Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-12 18:24 R dataframe-to-table chi_traffic_2020_Q1, R 3.390 s 0.266456
2021-10-12 18:25 R dataframe-to-table type_strings, R 0.488 s 0.232712
2021-10-12 18:25 R dataframe-to-table type_dict, R 0.053 s -0.247743
2021-10-12 18:25 R dataframe-to-table type_integers, R 0.010 s 0.938388
2021-10-12 18:25 R dataframe-to-table type_floats, R 0.013 s 0.921603
2021-10-12 18:25 R dataframe-to-table type_nested, R 0.529 s 0.235431
2021-10-12 18:31 R dataframe-to-table type_simple_features, R 3.371 s 0.770270
2021-10-12 18:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.214 s 0.378507
2021-10-12 18:31 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.465 s 0.899875
2021-10-12 18:32 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.207 s 0.950781
2021-10-12 18:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.509 s 0.862702
2021-10-12 18:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.315 s -1.271773
2021-10-12 18:32 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.059 s -1.408792
2021-10-12 18:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 0.707079
2021-10-12 18:33 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.406 s -0.849321
2021-10-12 18:33 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.047 s 1.058499
2021-10-12 18:33 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.155 s 0.887215
2021-10-12 18:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.104 s 1.155157
2021-10-12 18:34 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.223 s 0.878079
2021-10-12 18:34 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.570417
2021-10-12 18:35 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.966 s 0.398983
2021-10-12 18:35 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.694 s -0.024206
2021-10-12 18:35 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.512 s 0.266039
2021-10-12 18:36 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.843 s 0.449513
2021-10-12 18:38 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.238 s 0.624252
2021-10-12 18:38 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.289 s 0.498596
2021-10-12 18:40 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.689 s 0.679438
2021-10-12 18:41 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.812 s 1.500165
2021-10-12 18:42 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.522 s 1.495629
2021-10-12 18:42 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.390 s 0.822311
2021-10-12 18:43 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.198 s -0.053765
2021-10-12 18:44 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.895 s -0.507095
2021-10-12 18:45 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.868 s -0.751017
2021-10-12 18:46 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.534 s -0.603571
2021-10-12 18:47 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.729 s -0.764024
2021-10-12 18:48 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.275 s 1.049444
2021-10-12 18:49 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.242 s 0.027346
2021-10-12 18:50 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.474 s 1.020499
2021-10-12 18:51 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.172 s -0.099470
2021-10-12 18:51 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.596 s -0.883647
2021-10-12 18:51 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.881 s -0.177956
2021-10-12 18:51 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.572 s 0.471539
2021-10-12 18:51 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.088 s 0.760048
2021-10-12 18:51 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s 0.306141
2021-10-12 18:52 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.598 s 0.082139
2021-10-12 18:52 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.532 s -1.438258
2021-10-12 18:52 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.614 s 0.524277
2021-10-12 18:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.871 s 1.378060
2021-10-12 18:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.513 s 1.346297
2021-10-12 18:53 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.110 s 0.101491
2021-10-12 18:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.363 s -0.699539
2021-10-12 18:54 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.487 s -0.324830
2021-10-12 18:54 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.205 s 0.435742
2021-10-12 18:54 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.165 s 0.814637
2021-10-12 18:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.489 s 1.397967
2021-10-12 19:02 JavaScript Parse Table.from, tracks 0.000 s -4.367363
2021-10-12 19:02 JavaScript Parse serialize, tracks 0.004 s 0.745825
2021-10-12 19:02 JavaScript Parse readBatches, tracks 0.000 s -3.300798
2021-10-12 19:02 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.036208
2021-10-12 19:02 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.060599
2021-10-12 19:02 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.605 s -0.250443
2021-10-12 19:02 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.614 s -0.318238
2021-10-12 19:02 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.696834
2021-10-12 19:02 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.676956
2021-10-12 19:02 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.661 s 0.473820
2021-10-12 19:02 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.700 s 0.275986
2021-10-12 19:02 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.282456
2021-10-12 19:02 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.136625
2021-10-12 19:02 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.957 s -2.059611
2021-10-12 19:02 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.929 s -0.403104
2021-10-12 19:02 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.547272
2021-10-12 19:02 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574060
2021-10-12 19:02 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.518053
2021-10-12 19:02 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.539236
2021-10-12 19:02 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s -0.461213
2021-10-12 19:02 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.071173
2021-10-12 19:02 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.110046
2021-10-12 19:02 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.544794
2021-10-12 19:02 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -1.971989
2021-10-12 19:02 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.199227
2021-10-12 19:02 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.554769
2021-10-12 19:02 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.583826
2021-10-12 19:02 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.037690
2021-10-12 19:02 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.738162
2021-10-12 19:02 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.744514
2021-10-12 19:02 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.507 s 0.274213
2021-10-12 17:29 Python dataframe-to-table type_dict 0.011 s 1.197138
2021-10-12 17:24 Python csv-read uncompressed, file, fanniemae_2016Q4 1.162 s 0.365444
2021-10-12 17:25 Python csv-read gzip, file, fanniemae_2016Q4 6.019 s 2.481861
2021-10-12 17:29 Python dataframe-to-table type_strings 0.366 s 0.534966
2021-10-12 18:01 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.125 s 0.732792
2021-10-12 18:04 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.285 s 1.621942
2021-10-12 18:04 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.821 s 1.254864
2021-10-12 17:26 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.939 s -2.052282
2021-10-12 17:29 Python dataframe-to-table type_integers 0.011 s 0.007657
2021-10-12 17:50 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.053 s -0.325018
2021-10-12 18:02 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.131 s 1.805845
2021-10-12 18:03 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.144 s 1.583140
2021-10-12 18:05 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.089 s 0.419694
2021-10-12 18:00 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.057 s -1.859537
2021-10-12 18:02 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.710 s 1.477278
2021-10-12 17:45 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.270 s 0.199515
2021-10-12 17:25 Python csv-read gzip, streaming, fanniemae_2016Q4 14.780 s 0.774944
2021-10-12 17:50 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.070 s -0.232528
2021-10-12 17:59 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.855 s 0.170194
2021-10-12 18:00 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.650 s 0.835242
2021-10-12 18:08 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.431 s -0.832412
2021-10-12 17:33 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 65.218 s -1.153062
2021-10-12 18:07 Python file-write lz4, feather, table, fanniemae_2016Q4 1.137 s 1.505526
2021-10-12 18:00 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.890 s -1.874584
2021-10-12 18:01 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.534 s 1.670490
2021-10-12 18:02 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.017 s 1.551401
2021-10-12 17:29 Python dataset-filter nyctaxi_2010-01 4.369 s -1.366790
2021-10-12 17:27 Python csv-read gzip, file, nyctaxi_2010-01 9.045 s 0.060878
2021-10-12 18:01 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.284 s 0.681893
2021-10-12 17:29 Python dataframe-to-table type_simple_features 0.934 s -0.896477
2021-10-12 18:02 Python file-read lz4, feather, table, fanniemae_2016Q4 0.609 s -1.070266
2021-10-12 18:04 Python file-read lz4, feather, table, nyctaxi_2010-01 0.676 s -0.887001
2021-10-12 17:37 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.503 s -0.846715
2021-10-12 17:59 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.004 s 0.139886
2021-10-12 18:02 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.037 s 0.257027
2021-10-12 18:03 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.173 s 0.285468
2021-10-12 18:06 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.445 s 0.435759
2021-10-12 17:28 Python dataframe-to-table chi_traffic_2020_Q1 19.551 s 0.080626
2021-10-12 17:29 Python dataframe-to-table type_nested 2.853 s 1.436185
2021-10-12 18:08 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.845 s 0.032553
2021-10-12 17:49 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.093 s -2.004117
2021-10-12 18:01 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.272 s 0.981626
2021-10-12 18:05 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.518 s -0.739009
2021-10-12 18:06 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.915 s -1.005264
2021-10-12 18:07 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.907 s -0.424226
2021-10-12 17:24 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.831 s 0.938069
2021-10-12 17:26 Python csv-read uncompressed, file, nyctaxi_2010-01 1.006 s 0.313994
2021-10-12 18:03 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.036 s -0.093398
2021-10-12 17:26 Python csv-read gzip, streaming, nyctaxi_2010-01 10.924 s -2.610649
2021-10-12 17:45 Python dataset-read async=True, nyctaxi_multi_ipc_s3 163.990 s 3.677809
2021-10-12 18:01 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.618 s 1.620417
2021-10-12 18:11 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.804 s 0.565178
2021-10-12 18:10 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.387 s -1.578593
2021-10-12 18:10 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.337 s 0.347690
2021-10-12 18:10 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.676826
2021-10-12 18:09 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.853 s 0.012262
2021-10-12 18:11 Python wide-dataframe use_legacy_dataset=true 0.390 s 1.341460
2021-10-12 18:10 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.934 s -0.782051
2021-10-12 18:09 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.912 s -0.022467
2021-10-12 18:11 Python wide-dataframe use_legacy_dataset=false 0.613 s 1.361593
2021-10-12 17:29 Python dataframe-to-table type_floats 0.011 s 0.739443
2021-10-12 18:00 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.306 s -1.855240
2021-10-12 18:07 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.603 s -1.606415