Outliers: 6


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 07:11 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.838 s 0.932305
2021-10-10 07:11 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.257831
2021-10-10 07:12 Python csv-read gzip, streaming, fanniemae_2016Q4 14.776 s 0.831645
2021-10-10 07:13 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.753 s -0.856549
2021-10-10 07:14 Python csv-read gzip, streaming, nyctaxi_2010-01 10.737 s -1.012106
2021-10-10 07:16 Python dataframe-to-table chi_traffic_2020_Q1 19.819 s -0.661437
2021-10-10 07:16 Python dataframe-to-table type_dict 0.011 s 1.196495
2021-10-10 07:16 Python dataframe-to-table type_integers 0.011 s -1.446172
2021-10-10 07:16 Python dataframe-to-table type_floats 0.011 s -0.237268
2021-10-10 07:16 Python dataframe-to-table type_nested 2.866 s 0.622654
2021-10-10 07:16 Python dataframe-to-table type_simple_features 0.927 s -0.524991
2021-10-10 07:17 Python dataset-filter nyctaxi_2010-01 4.312 s 2.034541
2021-10-10 07:20 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.479 s 0.680253
2021-10-10 07:38 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.084 s -2.984618
2021-10-10 07:48 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.818 s 0.399697
2021-10-10 07:48 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.022 s 0.086412
2021-10-10 07:48 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.682 s 0.676332
2021-10-10 07:49 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.960 s 0.690814
2021-10-10 07:49 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.288 s 0.116436
2021-10-10 07:50 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.118 s 1.275957
2021-10-10 07:50 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 1.465291
2021-10-10 07:51 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.681 s 8.746166
2021-10-10 07:51 Python file-read lz4, feather, table, fanniemae_2016Q4 0.587 s 2.610399
2021-10-10 07:51 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.193 s 0.384839
2021-10-10 07:51 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.031 s 0.367184
2021-10-10 08:13 R dataframe-to-table type_dict, R 0.048 s 0.309399
2021-10-10 08:19 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.210 s 0.496405
2021-10-10 08:20 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.204 s 1.316739
2021-10-10 08:20 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.461 s 1.514234
2021-10-10 08:21 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.822770
2021-10-10 08:21 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.394 s -0.429022
2021-10-10 08:22 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.109 s 1.337386
2021-10-10 08:22 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.216 s 1.515499
2021-10-10 08:23 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.001 s -0.067068
2021-10-10 08:24 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.828 s 0.714635
2021-10-10 08:27 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.276 s 0.738768
2021-10-10 08:28 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.700 s 0.762020
2021-10-10 08:29 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.813 s 2.848564
2021-10-10 08:30 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.384 s 2.527348
2021-10-10 08:31 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.177 s 1.363806
2021-10-10 08:34 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.528 s -0.535024
2021-10-10 08:36 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.713 s -0.560060
2021-10-10 08:37 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.229 s 1.490370
2021-10-10 08:39 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.162 s 1.120680
2021-10-10 08:39 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.779281
2021-10-10 08:40 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.187 s -0.882610
2021-10-10 08:40 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.813538
2021-10-10 08:41 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.590 s -0.029637
2021-10-10 08:41 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s -0.321855
2021-10-10 08:42 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s -0.395924
2021-10-10 08:42 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.490 s -1.719585
2021-10-10 08:43 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.486 s 1.289598
2021-10-10 08:50 JavaScript Parse readBatches, tracks 0.000 s 0.763315
2021-10-10 08:32 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.888 s -0.497291
2021-10-10 08:50 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.881459
2021-10-10 08:50 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.899240
2021-10-10 08:50 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.630 s -0.424173
2021-10-10 08:50 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.620 s -0.445172
2021-10-10 08:50 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.658790
2021-10-10 08:50 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.682763
2021-10-10 08:50 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.625 s 1.222397
2021-10-10 08:50 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.698 s 0.258768
2021-10-10 08:50 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.568133
2021-10-10 08:50 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.828 s 1.286289
2021-10-10 08:50 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.829 s 1.564399
2021-10-10 08:50 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.511496
2021-10-10 08:50 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.556661
2021-10-10 08:50 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-10 08:50 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-10 08:50 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.198356
2021-10-10 08:50 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.105726
2021-10-10 08:50 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.305142
2021-10-10 08:50 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.206190
2021-10-10 08:50 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.693159
2021-10-10 08:50 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.021749
2021-10-10 08:50 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.815222
2021-10-10 08:50 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.781503
2021-10-10 07:52 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.107 s 1.088890
2021-10-10 07:52 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.908567
2021-10-10 07:53 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.780 s 0.912662
2021-10-10 07:53 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.080 s 0.614784
2021-10-10 07:55 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.467 s 0.373787
2021-10-10 07:56 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.450 s -0.857141
2021-10-10 07:56 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.003 s -2.575121
2021-10-10 07:57 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.503 s -4.806592
2021-10-10 07:58 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.944 s -1.426022
2021-10-10 07:59 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.927 s -0.807361
2021-10-10 07:59 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.649604
2021-10-10 07:59 Python file-write lz4, feather, table, nyctaxi_2010-01 1.794 s 0.922298
2021-10-10 08:00 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.411519
2021-10-10 08:00 Python wide-dataframe use_legacy_dataset=false 0.617 s 1.339651
2021-10-10 08:13 R dataframe-to-table type_strings, R 0.489 s 0.232330
2021-10-10 07:51 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.992 s 9.049537
2021-10-10 07:52 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.278 s 0.925457
2021-10-10 07:54 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.608 s -1.702218
2021-10-10 07:55 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.908 s -1.237570
2021-10-10 08:20 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 1.553900
2021-10-10 08:21 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.072 s -4.221406
2021-10-10 08:22 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -4.798981
2021-10-10 08:26 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.229 s 0.846244
2021-10-10 08:39 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.860 s 0.512379
2021-10-10 07:16 Python dataframe-to-table type_strings 0.367 s 0.444062
2021-10-10 07:49 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.788 s 0.680082
2021-10-10 08:33 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.870 s -0.932738
2021-10-10 08:38 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s 2.491257
2021-10-10 08:40 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.541120
2021-10-10 08:41 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.623 s -1.446914
2021-10-10 07:14 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.303770
2021-10-10 07:38 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.053 s -0.151138
2021-10-10 07:50 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.517 s 11.112156
2021-10-10 07:56 Python file-write lz4, feather, table, fanniemae_2016Q4 1.142 s 1.412990
2021-10-10 08:20 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.322 s -5.072736
2021-10-10 08:39 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.566 s 1.292113
2021-10-10 08:42 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.265435
2021-10-10 08:50 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.989600
2021-10-10 08:50 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.502 s 0.241818
2021-10-10 07:12 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.768236
2021-10-10 08:13 R dataframe-to-table type_floats, R 0.013 s 1.634887
2021-10-10 08:13 R dataframe-to-table type_nested, R 0.529 s 0.235796
2021-10-10 08:30 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.529 s 1.355757
2021-10-10 08:39 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.591 s -0.596143
2021-10-10 08:43 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.159 s 0.900933
2021-10-10 08:50 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.422012
2021-10-10 07:13 Python csv-read uncompressed, file, nyctaxi_2010-01 1.006 s 0.481381
2021-10-10 07:38 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.028 s -0.142745
2021-10-10 07:49 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.211 s 0.555434
2021-10-10 07:50 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.627 s 7.623261
2021-10-10 08:19 R dataframe-to-table type_simple_features, R 3.343 s 1.313126
2021-10-10 08:21 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.046 s 1.582522
2021-10-10 08:50 JavaScript Parse Table.from, tracks 0.000 s 0.574921
2021-10-10 08:50 JavaScript Parse serialize, tracks 0.003 s 2.212628
2021-10-10 08:50 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.727925
2021-10-10 08:50 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.237187
2021-10-10 07:24 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.613 s 0.483373
2021-10-10 07:52 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.167 s 1.799630
2021-10-10 07:58 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.926 s -0.382100
2021-10-10 07:59 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.340 s 0.298580
2021-10-10 08:00 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.949 s -5.089507
2021-10-10 08:13 R dataframe-to-table chi_traffic_2020_Q1, R 3.377 s 0.274306
2021-10-10 08:23 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.690 s 0.045447
2021-10-10 08:41 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.870 s 1.800485
2021-10-10 07:34 Python dataset-read async=True, nyctaxi_multi_ipc_s3 194.211 s -0.975825
2021-10-10 07:34 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.304 s 0.174171
2021-10-10 07:51 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.029 s 0.695272
2021-10-10 07:57 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.854 s -0.205929
2021-10-10 08:13 R dataframe-to-table type_integers, R 0.010 s 1.632404
2021-10-10 08:22 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.161 s 1.539918
2021-10-10 08:24 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.510 s 0.324573
2021-10-10 08:36 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 2.905603