Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 02:53 Python dataframe-to-table type_floats 0.011 s 0.353024
2021-10-13 03:15 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.041 s -0.133278
2021-10-13 03:27 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.818 s 0.373994
2021-10-13 03:28 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.090 s -2.326331
2021-10-13 03:29 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.285 s 0.124922
2021-10-13 03:31 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.169 s 0.167369
2021-10-13 03:31 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.365 s 0.535745
2021-10-13 03:31 Python file-read lz4, feather, table, nyctaxi_2010-01 0.674 s -0.004013
2021-10-13 03:33 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.656 s -1.786120
2021-10-13 03:34 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.992 s -1.591795
2021-10-13 03:38 Python file-write lz4, feather, table, nyctaxi_2010-01 1.795 s 0.550150
2021-10-13 03:39 Python wide-dataframe use_legacy_dataset=false 0.614 s 0.936251
2021-10-13 03:53 R dataframe-to-table type_nested, R 0.529 s 0.234114
2021-10-13 03:53 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.517 s -4.004005
2021-10-13 03:54 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.239 s -0.473932
2021-10-13 03:54 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.479 s 0.793628
2021-10-13 03:54 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s -1.319828
2021-10-13 04:10 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.745 s -1.148833
2021-10-13 04:10 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 1.073341
2021-10-13 04:12 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.473 s 0.981418
2021-10-13 04:13 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.615 s -2.379207
2021-10-13 04:14 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -1.035096
2021-10-13 04:15 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 3.025 s -5.375770
2021-10-13 04:15 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.555 s 0.625936
2021-10-13 04:15 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.115 s -2.145847
2021-10-13 04:16 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.214 s -2.693238
2021-10-13 04:24 JavaScript Parse serialize, tracks 0.004 s 0.619744
2021-10-13 04:24 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.113580
2021-10-13 04:24 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.675 s -0.481825
2021-10-13 04:24 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.619 s 1.342236
2021-10-13 04:24 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.714 s 0.195046
2021-10-13 04:24 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s 0.126695
2021-10-13 04:24 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.848 s 0.892093
2021-10-13 04:24 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.964 s -1.093531
2021-10-13 04:24 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.121619
2021-10-13 04:24 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.397772
2021-10-13 04:24 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.237394
2021-10-13 02:48 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.893 s 0.256081
2021-10-13 02:49 Python csv-read gzip, streaming, fanniemae_2016Q4 14.856 s -0.066764
2021-10-13 02:51 Python csv-read gzip, streaming, nyctaxi_2010-01 10.827 s -1.604209
2021-10-13 02:53 Python dataframe-to-table chi_traffic_2020_Q1 19.302 s 0.750827
2021-10-13 03:11 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.207 s 0.238186
2021-10-13 03:15 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.085 s -0.393707
2021-10-13 03:27 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.685 s 0.552806
2021-10-13 03:53 R dataframe-to-table type_strings, R 0.492 s 0.230345
2021-10-13 02:50 Python csv-read uncompressed, file, nyctaxi_2010-01 0.997 s 1.127878
2021-10-13 02:53 Python dataframe-to-table type_strings 0.365 s 0.565612
2021-10-13 02:53 Python dataframe-to-table type_nested 2.859 s 1.083019
2021-10-13 03:35 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.369 s -0.375818
2021-10-13 03:53 R dataframe-to-table type_dict, R 0.051 s 0.057629
2021-10-13 03:56 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.153 s 0.807150
2021-10-13 03:58 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.128 s -2.309441
2021-10-13 04:04 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.535 s 0.943351
2021-10-13 03:27 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.574 s -2.622014
2021-10-13 04:24 JavaScript Parse Table.from, tracks 0.000 s 0.417475
2021-10-13 04:24 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.428345
2021-10-13 04:24 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.286602
2021-10-13 02:50 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.766 s -0.983224
2021-10-13 02:51 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.922829
2021-10-13 03:30 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.017 s 0.633931
2021-10-13 03:30 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.055 s -0.481580
2021-10-13 03:34 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.718 s -2.429646
2021-10-13 03:53 R dataframe-to-table type_floats, R 0.013 s 0.845904
2021-10-13 02:54 Python dataset-filter nyctaxi_2010-01 4.404 s -2.985292
2021-10-13 03:30 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.368342
2021-10-13 03:32 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.355 s -2.303766
2021-10-13 03:38 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.392990
2021-10-13 03:55 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.057 s -1.220933
2021-10-13 04:02 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.034 s -2.425921
2021-10-13 04:13 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.163509
2021-10-13 04:14 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.193 s -1.453743
2021-10-13 04:17 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 0.944626
2021-10-13 03:29 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.615 s 1.486425
2021-10-13 03:30 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.005 s 1.501801
2021-10-13 03:37 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.872 s -0.320263
2021-10-13 03:55 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.043 s 1.515802
2021-10-13 02:48 Python csv-read uncompressed, file, fanniemae_2016Q4 1.156 s 0.532890
2021-10-13 03:15 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.093 s -1.808054
2021-10-13 03:35 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 0.768794
2021-10-13 03:55 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.399 s -0.424340
2021-10-13 03:57 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.031 s -0.380949
2021-10-13 04:24 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.465527
2021-10-13 02:57 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.642 s 0.492890
2021-10-13 03:29 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.279 s 0.430808
2021-10-13 03:56 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.218 s 0.799835
2021-10-13 03:58 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.554 s -0.137728
2021-10-13 04:16 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -1.044673
2021-10-13 02:49 Python csv-read gzip, file, fanniemae_2016Q4 6.025 s 1.140457
2021-10-13 02:53 Python dataframe-to-table type_integers 0.011 s -0.342315
2021-10-13 04:13 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.582 s -1.052058
2021-10-13 03:30 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.683 s 1.094315
2021-10-13 03:38 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.305 s 1.170478
2021-10-13 03:55 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s -0.103291
2021-10-13 04:08 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.552 s -1.075424
2021-10-13 04:13 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.291126
2021-10-13 04:24 JavaScript Parse readBatches, tracks 0.000 s 0.969756
2021-10-13 04:24 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.643 s -0.371143
2021-10-13 04:24 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.708972
2021-10-13 03:30 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.157 s 1.245515
2021-10-13 03:38 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.902 s -0.318482
2021-10-13 03:39 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.755 s 2.113309
2021-10-13 04:01 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.583 s -2.328224
2021-10-13 04:06 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.914 s -1.007199
2021-10-13 04:13 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.932 s -1.459896
2021-10-13 04:24 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.084251
2021-10-13 04:24 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.444722
2021-10-13 03:10 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.804 s -0.456285
2021-10-13 03:31 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.146 s 1.418482
2021-10-13 03:35 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.856 s 0.012711
2021-10-13 03:57 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.698 s -0.055336
2021-10-13 04:05 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.215 s -0.959281
2021-10-13 04:24 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.443594
2021-10-13 03:01 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.713 s -0.196689
2021-10-13 03:56 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.215 s -1.233037
2021-10-13 04:04 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.391 s 0.608585
2021-10-13 04:15 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.790 s -5.691109
2021-10-13 04:24 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.279490
2021-10-13 02:53 Python dataframe-to-table type_dict 0.011 s 1.339136
2021-10-13 03:28 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.331 s -2.224453
2021-10-13 03:36 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.365 s -0.151238
2021-10-13 04:07 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.876 s -0.958190
2021-10-13 04:24 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.857014
2021-10-13 03:29 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.540 s 1.473970
2021-10-13 03:39 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.824163
2021-10-13 03:53 R dataframe-to-table type_integers, R 0.009 s 0.860938
2021-10-13 03:56 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.109 s 0.751756
2021-10-13 04:16 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.368 s -2.438820
2021-10-13 04:17 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.498 s 0.472825
2021-10-13 04:24 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.106840
2021-10-13 03:28 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.926 s -2.438296
2021-10-13 03:32 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.812 s 1.233605
2021-10-13 03:36 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.918 s -1.187040
2021-10-13 03:53 R dataframe-to-table chi_traffic_2020_Q1, R 3.374 s 0.264032
2021-10-13 04:00 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.578 s -2.442059
2021-10-13 04:03 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.812 s 1.408258
2021-10-13 04:11 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s -0.214911
2021-10-13 04:14 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.608 s -1.770580
2021-10-13 04:24 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.053905
2021-10-13 04:24 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.276320
2021-10-13 04:24 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.636430
2021-10-13 03:29 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.140 s -0.039083
2021-10-13 03:37 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.992 s -1.406625
2021-10-13 03:54 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.782 s 0.719475
2021-10-13 04:24 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.097634
2021-10-13 04:24 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.337028
2021-10-13 04:24 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.654550
2021-10-13 04:24 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.491 s 0.564532