Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-28 07:33 Python csv-read gzip, streaming, fanniemae_2016Q4 14.970 s -0.932256
2021-09-28 07:37 Python dataframe-to-table type_nested 2.943 s 0.657081
2021-09-28 07:37 Python dataframe-to-table chi_traffic_2020_Q1 19.746 s 0.274863
2021-09-28 07:32 Python csv-read uncompressed, file, fanniemae_2016Q4 1.216 s -0.709012
2021-09-28 07:37 Python dataframe-to-table type_floats 0.012 s -0.157219
2021-09-28 07:38 Python dataset-filter nyctaxi_2010-01 4.371 s -0.531550
2021-09-28 07:37 Python dataframe-to-table type_integers 0.011 s -2.576332
2021-09-28 07:35 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.472408
2021-09-28 07:37 Python dataframe-to-table type_strings 0.366 s 0.754161
2021-09-28 07:34 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s 0.014191
2021-09-28 07:35 Python csv-read gzip, streaming, nyctaxi_2010-01 10.633 s -0.424638
2021-09-28 07:32 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.053 s -0.945886
2021-09-28 07:33 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.074061
2021-09-28 07:34 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.636 s -0.396427
2021-09-28 07:37 Python dataframe-to-table type_dict 0.012 s -1.580492
2021-09-28 07:37 Python dataframe-to-table type_simple_features 0.906 s 0.549795
2021-09-28 07:41 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.278 s -0.299731
2021-09-28 08:07 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.023 s -0.126771
2021-09-28 08:20 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.797 s 1.112771
2021-09-28 08:28 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.760 s 0.696325
2021-09-28 09:06 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.909 s 0.107721
2021-09-28 09:23 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.725 s 1.177300
2021-09-28 08:24 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.474 s 1.592397
2021-09-28 09:26 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s 0.120668
2021-09-28 08:27 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.355 s -0.308357
2021-09-28 08:19 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 1.022694
2021-09-28 09:09 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s -0.493765
2021-09-28 09:19 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.212 s 1.007490
2021-09-28 09:27 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 2.591 s 0.905931
2021-09-28 09:27 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.103 s -4.320657
2021-09-28 08:04 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.207 s 0.487187
2021-09-28 08:17 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.675 s 0.553617
2021-09-28 08:18 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.845 s -1.118502
2021-09-28 08:18 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.662 s -0.161671
2021-09-28 08:07 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.033 s -0.052561
2021-09-28 08:20 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.053 s -0.983296
2021-09-28 08:21 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.475491
2021-09-28 08:22 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.080 s 1.686653
2021-09-28 08:18 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.747 s -1.283666
2021-09-28 08:17 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.248 s -0.540813
2021-09-28 08:19 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.059 s -0.600966
2021-09-28 09:13 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.268 s 1.799611
2021-09-28 09:16 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.835 s -1.096795
2021-09-28 07:54 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 268.196 s 0.184584
2021-09-28 08:17 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.993 s -0.224185
2021-09-28 08:07 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.038 s -0.041629
2021-09-28 08:19 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.110 s 0.488896
2021-09-28 09:09 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.237 s 0.173320
2021-09-28 08:23 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.428 s 1.730699
2021-09-28 08:26 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.792 s 0.965047
2021-09-28 08:27 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.813 s 1.072468
2021-09-28 08:03 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.148 s 0.432305
2021-09-28 08:21 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 7.981 s 0.949781
2021-09-28 08:25 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.067 s 1.460125
2021-09-28 08:26 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.892 s 1.811331
2021-09-28 08:28 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.778653
2021-09-28 08:41 R dataframe-to-table type_dict, R 0.053 s -0.165906
2021-09-28 09:06 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.233 s 0.219998
2021-09-28 09:16 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.716 s 1.827752
2021-09-28 09:27 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.698 s 0.604150
2021-09-28 08:21 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.511 s 0.839058
2021-09-28 08:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.124 s 1.547098
2021-09-28 08:24 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.603 s 0.997188
2021-09-28 09:05 R dataframe-to-table type_simple_features, R 274.790 s -0.024792
2021-09-28 08:41 R dataframe-to-table type_integers, R 0.086 s -1.003965
2021-09-28 08:18 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.143 s -0.685395
2021-09-28 08:24 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.342 s -0.078302
2021-09-28 08:25 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.833 s 1.590417
2021-09-28 08:42 R dataframe-to-table type_nested, R 0.537 s -0.218116
2021-09-28 09:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s -0.180954
2021-09-28 09:17 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.576 s 1.093553
2021-09-28 08:18 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.277 s 0.186049
2021-09-28 08:19 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.376262
2021-09-28 08:20 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.360913
2021-09-28 08:41 R dataframe-to-table type_strings, R 0.488 s 0.873616
2021-09-28 09:25 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s 0.072498
2021-09-28 09:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.602 s 0.969726
2021-09-28 08:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 2.564 s -3.004666
2021-09-28 09:05 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.221 s 0.347400
2021-09-28 09:11 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.534 s -0.940208
2021-09-28 09:24 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.495310
2021-09-28 08:17 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.996 s 0.136085
2021-09-28 08:25 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.540278
2021-09-28 08:28 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.168538
2021-09-28 08:19 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.811 s -0.444124
2021-09-28 08:41 R dataframe-to-table type_floats, R 0.107 s 0.548020
2021-09-28 09:06 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.155320
2021-09-28 09:14 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 1.876961
2021-09-28 08:20 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.828 s 1.046222
2021-09-28 08:28 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.165353
2021-09-28 09:20 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.894 s 0.822463
2021-09-28 08:27 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.298 s 0.581571
2021-09-28 09:06 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.898 s 0.250166
2021-09-28 09:08 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.399 s -1.175568
2021-09-28 09:09 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.198 s -1.751929
2021-09-28 09:12 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.829 s 1.992444
2021-09-28 09:21 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.873 s 1.036694
2021-09-28 09:26 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.717996
2021-09-28 09:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.172 s 0.513934
2021-09-28 09:18 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -0.884131
2021-09-28 08:41 R dataframe-to-table chi_traffic_2020_Q1, R 5.372 s 0.625296
2021-09-28 09:07 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.947 s -1.426812
2021-09-28 09:10 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.979 s -0.681285
2021-09-28 09:27 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.753 s 0.744853
2021-09-28 09:28 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.514 s 0.205133
2021-09-28 09:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 7.874 s 0.888559
2021-09-28 09:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 1.344618
2021-09-28 09:29 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.100 s 0.797401
2021-09-28 09:30 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.477 s -1.160182
2021-09-28 09:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.396 s -0.803169
2021-09-28 09:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.801 s 1.369513
2021-09-28 09:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.161114
2021-09-28 09:38 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.013462
2021-09-28 09:38 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.576 s -0.009095
2021-09-28 09:38 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.632 s 0.816441
2021-09-28 09:38 JavaScript Parse serialize, tracks 0.005 s -0.173041
2021-09-28 09:38 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.144235
2021-09-28 09:38 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.469571
2021-09-28 09:38 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.012585
2021-09-28 09:38 JavaScript Parse Table.from, tracks 0.000 s -1.534825
2021-09-28 09:38 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.711 s 0.198535
2021-09-28 09:38 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.487483
2021-09-28 09:38 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.830726
2021-09-28 09:38 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.504 s 0.133697
2021-09-28 09:38 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.215019
2021-09-28 09:38 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.420338
2021-09-28 09:38 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.865 s 0.434366
2021-09-28 09:38 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.887 s 0.211901
2021-09-28 09:38 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-28 09:38 JavaScript Parse readBatches, tracks 0.000 s -1.782500
2021-09-28 09:38 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-28 09:38 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.199392
2021-09-28 09:38 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.928629
2021-09-28 09:38 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.204903
2021-09-28 09:38 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.620296
2021-09-28 09:38 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.928523
2021-09-28 09:38 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.676430
2021-09-28 09:38 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.619971
2021-09-28 09:38 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.464675
2021-09-28 09:38 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.233870
2021-09-28 09:38 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.617 s -0.070081
2021-09-28 09:38 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.926513
2021-09-28 09:38 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.632257
2021-09-28 09:08 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.053 s 0.541075
2021-09-28 09:09 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.110 s 1.478173
2021-09-28 09:10 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.374670
2021-09-28 09:22 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.514 s 1.383116
2021-09-28 09:28 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.986 s -0.121965
2021-09-28 09:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.566 s 0.497612