Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-01 15:40 Python dataframe-to-table type_simple_features 0.912 s 0.020549
2021-10-01 16:17 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -1.123319
2021-10-01 16:26 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.828 s 0.116489
2021-10-01 15:37 Python csv-read uncompressed, file, nyctaxi_2010-01 1.007 s 0.200043
2021-10-01 15:57 Python dataset-read async=True, nyctaxi_multi_ipc_s3 186.939 s 0.153477
2021-10-01 16:16 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.756 s 1.742056
2021-10-01 16:24 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.931 s -1.061002
2021-10-01 16:40 R dataframe-to-table chi_traffic_2020_Q1, R 5.467 s -1.162276
2021-10-01 16:19 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.986 s -1.602733
2021-10-01 16:20 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.398 s -1.248158
2021-10-01 16:03 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.019 s 0.042993
2021-10-01 16:18 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.666032
2021-10-01 16:25 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.993 s -1.087226
2021-10-01 16:03 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.025 s 0.154858
2021-10-01 16:25 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.969 s -0.252262
2021-10-01 16:40 R dataframe-to-table type_nested, R 0.535 s 0.883953
2021-10-01 16:16 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.836 s -1.327549
2021-10-01 16:17 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.250 s -2.288719
2021-10-01 16:18 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.299 s -1.267244
2021-10-01 16:18 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.346 s -1.487266
2021-10-01 16:21 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.612 s -1.025410
2021-10-01 16:22 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.987 s -1.066780
2021-10-01 15:37 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.496 s 0.518839
2021-10-01 15:40 Python dataframe-to-table type_integers 0.011 s 1.864912
2021-10-01 15:35 Python csv-read uncompressed, file, fanniemae_2016Q4 1.164 s 0.171168
2021-10-01 15:40 Python dataframe-to-table type_floats 0.011 s 1.823598
2021-10-01 15:41 Python dataset-filter nyctaxi_2010-01 4.355 s 0.354880
2021-10-01 16:16 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.121 s 0.885225
2021-10-01 16:17 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.290 s 0.021535
2021-10-01 16:26 Python file-write lz4, feather, table, nyctaxi_2010-01 1.839 s -1.572278
2021-10-01 16:40 R dataframe-to-table type_strings, R 0.497 s -2.545360
2021-10-01 15:40 Python dataframe-to-table type_nested 2.873 s 1.572894
2021-10-01 16:17 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.937 s -2.112846
2021-10-01 16:40 R dataframe-to-table type_integers, R 0.084 s 0.076551
2021-10-01 15:44 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.714 s -1.199253
2021-10-01 16:15 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.931 s 1.693110
2021-10-01 16:16 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.173 s 1.782387
2021-10-01 16:17 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.779 s -1.447867
2021-10-01 16:24 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.937 s -0.263287
2021-10-01 15:38 Python csv-read gzip, file, nyctaxi_2010-01 9.043 s 0.859988
2021-10-01 15:40 Python dataframe-to-table type_strings 0.374 s -0.377487
2021-10-01 15:58 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.102 s 1.047462
2021-10-01 16:15 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.859 s 0.269284
2021-10-01 16:23 Python file-write lz4, feather, table, fanniemae_2016Q4 1.159 s 0.216736
2021-10-01 16:40 R dataframe-to-table type_dict, R 0.061 s -1.084278
2021-10-01 15:40 Python dataframe-to-table type_dict 0.012 s -1.266872
2021-10-01 16:17 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.057 s -0.436534
2021-10-01 16:21 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.763 s -1.298800
2021-10-01 16:25 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.349 s 0.110642
2021-10-01 16:16 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.263 s 1.023166
2021-10-01 15:40 Python dataframe-to-table chi_traffic_2020_Q1 19.634 s 0.535581
2021-10-01 15:48 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.541 s 1.352415
2021-10-01 16:15 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.777 s 0.023862
2021-10-01 16:22 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.439 s -0.809427
2021-10-01 15:36 Python csv-read gzip, streaming, fanniemae_2016Q4 14.884 s -0.480954
2021-10-01 16:23 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.282 s -0.605014
2021-10-01 15:36 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.452786
2021-10-01 16:19 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.828831
2021-10-01 16:18 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.836915
2021-10-01 16:26 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.635693
2021-10-01 16:40 R dataframe-to-table type_floats, R 0.113 s -1.436679
2021-10-01 16:26 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.405 s -0.326076
2021-10-01 16:26 Python wide-dataframe use_legacy_dataset=true 0.396 s -0.443124
2021-10-01 17:04 R dataframe-to-table type_simple_features, R 275.089 s -0.401910
2021-10-01 17:04 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.696 s -3.659560
2021-10-01 17:04 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 8.309 s -2.911339
2021-10-01 17:04 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.207 s 0.483690
2021-10-01 17:05 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.067742
2021-10-01 17:05 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.863 s 0.625555
2021-10-01 17:07 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.057 s -0.152896
2021-10-01 17:06 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.378809
2021-10-01 17:06 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.906 s 0.703536
2021-10-01 17:07 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.167 s 0.472359
2021-10-01 17:15 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.843 s -2.386982
2021-10-01 17:26 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.968 s 1.127203
2021-10-01 17:37 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.039438
2021-10-01 15:35 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.936 s -0.458502
2021-10-01 15:37 Python csv-read gzip, streaming, nyctaxi_2010-01 10.494 s 0.469192
2021-10-01 16:02 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.026 s 0.138953
2021-10-01 16:15 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.097 s -0.629219
2021-10-01 16:19 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.484 s -1.624206
2021-10-01 16:23 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.821 s -0.741745
2021-10-01 17:06 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.392 s -0.488303
2021-10-01 17:18 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.213 s 0.723304
2021-10-01 17:26 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.601 s 1.074814
2021-10-01 17:37 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.507338
2021-10-01 17:25 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.176 s 1.202008
2021-10-01 17:26 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.567 s 1.407155
2021-10-01 17:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.931 s 1.091925
2021-10-01 17:37 JavaScript Parse serialize, tracks 0.005 s -0.764222
2021-10-01 17:37 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.694 s 0.300194
2021-10-01 17:37 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.046290
2021-10-01 17:07 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.108 s 1.674869
2021-10-01 17:08 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.242 s 0.050276
2021-10-01 17:21 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.576 s -0.782091
2021-10-01 17:08 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.250278
2021-10-01 17:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.259193
2021-10-01 17:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -1.205992
2021-10-01 17:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.156 s 1.096742
2021-10-01 17:16 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.571 s 0.786519
2021-10-01 17:20 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.894 s -0.446908
2021-10-01 17:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.512 s 0.592654
2021-10-01 17:37 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.603070
2021-10-01 17:10 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.503 s 0.859412
2021-10-01 17:13 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.621 s -1.188870
2021-10-01 17:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.353 s 0.892328
2021-10-01 17:37 JavaScript Parse readBatches, tracks 0.000 s -1.510960
2021-10-01 17:37 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.631 s 0.924006
2021-10-01 17:12 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.614 s -0.882923
2021-10-01 17:37 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.533 s -0.095952
2021-10-01 17:37 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.199598
2021-10-01 17:37 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.808393
2021-10-01 17:24 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.489 s 0.253331
2021-10-01 17:28 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.479 s -1.869958
2021-10-01 17:37 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.487257
2021-10-01 17:37 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.740240
2021-10-01 17:09 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.976 s -0.247019
2021-10-01 17:19 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.944 s -0.921410
2021-10-01 17:24 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.253 s 0.725706
2021-10-01 17:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.490 s 0.124285
2021-10-01 17:37 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.586588
2021-10-01 17:17 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.415 s -2.523764
2021-10-01 17:26 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.265574
2021-10-01 17:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.605 s 0.261855
2021-10-01 17:28 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.517 s 1.210013
2021-10-01 17:29 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.472177
2021-10-01 17:37 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.757593
2021-10-01 17:37 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.487257
2021-10-01 17:37 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.133521
2021-10-01 17:09 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.675 s 0.122957
2021-10-01 17:37 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.686249
2021-10-01 17:37 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.498 s 0.120672
2021-10-01 17:37 JavaScript Parse Table.from, tracks 0.000 s -1.094468
2021-10-01 17:37 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.052904
2021-10-01 17:37 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.578031
2021-10-01 17:37 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.006242
2021-10-01 17:11 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.167 s -1.187190
2021-10-01 17:25 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 1.237875
2021-10-01 17:37 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.875 s 0.179724
2021-10-01 17:37 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.512945
2021-10-01 17:37 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.226256
2021-10-01 17:37 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.287361
2021-10-01 17:37 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.999034
2021-10-01 17:15 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.062 s -0.923406
2021-10-01 17:22 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.758 s -0.529428
2021-10-01 17:37 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.911 s -0.188860
2021-10-01 17:37 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.668 s -0.350525
2021-10-01 17:23 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.293107
2021-10-01 17:37 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.465882