Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-06 08:11 Python dataframe-to-table type_dict 0.012 s -0.314941
2021-10-06 08:43 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.999 s -0.055351
2021-10-06 08:06 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.972 s -0.431021
2021-10-06 08:47 Python file-read lz4, feather, table, nyctaxi_2010-01 0.678 s -1.927596
2021-10-06 08:48 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 8.045 s -1.552369
2021-10-06 08:09 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s -0.013179
2021-10-06 08:12 Python dataframe-to-table type_nested 2.871 s 0.941541
2021-10-06 08:50 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.428 s -0.667838
2021-10-06 08:29 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.350 s -0.387932
2021-10-06 08:45 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.375218
2021-10-06 08:19 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.370 s 0.740105
2021-10-06 08:45 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.819 s -1.346568
2021-10-06 08:46 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.361 s -1.406820
2021-10-06 08:33 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.026 s -0.056564
2021-10-06 08:47 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.177 s -0.072694
2021-10-06 08:51 Python file-write lz4, feather, table, fanniemae_2016Q4 1.162 s 0.022234
2021-10-06 08:54 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.799 s 0.337605
2021-10-06 08:15 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 54.764 s 1.360470
2021-10-06 08:12 Python dataframe-to-table type_simple_features 0.914 s -0.080747
2021-10-06 08:33 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.014 s 0.332623
2021-10-06 08:43 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.029 s -0.104472
2021-10-06 08:44 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.825 s -0.012164
2021-10-06 08:48 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.082 s 0.844073
2021-10-06 08:54 Python file-write lz4, feather, table, nyctaxi_2010-01 1.786 s 1.376554
2021-10-06 08:11 Python dataframe-to-table type_strings 0.375 s -0.626944
2021-10-06 08:12 Python dataset-filter nyctaxi_2010-01 4.353 s 0.607333
2021-10-06 08:33 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.020 s 0.189432
2021-10-06 08:45 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.298 s -1.295565
2021-10-06 08:44 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.881 s -1.195775
2021-10-06 08:49 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.346 s 0.089980
2021-10-06 08:50 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.688 s 0.194769
2021-10-06 08:08 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.514 s 1.097989
2021-10-06 08:08 Python csv-read uncompressed, file, nyctaxi_2010-01 1.012 s 0.108867
2021-10-06 08:06 Python csv-read uncompressed, file, fanniemae_2016Q4 1.146 s 1.575963
2021-10-06 08:08 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.624595
2021-10-06 08:09 Python csv-read gzip, streaming, nyctaxi_2010-01 10.512 s 1.041491
2021-10-06 08:44 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.243 s -0.029369
2021-10-06 08:47 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.509 s -1.378957
2021-10-06 08:54 Python wide-dataframe use_legacy_dataset=false 0.624 s -0.651362
2021-10-06 09:07 R dataframe-to-table type_strings, R 0.488 s 1.790207
2021-10-06 08:43 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.884 s 0.006509
2021-10-06 08:43 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.794 s -0.466845
2021-10-06 08:44 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.292 s -0.139965
2021-10-06 08:49 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.442 s 0.815136
2021-10-06 08:53 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.826 s 0.617127
2021-10-06 08:51 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.798 s -0.486284
2021-10-06 08:51 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.241 s -0.134543
2021-10-06 09:08 R dataframe-to-table type_floats, R 0.107 s 0.984392
2021-10-06 08:11 Python dataframe-to-table type_integers 0.011 s 1.249656
2021-10-06 08:44 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.156 s -0.709608
2021-10-06 08:07 Python csv-read gzip, streaming, fanniemae_2016Q4 14.910 s -0.456028
2021-10-06 08:11 Python dataframe-to-table chi_traffic_2020_Q1 19.433 s 1.078553
2021-10-06 08:11 Python dataframe-to-table type_floats 0.012 s -0.295340
2021-10-06 08:52 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.789 s 0.622276
2021-10-06 08:53 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.352 s -0.106652
2021-10-06 09:07 R dataframe-to-table chi_traffic_2020_Q1, R 5.368 s 0.527814
2021-10-06 08:45 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.046 s 0.075734
2021-10-06 09:08 R dataframe-to-table type_nested, R 0.539 s -0.309881
2021-10-06 08:45 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.936 s -0.971547
2021-10-06 08:45 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.244 s -1.018166
2021-10-06 08:46 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.298 s -1.160324
2021-10-06 09:08 R dataframe-to-table type_dict, R 0.050 s 0.016564
2021-10-06 08:53 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.839 s 1.210986
2021-10-06 08:54 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.339 s 0.227951
2021-10-06 08:54 Python wide-dataframe use_legacy_dataset=true 0.394 s 0.077116
2021-10-06 09:08 R dataframe-to-table type_integers, R 0.085 s -0.455749
2021-10-06 08:29 Python dataset-read async=True, nyctaxi_multi_ipc_s3 195.853 s -0.836896
2021-10-06 08:46 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.016 s 1.301469
2021-10-06 08:52 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.780 s 1.163709
2021-10-06 09:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.250 s 0.209779
2021-10-06 09:40 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.266 s 0.878975
2021-10-06 09:55 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.890 s 0.781747
2021-10-06 10:05 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.275241
2021-10-06 09:38 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.850 s 0.800551
2021-10-06 09:50 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.640 s 1.498332
2021-10-06 09:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.517 s 1.246627
2021-10-06 09:33 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.036391
2021-10-06 09:35 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.132 s -0.315818
2021-10-06 09:51 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.234 s 1.517499
2021-10-06 09:54 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -0.763262
2021-10-06 09:56 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.362 s 0.321462
2021-10-06 09:57 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.484 s 0.130972
2021-10-06 09:36 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.250 s -0.501374
2021-10-06 09:37 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.992 s -0.592275
2021-10-06 09:52 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.506 s -3.014888
2021-10-06 09:53 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.447976
2021-10-06 09:55 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -1.133132
2021-10-06 10:04 JavaScript Parse readBatches, tracks 0.000 s 0.084444
2021-10-06 10:05 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.156648
2021-10-06 09:33 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.919 s 0.044178
2021-10-06 09:38 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.559 s -1.477760
2021-10-06 09:45 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.190 s 1.061992
2021-10-06 10:04 JavaScript Parse Table.from, tracks 0.000 s -0.841170
2021-10-06 10:05 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.873 s 0.706950
2021-10-06 09:34 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.396 s -0.609839
2021-10-06 09:48 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.461 s 1.188845
2021-10-06 09:50 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.022055
2021-10-06 10:04 JavaScript Parse serialize, tracks 0.005 s 0.468697
2021-10-06 10:05 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 09:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 0.771903
2021-10-06 09:57 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.192 s 0.746646
2021-10-06 10:05 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.713 s 0.190574
2021-10-06 10:05 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.604220
2021-10-06 09:41 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 0.801558
2021-10-06 09:47 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.792 s 1.201198
2021-10-06 10:05 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.595370
2021-10-06 09:44 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.561 s 0.621969
2021-10-06 09:54 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.523 s -0.828132
2021-10-06 10:05 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.470091
2021-10-06 09:32 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.237 s 0.173371
2021-10-06 09:36 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 0.776911
2021-10-06 09:53 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.873 s 0.797412
2021-10-06 10:05 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-06 09:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.572 s -1.662594
2021-10-06 09:42 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.721 s 0.896653
2021-10-06 09:46 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.822 s 1.206587
2021-10-06 10:05 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.139126
2021-10-06 09:35 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.184 s -0.616375
2021-10-06 09:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -3.740349
2021-10-06 09:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.920 s -0.020851
2021-10-06 10:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.184467
2021-10-06 10:05 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.047546
2021-10-06 09:32 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.928 s 0.147215
2021-10-06 09:53 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.584 s 0.684542
2021-10-06 10:05 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.620 s -0.360125
2021-10-06 10:05 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.561175
2021-10-06 10:05 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.526 s -0.298948
2021-10-06 09:43 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -0.765133
2021-10-06 09:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.483 s -1.600591
2021-10-06 10:05 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.148286
2021-10-06 10:05 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.996058
2021-10-06 09:37 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.672 s 0.165287
2021-10-06 10:05 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.717 s -0.600563
2021-10-06 10:05 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.552588
2021-10-06 10:05 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.016002
2021-10-06 10:05 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.990976
2021-10-06 09:31 R dataframe-to-table type_simple_features, R 275.440 s -0.797643
2021-10-06 09:34 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.062 s -0.897637
2021-10-06 09:53 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 0.763286
2021-10-06 10:05 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.824 s 1.422025
2021-10-06 10:05 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.205314
2021-10-06 10:05 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.041968
2021-10-06 09:44 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.405 s -0.612771
2021-10-06 09:53 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.167 s 1.193671
2021-10-06 09:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -4.274749
2021-10-06 10:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.172891
2021-10-06 10:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.597 s -0.255679
2021-10-06 10:05 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.367666
2021-10-06 10:05 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.998453