Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 19:33 Python csv-read uncompressed, file, fanniemae_2016Q4 1.170 s 0.161441
2021-10-10 19:34 Python csv-read gzip, streaming, fanniemae_2016Q4 14.778 s 0.760037
2021-10-10 19:34 Python csv-read gzip, file, fanniemae_2016Q4 6.024 s 1.635341
2021-10-10 19:35 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.622 s -0.007335
2021-10-10 19:35 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s -0.177942
2021-10-10 19:36 Python csv-read gzip, streaming, nyctaxi_2010-01 10.611 s -0.096359
2021-10-10 19:36 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.675531
2021-10-10 19:38 Python dataframe-to-table chi_traffic_2020_Q1 19.273 s 0.841129
2021-10-10 19:38 Python dataframe-to-table type_strings 0.368 s 0.407036
2021-10-10 19:38 Python dataframe-to-table type_dict 0.012 s 0.609939
2021-10-10 19:38 Python dataframe-to-table type_integers 0.011 s -1.605876
2021-10-10 19:38 Python dataframe-to-table type_floats 0.011 s -0.295724
2021-10-10 19:38 Python dataframe-to-table type_nested 2.889 s -0.594493
2021-10-10 19:38 Python dataframe-to-table type_simple_features 0.929 s -0.663947
2021-10-10 19:46 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.660 s -0.111964
2021-10-10 19:56 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.628 s -0.027589
2021-10-10 20:00 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.075 s -0.397437
2021-10-10 20:10 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.831 s 0.303857
2021-10-10 20:10 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.004 s 0.157477
2021-10-10 20:10 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.955 s 0.703393
2021-10-10 20:12 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.120 s 1.011413
2021-10-10 20:12 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.546 s 3.166804
2021-10-10 20:12 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.277 s 1.847929
2021-10-10 20:12 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 1.159483
2021-10-10 20:13 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.006 s 1.634125
2021-10-10 20:14 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.270 s 1.463153
2021-10-10 20:14 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.507138
2021-10-10 20:15 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.779 s 1.404228
2021-10-10 20:16 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.531 s -0.989494
2021-10-10 20:18 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.107 s -2.747665
2021-10-10 20:18 Python file-write lz4, feather, table, fanniemae_2016Q4 1.146 s 1.041161
2021-10-10 20:18 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.569 s -3.542970
2021-10-10 20:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.928 s -0.263055
2021-10-10 20:20 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.919 s -0.544312
2021-10-10 20:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.356 s -0.031268
2021-10-10 20:21 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.753673
2021-10-10 20:21 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.863 s -1.312467
2021-10-10 20:21 Python wide-dataframe use_legacy_dataset=false 0.614 s 1.613757
2021-10-10 20:35 R dataframe-to-table chi_traffic_2020_Q1, R 3.435 s 0.272558
2021-10-10 20:36 R dataframe-to-table type_strings, R 0.489 s 0.233772
2021-10-10 20:36 R dataframe-to-table type_integers, R 0.010 s 1.363620
2021-10-10 20:36 R dataframe-to-table type_nested, R 0.528 s 0.237162
2021-10-10 20:42 R dataframe-to-table type_simple_features, R 3.341 s 1.117292
2021-10-10 20:43 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.228 s 0.050593
2021-10-10 20:43 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.445 s 1.279584
2021-10-10 20:43 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -2.887832
2021-10-10 20:44 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 0.945567
2021-10-10 20:44 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.043 s 2.024098
2021-10-10 20:44 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.153 s 1.293719
2021-10-10 20:45 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.102 s 1.630669
2021-10-10 20:45 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.212 s 1.276765
2021-10-10 20:45 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -2.818448
2021-10-10 20:46 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.686 s 0.086411
2021-10-10 20:49 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.284 s 0.427616
2021-10-10 20:50 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.292 s 0.624463
2021-10-10 20:51 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.756 s 0.347561
2021-10-10 20:53 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.647 s -2.916294
2021-10-10 20:53 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.383 s 2.188228
2021-10-10 20:55 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.894 s -0.495415
2021-10-10 20:56 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.878 s -0.981203
2021-10-10 20:57 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.550252
2021-10-10 20:58 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.736 s -0.933408
2021-10-10 20:59 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 2.624444
2021-10-10 21:00 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.266 s -1.955282
2021-10-10 21:02 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.853 s 0.668483
2021-10-10 21:02 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.567 s 1.100064
2021-10-10 21:03 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s 0.031695
2021-10-10 21:03 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.592 s 1.252107
2021-10-10 21:03 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.529 s -1.109286
2021-10-10 21:03 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.292260
2021-10-10 21:04 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.654 s -1.037972
2021-10-10 21:04 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.262213
2021-10-10 21:05 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s -0.294731
2021-10-10 21:05 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.488 s -1.096871
2021-10-10 21:05 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.123137
2021-10-10 21:06 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.158 s 1.142877
2021-10-10 21:06 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.490 s 1.010082
2021-10-10 20:42 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.208 s 0.481116
2021-10-10 21:14 JavaScript Parse readBatches, tracks 0.000 s -1.359417
2021-10-10 21:13 JavaScript Parse Table.from, tracks 0.000 s -1.376763
2021-10-10 21:14 JavaScript Parse serialize, tracks 0.004 s 0.590949
2021-10-10 21:14 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.186339
2021-10-10 21:14 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.639 s -0.423539
2021-10-10 21:14 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.726 s -0.642430
2021-10-10 21:14 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.199185
2021-10-10 21:14 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.826 s -3.036863
2021-10-10 21:14 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.687 s -8.932356
2021-10-10 21:14 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.985343
2021-10-10 21:14 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.551428
2021-10-10 21:14 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.596024
2021-10-10 21:14 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.486534
2021-10-10 21:14 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.525547
2021-10-10 21:14 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.216211
2021-10-10 21:14 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.124616
2021-10-10 21:14 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.174850
2021-10-10 21:14 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.901204
2021-10-10 21:14 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.327523
2021-10-10 21:14 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.856402
2021-10-10 21:14 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.383236
2021-10-10 21:14 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.130772
2021-10-10 21:14 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.805349
2021-10-10 19:33 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.831 s 0.950664
2021-10-10 20:13 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.004 s 3.477914
2021-10-10 20:44 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.383 s 0.350221
2021-10-10 20:46 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.977 s 0.234657
2021-10-10 20:12 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.642 s 2.880343
2021-10-10 20:13 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.156 s 1.113859
2021-10-10 20:14 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.145 s 1.300202
2021-10-10 20:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.368 s -0.205308
2021-10-10 20:47 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.506 s 0.344009
2021-10-10 20:54 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.216 s -0.754517
2021-10-10 21:14 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.216805
2021-10-10 21:14 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -2.566904
2021-10-10 21:14 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.943 s -1.752787
2021-10-10 20:10 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.729 s 0.162461
2021-10-10 20:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.862 s -0.191207
2021-10-10 20:19 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.880 s -0.378818
2021-10-10 20:21 Python wide-dataframe use_legacy_dataset=true 0.388 s 3.234474
2021-10-10 21:02 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.878776
2021-10-10 19:39 Python dataset-filter nyctaxi_2010-01 4.319 s 1.591180
2021-10-10 20:00 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.066 s -1.521471
2021-10-10 20:11 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.194 s 0.879628
2021-10-10 20:11 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.792 s 0.488167
2021-10-10 20:36 R dataframe-to-table type_dict, R 0.049 s 0.370358
2021-10-10 20:36 R dataframe-to-table type_floats, R 0.013 s 1.353610
2021-10-10 20:42 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.449 s 1.305638
2021-10-10 20:47 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.837 s 0.641723
2021-10-10 21:02 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.591 s -0.577109
2021-10-10 20:00 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.059 s -0.719223
2021-10-10 20:15 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.096 s 0.495894
2021-10-10 19:42 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.089 s 0.740569
2021-10-10 20:12 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.671 s 3.784085
2021-10-10 20:13 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.017 s 1.188049
2021-10-10 20:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.449 s 0.523895
2021-10-10 20:44 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.046 s -2.321717
2021-10-10 20:52 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.816 s 1.788865
2021-10-10 21:02 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.182 s -0.678146
2021-10-10 21:04 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.875 s 1.464322
2021-10-10 21:14 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.073 s -3.481854
2021-10-10 21:14 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.033855
2021-10-10 21:14 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.792549
2021-10-10 19:56 Python dataset-read async=True, nyctaxi_multi_ipc_s3 193.007 s -0.994274
2021-10-10 20:11 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.286 s 0.119606
2021-10-10 20:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.170 s 0.978343
2021-10-10 20:17 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.915 s -1.137873
2021-10-10 20:21 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.436 s -4.977025
2021-10-10 21:01 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.467 s 2.489071
2021-10-10 21:14 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.187587
2021-10-10 21:14 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.691 s -2.861413