Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 10:58 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.050 s -0.051500
2021-10-06 10:59 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.464 s -1.150099
2021-10-06 10:21 Python csv-read gzip, file, nyctaxi_2010-01 9.037 s 2.806759
2021-10-06 10:56 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.021 s -0.613371
2021-10-06 11:03 Python file-write lz4, feather, table, fanniemae_2016Q4 1.152 s 0.734457
2021-10-06 11:07 Python wide-dataframe use_legacy_dataset=false 0.625 s -0.916893
2021-10-06 10:27 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.813 s -0.032461
2021-10-06 11:00 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.069 s 0.913478
2021-10-06 11:06 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.809 s 0.210919
2021-10-06 10:23 Python dataframe-to-table chi_traffic_2020_Q1 19.540 s 0.565136
2021-10-06 10:23 Python dataframe-to-table type_floats 0.011 s 1.460606
2021-10-06 10:24 Python dataset-filter nyctaxi_2010-01 4.360 s 0.295455
2021-10-06 10:40 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.932 s 0.560156
2021-10-06 10:59 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.305 s -1.126659
2021-10-06 10:56 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.831 s -0.149753
2021-10-06 10:23 Python dataframe-to-table type_simple_features 0.913 s -0.007002
2021-10-06 10:45 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.034 s -0.181739
2021-10-06 10:55 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.857 s 0.175453
2021-10-06 11:02 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.435 s 0.843927
2021-10-06 11:04 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.784 s 0.640119
2021-10-06 10:18 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.969 s -0.401733
2021-10-06 10:59 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.179 s -0.400080
2021-10-06 10:55 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.991 s 0.151017
2021-10-06 10:56 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.300 s -0.450385
2021-10-06 10:55 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.743 s 0.040450
2021-10-06 10:31 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.205 s 0.746760
2021-10-06 10:40 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.187 s 0.713525
2021-10-06 10:58 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.059 s -1.310482
2021-10-06 11:07 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.216105
2021-10-06 10:45 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.040 s -0.133864
2021-10-06 10:57 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.373928
2021-10-06 11:00 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.980 s -1.230137
2021-10-06 11:03 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.739 s -0.062174
2021-10-06 10:18 Python csv-read uncompressed, file, fanniemae_2016Q4 1.209 s -2.038308
2021-10-06 11:03 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.276 s -0.467943
2021-10-06 10:57 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.927 s -0.805237
2021-10-06 11:01 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.276 s 0.396791
2021-10-06 11:06 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.348 s 0.100201
2021-10-06 10:23 Python dataframe-to-table type_strings 0.370 s 0.172764
2021-10-06 10:45 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.026 s 0.144429
2021-10-06 10:56 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.257 s -0.371067
2021-10-06 10:59 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 0.990008
2021-10-06 11:03 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.345 s -0.079827
2021-10-06 10:57 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.895 s -1.391028
2021-10-06 10:19 Python csv-read gzip, file, fanniemae_2016Q4 6.038 s -1.556891
2021-10-06 10:20 Python csv-read uncompressed, file, nyctaxi_2010-01 1.024 s -1.108783
2021-10-06 10:57 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.151 s -0.464361
2021-10-06 10:21 Python csv-read gzip, streaming, nyctaxi_2010-01 10.494 s 1.178723
2021-10-06 10:23 Python dataframe-to-table type_dict 0.011 s 1.253765
2021-10-06 10:57 Python file-read lz4, feather, table, fanniemae_2016Q4 0.590 s 2.306078
2021-10-06 11:06 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.825 s 0.616083
2021-10-06 10:19 Python csv-read gzip, streaming, fanniemae_2016Q4 14.921 s -0.517008
2021-10-06 10:20 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.515 s 1.067699
2021-10-06 10:23 Python dataframe-to-table type_integers 0.011 s 0.963970
2021-10-06 11:06 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.367358
2021-10-06 10:23 Python dataframe-to-table type_nested 2.871 s 0.940546
2021-10-06 10:58 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.236 s -0.864335
2021-10-06 11:04 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.777 s 1.183554
2021-10-06 10:58 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.294 s -1.119261
2021-10-06 11:02 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.695 s 0.145018
2021-10-06 10:57 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.795 s -0.942969
2021-10-06 11:05 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.844 s 1.111683
2021-10-06 11:44 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.912 s 0.274002
2021-10-06 11:47 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.186 s -0.779394
2021-10-06 11:46 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.913 s 0.374969
2021-10-06 11:49 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.993 s -0.651176
2021-10-06 11:50 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.517 s 0.673497
2021-10-06 12:17 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.836 s 1.540754
2021-10-06 12:17 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.554 s -0.812198
2021-10-06 11:45 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.363702
2021-10-06 12:00 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.780 s 1.405062
2021-10-06 12:17 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s 0.194531
2021-10-06 11:47 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s 0.128935
2021-10-06 11:48 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.385899
2021-10-06 12:08 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -3.238016
2021-10-06 12:17 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.218059
2021-10-06 11:46 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.554 s 1.835139
2021-10-06 11:52 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.251 s 0.951874
2021-10-06 12:06 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.573 s 0.771836
2021-10-06 12:17 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.644 s 0.761430
2021-10-06 12:17 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.789333
2021-10-06 12:17 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.282887
2021-10-06 12:17 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.024381
2021-10-06 11:20 R dataframe-to-table type_strings, R 0.490 s 0.518724
2021-10-06 12:01 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.452 s 1.334112
2021-10-06 12:17 JavaScript Parse serialize, tracks 0.004 s 0.566469
2021-10-06 12:17 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.636430
2021-10-06 11:49 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.111106
2021-10-06 12:07 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.526 s -1.343971
2021-10-06 11:20 R dataframe-to-table chi_traffic_2020_Q1, R 5.377 s 0.346528
2021-10-06 12:06 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.991117
2021-10-06 12:09 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.478 s -0.623388
2021-10-06 12:05 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.993642
2021-10-06 12:06 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.873 s 0.781660
2021-10-06 12:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -1.031708
2021-10-06 12:17 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.514045
2021-10-06 11:56 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.564 s 0.528799
2021-10-06 12:09 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.200 s 0.702858
2021-10-06 12:17 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.856 s 0.601068
2021-10-06 11:20 R dataframe-to-table type_dict, R 0.050 s 0.053259
2021-10-06 11:20 R dataframe-to-table type_floats, R 0.107 s 0.908823
2021-10-06 11:45 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.244 s 0.091657
2021-10-06 12:17 JavaScript Parse readBatches, tracks 0.000 s 0.080797
2021-10-06 11:20 R dataframe-to-table type_integers, R 0.084 s 0.132029
2021-10-06 11:44 R dataframe-to-table type_simple_features, R 276.097 s -2.042652
2021-10-06 11:45 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.914 s 0.097376
2021-10-06 11:48 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.145 s -1.387364
2021-10-06 11:55 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.733 s 0.810983
2021-10-06 12:03 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.285 s -1.239448
2021-10-06 12:07 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.598 s 0.759306
2021-10-06 12:17 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.288270
2021-10-06 12:02 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.638 s 1.522680
2021-10-06 12:06 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.588 s 0.605562
2021-10-06 12:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.880 s 0.768747
2021-10-06 12:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.592 s 0.042655
2021-10-06 12:17 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500672
2021-10-06 11:20 R dataframe-to-table type_nested, R 0.541 s -1.315553
2021-10-06 11:57 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.292119
2021-10-06 12:17 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.152525
2021-10-06 12:17 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.027598
2021-10-06 11:48 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.256 s -0.866887
2021-10-06 11:51 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.862 s 0.705279
2021-10-06 12:17 JavaScript Parse Table.from, tracks 0.000 s 0.571559
2021-10-06 12:17 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.726 s 0.118229
2021-10-06 12:17 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.890864
2021-10-06 11:47 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.363 s 1.292625
2021-10-06 12:05 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.493 s -0.400591
2021-10-06 12:08 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s 0.249208
2021-10-06 12:17 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.024 s 0.213338
2021-10-06 11:53 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.297 s 0.814723
2021-10-06 11:59 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.825 s 1.108600
2021-10-06 12:09 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -2.965204
2021-10-06 12:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.095765
2021-10-06 12:17 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.665 s -0.458060
2021-10-06 12:17 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.092956
2021-10-06 11:44 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.265 s 0.090580
2021-10-06 11:55 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.832 s 0.005786
2021-10-06 12:06 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.419779
2021-10-06 11:58 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.183 s 1.284346
2021-10-06 12:04 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.251 s 0.274937
2021-10-06 12:17 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.567 s -0.189174
2021-10-06 12:17 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500672
2021-10-06 12:17 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.184454
2021-10-06 12:17 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.552799
2021-10-06 12:17 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.570524
2021-10-06 12:17 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.166501
2021-10-06 12:17 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.161633
2021-10-06 12:17 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.749420
2021-10-06 11:06 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.361 s -0.002333