Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 05:54 Python csv-read gzip, file, fanniemae_2016Q4 6.042 s -2.404982
2021-10-09 06:32 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.206 s 0.789841
2021-10-09 06:35 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.463 s -0.519741
2021-10-09 06:41 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.904 s -0.562334
2021-10-09 06:42 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.309876
2021-10-09 06:42 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.089200
2021-10-09 06:56 R dataframe-to-table type_integers, R 0.010 s 2.068801
2021-10-09 06:56 R dataframe-to-table type_floats, R 0.013 s 2.073086
2021-10-09 07:02 R dataframe-to-table type_simple_features, R 3.333 s 1.590186
2021-10-09 07:03 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.937 s -0.800148
2021-10-09 07:06 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.001 s -0.071926
2021-10-09 07:06 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.682 s 0.101235
2021-10-09 07:07 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.837 s 0.652132
2021-10-09 07:24 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s -0.471010
2021-10-09 07:24 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.111 s -0.938241
2021-10-09 07:33 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 1.348812
2021-10-09 07:33 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.610 s 1.606198
2021-10-09 07:33 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.516828
2021-10-09 07:33 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-09 07:33 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.613827
2021-10-09 07:33 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-09 07:33 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.173005
2021-10-09 06:19 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.029 s 0.117077
2021-10-09 06:31 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.939 s 1.333291
2021-10-09 06:33 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.799 s -0.447805
2021-10-09 06:55 R dataframe-to-table chi_traffic_2020_Q1, R 3.402 s 0.276388
2021-10-09 05:55 Python csv-read gzip, streaming, nyctaxi_2010-01 10.659 s -0.433049
2021-10-09 06:35 Python file-read lz4, feather, table, nyctaxi_2010-01 0.677 s -1.190649
2021-10-09 06:40 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.885 s -0.749916
2021-10-09 06:41 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.923 s -0.451030
2021-10-09 05:55 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.676 s -0.319644
2021-10-09 06:06 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.698 s 0.039533
2021-10-09 06:37 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.311 s 0.235680
2021-10-09 06:15 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.353 s 0.140499
2021-10-09 06:42 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.326 s 0.413431
2021-10-09 07:02 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.200 s 0.614808
2021-10-09 06:32 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.290 s 0.137871
2021-10-09 06:36 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.968 s -0.548968
2021-10-09 06:36 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.083 s 0.585288
2021-10-09 06:37 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.433 s 0.637250
2021-10-09 06:38 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.356 s -0.122644
2021-10-09 06:42 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.347 s 0.223616
2021-10-09 05:55 Python csv-read uncompressed, file, nyctaxi_2010-01 0.998 s 1.361070
2021-10-09 06:38 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.631 s 0.528492
2021-10-09 07:02 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.436 s 1.950376
2021-10-09 05:58 Python dataframe-to-table type_dict 0.012 s 0.499292
2021-10-09 05:58 Python dataframe-to-table type_floats 0.011 s 1.179272
2021-10-09 06:34 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.235 s -0.212783
2021-10-09 07:03 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.444 s 1.891998
2021-10-09 07:03 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.844042
2021-10-09 07:04 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.064 s -1.210074
2021-10-09 05:53 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.788 s 1.482013
2021-10-09 05:56 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.018700
2021-10-09 05:58 Python dataframe-to-table type_strings 0.374 s -0.074511
2021-10-09 05:58 Python dataframe-to-table type_integers 0.011 s 0.653541
2021-10-09 06:01 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 56.663 s 1.541179
2021-10-09 06:34 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.030 s 0.433150
2021-10-09 06:33 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.144 s 0.175288
2021-10-09 06:42 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.836 s -0.843277
2021-10-09 06:56 R dataframe-to-table type_strings, R 0.491 s 0.231982
2021-10-09 07:04 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.171 s 1.926017
2021-10-09 07:05 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.231 s 1.881372
2021-10-09 07:06 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.546 s -0.031242
2021-10-09 05:58 Python dataframe-to-table type_nested 2.873 s 0.416038
2021-10-09 06:33 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.300 s -1.418748
2021-10-09 06:34 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.036 s 0.460202
2021-10-09 06:56 R dataframe-to-table type_nested, R 0.539 s 0.233032
2021-10-09 05:58 Python dataframe-to-table type_simple_features 0.915 s 0.053638
2021-10-09 06:19 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.028 s -0.091290
2021-10-09 06:32 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.857 s -0.091761
2021-10-09 06:34 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.380 s -0.927357
2021-10-09 06:56 R dataframe-to-table type_dict, R 0.050 s 0.062434
2021-10-09 07:04 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 1.152166
2021-10-09 07:04 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.401 s -0.974872
2021-10-09 07:05 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.141 s -1.195942
2021-10-09 07:05 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.172379
2021-10-09 05:53 Python csv-read uncompressed, file, fanniemae_2016Q4 1.160 s 0.879446
2021-10-09 05:54 Python csv-read gzip, streaming, fanniemae_2016Q4 14.720 s 1.435644
2021-10-09 05:59 Python dataset-filter nyctaxi_2010-01 4.339 s 1.108736
2021-10-09 06:19 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.034 s -0.142470
2021-10-09 06:31 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.011 s 0.069351
2021-10-09 06:31 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.735 s 0.174094
2021-10-09 06:32 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.791 s 0.763280
2021-10-09 06:33 Python file-read lz4, feather, table, fanniemae_2016Q4 0.593 s 1.764075
2021-10-09 06:42 Python wide-dataframe use_legacy_dataset=false 0.625 s -0.851599
2021-10-09 05:58 Python dataframe-to-table chi_traffic_2020_Q1 19.528 s 0.186061
2021-10-09 06:31 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.809 s 0.469084
2021-10-09 06:35 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.096236
2021-10-09 06:39 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.880 s -1.148245
2021-10-09 06:40 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.843 s -0.203688
2021-10-09 06:15 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.343 s -0.356343
2021-10-09 06:33 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.938 s -0.442089
2021-10-09 06:35 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.312 s -0.504495
2021-10-09 06:39 Python file-write lz4, feather, table, fanniemae_2016Q4 1.163 s -0.054162
2021-10-09 06:39 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.343 s -1.323934
2021-10-09 07:03 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.208 s 1.287465
2021-10-09 07:09 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.232 s 0.868509
2021-10-09 07:15 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.895 s -0.718780
2021-10-09 07:11 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.688 s 0.886088
2021-10-09 07:10 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.282 s 0.695891
2021-10-09 07:12 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.823 s 1.667436
2021-10-09 07:14 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.195 s 0.654520
2021-10-09 07:13 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.395 s 1.218695
2021-10-09 07:16 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.855 s -0.692892
2021-10-09 07:13 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.542 s 1.015061
2021-10-09 07:22 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.855 s 0.398232
2021-10-09 07:20 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.234 s 1.219511
2021-10-09 07:24 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.899 s 0.147254
2021-10-09 07:23 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.609 s -0.127036
2021-10-09 07:33 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.908 s -0.064721
2021-10-09 07:33 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.303673
2021-10-09 07:33 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.134523
2021-10-09 07:22 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.589 s -0.227320
2021-10-09 07:33 JavaScript Parse serialize, tracks 0.005 s 0.416142
2021-10-09 07:33 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.265592
2021-10-09 07:33 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.648 s -0.531288
2021-10-09 07:33 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.783549
2021-10-09 07:33 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.102594
2021-10-09 07:33 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.251969
2021-10-09 07:23 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s 0.366972
2021-10-09 07:26 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.183 s -0.007180
2021-10-09 07:33 JavaScript Parse Table.from, tracks 0.000 s 0.212825
2021-10-09 07:22 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.571 s 0.253907
2021-10-09 07:33 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 1.362269
2021-10-09 07:33 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.728 s 0.060769
2021-10-09 07:33 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.529 s -0.239178
2021-10-09 07:25 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -0.339977
2021-10-09 07:26 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.500 s -0.149192
2021-10-09 07:33 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.907 s -0.707131
2021-10-09 07:33 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-09 07:33 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.031997
2021-10-09 07:33 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.497467
2021-10-09 07:19 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.286703
2021-10-09 07:24 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.730 s -2.148131
2021-10-09 07:25 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.059164
2021-10-09 07:33 JavaScript Parse readBatches, tracks 0.000 s 0.450387
2021-10-09 07:33 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.264874
2021-10-09 07:33 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.541 s -0.283662
2021-10-09 07:33 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.174397
2021-10-09 07:33 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.062264
2021-10-09 07:33 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.214762
2021-10-09 07:33 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.337191
2021-10-09 07:21 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.206664
2021-10-09 07:25 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s -0.592023
2021-10-09 07:17 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.535 s -0.753004
2021-10-09 07:23 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.427074
2021-10-09 07:22 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.171 s 0.587922
2021-10-09 07:18 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.711 s -0.575147
2021-10-09 07:22 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s -0.031509