Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-05 23:44 Python dataframe-to-table type_dict 0.012 s -0.220665
2021-10-05 23:40 Python csv-read gzip, streaming, fanniemae_2016Q4 14.891 s -0.359952
2021-10-06 00:22 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.956 s -1.185570
2021-10-06 00:23 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.490 s 0.532502
2021-10-05 23:41 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.563 s 0.745882
2021-10-05 23:42 Python csv-read gzip, streaming, nyctaxi_2010-01 10.513 s 1.059800
2021-10-06 00:02 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.245 s 0.311222
2021-10-05 23:44 Python dataframe-to-table type_strings 0.367 s 0.572605
2021-10-06 00:17 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.985 s 0.154154
2021-10-06 00:17 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.995 s 0.035373
2021-10-06 00:19 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.264 s -1.388676
2021-10-05 23:39 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.955 s -0.341957
2021-10-06 00:18 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.824 s -0.005010
2021-10-06 00:18 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.307 s -0.769873
2021-10-05 23:41 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.638926
2021-10-05 23:44 Python dataframe-to-table chi_traffic_2020_Q1 19.509 s 0.671355
2021-10-06 00:18 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.256 s -0.354279
2021-10-06 00:20 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.333 s -1.339606
2021-10-05 23:52 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.102 s 0.790005
2021-10-06 00:17 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.827 s 0.378736
2021-10-06 00:17 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.683 s 0.630102
2021-10-06 00:24 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.684 s 0.267275
2021-10-06 00:27 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.818 s 0.715228
2021-10-05 23:45 Python dataframe-to-table type_simple_features 0.912 s 0.059418
2021-10-05 23:45 Python dataset-filter nyctaxi_2010-01 4.351 s 0.659830
2021-10-06 00:19 Python file-read lz4, feather, table, fanniemae_2016Q4 0.601 s 0.377237
2021-10-06 00:21 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.234754
2021-10-06 00:27 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.355 s -0.298183
2021-10-06 00:28 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.673775
2021-10-05 23:42 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.234430
2021-10-06 00:20 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.052 s -0.984118
2021-10-06 00:25 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.306 s -0.691945
2021-10-05 23:45 Python dataframe-to-table type_nested 2.876 s 0.894408
2021-10-06 00:19 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.325371
2021-10-06 00:20 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.042 s 0.231395
2021-10-06 00:25 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.764 s -0.171819
2021-10-06 00:27 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.856 s 1.017842
2021-10-05 23:44 Python dataframe-to-table type_integers 0.011 s -0.631079
2021-10-05 23:48 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.212 s -1.027408
2021-10-06 00:06 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.042 s -0.080468
2021-10-06 00:06 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.020 s 0.028734
2021-10-06 00:18 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.914 s -1.761126
2021-10-06 00:25 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.270701
2021-10-06 00:26 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.792 s 0.638833
2021-10-05 23:39 Python csv-read uncompressed, file, fanniemae_2016Q4 1.161 s 0.720019
2021-10-05 23:41 Python csv-read uncompressed, file, nyctaxi_2010-01 1.019 s -0.490849
2021-10-05 23:44 Python dataframe-to-table type_floats 0.011 s 1.471318
2021-10-06 00:21 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.324 s -1.266241
2021-10-06 00:02 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.918 s 0.385351
2021-10-06 00:23 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.315 s 0.280917
2021-10-06 00:19 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.835 s -1.674985
2021-10-06 00:19 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.967 s -1.523282
2021-10-06 00:21 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.482 s -1.296777
2021-10-06 00:28 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.360 s 0.094149
2021-10-06 00:06 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.017 s 0.241797
2021-10-06 00:26 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.792 s 1.040059
2021-10-06 00:18 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.154 s -0.643501
2021-10-06 00:21 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -1.462870
2021-10-06 00:22 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.136 s 0.525357
2021-10-06 00:24 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.283 s 0.387250
2021-10-06 00:28 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.346970
2021-10-06 00:28 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.796 s 0.405060
2021-10-06 00:28 Python wide-dataframe use_legacy_dataset=false 0.623 s -0.639644
2021-10-06 01:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.262 s 0.921370
2021-10-06 01:18 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.829 s 0.602118
2021-10-06 01:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.456 s 1.338027
2021-10-06 00:42 R dataframe-to-table type_strings, R 0.489 s 1.357289
2021-10-06 01:07 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.250 s 0.016308
2021-10-06 01:07 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.920 s 0.030730
2021-10-06 01:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.070475
2021-10-06 01:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.867 s 0.862230
2021-10-06 01:39 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.116954
2021-10-06 00:42 R dataframe-to-table type_dict, R 0.051 s -0.034085
2021-10-06 01:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.729 s 0.894020
2021-10-06 01:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.581 s 0.161722
2021-10-06 01:32 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.178 s 0.811473
2021-10-06 00:43 R dataframe-to-table type_nested, R 0.542 s -1.837345
2021-10-06 01:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.396867
2021-10-06 01:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -2.864402
2021-10-06 01:06 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.248 s 0.226734
2021-10-06 01:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.546 s -0.871665
2021-10-06 01:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.278 s 1.755745
2021-10-06 01:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.185 s -0.934186
2021-10-06 01:07 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.917 s 0.224138
2021-10-06 00:42 R dataframe-to-table chi_traffic_2020_Q1, R 5.341 s -0.004323
2021-10-06 01:09 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.061 s -0.784844
2021-10-06 01:39 JavaScript Parse Table.from, tracks 0.000 s -0.774750
2021-10-06 01:39 JavaScript Parse serialize, tracks 0.005 s 0.479155
2021-10-06 00:43 R dataframe-to-table type_floats, R 0.106 s 0.025911
2021-10-06 01:16 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.305 s 0.831493
2021-10-06 01:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.585 s 0.714957
2021-10-06 01:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.191 s 0.931572
2021-10-06 01:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.820 s 1.285764
2021-10-06 01:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.787 s 1.348608
2021-10-06 01:39 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.882 s -0.032402
2021-10-06 01:39 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.524174
2021-10-06 01:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.639 s 1.598026
2021-10-06 01:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.235 s 1.523273
2021-10-06 01:28 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.159559
2021-10-06 01:39 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s -0.113852
2021-10-06 01:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.884926
2021-10-06 00:42 R dataframe-to-table type_integers, R 0.084 s -0.175516
2021-10-06 01:12 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.668 s 0.211671
2021-10-06 01:39 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.934779
2021-10-06 01:11 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.236 s 0.345159
2021-10-06 01:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.850 s 0.843827
2021-10-06 01:39 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.024 s 0.160861
2021-10-06 01:08 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.105391
2021-10-06 01:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.379 s 0.274232
2021-10-06 01:30 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.617 s -1.115194
2021-10-06 01:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.553 s 0.658670
2021-10-06 01:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.085401
2021-10-06 01:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.772512
2021-10-06 01:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.571 s 0.852816
2021-10-06 01:39 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.698941
2021-10-06 01:39 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.649700
2021-10-06 01:06 R dataframe-to-table type_simple_features, R 275.132 s -0.244398
2021-10-06 01:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.366 s 0.163289
2021-10-06 01:39 JavaScript Parse readBatches, tracks 0.000 s -0.544622
2021-10-06 01:39 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.107231
2021-10-06 01:39 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.525187
2021-10-06 01:08 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.010774
2021-10-06 01:39 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -1.575321
2021-10-06 01:39 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.590851
2021-10-06 01:39 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.106696
2021-10-06 01:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.846866
2021-10-06 01:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.603 s 0.764973
2021-10-06 01:39 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.557557
2021-10-06 01:39 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.048 s -2.958103
2021-10-06 01:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.164 s 0.648883
2021-10-06 01:28 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.175 s 0.664524
2021-10-06 01:39 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.687358
2021-10-06 01:39 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.312707
2021-10-06 01:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 1.601551
2021-10-06 01:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.897 s 0.796093
2021-10-06 01:39 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.908 s -0.081478
2021-10-06 01:39 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.524174
2021-10-06 01:39 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.497586
2021-10-06 01:39 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.660 s -0.370382
2021-10-06 01:39 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.648 s 0.705263
2021-10-06 01:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.142680
2021-10-06 01:12 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.995 s -0.858538
2021-10-06 01:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.524 s -0.989025
2021-10-06 01:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.109 s -2.731037
2021-10-06 01:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.482 s -1.571869
2021-10-06 01:39 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.649 s -0.404355
2021-10-06 01:39 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.670 s 0.442904
2021-10-06 01:39 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.201778
2021-10-06 01:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.513 s -0.090125