Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-02 02:54 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.383 s -1.050883
2021-10-02 03:41 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.055 s 0.114043
2021-10-02 03:43 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.689 s -0.060192
2021-10-02 03:56 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.752 s -0.483260
2021-10-02 04:02 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.106 s -3.518028
2021-10-02 04:11 JavaScript Parse serialize, tracks 0.005 s -0.694524
2021-10-02 04:11 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.653 s -0.403886
2021-10-02 03:39 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.225265
2021-10-02 02:15 Python dataframe-to-table type_floats 0.011 s 0.876935
2021-10-02 02:50 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.262 s 1.066299
2021-10-02 02:53 Python file-read lz4, feather, table, nyctaxi_2010-01 0.665 s 0.936822
2021-10-02 03:00 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.394 s -2.520273
2021-10-02 03:38 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.861 s 0.639527
2021-10-02 03:53 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.943 s -0.928010
2021-10-02 02:10 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.791 s -0.041853
2021-10-02 02:52 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.060 s -0.520469
2021-10-02 03:14 R dataframe-to-table type_integers, R 0.084 s 0.377551
2021-10-02 03:39 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.866 s 0.604320
2021-10-02 02:18 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.958 s -0.347071
2021-10-02 02:51 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.755 s -0.892599
2021-10-02 02:53 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.487 s -1.601290
2021-10-02 03:14 R dataframe-to-table chi_traffic_2020_Q1, R 5.347 s 0.991986
2021-10-02 03:38 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.208 s 0.528072
2021-10-02 03:38 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.198 s 0.587544
2021-10-02 02:15 Python dataframe-to-table type_nested 2.889 s 0.958703
2021-10-02 02:49 Python dataset-selectivity 1%, chi_traffic_2020_Q1 5.934 s 1.561152
2021-10-02 03:51 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.390 s 2.020549
2021-10-02 02:14 Python dataframe-to-table chi_traffic_2020_Q1 19.334 s 1.976867
2021-10-02 02:15 Python dataframe-to-table type_dict 0.012 s -0.787479
2021-10-02 02:55 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.746 s -1.094518
2021-10-02 03:52 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.193 s 1.091707
2021-10-02 04:00 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 1.095084
2021-10-02 04:00 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.184 s -1.080522
2021-10-02 04:11 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.465 s 0.586229
2021-10-02 02:11 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.669 s -0.056075
2021-10-02 02:12 Python csv-read gzip, streaming, nyctaxi_2010-01 10.665 s -0.169826
2021-10-02 02:32 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.319 s -0.171841
2021-10-02 02:52 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.316 s -1.399748
2021-10-02 02:53 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.340 s -1.522574
2021-10-02 03:41 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.176 s -0.067698
2021-10-02 04:01 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -1.293586
2021-10-02 04:03 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.201 s -1.839233
2021-10-02 04:11 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.686 s -0.396730
2021-10-02 04:11 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.860 s 0.503241
2021-10-02 04:11 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 02:56 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.977 s -0.970749
2021-10-02 02:57 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.853 s -0.910917
2021-10-02 03:00 Python wide-dataframe use_legacy_dataset=false 0.619 s 0.063129
2021-10-02 03:49 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.824 s 1.466846
2021-10-02 02:11 Python csv-read gzip, streaming, fanniemae_2016Q4 14.724 s -0.046179
2021-10-02 02:13 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.471754
2021-10-02 02:15 Python dataframe-to-table type_simple_features 0.912 s 0.038078
2021-10-02 02:32 Python dataset-read async=True, nyctaxi_multi_ipc_s3 191.340 s -0.316581
2021-10-02 02:49 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.906 s 0.015766
2021-10-02 02:51 Python file-read lz4, feather, table, fanniemae_2016Q4 0.593 s 1.649015
2021-10-02 02:10 Python csv-read uncompressed, file, fanniemae_2016Q4 1.186 s -0.157521
2021-10-02 02:58 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.979 s -1.783126
2021-10-02 03:47 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.609 s -1.019784
2021-10-02 03:55 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.579 s -0.884045
2021-10-02 04:02 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.356 s 0.720678
2021-10-02 04:11 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.431033
2021-10-02 04:11 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.014635
2021-10-02 02:15 Python dataframe-to-table type_integers 0.011 s 1.368211
2021-10-02 02:37 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.025 s -0.040472
2021-10-02 02:51 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.951 s -1.942917
2021-10-02 02:54 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.966 s -1.504167
2021-10-02 02:59 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 12.016 s -0.553247
2021-10-02 03:40 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.920 s 0.079025
2021-10-02 03:40 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.385 s -0.019285
2021-10-02 03:42 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.231 s 0.680602
2021-10-02 03:43 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.999 s -1.516613
2021-10-02 04:01 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -1.333920
2021-10-02 02:37 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.030 s 0.093490
2021-10-02 02:49 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.740 s 0.150864
2021-10-02 02:50 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.115 s 1.181447
2021-10-02 02:59 Python file-write snappy, parquet, table, nyctaxi_2010-01 8.001 s -1.125782
2021-10-02 03:14 R dataframe-to-table type_strings, R 0.494 s -1.094664
2021-10-02 03:14 R dataframe-to-table type_nested, R 0.544 s -2.590112
2021-10-02 03:45 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.159 s -1.038732
2021-10-02 04:00 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.168 s 1.622017
2021-10-02 04:02 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.926 s 1.001375
2021-10-02 04:11 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.130639
2021-10-02 03:58 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.244 s 1.238351
2021-10-02 04:00 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.574 s 1.156612
2021-10-02 04:03 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.481 s -1.977935
2021-10-02 04:11 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.107716
2021-10-02 04:11 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.595214
2021-10-02 04:11 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.574204
2021-10-02 02:50 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.189 s 1.267018
2021-10-02 03:00 Python wide-dataframe use_legacy_dataset=true 0.398 s -2.521044
2021-10-02 03:40 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.057089
2021-10-02 03:51 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.539 s 1.381338
2021-10-02 04:03 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.181 s 0.931203
2021-10-02 02:12 Python csv-read uncompressed, file, nyctaxi_2010-01 1.032 s -0.253767
2021-10-02 02:56 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.366 s -0.111474
2021-10-02 03:46 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.594 s -0.749435
2021-10-02 04:11 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.738 s 0.052011
2021-10-02 04:11 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.657603
2021-10-02 02:11 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.039611
2021-10-02 02:49 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.977 s 0.191387
2021-10-02 02:55 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.633 s -1.061535
2021-10-02 04:00 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.859 s 1.276240
2021-10-02 04:04 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.505 s 0.082079
2021-10-02 04:11 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.566616
2021-10-02 04:11 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.023284
2021-10-02 02:15 Python dataframe-to-table type_strings 0.377 s -0.640269
2021-10-02 02:15 Python dataset-filter nyctaxi_2010-01 4.349 s 0.562363
2021-10-02 02:23 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.948 s 1.161230
2021-10-02 02:37 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.033 s 0.025231
2021-10-02 02:50 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.777 s 1.160671
2021-10-02 02:50 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.835 s -1.130061
2021-10-02 03:14 R dataframe-to-table type_dict, R 0.050 s -0.041412
2021-10-02 04:02 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.604 s -0.093754
2021-10-02 04:11 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.662 s 0.362545
2021-10-02 02:51 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.288 s 0.351858
2021-10-02 02:52 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.070 s -2.049292
2021-10-02 02:53 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.889958
2021-10-02 02:57 Python file-write lz4, feather, table, fanniemae_2016Q4 1.155 s 0.541873
2021-10-02 03:00 Python file-write lz4, feather, table, nyctaxi_2010-01 1.804 s 0.411458
2021-10-02 03:00 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.803 s 0.345115
2021-10-02 03:38 R dataframe-to-table type_simple_features, R 275.863 s -1.849635
2021-10-02 03:42 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.224115
2021-10-02 03:49 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.034 s -0.716849
2021-10-02 04:11 JavaScript Parse Table.from, tracks 0.000 s -0.139769
2021-10-02 04:11 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.557672
2021-10-02 04:11 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.140541
2021-10-02 02:51 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.237 s -1.635195
2021-10-02 02:57 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.289 s -0.619892
2021-10-02 02:58 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.931 s -0.200521
2021-10-02 03:00 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.434 s -0.551599
2021-10-02 03:14 R dataframe-to-table type_floats, R 0.108 s 0.568257
2021-10-02 03:44 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.547 s -1.202206
2021-10-02 03:59 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.494 s -0.688763
2021-10-02 04:00 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.357219
2021-10-02 04:11 JavaScript Parse readBatches, tracks 0.000 s -0.267883
2021-10-02 04:11 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.000 s 0.804793
2021-10-02 04:11 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.557557
2021-10-02 03:54 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.897 s -0.586128
2021-10-02 03:57 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.280 s 1.109865
2021-10-02 04:11 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.115197
2021-10-02 04:11 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.138360
2021-10-02 04:11 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.912 s -0.175263
2021-10-02 04:11 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.557557
2021-10-02 04:11 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.056254
2021-10-02 04:11 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -1.185123
2021-10-02 04:11 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -1.114953
2021-10-02 03:41 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.132 s -0.166596
2021-10-02 04:01 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.606 s 0.959654
2021-10-02 04:11 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.040735