Outliers: 3


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 02:27 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.655088
2021-10-08 02:27 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.934333
2021-10-08 00:17 Python csv-read uncompressed, file, fanniemae_2016Q4 1.183 s -0.501270
2021-10-08 00:18 Python csv-read gzip, streaming, fanniemae_2016Q4 14.906 s -0.391538
2021-10-08 02:27 JavaScript Parse serialize, tracks 0.005 s -0.826152
2021-10-08 02:27 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.186654
2021-10-08 02:27 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.196184
2021-10-08 02:27 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.661 s -0.485691
2021-10-08 02:27 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.604271
2021-10-08 00:19 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.690839
2021-10-08 00:19 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.487 s 1.186043
2021-10-08 00:21 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.598498
2021-10-08 00:20 Python csv-read uncompressed, file, nyctaxi_2010-01 1.010 s 0.312460
2021-10-08 00:21 Python csv-read gzip, streaming, nyctaxi_2010-01 10.472 s 1.253627
2021-10-08 02:27 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.830 s 1.200141
2021-10-08 02:27 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.624 s 1.213891
2021-10-08 02:28 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.774574
2021-10-08 02:27 JavaScript Parse readBatches, tracks 0.000 s 0.091093
2021-10-08 02:27 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.672 s -0.474314
2021-10-08 02:27 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.742532
2021-10-08 02:27 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-08 02:27 JavaScript Parse Table.from, tracks 0.000 s 0.568399
2021-10-08 02:27 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.623998
2021-10-08 00:23 Python dataframe-to-table chi_traffic_2020_Q1 19.297 s 1.338068
2021-10-08 02:27 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.626801
2021-10-08 02:27 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.133860
2021-10-08 02:28 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.801978
2021-10-08 00:23 Python dataframe-to-table chi_traffic_2020_Q1 19.297 s 1.338068
2021-10-08 00:24 Python dataframe-to-table type_strings 0.370 s 0.070182
2021-10-08 00:24 Python dataframe-to-table type_dict 0.012 s -0.263449
2021-10-08 00:24 Python dataframe-to-table type_integers 0.011 s 1.139500
2021-10-08 02:27 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.715 s 0.180939
2021-10-08 02:27 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 02:28 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.450 s 1.284484
2021-10-08 00:25 Python dataframe-to-table type_floats 0.012 s -1.422821
2021-10-08 00:25 Python dataframe-to-table type_nested 2.869 s 0.770275
2021-10-08 02:27 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-10-08 02:27 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.074808
2021-10-08 02:27 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.435998
2021-10-08 00:26 Python dataframe-to-table type_simple_features 0.910 s 0.386677
2021-10-08 02:27 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.689756
2021-10-08 02:27 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.666733
2021-10-08 00:26 Python dataset-filter nyctaxi_2010-01 4.355 s 0.582314
2021-10-08 00:30 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.270 s -0.718769
2021-10-08 00:30 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 64.270 s -0.718769
2021-10-08 00:35 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.593 s 0.509806
2021-10-08 00:35 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.593 s 0.509806
2021-10-08 00:35 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.593 s 0.509806
2021-10-08 00:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 176.034 s 1.414144
2021-10-08 00:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 176.034 s 1.414144
2021-10-08 00:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 176.034 s 1.414144
2021-10-08 00:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 176.034 s 1.414144
2021-10-08 00:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 10.375 s -48.615281
2021-10-08 00:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 10.375 s -48.615281
2021-10-08 00:48 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 10.375 s -48.615281
2021-10-08 00:54 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.042 s -0.068201
2021-10-08 00:54 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.035 s -0.186389
2021-10-08 00:53 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.038 s -0.099892
2021-10-08 00:53 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.038 s -0.099892
2021-10-08 01:05 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.785 s 0.613710
2021-10-08 01:05 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.785 s 0.613710
2021-10-08 01:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.780 s -0.324457
2021-10-08 01:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.925 s 0.617732
2021-10-08 01:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.925 s 0.617732
2021-10-08 01:07 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.780 s -0.324457
2021-10-08 01:08 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.008 s -0.174284
2021-10-08 01:08 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.008 s -0.174284
2021-10-08 01:09 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.245 s 0.023850
2021-10-08 01:10 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.829 s -0.022474
2021-10-08 01:10 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.295 s -0.123163
2021-10-08 01:10 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.295 s -0.123163
2021-10-08 01:11 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.861 s -0.548444
2021-10-08 01:11 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.861 s -0.548444
2021-10-08 01:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.160 s -0.727681
2021-10-08 01:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.160 s -0.727681
2021-10-08 01:13 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.814 s -0.969287
2021-10-08 01:13 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.814 s -0.969287
2021-10-08 01:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.059775
2021-10-08 01:15 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.923 s -0.482777
2021-10-08 01:14 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.059775
2021-10-08 01:15 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.303719
2021-10-08 01:16 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.235 s -0.566539
2021-10-08 01:16 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.057 s -0.302703
2021-10-08 01:17 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.319 s -0.900954
2021-10-08 01:17 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.319 s -0.900954
2021-10-08 01:18 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.478610
2021-10-08 01:21 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.446 s -0.746950
2021-10-08 01:18 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.478610
2021-10-08 01:19 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.331 s -0.909071
2021-10-08 01:19 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.331 s -0.909071
2021-10-08 01:20 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.395969
2021-10-08 01:20 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.395969
2021-10-08 01:21 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.446 s -0.746950
2021-10-08 01:26 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 0.558524
2021-10-08 01:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.570918
2021-10-08 01:22 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.570918
2021-10-08 01:23 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.943 s -0.738355
2021-10-08 01:31 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.663 s 0.511776
2021-10-08 01:50 R dataframe-to-table type_floats, R 0.013 s 4.357778
2021-10-08 01:59 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.228 s 3.984349
2021-10-08 02:00 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.986 s 0.016959
2021-10-08 01:31 Python file-write lz4, feather, table, fanniemae_2016Q4 1.151 s 0.832462
2021-10-08 01:50 R dataframe-to-table type_dict, R 0.053 s -0.210116
2021-10-08 01:23 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.943 s -0.738355
2021-10-08 01:32 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.265 s -0.528012
2021-10-08 02:01 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 0.538781
2021-10-08 02:13 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.184932
2021-10-08 02:16 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.460826
2021-10-08 01:32 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.778 s 0.856865
2021-10-08 01:35 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.012116
2021-10-08 01:57 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.245 s 0.077450
2021-10-08 01:24 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.093 s 0.504113
2021-10-08 01:33 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.750 s 1.004055
2021-10-08 01:35 Python wide-dataframe use_legacy_dataset=false 0.620 s 0.437784
2021-10-08 02:03 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.255 s 0.729713
2021-10-08 01:33 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.829 s 1.040149
2021-10-08 01:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.237 s 0.312982
2021-10-08 01:24 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.093 s 0.504113
2021-10-08 01:34 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.799 s 0.874347
2021-10-08 01:57 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.318266
2021-10-08 01:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.568 s -0.987985
2021-10-08 01:34 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.311 s 2.183654
2021-10-08 02:07 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -0.847488
2021-10-08 02:10 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.780 s 1.075462
2021-10-08 01:34 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.291 s 0.886563
2021-10-08 01:49 R dataframe-to-table chi_traffic_2020_Q1, R 3.393 s 0.252547
2021-10-08 01:34 Python file-write lz4, feather, table, nyctaxi_2010-01 1.786 s 1.361403
2021-10-08 01:50 R dataframe-to-table type_nested, R 0.538 s 0.200045
2021-10-08 02:01 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.512 s 1.293789
2021-10-08 02:05 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.727 s 0.629989
2021-10-08 01:25 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.298 s 0.090067
2021-10-08 01:35 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.804 s -0.047654
2021-10-08 02:09 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.822 s 0.848959
2021-10-08 02:15 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.920892
2021-10-08 01:50 R dataframe-to-table type_strings, R 0.489 s 0.199964
2021-10-08 01:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.934 s -0.795625
2021-10-08 01:59 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.531847
2021-10-08 02:06 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -0.990315
2021-10-08 01:50 R dataframe-to-table type_integers, R 0.010 s 4.398281
2021-10-08 01:25 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.298 s 0.090067
2021-10-08 01:56 R dataframe-to-table type_simple_features, R 3.394 s 2.617549
2021-10-08 01:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.470 s 3.687314
2021-10-08 01:26 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.443 s 0.558524
2021-10-08 01:57 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.485 s 3.983529
2021-10-08 01:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.410 s -1.460718
2021-10-08 01:58 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.068 s -1.866833
2021-10-08 02:00 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.678 s 0.079942
2021-10-08 02:08 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.199 s 0.661818
2021-10-08 02:14 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.236 s 1.254497
2021-10-08 01:59 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.155 s 3.986748
2021-10-08 01:59 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.121 s 0.422298
2021-10-08 02:07 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.577 s -0.119434
2021-10-08 02:04 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.308 s 0.496376
2021-10-08 02:11 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.458 s 0.854805
2021-10-08 02:16 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.582 s 0.412951
2021-10-08 02:13 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.635 s 1.185966
2021-10-08 02:17 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.764449
2021-10-08 02:16 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.174 s 0.445306
2021-10-08 02:16 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.870 s 0.556580
2021-10-08 02:17 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.532029
2021-10-08 02:17 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -1.165484
2021-10-08 02:16 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.079456
2021-10-08 02:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.613 s -0.400404
2021-10-08 02:18 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.865613
2021-10-08 02:19 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.678861
2021-10-08 02:18 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.908 s 0.529627
2021-10-08 02:18 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.711 s -1.939248
2021-10-08 02:19 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s 0.094029
2021-10-08 02:19 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -1.345711
2021-10-08 02:20 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.194 s 0.476649
2021-10-08 02:20 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.506 s -0.904200
2021-10-08 02:27 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.140368
2021-10-08 02:27 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.549612
2021-10-08 00:17 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.978 s -0.428253
2021-10-08 02:27 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.920 s -0.301362
2021-10-08 02:27 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.619950