Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-09 08:26 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.881 s -0.693822
2021-10-09 08:27 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.366 s -0.785069
2021-10-09 08:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.402 s -1.379597
2021-10-09 08:28 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.397490
2021-10-09 08:28 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.444611
2021-10-09 08:41 R dataframe-to-table type_strings, R 0.491 s 0.231935
2021-10-09 08:42 R dataframe-to-table type_dict, R 0.051 s 0.022630
2021-10-09 08:42 R dataframe-to-table type_floats, R 0.013 s 1.994012
2021-10-09 08:42 R dataframe-to-table type_nested, R 0.536 s 0.233934
2021-10-09 08:49 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -1.441101
2021-10-09 08:49 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.956 s -1.819658
2021-10-09 08:51 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.987 s 0.097959
2021-10-09 08:52 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.681 s 0.106610
2021-10-09 08:52 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.538 s 0.047450
2021-10-09 09:02 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.866 s -0.927902
2021-10-09 09:19 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.300275
2021-10-09 07:42 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.464 s 1.143067
2021-10-09 07:42 Python csv-read gzip, streaming, nyctaxi_2010-01 10.458 s 1.158818
2021-10-09 07:45 Python dataframe-to-table chi_traffic_2020_Q1 19.736 s -0.392413
2021-10-09 07:45 Python dataframe-to-table type_strings 0.369 s 0.308270
2021-10-09 07:45 Python dataframe-to-table type_dict 0.012 s -0.481547
2021-10-09 07:45 Python dataframe-to-table type_nested 2.864 s 0.709470
2021-10-09 08:19 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.951 s -0.741239
2021-10-09 08:20 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.256 s -0.237584
2021-10-09 08:20 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.036 s 0.064656
2021-10-09 08:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.965 s -0.519978
2021-10-09 07:45 Python dataset-filter nyctaxi_2010-01 4.348 s 0.693069
2021-10-09 08:19 Python file-read lz4, feather, table, fanniemae_2016Q4 0.598 s 0.862643
2021-10-09 08:19 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.031 s 0.653549
2021-10-09 08:18 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.865 s -0.282585
2021-10-09 08:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.778 s 0.198964
2021-10-09 07:40 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.253223
2021-10-09 07:45 Python dataframe-to-table type_integers 0.011 s 0.784740
2021-10-09 08:18 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.278 s 0.649795
2021-10-09 08:28 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.848 s -1.248362
2021-10-09 08:50 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.408 s -1.429493
2021-10-09 08:50 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.150 s -1.901982
2021-10-09 08:19 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.250 s -0.534684
2021-10-09 08:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.185 s -1.795584
2021-10-09 09:19 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.378730
2021-10-09 08:07 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.006 s 0.246519
2021-10-09 08:48 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.210 s 1.169871
2021-10-09 08:50 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.066 s -1.601331
2021-10-09 08:51 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.348399
2021-10-09 07:45 Python dataframe-to-table type_floats 0.011 s 1.144510
2021-10-09 07:45 Python dataframe-to-table type_simple_features 0.912 s 0.217079
2021-10-09 08:20 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.312 s -0.488269
2021-10-09 08:27 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.893 s -0.404939
2021-10-09 08:28 Python wide-dataframe use_legacy_dataset=false 0.625 s -0.726244
2021-10-09 07:41 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.810405
2021-10-09 07:43 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.275587
2021-10-09 08:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.204 s 0.826625
2021-10-09 08:24 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.646 s 0.446455
2021-10-09 08:18 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.779 s 1.042434
2021-10-09 08:22 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.091 s 0.523963
2021-10-09 08:57 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.834 s -0.515154
2021-10-09 09:19 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.707 s -0.470886
2021-10-09 07:42 Python csv-read uncompressed, file, nyctaxi_2010-01 1.005 s 0.717781
2021-10-09 08:24 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.310 s 0.231263
2021-10-09 08:25 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.253 s 0.003759
2021-10-09 08:59 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 1.115590
2021-10-09 08:41 R dataframe-to-table chi_traffic_2020_Q1, R 3.419 s 0.275847
2021-10-09 08:53 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.845 s 0.594154
2021-10-09 07:40 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.936 s 0.011804
2021-10-09 07:49 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.135 s 0.223307
2021-10-09 08:02 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.386 s 0.120692
2021-10-09 08:17 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.964 s 0.703117
2021-10-09 08:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.307 s 0.276580
2021-10-09 08:23 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.447 s 0.531457
2021-10-09 08:25 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.264658
2021-10-09 08:55 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.238 s 0.822036
2021-10-09 08:59 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.546 s 0.879404
2021-10-09 08:07 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.004 s 0.464119
2021-10-09 09:00 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.182 s 1.291461
2021-10-09 07:41 Python csv-read gzip, streaming, fanniemae_2016Q4 14.874 s -0.040440
2021-10-09 08:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.845 s 0.248590
2021-10-09 08:17 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.712 s 0.395686
2021-10-09 07:53 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.581 s -0.449851
2021-10-09 08:06 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.011 s 0.957112
2021-10-09 08:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.901 s -1.337030
2021-10-09 08:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.863 s -0.427449
2021-10-09 08:55 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.300 s 0.566696
2021-10-09 08:57 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.688 s 0.880973
2021-10-09 08:02 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.404 s -0.219156
2021-10-09 08:17 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.968 s 0.301888
2021-10-09 08:19 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.287 s 0.489780
2021-10-09 08:21 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.136337
2021-10-09 08:49 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.596278
2021-10-09 09:03 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.699334
2021-10-09 08:18 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.129 s 0.897241
2021-10-09 08:50 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.165 s 1.868112
2021-10-09 09:01 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.903 s -0.877393
2021-10-09 09:19 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.504203
2021-10-09 09:19 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.129677
2021-10-09 09:04 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.720 s -0.770137
2021-10-09 09:08 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.170 s 0.628365
2021-10-09 09:06 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.240 s 0.685350
2021-10-09 09:07 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.490 s -0.032463
2021-10-09 09:11 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.087720
2021-10-09 09:05 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.287 s -2.199509
2021-10-09 09:11 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s -0.838574
2021-10-09 09:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.532 s -1.835697
2021-10-09 09:09 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.486560
2021-10-09 09:08 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.866 s 0.362539
2021-10-09 09:08 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.584 s -1.583464
2021-10-09 09:08 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s -0.547353
2021-10-09 09:08 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s -0.473790
2021-10-09 09:08 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.239715
2021-10-09 09:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.215126
2021-10-09 09:11 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.173 s 0.125685
2021-10-09 09:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.691 s -1.520703
2021-10-09 09:09 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s -0.206026
2021-10-09 09:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.899 s 0.801993
2021-10-09 09:11 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.491 s -2.121415
2021-10-09 09:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.508 s -0.847404
2021-10-09 09:19 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.676 s 0.415522
2021-10-09 09:19 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.510089
2021-10-09 09:19 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.051 s -3.091055
2021-10-09 09:19 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.286583
2021-10-09 09:19 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.631 s -0.460045
2021-10-09 09:19 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.879354
2021-10-09 09:19 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.529419
2021-10-09 09:19 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.752623
2021-10-09 09:19 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.507934
2021-10-09 09:19 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.594458
2021-10-09 09:19 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.712389
2021-10-09 09:19 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.695683
2021-10-09 09:19 JavaScript Parse Table.from, tracks 0.000 s 1.362248
2021-10-09 09:19 JavaScript Parse readBatches, tracks 0.000 s 1.149386
2021-10-09 09:19 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.483175
2021-10-09 09:19 JavaScript Parse serialize, tracks 0.005 s -0.846471
2021-10-09 09:19 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -2.409033
2021-10-09 09:19 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.394895
2021-10-09 09:19 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.887855
2021-10-09 09:19 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.907195
2021-10-09 09:19 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.565 s -0.855698
2021-10-09 09:19 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.274062
2021-10-09 09:19 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.539 s -0.320579
2021-10-09 09:19 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.949 s -1.725494
2021-10-09 09:19 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-09 09:19 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.763461
2021-10-09 08:21 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.536 s -0.915127
2021-10-09 08:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.926 s -0.485220
2021-10-09 08:42 R dataframe-to-table type_integers, R 0.010 s 1.992585
2021-10-09 08:48 R dataframe-to-table type_simple_features, R 3.308 s 1.550385
2021-10-09 08:48 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.205 s 0.559872
2021-10-09 08:48 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.444 s 1.887287
2021-10-09 08:49 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.442 s 1.836342
2021-10-09 08:51 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.221 s 1.827741