Outliers: 1


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 07:03 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.949 s -0.523852
2021-10-01 07:08 Python dataframe-to-table type_integers 0.011 s 2.048283
2021-10-01 07:04 Python csv-read uncompressed, file, fanniemae_2016Q4 1.156 s 0.284460
2021-10-01 07:30 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.017 s 0.075815
2021-10-01 07:42 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.173 s 1.810138
2021-10-01 07:46 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.952 s -1.440575
2021-10-01 07:09 Python dataframe-to-table type_nested 2.881 s 1.481370
2021-10-01 07:41 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.771 s 0.053325
2021-10-01 07:49 Python file-write lz4, feather, table, fanniemae_2016Q4 1.161 s -0.048122
2021-10-01 08:31 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.414 s -4.893770
2021-10-01 08:33 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.066 s -1.743576
2021-10-01 08:35 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.933 s 2.126384
2021-10-01 08:51 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.480 s 1.995049
2021-10-01 09:03 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.689 s 0.329430
2021-10-01 09:03 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.686442
2021-10-01 07:05 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.955141
2021-10-01 07:06 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.910918
2021-10-01 07:08 Python dataframe-to-table type_strings 0.375 s -0.440932
2021-10-01 07:45 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.023 s 0.884346
2021-10-01 07:47 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.406 s -1.353983
2021-10-01 07:52 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.315942
2021-10-01 08:06 R dataframe-to-table chi_traffic_2020_Q1, R 5.445 s -0.778564
2021-10-01 08:06 R dataframe-to-table type_floats, R 0.113 s -1.491270
2021-10-01 08:30 R dataframe-to-table type_simple_features, R 275.565 s -1.352512
2021-10-01 08:35 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.233 s 0.539297
2021-10-01 08:41 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.069 s -0.999909
2021-10-01 08:52 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.864 s 1.443086
2021-10-01 08:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -2.009299
2021-10-01 08:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.147 s 1.182136
2021-10-01 09:03 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.266 s 3.197771
2021-10-01 09:03 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.530900
2021-10-01 07:04 Python csv-read gzip, streaming, fanniemae_2016Q4 14.882 s -0.524924
2021-10-01 07:45 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.294 s -1.248817
2021-10-01 07:50 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.934 s -1.131308
2021-10-01 08:39 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.613 s -0.912656
2021-10-01 08:44 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.212 s 0.803374
2021-10-01 08:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.046826
2021-10-01 08:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.481 s 0.148029
2021-10-01 09:03 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.836 s 1.111984
2021-10-01 09:03 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.229247
2021-10-01 07:43 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.836 s -1.416540
2021-10-01 07:44 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.254 s -2.674972
2021-10-01 07:46 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.178451
2021-10-01 07:47 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.587 s -0.940034
2021-10-01 07:48 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.752 s -1.265287
2021-10-01 07:49 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.342 s 0.041991
2021-10-01 07:53 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.307531
2021-10-01 08:31 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.509415
2021-10-01 09:03 JavaScript Parse readBatches, tracks 0.000 s -1.849124
2021-10-01 09:03 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.801209
2021-10-01 07:12 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.421 s -0.312313
2021-10-01 07:43 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.300 s -1.396019
2021-10-01 08:48 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.766 s -0.684888
2021-10-01 08:52 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.579 s 1.385504
2021-10-01 07:05 Python csv-read uncompressed, file, nyctaxi_2010-01 1.008 s 0.189877
2021-10-01 08:45 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.940 s -0.811145
2021-10-01 09:03 JavaScript Parse Table.from, tracks 0.000 s -1.408263
2021-10-01 09:03 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.044505
2021-10-01 07:08 Python dataframe-to-table type_floats 0.011 s 1.279693
2021-10-01 09:03 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.697 s -0.234348
2021-10-01 09:03 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.735152
2021-10-01 09:03 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.002380
2021-10-01 07:05 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.536 s 0.249847
2021-10-01 07:42 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.914 s 2.111921
2021-10-01 07:43 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.758 s -1.176051
2021-10-01 08:07 R dataframe-to-table type_nested, R 0.538 s -0.412675
2021-10-01 08:35 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.282500
2021-10-01 08:50 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.251 s 0.995794
2021-10-01 08:52 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.176 s 1.245593
2021-10-01 07:06 Python csv-read gzip, streaming, nyctaxi_2010-01 10.510 s 0.303691
2021-10-01 07:08 Python dataframe-to-table chi_traffic_2020_Q1 19.668 s 0.425116
2021-10-01 07:26 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.250 s 0.284659
2021-10-01 07:41 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.997 s 0.083268
2021-10-01 07:46 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.466 s -1.547915
2021-10-01 08:06 R dataframe-to-table type_strings, R 0.489 s 0.930465
2021-10-01 08:33 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.383 s -0.043432
2021-10-01 08:36 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.535 s -0.689409
2021-10-01 08:39 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.618 s -1.222001
2021-10-01 08:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.208 s -5.375922
2021-10-01 09:03 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-01 07:08 Python dataframe-to-table type_dict 0.012 s -1.085195
2021-10-01 07:26 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.123 s -0.339477
2021-10-01 07:50 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.307 s -0.840706
2021-10-01 08:06 R dataframe-to-table type_integers, R 0.085 s -0.310110
2021-10-01 08:43 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.722 s -2.614496
2021-10-01 09:03 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.295 s 3.083432
2021-10-01 09:03 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.513168
2021-10-01 07:09 Python dataframe-to-table type_simple_features 0.913 s -0.104423
2021-10-01 07:49 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.958 s -0.968115
2021-10-01 08:30 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.672 s -4.444149
2021-10-01 08:41 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.843 s -2.349215
2021-10-01 08:52 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.567 s 1.507248
2021-10-01 08:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.920 s 1.153555
2021-10-01 08:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.181915
2021-10-01 08:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.198 s 0.002332
2021-10-01 09:03 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.915855
2021-10-01 09:03 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.189817
2021-10-01 09:03 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.060371
2021-10-01 07:09 Python dataset-filter nyctaxi_2010-01 4.350 s 0.471439
2021-10-01 07:30 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.021 s 0.210706
2021-10-01 07:51 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.028 s -1.776018
2021-10-01 09:03 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.876 s 0.525022
2021-10-01 07:16 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.565 s 1.456584
2021-10-01 07:30 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.089881
2021-10-01 07:41 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.898 s 0.080909
2021-10-01 07:43 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.115 s 1.174875
2021-10-01 07:44 Python file-read lz4, feather, table, fanniemae_2016Q4 0.620 s -3.309791
2021-10-01 07:44 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.026 s 0.704552
2021-10-01 07:44 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.325 s -1.355499
2021-10-01 07:49 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.783 s -0.561897
2021-10-01 07:52 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.366 s -0.972119
2021-10-01 07:52 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.355 s 0.080945
2021-10-01 08:31 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.853 s 0.719915
2021-10-01 08:32 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.915 s 0.202996
2021-10-01 08:36 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s 0.035771
2021-10-01 08:47 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.572 s -0.683517
2021-10-01 09:03 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.216580
2021-10-01 09:03 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.998211
2021-10-01 07:42 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.748 s 1.970988
2021-10-01 07:44 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.932 s -2.320848
2021-10-01 08:31 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.206 s 0.492574
2021-10-01 08:32 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.559 s 0.702724
2021-10-01 08:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.124 s 0.465996
2021-10-01 09:03 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.571584
2021-10-01 09:03 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.323034
2021-10-01 07:43 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.276 s 0.473656
2021-10-01 07:45 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.165837
2021-10-01 07:53 Python wide-dataframe use_legacy_dataset=false 0.624 s -1.070114
2021-10-01 08:34 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.176 s -0.122321
2021-10-01 08:43 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.229475
2021-10-01 08:49 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.289 s -3.004559
2021-10-01 09:03 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.003473
2021-10-01 07:51 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 10.011 s -0.785615
2021-10-01 07:53 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.848 s -0.053809
2021-10-01 08:37 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.176 s -1.297418
2021-10-01 08:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.468 s 1.691304
2021-10-01 09:03 JavaScript Parse serialize, tracks 0.005 s -0.520157
2021-10-01 07:52 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.945 s -0.097745
2021-10-01 08:06 R dataframe-to-table type_dict, R 0.062 s -1.261373
2021-10-01 08:46 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.891 s -0.356441
2021-10-01 08:52 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.092 s -0.101665
2021-10-01 08:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 1.168512
2021-10-01 08:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.230741
2021-10-01 08:54 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.619 s -0.344339
2021-10-01 09:03 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.503980
2021-10-01 09:03 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.477156
2021-10-01 09:03 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.509285
2021-10-01 09:03 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.123414
2021-10-01 09:03 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.518 s -0.175398