Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 17:31 Python dataframe-to-table type_simple_features 0.913 s 0.055097
2021-10-08 18:06 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.851 s 0.196249
2021-10-08 18:06 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.707 s 0.420634
2021-10-08 18:41 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.975 s 0.234706
2021-10-08 18:42 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.538 s 0.050509
2021-10-08 18:47 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.693 s 0.851451
2021-10-08 18:53 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.531 s -0.687872
2021-10-08 18:58 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s 0.640018
2021-10-08 18:59 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.534 s -2.219894
2021-10-08 18:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s -0.354369
2021-10-08 19:00 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.088107
2021-10-08 19:01 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -1.883435
2021-10-08 19:09 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.484069
2021-10-08 19:09 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.193940
2021-10-08 19:09 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.135055
2021-10-08 19:09 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.964893
2021-10-08 19:09 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.134942
2021-10-08 19:09 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.796444
2021-10-08 17:31 Python dataframe-to-table type_dict 0.011 s 1.125486
2021-10-08 17:32 Python dataset-filter nyctaxi_2010-01 4.332 s 1.634891
2021-10-08 17:27 Python csv-read gzip, file, fanniemae_2016Q4 6.035 s -0.756443
2021-10-08 17:28 Python csv-read uncompressed, file, nyctaxi_2010-01 0.989 s 2.403897
2021-10-08 17:31 Python dataframe-to-table type_integers 0.011 s 0.656474
2021-10-08 17:31 Python dataframe-to-table type_floats 0.011 s 0.613083
2021-10-08 17:26 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.979 s -0.396522
2021-10-08 17:26 Python csv-read uncompressed, file, fanniemae_2016Q4 1.184 s -0.525616
2021-10-08 18:07 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.201 s 1.085818
2021-10-08 17:29 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.279753
2021-10-08 17:49 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.552 s 0.001808
2021-10-08 18:06 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.949 s 1.306264
2021-10-08 17:49 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.965 s -0.516034
2021-10-08 18:07 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.792 s 0.916490
2021-10-08 17:39 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 84.791 s 0.349407
2021-10-08 18:06 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.988 s 0.149696
2021-10-08 18:08 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.781 s -0.227489
2021-10-08 18:08 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.937 s -0.616642
2021-10-08 18:09 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.284 s -0.607845
2021-10-08 17:28 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.498 s 0.994561
2021-10-08 17:31 Python dataframe-to-table type_strings 0.368 s 0.465276
2021-10-08 17:53 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.047 s -0.144667
2021-10-08 17:53 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.031 s -0.122224
2021-10-08 17:27 Python csv-read gzip, streaming, fanniemae_2016Q4 14.912 s -0.393140
2021-10-08 17:29 Python csv-read gzip, streaming, nyctaxi_2010-01 10.492 s 0.978779
2021-10-08 17:31 Python dataframe-to-table chi_traffic_2020_Q1 19.496 s 0.421669
2021-10-08 17:31 Python dataframe-to-table type_nested 2.881 s 0.344025
2021-10-08 18:07 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.858 s -0.356852
2021-10-08 18:09 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.033 s 0.254812
2021-10-08 18:07 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.284 s 0.421577
2021-10-08 18:08 Python file-read lz4, feather, table, fanniemae_2016Q4 0.605 s -0.272436
2021-10-08 18:10 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.311964
2021-10-08 18:10 Python file-read lz4, feather, table, nyctaxi_2010-01 0.672 s -0.514674
2021-10-08 18:12 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.436 s 0.580799
2021-10-08 18:09 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.039 s 0.381789
2021-10-08 18:07 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.132 s 0.785477
2021-10-08 18:08 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.167891
2021-10-08 18:10 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.326 s -0.756010
2021-10-08 18:11 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.959 s -0.683243
2021-10-08 18:11 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 0.585192
2021-10-08 18:08 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.237 s -0.476178
2021-10-08 18:10 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.463 s -0.702033
2021-10-08 18:13 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.364 s -0.220284
2021-10-08 18:12 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.323 s -0.001119
2021-10-08 18:38 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.204 s 0.580909
2021-10-08 18:43 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.841 s 0.592923
2021-10-08 18:49 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.537 s 1.357628
2021-10-08 18:50 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.182 s 1.447504
2021-10-08 18:13 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.689 s -0.008228
2021-10-08 18:14 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.241 s -0.120086
2021-10-08 18:14 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.840 s -0.889219
2021-10-08 18:14 Python file-write lz4, feather, table, fanniemae_2016Q4 1.158 s 0.255941
2021-10-08 18:15 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.886 s -0.834646
2021-10-08 18:39 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.554 s 1.763671
2021-10-08 18:15 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.855 s -0.485168
2021-10-08 18:39 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.292 s -2.614935
2021-10-08 18:39 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.452 s 2.507111
2021-10-08 18:41 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.677 s 0.136672
2021-10-08 18:16 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.919 s -0.443419
2021-10-08 18:32 R dataframe-to-table type_nested, R 0.539 s 0.225798
2021-10-08 18:40 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.135 s -0.698878
2021-10-08 19:02 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.497 s 0.008872
2021-10-08 18:38 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.462 s 2.607095
2021-10-08 18:40 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.380 s 0.480786
2021-10-08 18:40 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.059 s -0.408263
2021-10-08 18:47 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.471033
2021-10-08 19:00 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.112 s -1.327701
2021-10-08 19:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.211 s -1.929650
2021-10-08 18:16 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.897 s -0.606437
2021-10-08 18:16 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.366 s -0.926858
2021-10-08 19:01 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.179 s 0.401773
2021-10-08 18:39 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.981 s -3.646492
2021-10-08 18:52 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.850 s -0.560895
2021-10-08 18:40 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.176 s 2.587355
2021-10-08 18:17 Python file-write lz4, feather, table, nyctaxi_2010-01 1.807 s 0.166706
2021-10-08 18:17 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.335 s 0.014722
2021-10-08 18:17 Python wide-dataframe use_legacy_dataset=false 0.626 s -1.183377
2021-10-08 18:17 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.249113
2021-10-08 18:56 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.234 s 1.374053
2021-10-08 18:57 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.486 s 0.815097
2021-10-08 19:09 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.033 s -2.161061
2021-10-08 18:17 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.807 s -0.070946
2021-10-08 18:31 R dataframe-to-table chi_traffic_2020_Q1, R 3.410 s 0.271545
2021-10-08 17:35 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.576 s 0.831783
2021-10-08 17:53 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.031 s 0.017238
2021-10-08 18:31 R dataframe-to-table type_strings, R 0.494 s 0.224104
2021-10-08 18:32 R dataframe-to-table type_integers, R 0.010 s 2.860129
2021-10-08 18:41 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.240 s 2.493809
2021-10-08 18:49 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.401 s 0.072988
2021-10-08 18:54 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.723 s -0.788801
2021-10-08 19:09 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.101106
2021-10-08 19:09 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.828716
2021-10-08 18:41 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.214305
2021-10-08 19:00 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.653 s -0.975662
2021-10-08 19:09 JavaScript Parse Table.from, tracks 0.000 s -0.152397
2021-10-08 19:09 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 18:38 R dataframe-to-table type_simple_features, R 3.302 s 2.015138
2021-10-08 19:09 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.650 s 0.754274
2021-10-08 19:09 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.490530
2021-10-08 18:58 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.416794
2021-10-08 19:09 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.700 s 0.262655
2021-10-08 19:09 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -1.040901
2021-10-08 19:09 JavaScript Parse serialize, tracks 0.005 s -0.567749
2021-10-08 19:09 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.311 s 2.194126
2021-10-08 19:09 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.570816
2021-10-08 18:32 R dataframe-to-table type_floats, R 0.013 s 2.847054
2021-10-08 18:45 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.271 s 0.587730
2021-10-08 18:55 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.837345
2021-10-08 18:58 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.860 s 0.468502
2021-10-08 19:09 JavaScript Parse readBatches, tracks 0.000 s -0.245081
2021-10-08 19:09 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.557 s -0.766395
2021-10-08 18:45 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 0.576530
2021-10-08 18:59 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.606 s 0.358769
2021-10-08 19:09 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.203857
2021-10-08 19:09 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.451547
2021-10-08 19:09 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.214490
2021-10-08 18:31 R dataframe-to-table type_dict, R 0.050 s 0.038490
2021-10-08 18:38 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.210 s 0.547863
2021-10-08 18:58 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s 0.333746
2021-10-08 18:58 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.058012
2021-10-08 19:09 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.252 s 2.404741
2021-10-08 19:09 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.146580
2021-10-08 19:09 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.474348
2021-10-08 19:09 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.871 s 0.155064
2021-10-08 19:09 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.698717
2021-10-08 18:51 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.721149
2021-10-08 18:58 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.332096
2021-10-08 19:00 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.927 s 0.396677
2021-10-08 19:09 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.590369
2021-10-08 19:09 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.920 s -0.275364
2021-10-08 19:09 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.790706