Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 15:09 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.038 s -0.813010
2021-09-29 15:11 Python csv-read gzip, file, fanniemae_2016Q4 6.047 s -4.147170
2021-09-29 15:14 Python dataframe-to-table type_floats 0.011 s 0.123331
2021-09-29 15:14 Python dataframe-to-table type_strings 0.369 s 0.244775
2021-09-29 15:12 Python csv-read gzip, streaming, nyctaxi_2010-01 10.625 s -0.356952
2021-09-29 15:22 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.219 s 2.607531
2021-09-29 15:11 Python csv-read uncompressed, file, nyctaxi_2010-01 0.994 s 0.434647
2021-09-29 15:14 Python dataframe-to-table type_nested 2.969 s -0.843106
2021-09-29 15:09 Python csv-read uncompressed, file, fanniemae_2016Q4 1.183 s -0.197839
2021-09-29 15:14 Python dataframe-to-table chi_traffic_2020_Q1 19.802 s -0.043167
2021-09-29 15:14 Python dataframe-to-table type_dict 0.013 s -1.856032
2021-09-29 15:12 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.918482
2021-09-29 15:15 Python dataframe-to-table type_simple_features 0.907 s 0.290565
2021-09-29 15:11 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.628 s -0.323629
2021-09-29 15:14 Python dataframe-to-table type_integers 0.011 s 0.631988
2021-09-29 15:10 Python csv-read gzip, streaming, fanniemae_2016Q4 14.982 s -0.828342
2021-09-29 15:15 Python dataset-filter nyctaxi_2010-01 4.394 s -1.082318
2021-09-29 15:18 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.225 s -0.465552
2021-09-29 15:55 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.515 s -1.518266
2021-09-29 15:59 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.746580
2021-09-29 15:51 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.024 s 0.658216
2021-09-29 15:58 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.801 s 0.979775
2021-09-29 15:59 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.282 s 0.644142
2021-09-29 15:51 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.047 s -0.605937
2021-09-29 16:59 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.186 s 0.597179
2021-09-29 15:56 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.768 s 2.322772
2021-09-29 16:13 R dataframe-to-table type_dict, R 0.052 s -0.015442
2021-09-29 15:50 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.105 s 0.899029
2021-09-29 15:55 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.444 s 1.445854
2021-09-29 16:13 R dataframe-to-table type_strings, R 0.493 s -0.879444
2021-09-29 16:46 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.297 s 1.387276
2021-09-29 17:00 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.510 s 0.811497
2021-09-29 15:32 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.156 s 0.740465
2021-09-29 15:37 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.008 s 0.185917
2021-09-29 15:50 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.165 s -1.538074
2021-09-29 15:48 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.036 s -0.155965
2021-09-29 15:48 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.041 s -1.493485
2021-09-29 15:56 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.513 s 1.511328
2021-09-29 15:48 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.801 s -0.000189
2021-09-29 15:52 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.815 s 0.914031
2021-09-29 15:53 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.068 s 1.445022
2021-09-29 15:58 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.841 s 2.224280
2021-09-29 16:38 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.947 s -0.282981
2021-09-29 15:32 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.266 s -0.214091
2021-09-29 15:49 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.859 s -1.216494
2021-09-29 15:50 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.332273
2021-09-29 16:37 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.238 s 0.152710
2021-09-29 16:59 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 2.109213
2021-09-29 15:49 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.286 s -1.499113
2021-09-29 15:51 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.772 s 1.112001
2021-09-29 15:52 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.514347
2021-09-29 16:51 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.223 s 0.748345
2021-09-29 15:48 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.815 s 0.480591
2021-09-29 15:56 Python file-write lz4, feather, table, fanniemae_2016Q4 1.144 s 1.553561
2021-09-29 15:58 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.324 s 1.623691
2021-09-29 15:59 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.062730
2021-09-29 16:13 R dataframe-to-table chi_traffic_2020_Q1, R 5.350 s 1.046964
2021-09-29 16:37 R dataframe-to-table type_simple_features, R 275.532 s -1.453076
2021-09-29 16:43 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.561 s -2.055882
2021-09-29 17:01 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.350 s 1.895617
2021-09-29 15:59 Python file-write lz4, feather, table, nyctaxi_2010-01 1.826 s -0.776187
2021-09-29 15:59 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.827 s 0.099843
2021-09-29 15:49 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.316 s -1.974013
2021-09-29 15:52 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.987 s 0.805246
2021-09-29 16:43 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s -0.008335
2021-09-29 16:52 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.819 s 2.484474
2021-09-29 16:56 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 0.795717
2021-09-29 17:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -1.789347
2021-09-29 15:53 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.474 s 0.880729
2021-09-29 15:56 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.079 s 1.158896
2021-09-29 16:53 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.804 s 2.255805
2021-09-29 15:36 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.030 s 0.061652
2021-09-29 16:13 R dataframe-to-table type_integers, R 0.084 s 0.901890
2021-09-29 16:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.412354
2021-09-29 15:50 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.796 s 1.127657
2021-09-29 15:52 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.426379
2021-09-29 15:54 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.100 s 1.364758
2021-09-29 16:39 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.570 s -1.464748
2021-09-29 15:49 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.751 s -0.924098
2021-09-29 16:14 R dataframe-to-table type_nested, R 0.535 s 0.501289
2021-09-29 16:39 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.365168
2021-09-29 16:40 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.664377
2021-09-29 16:41 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.143 s -0.955023
2021-09-29 16:42 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.168715
2021-09-29 16:50 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.587 s 0.735173
2021-09-29 17:00 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.600 s 2.007712
2021-09-29 17:02 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.474 s -0.336366
2021-09-29 17:10 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.194526
2021-09-29 15:36 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.055 s -0.266243
2021-09-29 16:14 R dataframe-to-table type_floats, R 0.112 s -1.095886
2021-09-29 16:40 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.399 s -1.145005
2021-09-29 16:42 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.971 s -0.226759
2021-09-29 16:44 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.854 s 1.321189
2021-09-29 16:50 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.395 s 1.283916
2021-09-29 16:59 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.578233
2021-09-29 15:50 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.820 s -5.477454
2021-09-29 16:38 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s 0.132529
2021-09-29 16:59 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.568 s 2.700586
2021-09-29 17:01 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.972 s 1.997206
2021-09-29 17:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.532 s 1.125110
2021-09-29 15:50 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.456986
2021-09-29 15:54 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.437 s 1.322554
2021-09-29 15:57 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.745 s 1.098264
2021-09-29 16:58 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.926639
2021-09-29 17:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.490 s 0.149690
2021-09-29 16:39 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.934 s -0.907148
2021-09-29 16:55 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.666 s 2.234330
2021-09-29 16:59 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.875 s 2.210914
2021-09-29 16:46 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.295 s 1.239230
2021-09-29 16:57 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.257 s 0.733530
2021-09-29 17:02 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.155 s 2.035729
2021-09-29 16:41 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.201 s -1.839893
2021-09-29 16:54 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.469 s 2.089885
2021-09-29 17:02 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.761964
2021-09-29 16:38 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.908 s 0.126621
2021-09-29 16:42 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.251 s -0.575069
2021-09-29 16:48 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.729 s 1.382137
2021-09-29 16:48 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.830 s 0.219872
2021-09-29 17:00 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.626 s -2.847315
2021-09-29 17:10 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.536369
2021-09-29 17:10 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.092198
2021-09-29 17:10 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.871475
2021-09-29 17:10 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.709 s 0.213769
2021-09-29 17:10 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.094245
2021-09-29 17:10 JavaScript Parse readBatches, tracks 0.000 s -0.671220
2021-09-29 17:10 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-29 17:10 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.921832
2021-09-29 17:10 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-29 17:10 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-29 17:10 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.490 s 0.349167
2021-09-29 17:10 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.340622
2021-09-29 17:10 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.512297
2021-09-29 17:10 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.979426
2021-09-29 17:10 JavaScript Parse serialize, tracks 0.005 s 0.430494
2021-09-29 17:10 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.167608
2021-09-29 17:10 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.428074
2021-09-29 17:10 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.593 s -0.037117
2021-09-29 17:10 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.706 s -0.492230
2021-09-29 17:10 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.581832
2021-09-29 17:10 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.558 s -0.004708
2021-09-29 17:10 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-29 17:10 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.799739
2021-09-29 17:10 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.896 s 0.075278
2021-09-29 17:10 JavaScript Parse Table.from, tracks 0.000 s -0.480115
2021-09-29 17:10 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.641369
2021-09-29 17:10 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.449261
2021-09-29 17:10 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.887 s -0.069395
2021-09-29 17:10 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.936269
2021-09-29 17:10 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.565760