Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-11 14:26 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.069 s -0.272453
2021-10-11 14:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.815 s 0.405798
2021-10-11 14:43 Python file-write uncompressed, feather, table, fanniemae_2016Q4 4.805 s 2.972724
2021-10-11 15:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s -0.406352
2021-10-11 15:31 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.875 s 1.469194
2021-10-11 15:31 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.600 s -0.101943
2021-10-11 15:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.489 s -1.116318
2021-10-11 15:40 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.622158
2021-10-11 15:40 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.696 s 0.287293
2021-10-11 15:40 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.403718
2021-10-11 15:40 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.896 s 0.196865
2021-10-11 15:40 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.574060
2021-10-11 15:40 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.413290
2021-10-11 15:40 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.279037
2021-10-11 15:40 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.116853
2021-10-11 14:22 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.233 s 0.216139
2021-10-11 14:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.297 s -1.038657
2021-10-11 14:38 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.011 s 2.222288
2021-10-11 14:38 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.047 s -0.058193
2021-10-11 14:00 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.850 s 0.718265
2021-10-11 14:26 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.045 s -0.255288
2021-10-11 14:36 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.782 s -0.335180
2021-10-11 14:01 Python csv-read gzip, file, fanniemae_2016Q4 6.026 s 0.890192
2021-10-11 14:39 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.118 s 2.090947
2021-10-11 14:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.525 s 2.355641
2021-10-11 14:26 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.083 s -1.950389
2021-10-11 14:38 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.699 s 2.185129
2021-10-11 14:39 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.186 s 1.247160
2021-10-11 14:40 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.166 s 1.865835
2021-10-11 14:00 Python csv-read uncompressed, file, fanniemae_2016Q4 1.161 s 0.664263
2021-10-11 14:38 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -1.352786
2021-10-11 14:39 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.050 s -0.832960
2021-10-11 14:01 Python csv-read gzip, streaming, fanniemae_2016Q4 14.782 s 0.712141
2021-10-11 14:05 Python dataset-filter nyctaxi_2010-01 4.319 s 1.472037
2021-10-11 14:13 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.772 s 0.504236
2021-10-11 14:36 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.917 s 0.569349
2021-10-11 14:37 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.286 s -0.023088
2021-10-11 14:40 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.349 s 1.006518
2021-10-11 15:08 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.231 s 0.272146
2021-10-11 15:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.070 s -2.165838
2021-10-11 15:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.591 s 1.433211
2021-10-11 14:02 Python csv-read gzip, streaming, nyctaxi_2010-01 10.620 s -0.275348
2021-10-11 14:05 Python dataframe-to-table type_floats 0.011 s -0.454411
2021-10-11 14:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.192 s 0.868459
2021-10-11 14:37 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.600 s 2.448817
2021-10-11 14:37 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.683166
2021-10-11 14:43 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.947 s -1.173408
2021-10-11 14:44 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.521 s -2.087391
2021-10-11 14:01 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.636 s -0.123220
2021-10-11 14:02 Python csv-read uncompressed, file, nyctaxi_2010-01 1.011 s -0.007047
2021-10-11 14:04 Python dataframe-to-table chi_traffic_2020_Q1 19.959 s -1.048560
2021-10-11 14:05 Python dataframe-to-table type_simple_features 0.932 s -0.845002
2021-10-11 15:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.208223
2021-10-11 14:05 Python dataframe-to-table type_strings 0.367 s 0.410983
2021-10-11 14:36 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.968 s 0.240859
2021-10-11 14:40 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.807 s 1.588866
2021-10-11 14:41 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.098 s 0.485878
2021-10-11 14:44 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.970 s -1.023276
2021-10-11 14:44 Python file-write lz4, feather, table, fanniemae_2016Q4 1.139 s 1.422252
2021-10-11 14:03 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 1.029160
2021-10-11 14:05 Python dataframe-to-table type_dict 0.012 s 0.556858
2021-10-11 14:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.572 s -1.111977
2021-10-11 15:40 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.565084
2021-10-11 14:05 Python dataframe-to-table type_nested 2.898 s -1.120555
2021-10-11 14:05 Python dataframe-to-table type_integers 0.011 s -1.801142
2021-10-11 14:40 Python file-read lz4, feather, table, nyctaxi_2010-01 0.675 s -0.847376
2021-10-11 14:44 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.879 s -0.365357
2021-10-11 14:46 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.920 s -0.067718
2021-10-11 15:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.165 s 1.093962
2021-10-11 15:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.122 s 0.004157
2021-10-11 15:14 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.247 s 0.684903
2021-10-11 15:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.284 s 0.674342
2021-10-11 15:26 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 1.965984
2021-10-11 14:08 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.689 s 0.198962
2021-10-11 14:22 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.297 s 0.345805
2021-10-11 14:37 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.791 s 0.374512
2021-10-11 14:42 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.442 s 0.584507
2021-10-11 14:46 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.932 s -0.662487
2021-10-11 14:45 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.888 s -0.363127
2021-10-11 14:47 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.635083
2021-10-11 14:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.320 s 0.869366
2021-10-11 14:46 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.306 s 2.272968
2021-10-11 14:47 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.833 s -0.345280
2021-10-11 14:47 Python wide-dataframe use_legacy_dataset=false 0.613 s 1.454858
2021-10-11 14:47 Python wide-dataframe use_legacy_dataset=true 0.388 s 2.206289
2021-10-11 15:01 R dataframe-to-table chi_traffic_2020_Q1, R 3.385 s 0.269653
2021-10-11 15:01 R dataframe-to-table type_floats, R 0.013 s 1.137627
2021-10-11 15:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.969 s 0.337498
2021-10-11 15:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.194 s 0.316620
2021-10-11 15:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.896 s -0.441213
2021-10-11 15:40 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s 0.210777
2021-10-11 15:01 R dataframe-to-table type_nested, R 0.535 s 0.233815
2021-10-11 15:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.387 s 1.370305
2021-10-11 15:01 R dataframe-to-table type_integers, R 0.010 s 1.157894
2021-10-11 15:09 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.421 s -1.883467
2021-10-11 15:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.858 s -0.457662
2021-10-11 15:32 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.161 s 0.998946
2021-10-11 15:07 R dataframe-to-table type_simple_features, R 3.385 s 0.953755
2021-10-11 15:10 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.238 s 1.079859
2021-10-11 15:29 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.958588
2021-10-11 15:40 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.097160
2021-10-11 15:40 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.591460
2021-10-11 15:25 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.249 s -0.653666
2021-10-11 15:29 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.578 s -0.494781
2021-10-11 15:40 JavaScript Parse readBatches, tracks 0.000 s -0.322417
2021-10-11 15:11 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s -0.021725
2021-10-11 15:40 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.998059
2021-10-11 15:40 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.297152
2021-10-11 15:01 R dataframe-to-table type_dict, R 0.061 s -1.840195
2021-10-11 15:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.487 s 0.520835
2021-10-11 15:18 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.537 s 0.903887
2021-10-11 15:27 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.174 s -0.175975
2021-10-11 15:40 JavaScript Parse serialize, tracks 0.004 s 0.512076
2021-10-11 15:40 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.689 s -0.103930
2021-10-11 15:40 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.108171
2021-10-11 15:40 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.852 s 0.658214
2021-10-11 15:01 R dataframe-to-table type_strings, R 0.490 s 0.232130
2021-10-11 15:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.532 s -0.433802
2021-10-11 15:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s 0.110454
2021-10-11 15:40 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.535349
2021-10-11 15:40 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.563107
2021-10-11 15:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 1.658399
2021-10-11 15:40 JavaScript Parse Table.from, tracks 0.000 s -0.249967
2021-10-11 15:40 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.223573
2021-10-11 15:40 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.305607
2021-10-11 15:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.063 s -1.088351
2021-10-11 15:10 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.860142
2021-10-11 15:29 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.855 s 0.563425
2021-10-11 15:40 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.197371
2021-10-11 15:40 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.538 s -0.263694
2021-10-11 15:40 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.431681
2021-10-11 15:33 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.505 s -0.333791
2021-10-11 15:40 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.591 s -0.330859
2021-10-11 15:40 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.824669
2021-10-11 15:40 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.478 s 0.737856
2021-10-11 15:08 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.315 s -1.657488
2021-10-11 15:08 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.443 s 1.087881
2021-10-11 15:40 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.138533
2021-10-11 15:08 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.454 s 1.112236
2021-10-11 15:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -1.143911
2021-10-11 15:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s -0.123073
2021-10-11 15:32 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.206 s -0.048755
2021-10-11 15:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s -0.130796
2021-10-11 15:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.224 s 0.102765
2021-10-11 15:16 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.738 s 0.464464
2021-10-11 15:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.842 s 0.591611
2021-10-11 15:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.814 s 1.643369
2021-10-11 15:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.737 s -0.839266
2021-10-11 15:30 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s 0.156175