Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 02:39 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.627 s -0.327302
2021-09-29 02:40 Python csv-read gzip, streaming, nyctaxi_2010-01 10.625 s -0.367671
2021-09-29 02:42 Python dataframe-to-table type_integers 0.011 s 0.203834
2021-09-29 02:42 Python dataframe-to-table type_floats 0.011 s -0.079222
2021-09-29 02:39 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -1.365947
2021-09-29 03:00 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.191 s 0.553699
2021-09-29 02:37 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.089 s -0.906545
2021-09-29 02:42 Python dataframe-to-table chi_traffic_2020_Q1 19.819 s -0.158091
2021-09-29 02:43 Python dataset-filter nyctaxi_2010-01 4.397 s -1.297679
2021-09-29 02:50 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.124 s 3.378393
2021-09-29 02:43 Python dataframe-to-table type_simple_features 0.902 s 1.195861
2021-09-29 02:38 Python csv-read gzip, streaming, fanniemae_2016Q4 15.020 s -0.906532
2021-09-29 03:04 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.039 s -0.060070
2021-09-29 02:38 Python csv-read uncompressed, file, fanniemae_2016Q4 1.168 s 0.040596
2021-09-29 02:42 Python dataframe-to-table type_strings 0.368 s 0.392743
2021-09-29 02:39 Python csv-read uncompressed, file, nyctaxi_2010-01 1.017 s 0.031033
2021-09-29 02:42 Python dataframe-to-table type_dict 0.011 s 1.423402
2021-09-29 02:46 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.956 s -0.840316
2021-09-29 03:00 Python dataset-read async=True, nyctaxi_multi_ipc_s3 182.061 s 0.666429
2021-09-29 02:43 Python dataframe-to-table type_nested 2.924 s 1.717090
2021-09-29 02:40 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.500453
2021-09-29 03:04 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.022 s 0.184136
2021-09-29 03:04 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.014 s 0.107703
2021-09-29 03:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.812 s 0.502954
2021-09-29 03:21 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.974 s 0.900992
2021-09-29 03:17 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.790 s 0.061889
2021-09-29 03:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.278 s -1.382656
2021-09-29 04:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.571 s 1.076139
2021-09-29 03:18 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.021 s -0.969617
2021-09-29 03:20 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.055 s -1.125923
2021-09-29 03:24 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.335 s 0.028728
2021-09-29 03:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.798 s 1.886615
2021-09-29 03:27 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.363 s -0.866658
2021-09-29 04:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.186004
2021-09-29 04:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.447537
2021-09-29 04:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.471 s 0.193698
2021-09-29 04:39 JavaScript Parse Table.from, tracks 0.000 s -0.165436
2021-09-29 04:39 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.887469
2021-09-29 03:17 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.989 s 0.164347
2021-09-29 03:22 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 1.444042
2021-09-29 03:28 Python file-write lz4, feather, table, nyctaxi_2010-01 1.835 s -1.270672
2021-09-29 03:28 Python wide-dataframe use_legacy_dataset=true 0.396 s -0.428377
2021-09-29 04:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.492516
2021-09-29 04:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.157 s 2.737872
2021-09-29 03:19 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.746 s -0.800723
2021-09-29 03:23 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.441 s 1.384864
2021-09-29 03:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.278 s 0.689523
2021-09-29 03:28 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.169711
2021-09-29 04:06 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.247 s 0.054179
2021-09-29 04:08 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.321897
2021-09-29 04:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.661 s 2.711856
2021-09-29 04:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.269 s -0.108101
2021-09-29 04:39 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.082248
2021-09-29 03:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.680 s -0.670730
2021-09-29 03:20 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.110 s 0.409249
2021-09-29 03:27 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.780 s 1.183108
2021-09-29 03:28 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.753 s 0.718120
2021-09-29 03:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.228500
2021-09-29 03:21 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.145815
2021-09-29 04:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.113 s 1.237957
2021-09-29 04:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.851 s 1.478684
2021-09-29 04:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.499 s 0.216182
2021-09-29 03:42 R dataframe-to-table type_strings, R 0.493 s -0.942719
2021-09-29 04:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.540458
2021-09-29 04:39 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-29 04:39 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.780174
2021-09-29 04:39 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.368536
2021-09-29 03:20 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.830 s 0.929797
2021-09-29 03:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.829 s 0.905175
2021-09-29 03:42 R dataframe-to-table type_integers, R 0.083 s 1.101485
2021-09-29 04:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.536 s -1.007113
2021-09-29 04:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.703 s -1.444154
2021-09-29 04:07 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.241 s 0.113737
2021-09-29 04:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.958 s 0.416915
2021-09-29 04:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.393 s 1.476422
2021-09-29 04:39 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.146760
2021-09-29 03:42 R dataframe-to-table chi_traffic_2020_Q1, R 5.373 s 0.581372
2021-09-29 04:06 R dataframe-to-table type_simple_features, R 275.270 s -1.018571
2021-09-29 04:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.669 s 1.704596
2021-09-29 04:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.305 s 1.298016
2021-09-29 04:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.837 s 2.329061
2021-09-29 04:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.505 s 1.606365
2021-09-29 04:39 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.683 s 0.364060
2021-09-29 03:18 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.872 s -1.774000
2021-09-29 03:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.072 s -1.104892
2021-09-29 04:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.495 s -0.936168
2021-09-29 04:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.578 s 2.575345
2021-09-29 04:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.473 s -0.055757
2021-09-29 04:39 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.101683
2021-09-29 03:19 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.813 s -0.618864
2021-09-29 03:25 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.088 s 1.126461
2021-09-29 04:07 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.291 s -1.803855
2021-09-29 04:39 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.877828
2021-09-29 04:39 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.989156
2021-09-29 04:39 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.375425
2021-09-29 04:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.976802
2021-09-29 03:18 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.321 s -2.593614
2021-09-29 03:25 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.193051
2021-09-29 04:07 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.924 s -0.054255
2021-09-29 04:39 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -0.121022
2021-09-29 04:39 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-29 04:39 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.254523
2021-09-29 03:43 R dataframe-to-table type_nested, R 0.535 s 0.435438
2021-09-29 04:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.271 s -1.693664
2021-09-29 03:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.465 s 0.956164
2021-09-29 03:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.089 s 1.502865
2021-09-29 03:42 R dataframe-to-table type_floats, R 0.108 s 0.182579
2021-09-29 04:08 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.944 s -1.332411
2021-09-29 04:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.445032
2021-09-29 04:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 1.529068
2021-09-29 04:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.986864
2021-09-29 03:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.635 s 0.620962
2021-09-29 04:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.185 s -0.992740
2021-09-29 04:28 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.255104
2021-09-29 04:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.605 s 2.634198
2021-09-29 04:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.741958
2021-09-29 04:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.741 s 1.403911
2021-09-29 04:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.964 s 2.679399
2021-09-29 04:39 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.021337
2021-09-29 03:19 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.175 s -2.404284
2021-09-29 03:19 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.302 s -1.706390
2021-09-29 03:19 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.487022
2021-09-29 03:24 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.526 s 1.142772
2021-09-29 03:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.857 s 2.122434
2021-09-29 03:42 R dataframe-to-table type_dict, R 0.054 s -0.233326
2021-09-29 04:07 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.919 s 0.018562
2021-09-29 04:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.400 s -1.211564
2021-09-29 04:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.580 s 3.149603
2021-09-29 04:39 JavaScript Parse serialize, tracks 0.005 s -0.573018
2021-09-29 04:39 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.500 s 0.185374
2021-09-29 03:26 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.744 s 1.169192
2021-09-29 04:09 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s 0.029895
2021-09-29 04:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.191 s 0.251278
2021-09-29 04:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 2.637060
2021-09-29 04:39 JavaScript Parse readBatches, tracks 0.000 s -0.014342
2021-09-29 04:39 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.561 s 0.064163
2021-09-29 04:39 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.685 s -0.139207
2021-09-29 04:39 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.849 s 0.835403
2021-09-29 04:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -1.059691
2021-09-29 04:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.210 s 1.067947
2021-09-29 04:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.798 s 2.834824
2021-09-29 04:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.877 s 2.537008
2021-09-29 04:39 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-29 04:39 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.174921
2021-09-29 04:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.466 s 2.471275
2021-09-29 04:39 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.887 s 0.271466
2021-09-29 04:39 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-29 04:39 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.112446
2021-09-29 04:39 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.192383