Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 07:53 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.832 s -1.135521
2021-09-30 08:37 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.203 s -4.019535
2021-09-30 08:45 Python wide-dataframe use_legacy_dataset=false 0.623 s -1.083274
2021-09-30 08:59 R dataframe-to-table type_nested, R 0.536 s 0.173693
2021-09-30 07:52 Python csv-read gzip, streaming, fanniemae_2016Q4 14.758 s -0.558245
2021-09-30 08:36 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.161 s -1.185829
2021-09-30 08:39 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 1.285974
2021-09-30 08:45 Python file-write lz4, feather, table, nyctaxi_2010-01 1.800 s 0.563446
2021-09-30 08:59 R dataframe-to-table type_strings, R 0.491 s -0.079136
2021-09-30 08:21 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.066299
2021-09-30 07:56 Python dataframe-to-table type_dict 0.012 s 0.080289
2021-09-30 08:36 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.798 s -3.223707
2021-09-30 08:38 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.026 s 0.052047
2021-09-30 08:37 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.025 s 0.652030
2021-09-30 08:45 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.087462
2021-09-30 08:15 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.310 s -0.122824
2021-09-30 08:34 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.995 s 0.112171
2021-09-30 08:44 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.351 s -0.064329
2021-09-30 08:45 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.389 s -0.224883
2021-09-30 08:35 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.315 s -1.730150
2021-09-30 08:36 Python file-read lz4, feather, table, fanniemae_2016Q4 0.597 s 0.840811
2021-09-30 08:38 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.161 s 0.028937
2021-09-30 08:42 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.306 s -0.819208
2021-09-30 07:56 Python dataset-filter nyctaxi_2010-01 4.443 s -2.590764
2021-09-30 08:36 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.896 s -4.361547
2021-09-30 08:34 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.740 s 0.256232
2021-09-30 08:39 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.643 s 0.130898
2021-09-30 08:42 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.831 s -0.937433
2021-09-30 07:51 Python csv-read uncompressed, file, fanniemae_2016Q4 1.178 s -0.121165
2021-09-30 07:54 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.603833
2021-09-30 08:42 Python file-write lz4, feather, table, fanniemae_2016Q4 1.156 s 0.446344
2021-09-30 08:59 R dataframe-to-table type_floats, R 0.108 s 0.111442
2021-09-30 08:05 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 92.834 s 2.085767
2021-09-30 08:41 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.661 s 0.432965
2021-09-30 08:41 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.292 s 0.425915
2021-09-30 08:44 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.844 s 0.666222
2021-09-30 08:15 Python dataset-read async=True, nyctaxi_multi_ipc_s3 211.852 s -2.804670
2021-09-30 08:37 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.038 s 0.046819
2021-09-30 08:38 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.747883
2021-09-30 08:40 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.436 s 1.252335
2021-09-30 08:59 R dataframe-to-table type_dict, R 0.027 s 2.798428
2021-09-30 07:52 Python csv-read gzip, file, fanniemae_2016Q4 6.033 s -0.736292
2021-09-30 07:53 Python csv-read uncompressed, file, nyctaxi_2010-01 1.010 s 0.155172
2021-09-30 07:56 Python dataframe-to-table type_floats 0.011 s 0.167707
2021-09-30 08:35 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.850 s -3.472380
2021-09-30 08:58 R dataframe-to-table chi_traffic_2020_Q1, R 5.374 s 0.587263
2021-09-30 08:20 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.018 s 0.242764
2021-09-30 08:35 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.865 s -1.331820
2021-09-30 08:43 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.828 s 0.518418
2021-09-30 08:45 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.818 s 0.171991
2021-09-30 08:59 R dataframe-to-table type_integers, R 0.084 s 0.456430
2021-09-30 07:55 Python dataframe-to-table chi_traffic_2020_Q1 19.499 s 1.703640
2021-09-30 08:21 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.002 s 0.276434
2021-09-30 08:34 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.840 s 0.359747
2021-09-30 08:37 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.022 s 0.920332
2021-09-30 08:40 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.236 s 0.714310
2021-09-30 08:43 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.795 s 1.610995
2021-09-30 07:53 Python csv-read gzip, streaming, nyctaxi_2010-01 10.813 s -1.108011
2021-09-30 07:56 Python dataframe-to-table type_strings 0.371 s 0.071429
2021-09-30 07:56 Python dataframe-to-table type_integers 0.011 s -2.003839
2021-09-30 07:56 Python dataframe-to-table type_simple_features 0.934 s -3.263664
2021-09-30 08:00 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 66.150 s -1.368507
2021-09-30 08:36 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.371942
2021-09-30 08:44 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.847 s 1.890815
2021-09-30 07:56 Python dataframe-to-table type_nested 2.852 s 4.453112
2021-09-30 08:34 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.040 s -1.359245
2021-09-30 09:28 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.122567
2021-09-30 09:43 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.481 s 1.724133
2021-09-30 09:56 JavaScript Parse serialize, tracks 0.005 s -0.057955
2021-09-30 07:51 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.828 s -0.556840
2021-09-30 08:35 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.283 s -1.332665
2021-09-30 08:38 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.161939
2021-09-30 09:33 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.764 s 1.051211
2021-09-30 09:56 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.129213
2021-09-30 09:56 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.615982
2021-09-30 09:32 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 1.215775
2021-09-30 09:26 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.135 s -0.369944
2021-09-30 09:31 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.304 s 1.076550
2021-09-30 09:43 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.260 s 0.521105
2021-09-30 09:44 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.587 s 1.771266
2021-09-30 09:47 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.348 s 1.681711
2021-09-30 09:56 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.529902
2021-09-30 09:56 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.103837
2021-09-30 09:25 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.754086
2021-09-30 09:25 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.385 s -0.422256
2021-09-30 09:28 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.981 s -0.719332
2021-09-30 09:44 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.877 s 1.941767
2021-09-30 09:45 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.206369
2021-09-30 09:48 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.143 s 1.722561
2021-09-30 09:24 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.923 s -0.026454
2021-09-30 09:45 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.234055
2021-09-30 09:56 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.023 s 0.727886
2021-09-30 09:42 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.284 s -0.999565
2021-09-30 09:56 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.430344
2021-09-30 09:56 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.450459
2021-09-30 09:56 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -2.480955
2021-09-30 09:22 R dataframe-to-table type_simple_features, R 275.481 s -1.332168
2021-09-30 09:23 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.958 s -0.379738
2021-09-30 09:36 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.398 s 0.662909
2021-09-30 09:39 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.806 s 1.938228
2021-09-30 09:47 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.474 s -0.467818
2021-09-30 09:56 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.632 s -0.188070
2021-09-30 09:56 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.724 s -0.734404
2021-09-30 09:56 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.999900
2021-09-30 09:24 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.171303
2021-09-30 09:30 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.844 s 1.281030
2021-09-30 09:56 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.781183
2021-09-30 09:56 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.468 s 0.688198
2021-09-30 09:26 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.166 s 0.364237
2021-09-30 09:44 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.209 s -1.067166
2021-09-30 09:56 JavaScript Parse readBatches, tracks 0.000 s -0.636505
2021-09-30 09:56 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.513673
2021-09-30 09:25 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s -0.051423
2021-09-30 09:46 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.608 s -0.052215
2021-09-30 09:27 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.262544
2021-09-30 09:35 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.588 s 0.673492
2021-09-30 09:48 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.492 s 0.139261
2021-09-30 09:56 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.326383
2021-09-30 09:56 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.971211
2021-09-30 09:23 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.246 s 0.065350
2021-09-30 09:40 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.462 s 2.007618
2021-09-30 09:46 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.951 s 1.671743
2021-09-30 09:38 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.814 s 2.275459
2021-09-30 09:56 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-30 09:56 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.929619
2021-09-30 09:25 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.936 s -1.034955
2021-09-30 09:27 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.266 s -1.390013
2021-09-30 09:29 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.556 s -1.782286
2021-09-30 09:34 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.837 s -1.277759
2021-09-30 09:47 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.749230
2021-09-30 09:56 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.867 s 0.412961
2021-09-30 09:45 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 2.189765
2021-09-30 09:45 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.597 s 1.686007
2021-09-30 09:47 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.550 s 0.778771
2021-09-30 09:48 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.197 s 0.410657
2021-09-30 09:56 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.012780
2021-09-30 09:41 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.673 s 1.818362
2021-09-30 09:23 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.232 s 0.225670
2021-09-30 09:37 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.241 s 0.336472
2021-09-30 09:45 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.516 s -0.111317
2021-09-30 09:56 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-30 09:56 JavaScript Parse Table.from, tracks 0.000 s -0.778994
2021-09-30 09:56 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.023 s 0.724307
2021-09-30 09:56 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.700 s 0.293030
2021-09-30 09:56 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.290626
2021-09-30 09:56 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.313307
2021-09-30 09:56 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.469709
2021-09-30 09:56 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.528 s 0.037368
2021-09-30 09:56 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.853 s 0.972473