Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 11:09 Python dataframe-to-table type_integers 0.011 s -0.289723
2021-10-13 11:09 Python dataframe-to-table type_floats 0.011 s 0.597957
2021-10-13 11:09 Python dataframe-to-table type_nested 2.837 s 2.218992
2021-10-13 11:27 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.251 s 0.209983
2021-10-13 11:41 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.690 s 0.465346
2021-10-13 11:42 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.617 s 1.315548
2021-10-13 11:42 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.299 s -0.258608
2021-10-13 11:48 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.314 s -0.085552
2021-10-13 11:49 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.873 s -0.545698
2021-10-13 11:52 Python wide-dataframe use_legacy_dataset=false 0.619 s 0.066646
2021-10-13 12:06 R dataframe-to-table type_dict, R 0.059 s -1.701985
2021-10-13 12:07 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.565 s -0.476769
2021-10-13 12:37 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.820 s 1.570588
2021-10-13 12:37 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.477537
2021-10-13 12:37 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.168170
2021-10-13 12:37 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.973633
2021-10-13 11:31 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.088 s -1.462027
2021-10-13 11:42 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.139 s 0.079280
2021-10-13 11:46 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.093 s 0.308724
2021-10-13 11:47 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.448 s 0.322974
2021-10-13 11:51 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.339 s 0.540732
2021-10-13 12:10 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.526 s 0.134133
2021-10-13 11:04 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.889 s 0.321417
2021-10-13 11:07 Python csv-read gzip, streaming, nyctaxi_2010-01 10.662 s -0.438459
2021-10-13 11:09 Python dataframe-to-table type_strings 0.362 s 0.738059
2021-10-13 12:06 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.526 s -2.902484
2021-10-13 12:06 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.222 s 0.438182
2021-10-13 12:07 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.318 s -1.151346
2021-10-13 12:09 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 0.715884
2021-10-13 11:06 Python csv-read uncompressed, file, nyctaxi_2010-01 1.014 s -0.406289
2021-10-13 11:43 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.551 s 1.247910
2021-10-13 11:43 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.685 s 0.988396
2021-10-13 12:06 R dataframe-to-table type_integers, R 0.009 s 0.770932
2021-10-13 12:06 R dataframe-to-table type_nested, R 0.535 s 0.232497
2021-10-13 12:17 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.389 s 0.799410
2021-10-13 11:50 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.813 s 0.504499
2021-10-13 11:52 Python file-write lz4, feather, table, nyctaxi_2010-01 1.792 s 0.635741
2021-10-13 12:08 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.147 s 0.722879
2021-10-13 11:07 Python csv-read gzip, file, nyctaxi_2010-01 9.041 s 1.160854
2021-10-13 11:06 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.722 s -0.718790
2021-10-13 11:05 Python csv-read gzip, streaming, fanniemae_2016Q4 14.842 s 0.071171
2021-10-13 11:45 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.286 s 1.170129
2021-10-13 12:09 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.025 s -0.296976
2021-10-13 11:09 Python dataset-filter nyctaxi_2010-01 4.394 s -1.860236
2021-10-13 11:48 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.747 s 0.238667
2021-10-13 11:50 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.915 s -0.163943
2021-10-13 11:51 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.325 s 0.532831
2021-10-13 12:06 R dataframe-to-table type_strings, R 0.494 s 0.229726
2021-10-13 12:08 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.034 s 2.801157
2021-10-13 12:10 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.693 s 0.002816
2021-10-13 12:19 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.893 s -0.573807
2021-10-13 11:04 Python csv-read uncompressed, file, fanniemae_2016Q4 1.151 s 0.664925
2021-10-13 11:17 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.315 s -0.656311
2021-10-13 11:44 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.129910
2021-10-13 11:51 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.886 s -0.161859
2021-10-13 12:07 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.458 s 0.716562
2021-10-13 12:07 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.039 s -0.860772
2021-10-13 12:08 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.109 s 0.713713
2021-10-13 11:31 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.108 s -0.663360
2021-10-13 11:49 Python file-write lz4, feather, table, fanniemae_2016Q4 1.149 s 0.515188
2021-10-13 12:13 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.295 s 0.373563
2021-10-13 11:31 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.051 s -0.257987
2021-10-13 11:43 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.014 s 1.271189
2021-10-13 11:52 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.770 s 1.397824
2021-10-13 12:17 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.657 s -3.064128
2021-10-13 11:05 Python csv-read gzip, file, fanniemae_2016Q4 6.022 s 1.697585
2021-10-13 11:13 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 68.498 s -2.103949
2021-10-13 11:41 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.811 s 0.408265
2021-10-13 11:42 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.331 s -1.937049
2021-10-13 11:43 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.115995
2021-10-13 11:09 Python dataframe-to-table chi_traffic_2020_Q1 19.091 s 1.305607
2021-10-13 11:09 Python dataframe-to-table type_dict 0.011 s 1.313629
2021-10-13 11:27 Python dataset-read async=True, nyctaxi_multi_ipc_s3 188.179 s -0.181046
2021-10-13 11:44 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.019 s 0.539517
2021-10-13 12:15 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.842 s -0.693807
2021-10-13 11:44 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.118 s 1.566788
2021-10-13 11:45 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.802 s 1.230386
2021-10-13 11:52 Python wide-dataframe use_legacy_dataset=true 0.392 s 0.202012
2021-10-13 12:11 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.839 s 0.422710
2021-10-13 11:45 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.201738
2021-10-13 12:06 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.720 s 0.658998
2021-10-13 12:09 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.216 s -1.181175
2021-10-13 11:41 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.103 s -2.240075
2021-10-13 11:41 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.928 s 0.500214
2021-10-13 11:44 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.156 s 1.178068
2021-10-13 11:47 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.359 s 0.403890
2021-10-13 11:49 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.356 s -0.040197
2021-10-13 11:42 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.932 s -2.254579
2021-10-13 12:06 R dataframe-to-table type_floats, R 0.013 s 0.761605
2021-10-13 11:43 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.054 s -0.155108
2021-10-13 11:49 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.804 s 0.420595
2021-10-13 11:43 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -0.319487
2021-10-13 12:15 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.812 s 1.229767
2021-10-13 12:20 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.890 s -1.241164
2021-10-13 12:21 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.519 s -0.363834
2021-10-13 12:05 R dataframe-to-table chi_traffic_2020_Q1, R 3.432 s 0.261781
2021-10-13 12:22 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.754 s -1.327505
2021-10-13 12:23 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.276 s 0.688345
2021-10-13 12:30 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.489 s 1.379619
2021-10-13 12:08 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.375 s 0.960346
2021-10-13 12:29 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.120 s 2.719816
2021-10-13 12:37 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.751677
2021-10-13 12:37 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.712188
2021-10-13 12:37 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.126345
2021-10-13 12:37 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.064624
2021-10-13 12:37 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.426806
2021-10-13 12:37 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.140373
2021-10-13 12:37 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.121543
2021-10-13 12:25 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.466 s 1.573736
2021-10-13 12:28 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.947 s -1.489789
2021-10-13 12:37 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.696 s -0.193843
2021-10-13 12:27 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.529 s -0.696977
2021-10-13 12:28 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.358 s 1.320914
2021-10-13 12:29 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.217 s -3.528260
2021-10-13 12:37 JavaScript Parse Table.from, tracks 0.000 s -0.251828
2021-10-13 12:37 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.614248
2021-10-13 12:37 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.871 s 0.730230
2021-10-13 12:37 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.920807
2021-10-13 12:26 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 1.083317
2021-10-13 12:37 JavaScript Parse readBatches, tracks 0.000 s -0.062840
2021-10-13 12:24 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.261 s -1.823601
2021-10-13 12:26 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.193 s -1.219448
2021-10-13 12:28 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.117 s -2.766943
2021-10-13 12:37 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.540 s -0.313327
2021-10-13 12:37 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.292267
2021-10-13 12:26 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.835 s 1.121814
2021-10-13 12:29 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.496 s -2.076363
2021-10-13 12:27 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.819821
2021-10-13 12:37 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 6.027 s -1.361017
2021-10-13 12:27 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.763 s -2.625886
2021-10-13 12:37 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.691 s 0.317762
2021-10-13 12:37 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.447689
2021-10-13 12:13 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.412 s -0.923415
2021-10-13 12:18 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.297 s -3.357104
2021-10-13 12:37 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.634 s -0.365366
2021-10-13 12:37 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.678414
2021-10-13 12:37 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.587000
2021-10-13 12:26 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.195 s -2.399508
2021-10-13 12:37 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.327710
2021-10-13 12:37 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s 0.035791
2021-10-13 12:26 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.589 s -1.682549
2021-10-13 12:28 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.609 s -0.257621
2021-10-13 12:37 JavaScript Parse serialize, tracks 0.005 s -0.861324
2021-10-13 12:26 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.517 s 4.176852
2021-10-13 12:37 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.033960
2021-10-13 12:37 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.155263
2021-10-13 12:37 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.596024