Outliers: 2


Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 01:06 Python csv-read uncompressed, file, fanniemae_2016Q4 1.169 s 0.139167
2021-10-13 01:08 Python csv-read uncompressed, file, nyctaxi_2010-01 0.999 s 0.982060
2021-10-13 01:09 Python csv-read gzip, streaming, nyctaxi_2010-01 10.958 s -2.663037
2021-10-13 01:09 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.347540
2021-10-13 01:11 Python dataframe-to-table chi_traffic_2020_Q1 19.239 s 0.943757
2021-10-13 01:11 Python dataframe-to-table type_dict 0.011 s 1.082620
2021-10-13 01:11 Python dataframe-to-table type_floats 0.011 s 0.421999
2021-10-13 01:11 Python dataframe-to-table type_nested 2.852 s 1.475771
2021-10-13 01:12 Python dataset-filter nyctaxi_2010-01 4.407 s -3.380668
2021-10-13 01:15 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 57.276 s 1.182289
2021-10-13 01:29 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.307 s 0.173785
2021-10-13 01:34 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.083 s -1.491725
2021-10-13 01:34 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.045 s -0.195177
2021-10-13 01:44 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.996 s 0.174223
2021-10-13 01:45 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.330 s -2.278225
2021-10-13 01:46 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.542 s 1.499455
2021-10-13 01:48 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.092910
2021-10-13 01:49 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.807 s 1.325175
2021-10-13 02:13 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 0.799150
2021-10-13 02:13 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.219 s 0.817073
2021-10-13 02:14 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.009 s -0.122781
2021-10-13 02:17 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.640 s -3.033408
2021-10-13 02:19 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.035 s -2.434225
2021-10-13 02:24 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.925 s -1.235669
2021-10-13 02:25 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.878 s -0.990200
2021-10-13 02:26 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.554 s -1.095493
2021-10-13 02:27 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.748 s -1.214177
2021-10-13 02:28 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.246 s -0.460717
2021-10-13 02:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.627 s -0.492579
2021-10-13 02:33 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s 0.176745
2021-10-13 02:33 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.494 s -1.878412
2021-10-13 02:42 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.094201
2021-10-13 02:42 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.600 s -0.266789
2021-10-13 02:42 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.646 s 0.796794
2021-10-13 02:42 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.878 s 0.079264
2021-10-13 02:42 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.877504
2021-10-13 02:42 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.800885
2021-10-13 01:51 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.017 s -1.791083
2021-10-13 01:52 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.893 s -0.287759
2021-10-13 01:54 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.865 s -0.197277
2021-10-13 01:55 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.342 s 0.412429
2021-10-13 02:10 R dataframe-to-table type_dict, R 0.052 s -0.214166
2021-10-13 02:10 R dataframe-to-table type_floats, R 0.013 s 0.864301
2021-10-13 02:13 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.047 s 1.042046
2021-10-13 01:11 Python dataframe-to-table type_strings 0.372 s 0.056224
2021-10-13 01:46 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.602 s 1.630501
2021-10-13 01:48 Python file-read lz4, feather, table, nyctaxi_2010-01 0.666 s 0.242184
2021-10-13 01:56 Python wide-dataframe use_legacy_dataset=true 0.393 s -0.021263
2021-10-13 01:06 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.913 s 0.017019
2021-10-13 02:11 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.256 s -0.226896
2021-10-13 02:11 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.472 s 0.813092
2021-10-13 02:14 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.697 s -0.044621
2021-10-13 02:16 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.130 s -2.309156
2021-10-13 02:20 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.811 s 1.533866
2021-10-13 01:07 Python csv-read gzip, streaming, fanniemae_2016Q4 14.851 s -0.031390
2021-10-13 01:44 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.845 s 0.225138
2021-10-13 01:44 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.686 s 0.512066
2021-10-13 01:45 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.913 s -2.229261
2021-10-13 01:47 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.102 s 1.936265
2021-10-13 02:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.485 s 0.830997
2021-10-13 02:15 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.542 s -0.021848
2021-10-13 02:18 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.586 s -2.338197
2021-10-13 02:42 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-13 01:50 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.629 s -1.597097
2021-10-13 01:46 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.137 s 0.105860
2021-10-13 02:13 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.164 s 0.823037
2021-10-13 02:30 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.166 s 0.450430
2021-10-13 02:30 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.617 s -2.665041
2021-10-13 02:32 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 3.015 s -5.818278
2021-10-13 02:42 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.462400
2021-10-13 02:42 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s 0.147082
2021-10-13 01:29 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.978 s -0.793087
2021-10-13 01:47 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.001 s 1.006818
2021-10-13 01:48 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.157 s 1.311243
2021-10-13 01:55 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.905 s -0.356235
2021-10-13 02:13 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -0.272510
2021-10-13 02:42 JavaScript Parse readBatches, tracks 0.000 s 0.287191
2021-10-13 01:47 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.019 s 1.406654
2021-10-13 01:49 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.345 s -2.180371
2021-10-13 01:55 Python file-write lz4, feather, table, nyctaxi_2010-01 1.795 s 0.533571
2021-10-13 02:42 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.649 s -0.424916
2021-10-13 02:42 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.722 s 0.146620
2021-10-13 02:42 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.684435
2021-10-13 02:42 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.507 s 0.280885
2021-10-13 01:08 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.665 s -0.292062
2021-10-13 01:48 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.312 s 1.047180
2021-10-13 01:51 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.712 s -2.345613
2021-10-13 01:54 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.976 s -1.120719
2021-10-13 01:56 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.752 s 2.284107
2021-10-13 01:56 Python wide-dataframe use_legacy_dataset=false 0.620 s -0.027361
2021-10-13 02:11 R dataframe-to-table type_nested, R 0.531 s 0.233681
2021-10-13 01:11 Python dataframe-to-table type_integers 0.011 s -0.244764
2021-10-13 01:34 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.065 s -0.136546
2021-10-13 01:47 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 0.687533
2021-10-13 01:47 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.060 s -0.647852
2021-10-13 01:53 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.353 s -0.043909
2021-10-13 01:53 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.914 s -1.119957
2021-10-13 02:42 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.099314
2021-10-13 02:42 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.663760
2021-10-13 02:42 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.462651
2021-10-13 01:08 Python csv-read gzip, file, fanniemae_2016Q4 6.024 s 1.341347
2021-10-13 01:46 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s 0.077442
2021-10-13 01:55 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.286 s 1.725749
2021-10-13 02:11 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.323 s -1.663019
2021-10-13 02:29 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.467 s 1.571386
2021-10-13 01:45 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.127 s -3.373967
2021-10-13 02:12 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.040 s -1.009123
2021-10-13 02:30 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.975 s -2.693901
2021-10-13 02:42 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.895209
2021-10-13 01:46 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.363 s -2.879090
2021-10-13 01:47 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.689 s 1.080883
2021-10-13 02:10 R dataframe-to-table type_integers, R 0.009 s 0.879557
2021-10-13 02:31 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.828640
2021-10-13 02:42 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.172177
2021-10-13 02:42 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.232574
2021-10-13 02:42 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.893 s 0.321580
2021-10-13 02:11 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.240 s -0.548728
2021-10-13 02:12 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.562 s 0.074993
2021-10-13 02:22 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.393 s 0.372699
2021-10-13 02:23 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.190 s 0.377494
2021-10-13 02:31 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.579 s -0.607178
2021-10-13 02:31 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s -0.541582
2021-10-13 02:42 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.762593
2021-10-13 02:42 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.107165
2021-10-13 01:52 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.438 s -0.739952
2021-10-13 02:10 R dataframe-to-table type_strings, R 0.492 s 0.230109
2021-10-13 02:31 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.532 s -1.290938
2021-10-13 02:34 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.161 s 1.076533
2021-10-13 01:19 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.452 s -1.607049
2021-10-13 01:52 Python file-write lz4, feather, table, fanniemae_2016Q4 1.150 s 0.486869
2021-10-13 02:10 R dataframe-to-table chi_traffic_2020_Q1, R 3.343 s 0.264763
2021-10-13 02:33 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -1.031751
2021-10-13 02:42 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.428210
2021-10-13 02:13 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.214 s -1.128020
2021-10-13 02:21 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.543 s 0.581879
2021-10-13 02:32 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.809 s -8.394015
2021-10-13 02:34 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -0.896841
2021-10-13 02:42 JavaScript Parse Table.from, tracks 0.000 s 0.421356
2021-10-13 02:42 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.423780
2021-10-13 02:42 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.798925
2021-10-13 02:42 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.145022
2021-10-13 02:31 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.193 s -1.427462
2021-10-13 02:42 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.159577
2021-10-13 02:42 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.193730
2021-10-13 02:28 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.367924
2021-10-13 02:34 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.920971
2021-10-13 02:42 JavaScript Parse serialize, tracks 0.005 s -0.605888