Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 01:33 Python dataframe-to-table type_integers 0.011 s 1.608786
2021-09-30 01:50 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.072 s 0.569415
2021-09-30 01:33 Python dataframe-to-table type_nested 2.881 s 1.599617
2021-09-30 01:50 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.279 s 0.141610
2021-09-30 01:30 Python csv-read uncompressed, file, nyctaxi_2010-01 1.021 s -0.056627
2021-09-30 01:30 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.676 s -0.364160
2021-09-30 01:33 Python dataframe-to-table type_strings 0.371 s -0.062035
2021-09-30 01:37 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.896 s -0.424916
2021-09-30 01:29 Python csv-read gzip, streaming, fanniemae_2016Q4 14.652 s -0.293975
2021-09-30 01:41 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.776 s 1.554161
2021-09-30 01:54 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.006 s 0.231042
2021-09-30 01:33 Python dataframe-to-table type_dict 0.012 s 0.132194
2021-09-30 01:54 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.046 s -0.146529
2021-09-30 01:28 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.734 s -0.306251
2021-09-30 01:33 Python dataframe-to-table type_simple_features 0.916 s -0.335670
2021-09-30 01:29 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -1.000834
2021-09-30 01:28 Python csv-read uncompressed, file, fanniemae_2016Q4 1.153 s 0.325036
2021-09-30 01:54 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.042 s -0.103077
2021-09-30 01:31 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.334030
2021-09-30 01:30 Python csv-read gzip, streaming, nyctaxi_2010-01 10.657 s -0.341403
2021-09-30 01:33 Python dataset-filter nyctaxi_2010-01 4.343 s 0.703565
2021-09-30 01:33 Python dataframe-to-table chi_traffic_2020_Q1 19.514 s 1.245756
2021-09-30 01:33 Python dataframe-to-table type_floats 0.011 s 1.222446
2021-09-30 02:09 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.288 s -1.138580
2021-09-30 02:14 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.768 s -0.442831
2021-09-30 02:32 R dataframe-to-table type_dict, R 0.062 s -1.268405
2021-09-30 03:00 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.232 s 0.609580
2021-09-30 03:03 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.168 s -1.277595
2021-09-30 03:29 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.725687
2021-09-30 03:29 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.187417
2021-09-30 02:11 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s -0.047912
2021-09-30 02:18 Python wide-dataframe use_legacy_dataset=false 0.619 s -0.181690
2021-09-30 03:15 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.150892
2021-09-30 03:17 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s 1.626902
2021-09-30 03:18 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.861 s 1.498012
2021-09-30 03:29 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.496 s 0.160087
2021-09-30 02:08 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.256 s 1.408132
2021-09-30 02:56 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.871 s 0.517996
2021-09-30 02:09 Python file-read lz4, feather, table, fanniemae_2016Q4 0.615 s -2.320365
2021-09-30 02:56 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.196 s 0.630377
2021-09-30 03:14 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.761 s -0.565450
2021-09-30 02:09 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.252 s -2.943977
2021-09-30 03:16 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.490 s 0.089003
2021-09-30 03:19 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.224565
2021-09-30 03:20 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.687427
2021-09-30 03:29 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.177534
2021-09-30 03:29 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.906279
2021-09-30 03:29 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.985402
2021-09-30 02:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.947 s 0.446348
2021-09-30 02:08 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.783 s -1.762012
2021-09-30 02:56 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.277 s -0.271595
2021-09-30 03:10 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.226 s 0.484275
2021-09-30 03:16 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.243 s 1.640755
2021-09-30 02:10 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.836928
2021-09-30 02:08 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.235357
2021-09-30 02:14 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.403 s -0.520083
2021-09-30 02:14 Python file-write lz4, feather, table, fanniemae_2016Q4 1.164 s -0.288395
2021-09-30 02:58 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.909 s 0.480275
2021-09-30 03:13 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.575 s -0.806655
2021-09-30 03:20 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.475 s -0.506699
2021-09-30 03:21 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.162 s 1.176009
2021-09-30 03:28 JavaScript Parse Table.from, tracks 0.000 s 0.451438
2021-09-30 03:08 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.583 s 0.604252
2021-09-30 03:18 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.175 s 0.138364
2021-09-30 03:20 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.351 s 1.051218
2021-09-30 02:13 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.742 s -1.223649
2021-09-30 02:16 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.927 s -0.218480
2021-09-30 02:57 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.287 s 1.463190
2021-09-30 02:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.291 s -1.199608
2021-09-30 02:18 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.804 s 0.307936
2021-09-30 03:29 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.882 s 0.056164
2021-09-30 03:29 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.639000
2021-09-30 03:28 JavaScript Parse serialize, tracks 0.003 s 3.374477
2021-09-30 02:07 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.929 s 1.850946
2021-09-30 02:11 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.464 s -1.524259
2021-09-30 02:12 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.586 s -0.961177
2021-09-30 02:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.928 s -1.041790
2021-09-30 02:17 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.978 s -0.330413
2021-09-30 02:17 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.338 s 0.214146
2021-09-30 02:59 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.434632
2021-09-30 03:01 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.125078
2021-09-30 03:07 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.099 s -1.152651
2021-09-30 03:18 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 1.210879
2021-09-30 03:28 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.531141
2021-09-30 03:29 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.512539
2021-09-30 03:29 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.216158
2021-09-30 02:58 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.380 s 0.063678
2021-09-30 03:11 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.940 s -0.828603
2021-09-30 03:18 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.577 s 1.468585
2021-09-30 02:09 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.957 s -3.251047
2021-09-30 02:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.084 s -1.468791
2021-09-30 02:11 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.929 s -1.316727
2021-09-30 02:12 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.405 s -1.387070
2021-09-30 02:17 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.355 s -0.316551
2021-09-30 03:01 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.964 s 0.374951
2021-09-30 03:12 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.903 s -0.584726
2021-09-30 02:15 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.264 s -0.463541
2021-09-30 02:17 Python file-write lz4, feather, table, nyctaxi_2010-01 1.805 s 0.326747
2021-09-30 03:18 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.407673
2021-09-30 03:19 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.513 s 0.431727
2021-09-30 03:21 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.490 s 0.129185
2021-09-30 03:28 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -3.117591
2021-09-30 02:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.793 s 0.564923
2021-09-30 02:08 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.115 s 1.234985
2021-09-30 02:10 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.045 s -0.478297
2021-09-30 02:32 R dataframe-to-table type_nested, R 0.540 s -1.319823
2021-09-30 03:05 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.621 s -1.276672
2021-09-30 03:09 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.402 s -0.044874
2021-09-30 02:06 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.754 s 0.128896
2021-09-30 02:32 R dataframe-to-table type_integers, R 0.084 s 0.470403
2021-09-30 02:56 R dataframe-to-table type_simple_features, R 274.647 s 0.418780
2021-09-30 02:59 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.117 s 0.986486
2021-09-30 03:00 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.396070
2021-09-30 02:07 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.760 s 1.734764
2021-09-30 02:32 R dataframe-to-table type_strings, R 0.494 s -1.540004
2021-09-30 03:29 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.045 s 2.142189
2021-09-30 02:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.174 s 1.902150
2021-09-30 02:08 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.849 s -1.745188
2021-09-30 02:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.995 s -1.209819
2021-09-30 02:18 Python wide-dataframe use_legacy_dataset=true 0.396 s -0.455551
2021-09-30 02:32 R dataframe-to-table chi_traffic_2020_Q1, R 5.432 s -0.552234
2021-09-30 03:28 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -3.213615
2021-09-30 02:14 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.009 s -1.220619
2021-09-30 02:32 R dataframe-to-table type_floats, R 0.112 s -1.136176
2021-09-30 02:57 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.873 s 0.517911
2021-09-30 03:07 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.371349
2021-09-30 03:29 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.119374
2021-09-30 03:29 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.598719
2021-09-30 02:59 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.168 s 0.342971
2021-09-30 03:02 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.518 s 0.131547
2021-09-30 03:04 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.602 s -0.818476
2021-09-30 03:17 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.582 s 1.361194
2021-09-30 03:21 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.606159
2021-09-30 03:28 JavaScript Parse readBatches, tracks 0.000 s -0.356284
2021-09-30 03:29 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.982884
2021-09-30 02:58 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.457451
2021-09-30 03:19 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.913 s 1.200559
2021-09-30 03:29 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.517103
2021-09-30 03:20 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.598 s -0.040517
2021-09-30 03:29 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.582417
2021-09-30 03:29 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.467960
2021-09-30 03:28 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.290 s 3.418190
2021-09-30 03:28 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.289 s 3.375530
2021-09-30 03:28 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.684 s 0.359427
2021-09-30 03:29 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.875 s 0.554320
2021-09-30 03:28 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.559662
2021-09-30 03:28 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.675 s 0.118059
2021-09-30 03:29 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500638
2021-09-30 03:29 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.139937