Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 15:04 Python file-read snappy, parquet, table, nyctaxi_2010-01 0.993 s 1.316431
2021-10-13 15:09 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.357 s -0.326110
2021-10-13 15:13 Python wide-dataframe use_legacy_dataset=false 0.612 s 1.287890
2021-10-13 15:26 R dataframe-to-table type_strings, R 0.494 s 0.229630
2021-10-13 15:26 R dataframe-to-table type_dict, R 0.062 s -2.203152
2021-10-13 15:26 R dataframe-to-table type_integers, R 0.009 s 0.737368
2021-10-13 15:28 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.043 s -0.888579
2021-10-13 15:29 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.387 s 0.315930
2021-10-13 15:29 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.213 s 0.683995
2021-10-13 15:30 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.007 s -0.077282
2021-10-13 15:30 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.692 s 0.013539
2021-10-13 15:31 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.541 s -0.022307
2021-10-13 15:32 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.103 s -2.588091
2021-10-13 15:34 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.557 s -2.564376
2021-10-13 15:35 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.153 s -3.631851
2021-10-13 15:36 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.817 s 0.607816
2021-10-13 15:37 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.652 s -2.619211
2021-10-13 15:41 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.892 s -1.286790
2021-10-13 15:42 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.529 s -0.641524
2021-10-13 15:46 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.590 s -0.167174
2021-10-13 15:46 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.808 s 1.808288
2021-10-13 15:47 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.531 s -0.961747
2021-10-13 15:48 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.757 s -2.273144
2021-10-13 15:58 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.893 s -0.334894
2021-10-13 14:27 Python dataframe-to-table type_nested 2.852 s 1.369709
2021-10-13 14:49 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.053 s 0.074727
2021-10-13 14:35 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.624 s -0.878405
2021-10-13 15:03 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.518 s 1.454886
2021-10-13 15:06 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.833 s 0.838025
2021-10-13 15:26 R dataframe-to-table type_nested, R 0.537 s 0.231884
2021-10-13 15:29 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.164 s 0.686916
2021-10-13 14:31 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.073 s 0.397584
2021-10-13 15:27 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.547 s -2.842975
2021-10-13 15:27 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.216 s 0.702799
2021-10-13 15:39 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.301 s -3.102215
2021-10-13 15:40 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.890 s -0.555545
2021-10-13 15:01 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.978 s 0.250100
2021-10-13 14:49 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.069 s -0.754048
2021-10-13 15:01 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.639 s 0.979544
2021-10-13 15:03 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.599 s 1.405605
2021-10-13 15:05 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.144 s 1.260898
2021-10-13 15:05 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.310 s 0.902684
2021-10-13 15:01 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.840 s 0.246603
2021-10-13 15:12 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.877 s -0.035765
2021-10-13 15:27 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.752 s 0.616658
2021-10-13 15:38 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.385 s 1.199603
2021-10-13 14:23 Python csv-read gzip, streaming, fanniemae_2016Q4 14.600 s 2.964425
2021-10-13 14:25 Python csv-read gzip, file, nyctaxi_2010-01 9.048 s -1.247593
2021-10-13 14:27 Python dataframe-to-table type_integers 0.011 s -0.198827
2021-10-13 15:11 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.945 s -0.739040
2021-10-13 14:24 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.479 s 0.989402
2021-10-13 15:02 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.262 s -0.288684
2021-10-13 15:49 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.115 s -1.903243
2021-10-13 14:24 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s -0.064248
2021-10-13 14:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.036 s -0.461550
2021-10-13 15:26 R dataframe-to-table chi_traffic_2020_Q1, R 3.465 s 0.260752
2021-10-13 15:49 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.678 s -1.357064
2021-10-13 14:45 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.187 s 0.252165
2021-10-13 15:03 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.118 s 1.297068
2021-10-13 15:04 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.047 s 0.011338
2021-10-13 15:47 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s -0.329255
2021-10-13 15:48 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.910 s -0.256371
2021-10-13 14:22 Python csv-read uncompressed, file, fanniemae_2016Q4 1.161 s 0.354618
2021-10-13 14:24 Python csv-read uncompressed, file, nyctaxi_2010-01 1.003 s 0.557808
2021-10-13 14:27 Python dataframe-to-table chi_traffic_2020_Q1 19.589 s -0.087893
2021-10-13 14:49 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.053 s -0.267005
2021-10-13 15:13 Python wide-dataframe use_legacy_dataset=true 0.393 s -0.003804
2021-10-13 15:29 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.060 s -0.985295
2021-10-13 15:03 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.286 s 0.117803
2021-10-13 15:09 Python file-write lz4, feather, table, fanniemae_2016Q4 1.144 s 0.961888
2021-10-13 15:27 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.451 s 0.686383
2021-10-13 14:27 Python dataframe-to-table type_strings 0.368 s 0.302293
2021-10-13 15:02 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.257 s 1.418634
2021-10-13 15:04 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.015 s 1.209875
2021-10-13 15:08 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.028 s -1.926538
2021-10-13 15:33 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.675 s -3.387919
2021-10-13 15:44 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.273 s 1.281790
2021-10-13 15:49 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.497 s -2.014991
2021-10-13 14:25 Python csv-read gzip, streaming, nyctaxi_2010-01 10.467 s 1.001285
2021-10-13 14:28 Python dataset-filter nyctaxi_2010-01 4.365 s -0.571181
2021-10-13 15:02 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.846 s -0.303094
2021-10-13 15:04 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.175 s 0.881601
2021-10-13 15:06 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.344 s -2.816760
2021-10-13 15:10 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.883 s -0.780210
2021-10-13 15:26 R dataframe-to-table type_floats, R 0.013 s 0.728154
2021-10-13 14:22 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.677 s 3.114600
2021-10-13 15:10 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.840 s 0.040302
2021-10-13 15:44 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s -2.095464
2021-10-13 14:27 Python dataframe-to-table type_dict 0.011 s 1.031853
2021-10-13 14:27 Python dataframe-to-table type_floats 0.011 s 0.478941
2021-10-13 15:04 Python file-read lz4, feather, table, fanniemae_2016Q4 0.618 s -1.390764
2021-10-13 15:12 Python file-write lz4, feather, table, nyctaxi_2010-01 1.794 s 0.512682
2021-10-13 15:47 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.590 s -1.716770
2021-10-13 15:47 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.193 s -1.202473
2021-10-13 15:02 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.018 s -0.326966
2021-10-13 15:09 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.862 s -0.045633
2021-10-13 15:27 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.322 s -1.370801
2021-10-13 15:28 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.566 s -0.660161
2021-10-13 15:47 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.593 s 0.860427
2021-10-13 15:05 Python file-read lz4, feather, table, nyctaxi_2010-01 0.669 s 0.141032
2021-10-13 15:07 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.619 s -1.570343
2021-10-13 15:08 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.706 s -2.869479
2021-10-13 15:43 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.769 s -1.672389
2021-10-13 15:45 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.469 s 1.178106
2021-10-13 15:29 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.218 s -1.290835
2021-10-13 15:46 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s -2.060497
2021-10-13 15:49 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.361 s 0.461700
2021-10-13 15:50 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.215 s -2.460276
2021-10-13 15:50 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.151 s 0.999465
2021-10-13 15:50 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.494 s 0.827064
2021-10-13 15:58 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.858 s 0.997225
2021-10-13 15:58 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.279686
2021-10-13 15:58 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.057518
2021-10-13 15:58 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.762593
2021-10-13 15:58 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.444722
2021-10-13 15:58 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s 0.088635
2021-10-13 15:58 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.683 s -0.483639
2021-10-13 15:58 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.465527
2021-10-13 15:58 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.984007
2021-10-13 15:58 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.227223
2021-10-13 15:58 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.593265
2021-10-13 15:58 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.723631
2021-10-13 15:58 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.553 s -0.515241
2021-10-13 15:58 JavaScript Parse readBatches, tracks 0.000 s -1.089224
2021-10-13 15:58 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.680 s -0.441449
2021-10-13 15:58 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.671 s 0.270169
2021-10-13 15:58 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.860682
2021-10-13 15:58 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.483529
2021-10-13 15:58 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.057618
2021-10-13 15:58 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.687972
2021-10-13 15:58 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.154114
2021-10-13 15:58 JavaScript Parse serialize, tracks 0.005 s -0.802527
2021-10-13 15:58 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.694414
2021-10-13 15:58 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.953787
2021-10-13 15:58 JavaScript Parse Table.from, tracks 0.000 s -0.586210
2021-10-13 15:58 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.065179
2021-10-13 15:58 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.036706
2021-10-13 15:58 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.738 s 0.052207
2021-10-13 15:58 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.789333
2021-10-13 15:58 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.209443
2021-10-13 15:03 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.717 s 0.777588
2021-10-13 15:05 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.174 s 0.105762
2021-10-13 15:10 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.291 s 0.566111
2021-10-13 15:12 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.340 s 0.455525
2021-10-13 15:12 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.324 s 0.551524
2021-10-13 15:12 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.752 s 1.877201
2021-10-13 15:29 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.135 s -1.252813