Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 20:42 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.843 s 0.036458
2021-10-12 21:36 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.290205
2021-10-12 21:36 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.689 s 0.335473
2021-10-12 21:36 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.926 s -0.334733
2021-10-12 21:36 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.108133
2021-10-12 21:36 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.227389
2021-10-12 19:57 Python csv-read uncompressed, file, fanniemae_2016Q4 1.155 s 0.564805
2021-10-12 20:41 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.303 s 0.011458
2021-10-12 19:57 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.859 s 0.614212
2021-10-12 21:36 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.226926
2021-10-12 19:57 Python csv-read gzip, streaming, fanniemae_2016Q4 14.791 s 0.646793
2021-10-12 19:58 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.431669
2021-10-12 19:58 Python csv-read uncompressed, file, nyctaxi_2010-01 1.000 s 0.869650
2021-10-12 19:58 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.938 s -1.979106
2021-10-12 19:59 Python csv-read gzip, streaming, nyctaxi_2010-01 10.930 s -2.548512
2021-10-12 20:00 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.059743
2021-10-12 20:39 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.527 s -0.822176
2021-10-12 20:59 R dataframe-to-table type_strings, R 0.490 s 0.232063
2021-10-12 21:36 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.078648
2021-10-12 20:24 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.038 s -0.116042
2021-10-12 20:37 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.026 s 0.481621
2021-10-12 20:38 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.003019
2021-10-12 20:01 Python dataframe-to-table type_floats 0.011 s 0.631687
2021-10-12 20:34 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.304 s -1.771845
2021-10-12 20:35 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.279 s 0.512052
2021-10-12 20:43 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.862 s -0.126829
2021-10-12 20:44 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.902 s -0.289857
2021-10-12 21:08 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.102 s 1.300300
2021-10-12 21:08 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.427682
2021-10-12 21:15 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.817 s 0.848737
2021-10-12 21:25 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.159 s 1.201224
2021-10-12 21:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.548 s 0.766403
2021-10-12 20:01 Python dataframe-to-table type_dict 0.011 s 1.248058
2021-10-12 20:35 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.126 s 0.711404
2021-10-12 20:38 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.801 s 1.470855
2021-10-12 20:41 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.940 s -0.682877
2021-10-12 20:42 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.495 s -1.406227
2021-10-12 21:36 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.756725
2021-10-12 20:24 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.067 s -0.188641
2021-10-12 20:36 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.990 s 1.767699
2021-10-12 20:45 Python wide-dataframe use_legacy_dataset=false 0.614 s 1.031044
2021-10-12 20:59 R dataframe-to-table type_nested, R 0.540 s 0.232568
2021-10-12 21:06 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -1.662873
2021-10-12 21:25 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.588 s -2.027036
2021-10-12 21:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.553369
2021-10-12 21:36 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.883 s -0.029006
2021-10-12 21:36 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.520119
2021-10-12 20:01 Python dataframe-to-table type_integers 0.011 s -0.007087
2021-10-12 20:10 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.062 s -1.305924
2021-10-12 20:39 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.084 s 0.444618
2021-10-12 20:44 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.635617
2021-10-12 20:59 R dataframe-to-table type_dict, R 0.051 s 0.096943
2021-10-12 21:09 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.694 s -0.015103
2021-10-12 21:23 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 0.256989
2021-10-12 20:34 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.920 s 0.545949
2021-10-12 20:34 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.059 s -1.859352
2021-10-12 21:22 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.271 s 1.865143
2021-10-12 21:26 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.536 s -2.045376
2021-10-12 21:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.877 s 1.060975
2021-10-12 21:36 JavaScript Parse Table.from, tracks 0.000 s 0.084579
2021-10-12 21:36 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.591 s -0.260980
2021-10-12 20:01 Python dataframe-to-table chi_traffic_2020_Q1 19.378 s 0.565701
2021-10-12 20:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.669 s 0.668413
2021-10-12 20:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.892 s -1.864656
2021-10-12 20:36 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.677 s 1.745598
2021-10-12 21:24 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.475 s 0.910436
2021-10-12 21:25 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.595 s -0.662692
2021-10-12 21:36 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.500672
2021-10-12 20:02 Python dataframe-to-table type_nested 2.861 s 0.960100
2021-10-12 20:35 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.588 s 1.839634
2021-10-12 20:36 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 0.997186
2021-10-12 21:36 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-12 21:36 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.190128
2021-10-12 20:02 Python dataset-filter nyctaxi_2010-01 4.366 s -1.155079
2021-10-12 20:43 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.913 s -0.063888
2021-10-12 20:44 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.353 s -0.091004
2021-10-12 20:59 R dataframe-to-table type_integers, R 0.009 s 0.920792
2021-10-12 21:13 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 0.423575
2021-10-12 21:16 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.391 s 0.686632
2021-10-12 21:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s 0.220575
2021-10-12 21:36 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.139499
2021-10-12 21:36 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.426247
2021-10-12 20:02 Python dataframe-to-table type_simple_features 0.933 s -0.828219
2021-10-12 20:45 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.875 s -1.880631
2021-10-12 21:06 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 0.852964
2021-10-12 21:19 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.710011
2021-10-12 21:36 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.566 s -0.152251
2021-10-12 20:37 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.166 s 1.505149
2021-10-12 21:06 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.218 s 0.320080
2021-10-12 21:06 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.238 s -0.507539
2021-10-12 21:25 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.878 s -0.077595
2021-10-12 21:36 JavaScript Parse serialize, tracks 0.004 s 0.650164
2021-10-12 20:05 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.907 s -0.472013
2021-10-12 20:23 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.080 s -1.468003
2021-10-12 20:37 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.113 s 1.985554
2021-10-12 21:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.118125
2021-10-12 21:08 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.163 s 0.867039
2021-10-12 21:16 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.520 s 1.558401
2021-10-12 21:20 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.536 s -0.667029
2021-10-12 21:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s 0.241316
2021-10-12 20:40 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.440 s 0.459438
2021-10-12 20:59 R dataframe-to-table chi_traffic_2020_Q1, R 3.366 s 0.266478
2021-10-12 21:07 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.066 s -1.478500
2021-10-12 21:25 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.559734
2021-10-12 20:37 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.134 s 1.658821
2021-10-12 21:10 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.845 s 0.417022
2021-10-12 21:18 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.897 s -0.573395
2021-10-12 21:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 0.215988
2021-10-12 21:36 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.547272
2021-10-12 20:01 Python dataframe-to-table type_strings 0.367 s 0.449410
2021-10-12 20:35 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.556 s 1.441293
2021-10-12 20:44 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.343 s 0.409257
2021-10-12 21:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.164 s 0.826077
2021-10-12 21:36 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.521760
2021-10-12 21:36 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.175566
2021-10-12 21:36 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.116060
2021-10-12 21:36 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.532 s -0.144911
2021-10-12 20:19 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.765 s -0.009227
2021-10-12 20:19 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.377 s 0.131501
2021-10-12 20:33 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.815 s 0.397641
2021-10-12 20:36 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.282 s 1.020365
2021-10-12 20:38 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.306 s 1.343551
2021-10-12 20:41 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.903 s -0.921568
2021-10-12 21:05 R dataframe-to-table type_simple_features, R 3.346 s 0.753432
2021-10-12 21:06 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.506 s 0.868325
2021-10-12 21:18 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.191 s 0.332844
2021-10-12 21:22 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.727 s -0.737249
2021-10-12 21:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.615 s 0.298298
2021-10-12 21:36 JavaScript Parse readBatches, tracks 0.000 s 0.631868
2021-10-12 21:36 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.697 s -0.239528
2021-10-12 21:36 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.205483
2021-10-12 21:08 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.195680
2021-10-12 21:09 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.978 s 0.254651
2021-10-12 21:14 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.695 s 0.620811
2021-10-12 21:28 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.495 s -2.253393
2021-10-12 21:36 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.067012
2021-10-12 20:36 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.026 s 0.720078
2021-10-12 20:42 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.720595
2021-10-12 20:45 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.639561
2021-10-12 21:08 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.212 s 0.861487
2021-10-12 21:12 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.265 s 0.384012
2021-10-12 21:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -0.551969
2021-10-12 21:36 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.070914
2021-10-12 21:36 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.316178
2021-10-12 21:36 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.512005
2021-10-12 20:59 R dataframe-to-table type_floats, R 0.013 s 0.904612
2021-10-12 21:07 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.412 s -1.154896
2021-10-12 21:10 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.577 s -0.370170
2021-10-12 21:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.364 s -1.080972