Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-10 21:20 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.842 s 0.830509
2021-10-10 21:20 Python csv-read uncompressed, file, fanniemae_2016Q4 1.171 s 0.105489
2021-10-10 21:21 Python csv-read gzip, streaming, fanniemae_2016Q4 14.785 s 0.695387
2021-10-10 21:21 Python csv-read gzip, file, fanniemae_2016Q4 6.027 s 0.717106
2021-10-10 21:22 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.643 s -0.163663
2021-10-10 21:22 Python csv-read uncompressed, file, nyctaxi_2010-01 1.016 s -0.452792
2021-10-10 21:22 Python csv-read gzip, streaming, nyctaxi_2010-01 10.635 s -0.300891
2021-10-10 21:23 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.991355
2021-10-10 21:25 Python dataframe-to-table chi_traffic_2020_Q1 19.703 s -0.349283
2021-10-10 21:25 Python dataframe-to-table type_strings 0.367 s 0.408064
2021-10-10 21:25 Python dataframe-to-table type_dict 0.012 s 0.501741
2021-10-10 21:25 Python dataframe-to-table type_integers 0.011 s -1.701642
2021-10-10 21:25 Python dataframe-to-table type_floats 0.011 s -0.235603
2021-10-10 21:25 Python dataframe-to-table type_nested 2.876 s 0.109624
2021-10-10 21:25 Python dataframe-to-table type_simple_features 0.929 s -0.642543
2021-10-10 21:25 Python dataset-filter nyctaxi_2010-01 4.317 s 1.658558
2021-10-10 21:47 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.072 s -1.756967
2021-10-10 21:47 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.085 s -0.523013
2021-10-10 21:47 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.044 s -0.421912
2021-10-10 21:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.978 s 0.285227
2021-10-10 21:59 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.715 s 0.304690
2021-10-10 21:59 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.960 s 0.540987
2021-10-10 22:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.784 s 0.661570
2021-10-10 22:00 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.291 s -0.090354
2021-10-10 22:00 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.128 s 0.618648
2021-10-10 22:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.098089
2021-10-10 22:01 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.690 s 3.217079
2021-10-10 22:01 Python file-read lz4, feather, table, fanniemae_2016Q4 0.594 s 1.352378
2021-10-10 22:01 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.997 s 3.351086
2021-10-10 22:01 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.090 s -1.837500
2021-10-10 22:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.109 s 1.676340
2021-10-10 22:02 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.985 s 3.123449
2021-10-10 22:03 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.277 s 1.465227
2021-10-10 22:03 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.773 s 1.495614
2021-10-10 22:04 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.530 s -0.960158
2021-10-10 22:06 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.321 s 0.160318
2021-10-10 22:08 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.964 s -1.481544
2021-10-10 22:09 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.924 s -0.604259
2021-10-10 22:09 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.379 s -1.383418
2021-10-10 22:09 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.376 s -0.575145
2021-10-10 22:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.838 s -1.731374
2021-10-10 22:10 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.873 s -1.678875
2021-10-10 22:10 Python wide-dataframe use_legacy_dataset=true 0.389 s 2.253894
2021-10-10 22:10 Python wide-dataframe use_legacy_dataset=false 0.611 s 2.209292
2021-10-10 22:23 R dataframe-to-table chi_traffic_2020_Q1, R 3.427 s 0.272360
2021-10-10 22:23 R dataframe-to-table type_dict, R 0.049 s 0.388944
2021-10-10 22:24 R dataframe-to-table type_nested, R 0.529 s 0.236742
2021-10-10 22:30 R dataframe-to-table type_simple_features, R 3.336 s 1.093789
2021-10-10 22:30 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.223 s 0.326194
2021-10-10 22:30 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.211 s 0.838985
2021-10-10 22:31 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.319 s -2.548069
2021-10-10 22:32 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.384 s 0.278746
2021-10-10 22:32 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.049 s 0.916703
2021-10-10 22:32 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.165 s 1.262680
2021-10-10 22:32 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 1.247911
2021-10-10 22:32 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -2.568893
2021-10-10 22:07 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.933 s -1.195869
2021-10-10 22:33 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.025 s -0.335115
2021-10-10 22:33 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.690 s 0.056553
2021-10-10 22:34 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.534 s 0.065451
2021-10-10 22:35 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.835 s 0.658280
2021-10-10 22:36 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.322 s 0.162607
2021-10-10 22:39 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.739 s 0.460635
2021-10-10 22:39 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 2.006160
2021-10-10 22:41 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.388 s 1.498936
2021-10-10 22:43 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.897 s -0.537233
2021-10-10 22:44 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.876 s -0.924789
2021-10-10 22:45 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.530 s -0.469235
2021-10-10 22:46 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.732 s -0.830790
2021-10-10 22:47 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 1.909701
2021-10-10 22:49 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.475 s 1.484882
2021-10-10 22:49 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.198 s -1.995431
2021-10-10 22:50 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s -0.500244
2021-10-10 22:50 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 0.044499
2021-10-10 22:50 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.345778
2021-10-10 22:50 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.248263
2021-10-10 22:50 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.592 s 1.312349
2021-10-10 22:51 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.526 s -0.671349
2021-10-10 22:51 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -0.475268
2021-10-10 22:51 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.893 s 0.729129
2021-10-10 22:52 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.699 s -1.752699
2021-10-10 22:52 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.559519
2021-10-10 22:53 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s -0.031393
2021-10-10 22:53 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.161 s 0.946532
2021-10-10 23:01 JavaScript Parse Table.from, tracks 0.000 s 0.528732
2021-10-10 23:01 JavaScript Parse readBatches, tracks 0.000 s 0.706815
2021-10-10 23:01 JavaScript Parse serialize, tracks 0.005 s -0.368578
2021-10-10 23:01 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.832762
2021-10-10 23:01 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.835147
2021-10-10 23:01 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.580 s -0.336320
2021-10-10 23:01 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 1.226734
2021-10-10 23:01 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.743 s -1.239368
2021-10-10 23:01 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.763 s -0.184407
2021-10-10 23:01 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.488304
2021-10-10 23:01 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.574126
2021-10-10 23:01 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.900 s -0.611496
2021-10-10 23:01 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.941 s -0.667558
2021-10-10 23:01 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.658501
2021-10-10 23:01 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.486534
2021-10-10 23:01 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.525547
2021-10-10 23:01 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.083240
2021-10-10 23:01 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.132816
2021-10-10 23:01 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.023221
2021-10-10 23:01 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.403926
2021-10-10 23:01 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.140965
2021-10-10 23:01 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.947966
2021-10-10 23:01 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.045 s 1.623214
2021-10-10 23:01 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.118077
2021-10-10 23:01 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.786784
2021-10-10 23:01 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.530 s -0.163550
2021-10-10 22:37 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.291 s 0.631478
2021-10-10 21:58 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.809 s 0.445336
2021-10-10 21:59 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.207 s 0.524903
2021-10-10 21:42 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.140 s 0.280963
2021-10-10 22:00 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.524 s 3.276819
2021-10-10 22:02 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.134 s 1.483745
2021-10-10 22:02 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.175 s 0.080692
2021-10-10 22:48 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.256 s -1.143375
2021-10-10 22:08 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.973 s -0.900068
2021-10-10 22:23 R dataframe-to-table type_integers, R 0.010 s 1.330375
2021-10-10 22:50 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.856 s 0.594595
2021-10-10 23:01 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.633 s -0.412167
2021-10-10 23:01 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.790466
2021-10-10 22:31 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.560 s 0.412173
2021-10-10 22:52 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.387286
2021-10-10 22:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.503 s -0.213947
2021-10-10 21:28 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.235 s 0.991313
2021-10-10 21:33 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.313 s -0.732377
2021-10-10 22:00 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.645 s 2.698980
2021-10-10 22:03 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.087819
2021-10-10 22:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 10.165 s -3.144285
2021-10-10 22:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.148 s 0.932038
2021-10-10 22:07 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.528 s -2.801518
2021-10-10 22:23 R dataframe-to-table type_strings, R 0.489 s 0.233773
2021-10-10 22:24 R dataframe-to-table type_floats, R 0.013 s 1.322371
2021-10-10 22:30 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.482 s 1.266083
2021-10-10 22:30 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.450 s 1.249788
2021-10-10 22:42 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.235 s -1.707544
2021-10-10 23:01 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.021 s 1.428090
2021-10-10 23:01 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.792140
2021-10-10 23:01 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.139316
2021-10-10 21:42 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.240 s -0.489469
2021-10-10 22:04 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.083 s 0.602669
2021-10-10 22:05 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.446 s 0.551256
2021-10-10 22:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.937 s -1.271352
2021-10-10 22:31 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.056 s -2.454262
2021-10-10 22:32 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.111 s 0.917987
2021-10-10 22:40 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.580 s -0.527274
2021-10-10 22:53 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.490 s -1.279154