
2021-10-08 20:44 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -1.211303
2021-10-08 20:21 R dataframe-to-table type_simple_features, R 3.304 s 1.942325
2021-10-08 20:23 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.172 s 2.465700
2021-10-08 20:44 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -1.441324
2021-10-08 20:14 R dataframe-to-table type_dict, R 0.051 s 0.008193
2021-10-08 20:22 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.445 s 2.396691
2021-10-08 20:24 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.974 s 0.252802
2021-10-08 20:24 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.684 s 0.076111
2021-10-08 20:42 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.398955