Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 20:59 Python csv-read gzip, streaming, fanniemae_2016Q4 14.911 s -0.329160
2021-10-08 21:36 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.963 s 0.894910
2021-10-08 21:39 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.327 s -0.767171
2021-10-08 21:39 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.284 s -0.511424
2021-10-08 21:46 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.900 s -0.613613
2021-10-08 22:21 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.897 s -0.745246
2021-10-08 22:25 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.849279
2021-10-08 22:27 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.495 s -0.991008
2021-10-08 22:28 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.098906
2021-10-08 22:32 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.507 s -0.816867
2021-10-08 22:39 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.960 s -1.214845
2021-10-08 22:39 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.413419
2021-10-08 22:39 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.168065
2021-10-08 22:39 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.257419
2021-10-08 22:39 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.216790
2021-10-08 22:39 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.464551
2021-10-08 22:39 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.173714
2021-10-08 22:39 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -1.338349
2021-10-08 22:39 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.785624
2021-10-08 22:39 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.556 s -0.780500
2021-10-08 21:04 Python dataset-filter nyctaxi_2010-01 4.342 s 1.116163
2021-10-08 21:37 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.220 s 0.569036
2021-10-08 21:37 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.792 s 0.862003
2021-10-08 21:41 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.935 s -0.523830
2021-10-08 21:03 Python dataframe-to-table type_floats 0.011 s 0.388701
2021-10-08 21:38 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.293 s -0.355904
2021-10-08 21:46 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.923 s -0.492017
2021-10-08 20:58 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.971 s -0.276076
2021-10-08 20:59 Python csv-read uncompressed, file, fanniemae_2016Q4 1.191 s -0.952613
2021-10-08 21:04 Python dataframe-to-table type_simple_features 0.911 s 0.386851
2021-10-08 21:21 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.083 s 0.489874
2021-10-08 21:36 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.765 s -0.145830
2021-10-08 21:38 Python file-read lz4, feather, table, fanniemae_2016Q4 0.600 s 0.459960
2021-10-08 21:38 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.077 s -1.175195
2021-10-08 21:04 Python dataframe-to-table type_nested 2.873 s 0.547967
2021-10-08 21:37 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.282 s 0.497474
2021-10-08 21:37 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.130 s 0.841027
2021-10-08 21:38 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.926 s -0.362690
2021-10-08 21:40 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.439 s -0.544916
2021-10-08 21:07 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 59.417 s 0.657472
2021-10-08 21:44 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.299 s -0.796249
2021-10-08 21:00 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.663325
2021-10-08 21:25 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.031 s 0.085012
2021-10-08 21:41 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.087 s 0.525819
2021-10-08 21:47 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.805 s 0.016719
2021-10-08 21:00 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.484 s 1.078921
2021-10-08 21:25 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.011 s 0.179054
2021-10-08 21:01 Python csv-read gzip, file, nyctaxi_2010-01 9.046 s -0.289301
2021-10-08 21:03 Python dataframe-to-table type_strings 0.374 s -0.543836
2021-10-08 21:42 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.455 s 0.433645
2021-10-08 21:47 Python file-write lz4, feather, table, nyctaxi_2010-01 1.818 s -0.511542
2021-10-08 21:47 Python wide-dataframe use_legacy_dataset=false 0.622 s 0.074790
2021-10-08 21:42 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.311 s 0.105714
2021-10-08 21:45 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.908 s -1.179529
2021-10-08 21:47 Python wide-dataframe use_legacy_dataset=true 0.393 s 0.714634
2021-10-08 21:00 Python csv-read uncompressed, file, nyctaxi_2010-01 1.005 s 0.797251
2021-10-08 21:03 Python dataframe-to-table chi_traffic_2020_Q1 19.797 s -0.873540
2021-10-08 21:03 Python dataframe-to-table type_dict 0.012 s -0.005461
2021-10-08 21:03 Python dataframe-to-table type_integers 0.011 s -1.878482
2021-10-08 21:47 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.365 s -0.607183
2021-10-08 21:01 Python csv-read gzip, streaming, nyctaxi_2010-01 10.479 s 1.066537
2021-10-08 21:11 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.169 s 0.347912
2021-10-08 21:25 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.022 s 0.157995
2021-10-08 22:10 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.235 s 2.293819
2021-10-08 21:38 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.254 s -0.744487
2021-10-08 21:43 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.739 s -0.266441
2021-10-08 21:44 Python file-write lz4, feather, table, fanniemae_2016Q4 1.160 s 0.110805
2021-10-08 21:45 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.862 s -0.469156
2021-10-08 22:39 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.467365
2021-10-08 21:36 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.821 s 0.396104
2021-10-08 21:44 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.809 s -0.577496
2021-10-08 22:39 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.470403
2021-10-08 21:21 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.609 s -0.035890
2021-10-08 21:36 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.001 s 0.111392
2021-10-08 21:37 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.860 s -0.342936
2021-10-08 21:38 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.783 s -0.227385
2021-10-08 21:39 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.011 s 1.719362
2021-10-08 21:40 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 1.263294
2021-10-08 21:40 Python file-read lz4, feather, table, nyctaxi_2010-01 0.671 s -0.330022
2021-10-08 21:43 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.290 s 0.294094
2021-10-08 21:46 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.357 s -0.371400
2021-10-08 22:30 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.618 s -0.411660
2021-10-08 22:39 JavaScript Parse Table.from, tracks 0.000 s -0.123870
2021-10-08 22:39 JavaScript Parse serialize, tracks 0.005 s 0.421288
2021-10-08 22:11 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.688 s 0.046362
2021-10-08 22:01 R dataframe-to-table chi_traffic_2020_Q1, R 3.392 s 0.272290
2021-10-08 22:01 R dataframe-to-table type_dict, R 0.050 s 0.055133
2021-10-08 22:08 R dataframe-to-table type_simple_features, R 3.326 s 1.885563
2021-10-08 22:01 R dataframe-to-table type_strings, R 0.490 s 0.226175
2021-10-08 22:02 R dataframe-to-table type_integers, R 0.010 s 2.586860
2021-10-08 22:13 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.853 s 0.509378
2021-10-08 22:19 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.557 s 0.584071
2021-10-08 22:20 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.191 s 0.931346
2021-10-08 22:15 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.264 s 0.638968
2021-10-08 22:29 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.598 s 0.372684
2021-10-08 22:27 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.827551
2021-10-08 22:28 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.589 s 0.206360
2021-10-08 22:28 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.186 s -0.908152
2021-10-08 22:11 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.104802
2021-10-08 22:11 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.975 s 0.233047
2021-10-08 22:12 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.540 s 0.025513
2021-10-08 22:02 R dataframe-to-table type_floats, R 0.012 s 2.590399
2021-10-08 22:02 R dataframe-to-table type_nested, R 0.537 s 0.227553
2021-10-08 22:24 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.718 s -0.689808
2021-10-08 22:19 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 1.122384
2021-10-08 22:08 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.542 s 2.265277
2021-10-08 22:39 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.657 s -0.502605
2021-10-08 22:39 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.710944
2021-10-08 22:39 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.774 s -0.269555
2021-10-08 22:08 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.443 s 2.393270
2021-10-08 22:28 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.854 s 0.455466
2021-10-08 22:29 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.617417
2021-10-08 22:39 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.265728
2021-10-08 22:10 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.164 s 2.370054
2021-10-08 22:10 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.138 s -0.959996
2021-10-08 22:30 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.915 s 0.354771
2021-10-08 22:39 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.252298
2021-10-08 22:39 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.883429
2021-10-08 22:08 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.205 s 0.563068
2021-10-08 22:09 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s -0.153398
2021-10-08 22:09 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.943 s -1.235447
2021-10-08 22:17 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.791902
2021-10-08 22:29 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -1.369975
2021-10-08 22:39 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.701172
2021-10-08 22:39 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.706 s -0.419291
2021-10-08 22:39 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s -2.300990
2021-10-08 22:39 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s 1.735152
2021-10-08 22:08 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.213 s 0.528361
2021-10-08 22:09 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.485916
2021-10-08 22:10 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.398 s -0.717370
2021-10-08 22:17 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.712 s 0.712684
2021-10-08 22:23 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.540 s -0.864929
2021-10-08 22:26 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.234 s 1.363887
2021-10-08 22:39 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.726 s -0.684332
2021-10-08 22:39 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.779168
2021-10-08 22:15 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.290 s 0.613660
2021-10-08 22:22 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.852 s -0.606192
2021-10-08 22:28 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.568 s 0.460577
2021-10-08 22:39 JavaScript Parse readBatches, tracks 0.000 s 0.122921
2021-10-08 22:39 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 3.015 s -3.540560
2021-10-08 22:39 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s 1.873763
2021-10-08 22:31 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.209 s -1.403065
2021-10-08 22:31 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.167 s 0.402096
2021-10-08 22:10 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.050 s 1.068501
2021-10-08 22:30 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.114 s -1.654959
2021-10-08 22:30 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.360 s 0.073759
2021-10-08 22:31 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.484 s -0.912945
2021-10-08 22:39 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.713336
2021-10-08 22:39 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.400416