Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-27 18:44 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.121 s -0.670558
2021-09-27 18:46 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.483 s 1.053494
2021-09-27 19:06 R dataframe-to-table type_dict, R 0.042 s 1.403634
2021-09-27 17:58 Python dataframe-to-table type_integers 0.011 s 0.043152
2021-09-27 17:53 Python csv-read uncompressed, file, fanniemae_2016Q4 1.145 s 0.326506
2021-09-27 17:58 Python dataframe-to-table type_nested 2.959 s -0.196557
2021-09-27 18:42 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.976 s 0.272532
2021-09-27 18:43 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.139 s -0.423712
2021-09-27 18:48 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.153 s 1.654164
2021-09-27 18:44 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.079 s -1.354969
2021-09-27 17:58 Python dataframe-to-table type_strings 0.368 s 0.548473
2021-09-27 18:25 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.153 s 0.762983
2021-09-27 18:53 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.071092
2021-09-27 17:58 Python dataframe-to-table type_simple_features 0.904 s 0.796450
2021-09-27 17:58 Python dataset-filter nyctaxi_2010-01 4.364 s -0.341007
2021-09-27 18:30 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.046 s -0.124167
2021-09-27 18:43 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.663 s -0.301076
2021-09-27 18:46 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.990 s 0.988105
2021-09-27 18:52 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.823 s 1.096653
2021-09-27 19:06 R dataframe-to-table chi_traffic_2020_Q1, R 5.379 s 0.543634
2021-09-27 17:55 Python csv-read gzip, streaming, nyctaxi_2010-01 10.791 s -1.046419
2021-09-27 17:57 Python dataframe-to-table chi_traffic_2020_Q1 19.996 s -1.110463
2021-09-27 18:45 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.857 s 1.016717
2021-09-27 18:45 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.889 s 0.825906
2021-09-27 18:50 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.053 s 1.702478
2021-09-27 18:53 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.781 s 0.593349
2021-09-27 18:53 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.206597
2021-09-27 17:54 Python csv-read gzip, file, fanniemae_2016Q4 6.032 s -0.828027
2021-09-27 18:43 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.286 s -0.540404
2021-09-27 18:49 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.487 s 1.827658
2021-09-27 17:55 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.833 s -1.164154
2021-09-27 18:30 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.012 s 0.073399
2021-09-27 18:42 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.699 s 0.467165
2021-09-27 18:43 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.725 s 0.013451
2021-09-27 18:44 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.803 s 0.326559
2021-09-27 17:56 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -0.920114
2021-09-27 18:42 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.995 s -0.371175
2021-09-27 18:43 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.843 s -1.191061
2021-09-27 19:06 R dataframe-to-table type_strings, R 0.496 s -2.202086
2021-09-27 18:44 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.291 s -0.001502
2021-09-27 18:45 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.045 s -0.528962
2021-09-27 18:46 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.100719
2021-09-27 19:07 R dataframe-to-table type_nested, R 0.540 s -1.251075
2021-09-27 17:58 Python dataframe-to-table type_floats 0.011 s 0.009366
2021-09-27 18:42 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.259 s -1.006308
2021-09-27 18:47 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.087 s 1.968811
2021-09-27 18:49 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.277 s 0.438129
2021-09-27 18:44 Python file-read lz4, feather, table, fanniemae_2016Q4 0.613 s -2.166344
2021-09-27 18:49 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.618 s 1.014247
2021-09-27 17:55 Python csv-read uncompressed, file, nyctaxi_2010-01 1.027 s -0.108772
2021-09-27 18:25 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.308 s 0.103660
2021-09-27 18:30 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.038 s -0.123935
2021-09-27 18:50 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.834 s 1.873617
2021-09-27 18:51 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.899 s 1.969902
2021-09-27 18:15 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 273.863 s -0.032261
2021-09-27 18:42 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.814 s 0.479980
2021-09-27 18:48 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 1.972493
2021-09-27 19:07 R dataframe-to-table type_floats, R 0.107 s 0.631526
2021-09-27 18:45 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.245020
2021-09-27 18:52 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.353 s -0.159813
2021-09-27 18:53 Python file-write lz4, feather, table, nyctaxi_2010-01 1.809 s 0.129232
2021-09-27 19:06 R dataframe-to-table type_integers, R 0.083 s 0.988049
2021-09-27 19:43 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.582 s 0.942953
2021-09-27 19:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.292 s -3.560376
2021-09-27 19:43 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.645945
2021-09-27 20:04 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.903 s -0.081615
2021-09-27 20:04 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-27 17:53 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.867 s -0.823724
2021-09-27 17:54 Python csv-read gzip, streaming, fanniemae_2016Q4 14.782 s -0.811617
2021-09-27 17:58 Python dataframe-to-table type_dict 0.012 s -1.768002
2021-09-27 18:01 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.616 s -0.619704
2021-09-27 18:50 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.027209
2021-09-27 18:51 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.784 s 1.100273
2021-09-27 18:52 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.293 s 0.663908
2021-09-27 19:31 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.635 s -22.025635
2021-09-27 19:35 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.251 s -0.555436
2021-09-27 19:50 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.259 s 0.460663
2021-09-27 19:42 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.837 s -1.404317
2021-09-27 19:52 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.580 s 1.043471
2021-09-27 19:47 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.517 s 1.078668
2021-09-27 19:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.988 s -0.163928
2021-09-27 19:54 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.565355
2021-09-27 19:34 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.180 s -0.638181
2021-09-27 19:36 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.537 s -1.132292
2021-09-27 19:49 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.574092
2021-09-27 20:04 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.049653
2021-09-27 20:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.771804
2021-09-27 19:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.477 s -1.152765
2021-09-27 20:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.136519
2021-09-27 20:04 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.685 s -0.146660
2021-09-27 20:04 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.526387
2021-09-27 19:36 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.669 s 1.730749
2021-09-27 19:41 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.759 s 1.772473
2021-09-27 19:48 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.729 s 0.874114
2021-09-27 20:04 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.448627
2021-09-27 20:04 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.622277
2021-09-27 19:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.409958
2021-09-27 19:32 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.912 s 0.107073
2021-09-27 19:33 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.105400
2021-09-27 19:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.394 s -0.286561
2021-09-27 20:04 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.716 s 0.172340
2021-09-27 19:35 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.306747
2021-09-27 19:46 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.875 s 0.839656
2021-09-27 19:51 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.999751
2021-09-27 19:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.886 s 0.910411
2021-09-27 20:04 JavaScript Parse Table.from, tracks 0.000 s -2.243835
2021-09-27 20:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.135671
2021-09-27 20:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.345892
2021-09-27 20:04 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.496 s 0.215600
2021-09-27 19:30 R dataframe-to-table type_simple_features, R 274.219 s 1.087308
2021-09-27 19:33 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.410 s -1.814510
2021-09-27 19:52 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.190453
2021-09-27 19:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -1.301872
2021-09-27 20:04 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.069294
2021-09-27 19:31 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.311 s -14.481087
2021-09-27 19:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 8.284 s -14.042004
2021-09-27 19:36 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.984 s -1.015852
2021-09-27 20:04 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.895 s -0.302374
2021-09-27 20:04 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.060048
2021-09-27 19:56 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.818 s -0.043972
2021-09-27 20:04 JavaScript Parse serialize, tracks 0.005 s 0.453651
2021-09-27 20:04 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.590 s -0.067226
2021-09-27 20:04 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.038618
2021-09-27 19:37 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.841 s 2.067508
2021-09-27 19:40 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.310 s 1.883858
2021-09-27 19:45 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s 0.671049
2021-09-27 19:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.496798
2021-09-27 19:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.503 s 0.167952
2021-09-27 20:04 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.318092
2021-09-27 19:39 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.268 s 2.022061
2021-09-27 19:52 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.693 s 0.788505
2021-09-27 20:04 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.078863
2021-09-27 20:04 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.051247
2021-09-27 19:30 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.627 s -18.330240
2021-09-27 19:32 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.571 s -1.830828
2021-09-27 19:44 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.232 s 0.576361
2021-09-27 19:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.136 s -0.417806
2021-09-27 19:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.584 s 0.252507
2021-09-27 19:52 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.195 s -0.117231
2021-09-27 19:52 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.755 s 0.783509
2021-09-27 19:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s 0.329107
2021-09-27 20:04 JavaScript Parse readBatches, tracks 0.000 s -2.501060
2021-09-27 20:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.581 s 0.035399
2021-09-27 20:04 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-27 20:04 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.566637
2021-09-27 20:04 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.212590
2021-09-27 20:04 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.123374
2021-09-27 20:04 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.204110
2021-09-27 20:04 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s 0.050789