Outliers: 4


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 06:13 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.039 s -0.255186
2021-09-30 06:24 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.807 s 0.512987
2021-09-30 07:13 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.253 s -0.009685
2021-09-30 06:24 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.960 s 0.361163
2021-09-30 06:28 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.005 s 0.137900
2021-09-30 06:49 R dataframe-to-table type_nested, R 0.538 s -0.217645
2021-09-30 06:08 Python dataset-read async=True, nyctaxi_multi_ipc_s3 206.108 s -2.203804
2021-09-30 06:13 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.034 s -0.000367
2021-09-30 06:25 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.864 s -1.326510
2021-09-30 06:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.168 s -1.604913
2021-09-30 06:28 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.188380
2021-09-30 05:46 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.825 s -1.120396
2021-09-30 06:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.792 s -3.261907
2021-09-30 06:31 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.624 s 0.600182
2021-09-30 06:33 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.810 s 0.640762
2021-09-30 07:15 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.934 s -0.960093
2021-09-30 05:45 Python csv-read gzip, streaming, fanniemae_2016Q4 14.767 s -0.571918
2021-09-30 06:26 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.856 s -3.979308
2021-09-30 06:26 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.473812
2021-09-30 06:27 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.002 s 0.188284
2021-09-30 05:44 Python csv-read uncompressed, file, fanniemae_2016Q4 1.151 s 0.327427
2021-09-30 06:27 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.214 s -5.280915
2021-09-30 06:27 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.084 s -1.485660
2021-09-30 06:35 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.849 s -0.080123
2021-09-30 06:49 R dataframe-to-table type_floats, R 0.108 s 0.362073
2021-09-30 06:35 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s 0.190150
2021-09-30 07:13 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.249 s 0.031082
2021-09-30 07:14 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.147087
2021-09-30 05:46 Python csv-read gzip, file, fanniemae_2016Q4 6.025 s 1.046378
2021-09-30 06:26 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.897 s -5.056240
2021-09-30 05:50 Python dataframe-to-table type_nested 2.874 s 3.775361
2021-09-30 06:25 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.289 s -1.537220
2021-09-30 06:27 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.010 s 1.662992
2021-09-30 06:28 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.152 s 0.070903
2021-09-30 06:33 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.802 s 1.508958
2021-09-30 06:34 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.362 s -0.712695
2021-09-30 05:49 Python dataframe-to-table chi_traffic_2020_Q1 19.655 s 0.790198
2021-09-30 05:49 Python dataframe-to-table type_floats 0.012 s -0.968448
2021-09-30 06:24 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.048 s -1.616872
2021-09-30 06:29 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.085 s 1.241200
2021-09-30 06:30 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.248166
2021-09-30 07:15 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.796298
2021-09-30 05:47 Python csv-read gzip, streaming, nyctaxi_2010-01 10.789 s -1.019846
2021-09-30 06:30 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.223 s 0.783340
2021-09-30 05:47 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.373864
2021-09-30 05:50 Python dataframe-to-table type_simple_features 0.938 s -4.355152
2021-09-30 06:09 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.318 s -0.170819
2021-09-30 06:24 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.737 s 0.273285
2021-09-30 06:32 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.777 s -0.520596
2021-09-30 06:32 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.295540
2021-09-30 06:35 Python file-write lz4, feather, table, nyctaxi_2010-01 1.803 s 0.431738
2021-09-30 06:34 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.863 s 1.598500
2021-09-30 05:58 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 92.521 s 2.159968
2021-09-30 07:15 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.385 s -0.450263
2021-09-30 05:44 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.829 s -0.561930
2021-09-30 05:49 Python dataframe-to-table type_strings 0.365 s 0.714236
2021-09-30 05:49 Python dataframe-to-table type_integers 0.011 s -1.823810
2021-09-30 06:25 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.313 s -1.681788
2021-09-30 06:32 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.236 s -0.210840
2021-09-30 06:34 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.884 s 0.395958
2021-09-30 07:13 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.943 s -0.239145
2021-09-30 07:15 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.063 s -1.215946
2021-09-30 05:49 Python dataframe-to-table type_dict 0.012 s -0.834417
2021-09-30 06:35 Python wide-dataframe use_legacy_dataset=false 0.612 s 1.137618
2021-09-30 05:46 Python csv-read uncompressed, file, nyctaxi_2010-01 0.993 s 0.454910
2021-09-30 06:26 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.282 s 1.380080
2021-09-30 06:28 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.283318
2021-09-30 06:49 R dataframe-to-table type_dict, R 0.027 s 2.945569
2021-09-30 05:50 Python dataset-filter nyctaxi_2010-01 4.401 s -1.270287
2021-09-30 06:29 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.637 s 0.159501
2021-09-30 06:31 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.263 s 0.683422
2021-09-30 07:13 R dataframe-to-table type_simple_features, R 274.746 s 0.106977
2021-09-30 05:53 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 66.569 s -1.482252
2021-09-30 06:13 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.015 s 0.279547
2021-09-30 06:35 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.076514
2021-09-30 06:49 R dataframe-to-table type_strings, R 0.490 s 0.279161
2021-09-30 06:49 R dataframe-to-table type_integers, R 0.083 s 1.081681
2021-09-30 07:16 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.147 s -1.236808
2021-09-30 07:18 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.968 s -0.047654
2021-09-30 07:14 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.948 s -0.300339
2021-09-30 07:17 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 2.031087
2021-09-30 07:16 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.204 s -1.942133
2021-09-30 07:17 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.224 s 0.877442
2021-09-30 07:20 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.841 s 1.333823
2021-09-30 07:18 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.317507
2021-09-30 07:19 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.521 s -0.197124
2021-09-30 07:38 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.151 s 1.768990
2021-09-30 07:46 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.640 s -0.293867
2021-09-30 07:36 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.610 s -0.319539
2021-09-30 07:38 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.496 s 0.131721
2021-09-30 07:46 JavaScript Parse readBatches, tracks 0.000 s 0.039115
2021-09-30 07:46 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.557652
2021-09-30 07:46 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.610610
2021-09-30 07:46 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.059884
2021-09-30 07:46 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.136481
2021-09-30 07:21 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.266 s 1.314768
2021-09-30 07:27 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.222 s 0.735908
2021-09-30 07:31 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.668 s 1.999234
2021-09-30 07:46 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.688373
2021-09-30 07:46 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.324745
2021-09-30 07:46 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.478145
2021-09-30 07:23 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.730 s 1.273669
2021-09-30 07:29 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.810 s 1.893204
2021-09-30 07:37 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.237873
2021-09-30 07:46 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.684 s -5.357491
2021-09-30 07:46 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.012 s -2.175437
2021-09-30 07:46 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.926273
2021-09-30 07:46 JavaScript Parse Table.from, tracks 0.000 s -0.118750
2021-09-30 07:37 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.474 s -0.416431
2021-09-30 07:22 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 1.298684
2021-09-30 07:24 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.702975
2021-09-30 07:37 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.736501
2021-09-30 07:46 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.872315
2021-09-30 07:46 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.634437
2021-09-30 07:37 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.599 s 0.080043
2021-09-30 07:46 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.586 s -0.053471
2021-09-30 07:46 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.021167
2021-09-30 07:32 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.850739
2021-09-30 07:35 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.114 s -7.782961
2021-09-30 07:36 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.950 s 1.740102
2021-09-30 07:28 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.822 s 2.144049
2021-09-30 07:46 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.989446
2021-09-30 07:26 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.399 s 0.535990
2021-09-30 07:35 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.368090
2021-09-30 07:34 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.189 s 0.401711
2021-09-30 07:38 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -1.128115
2021-09-30 07:46 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.802 s -2.191847
2021-09-30 07:33 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.492 s -0.451627
2021-09-30 07:35 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.873 s 2.009226
2021-09-30 07:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.508 s 1.060449
2021-09-30 07:46 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.900433
2021-09-30 07:25 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.579 s 0.870572
2021-09-30 07:46 JavaScript Parse serialize, tracks 0.005 s -0.642114
2021-09-30 07:46 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.485313
2021-09-30 07:46 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.025096
2021-09-30 07:30 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.465 s 1.992290
2021-09-30 07:35 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 1.772760
2021-09-30 07:46 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.409492
2021-09-30 07:33 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.264 s 0.245920
2021-09-30 07:34 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.577 s 1.991951
2021-09-30 07:46 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-30 07:46 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.097070
2021-09-30 07:35 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.562 s 2.434590
2021-09-30 07:46 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.943 s -1.334300
2021-09-30 07:46 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.128982
2021-09-30 07:46 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.409978
2021-09-30 07:46 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.388440
2021-09-30 07:46 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.571 s -0.951323
2021-09-30 06:49 R dataframe-to-table chi_traffic_2020_Q1, R 5.367 s 0.737485