Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 07:48 Python csv-read gzip, file, fanniemae_2016Q4 6.028 s 0.380974
2021-10-13 07:48 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s -0.319001
2021-10-13 07:50 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 0.900548
2021-10-13 07:52 Python dataframe-to-table type_nested 2.864 s 0.764202
2021-10-13 08:30 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.910 s -1.109617
2021-10-13 08:48 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.567 s -0.960641
2021-10-13 09:04 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.276 s 0.590827
2021-10-13 09:07 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.159818
2021-10-13 09:00 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.919 s -1.153467
2021-10-13 09:07 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.601 s -3.485049
2021-10-13 09:07 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.595 s 0.549822
2021-10-13 09:18 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.379269
2021-10-13 09:08 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.801 s -4.098128
2021-10-13 09:10 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.216 s -3.389727
2021-10-13 09:18 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.640935
2021-10-13 09:18 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.244367
2021-10-13 09:18 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.426806
2021-10-13 09:18 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.010301
2021-10-13 09:18 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.028 s -2.210310
2021-10-13 09:18 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.805708
2021-10-13 08:30 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.856 s 0.017728
2021-10-13 08:49 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.366 s 1.445597
2021-10-13 07:47 Python csv-read gzip, streaming, fanniemae_2016Q4 14.833 s 0.212235
2021-10-13 08:24 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.682 s 1.043989
2021-10-13 08:24 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.004 s 1.423855
2021-10-13 08:32 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.904 s -0.436077
2021-10-13 08:33 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.276 s 1.991402
2021-10-13 08:54 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.593 s -2.478546
2021-10-13 09:10 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.501 s 0.118012
2021-10-13 09:18 JavaScript Parse serialize, tracks 0.004 s 0.577591
2021-10-13 07:47 Python csv-read uncompressed, file, fanniemae_2016Q4 1.143 s 0.912432
2021-10-13 08:30 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.334 s 0.147829
2021-10-13 08:47 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.514 s -3.142072
2021-10-13 08:23 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.291 s 0.039754
2021-10-13 08:25 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.126214
2021-10-13 08:56 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.142 s -3.203146
2021-10-13 09:18 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s 0.069359
2021-10-13 09:18 JavaScript DataFrame Iterate 1,000,000, tracks 0.053 s 0.056933
2021-10-13 07:51 Python dataframe-to-table type_integers 0.011 s 0.088100
2021-10-13 07:52 Python dataset-filter nyctaxi_2010-01 4.411 s -2.939039
2021-10-13 08:09 Python dataset-read async=True, nyctaxi_multi_ipc_s3 190.332 s -0.530597
2021-10-13 08:24 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.027 s 0.413520
2021-10-13 08:28 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.651 s -1.709281
2021-10-13 08:46 R dataframe-to-table chi_traffic_2020_Q1, R 3.474 s 0.261758
2021-10-13 09:05 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.468 s 1.364289
2021-10-13 09:10 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.118 s 3.227185
2021-10-13 09:18 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.596024
2021-10-13 08:13 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.075 s -0.237972
2021-10-13 08:26 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.171650
2021-10-13 08:47 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.242 s -0.579467
2021-10-13 08:50 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.693 s -0.000804
2021-10-13 09:01 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.927 s -2.059181
2021-10-13 09:18 JavaScript Parse readBatches, tracks 0.000 s -0.412143
2021-10-13 09:18 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.858 s 0.609172
2021-10-13 08:09 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.253 s 0.208412
2021-10-13 08:24 Python file-read lz4, feather, table, fanniemae_2016Q4 0.599 s 0.364645
2021-10-13 08:27 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.357 s -2.435420
2021-10-13 08:31 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.869 s -0.305946
2021-10-13 08:33 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.756 s 1.907827
2021-10-13 08:57 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.668 s -3.895471
2021-10-13 09:09 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.116 s -2.128203
2021-10-13 09:09 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.495 s -1.781404
2021-10-13 09:18 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.653 s 0.660756
2021-10-13 09:18 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.812004
2021-10-13 08:13 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.089 s -1.575661
2021-10-13 08:50 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 14.031 s -0.374927
2021-10-13 09:09 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.560 s 0.539415
2021-10-13 09:18 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.614 s -0.316619
2021-10-13 09:18 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.702 s 0.256556
2021-10-13 09:18 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.843 s 1.309360
2021-10-13 09:18 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.295578
2021-10-13 07:51 Python dataframe-to-table type_strings 0.363 s 0.715675
2021-10-13 08:23 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.918 s -2.056109
2021-10-13 08:23 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.145 s -0.272287
2021-10-13 08:33 Python file-write lz4, feather, table, nyctaxi_2010-01 1.796 s 0.450988
2021-10-13 08:47 R dataframe-to-table type_nested, R 0.542 s 0.230571
2021-10-13 09:05 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.274 s -3.547036
2021-10-13 09:06 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.536 s 3.755631
2021-10-13 08:22 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.789 s 0.540967
2021-10-13 08:22 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.094 s -2.173707
2021-10-13 08:26 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.278 s 1.293989
2021-10-13 08:46 R dataframe-to-table type_floats, R 0.013 s 0.795612
2021-10-13 08:47 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.724 s 0.689546
2021-10-13 08:49 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.165 s 0.753462
2021-10-13 08:49 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.217 s 0.749020
2021-10-13 09:09 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.357 s 1.722246
2021-10-13 09:18 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 1.048838
2021-10-13 07:59 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.138 s 1.093491
2021-10-13 08:26 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.790 s 1.419860
2021-10-13 08:28 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.712 s -2.443494
2021-10-13 08:32 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.350 s 0.024856
2021-10-13 09:03 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.776 s -1.821716
2021-10-13 09:06 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.190 s -2.112797
2021-10-13 09:07 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.821 s 1.595522
2021-10-13 09:07 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.194 s -1.511928
2021-10-13 09:18 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.485 s 0.658163
2021-10-13 07:51 Python dataframe-to-table type_dict 0.011 s 1.260683
2021-10-13 08:22 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.735 s 0.095245
2021-10-13 08:33 Python wide-dataframe use_legacy_dataset=false 0.615 s 0.756640
2021-10-13 09:18 JavaScript Parse Table.from, tracks 0.000 s 0.423407
2021-10-13 07:49 Python csv-read gzip, streaming, nyctaxi_2010-01 10.651 s -0.322523
2021-10-13 08:52 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.140 s -2.498799
2021-10-13 08:59 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.296 s -3.994018
2021-10-13 09:08 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.973 s -2.414507
2021-10-13 09:18 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.383178
2021-10-13 07:47 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.897 s 0.253512
2021-10-13 08:30 Python file-write lz4, feather, table, fanniemae_2016Q4 1.141 s 1.106861
2021-10-13 08:47 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.479 s 0.742676
2021-10-13 08:51 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.537 s 0.021802
2021-10-13 09:18 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s 0.078303
2021-10-13 09:18 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.634463
2021-10-13 09:18 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.349819
2021-10-13 09:18 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.604220
2021-10-13 09:18 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.669542
2021-10-13 07:48 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.665 s -0.285682
2021-10-13 08:22 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.947 s 0.424055
2021-10-13 08:24 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.280 s 0.157265
2021-10-13 08:29 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 14.000 s -1.603176
2021-10-13 08:33 Python wide-dataframe use_legacy_dataset=true 0.386 s 2.463943
2021-10-13 08:48 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.060 s -1.212358
2021-10-13 09:18 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.593 s -0.224662
2021-10-13 07:51 Python dataframe-to-table type_floats 0.011 s 0.541391
2021-10-13 08:23 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.335 s -2.152369
2021-10-13 08:25 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.186 s 0.879635
2021-10-13 08:56 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.815 s 0.959422
2021-10-13 09:02 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.555 s -1.184499
2021-10-13 09:18 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.447689
2021-10-13 07:51 Python dataframe-to-table chi_traffic_2020_Q1 19.309 s 0.717034
2021-10-13 08:13 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.045 s -0.181613
2021-10-13 08:29 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.385 s -0.462236
2021-10-13 08:46 R dataframe-to-table type_strings, R 0.497 s 0.228894
2021-10-13 08:46 R dataframe-to-table type_integers, R 0.009 s 0.804309
2021-10-13 08:53 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.684 s -3.197362
2021-10-13 07:55 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.183 s 0.945718
2021-10-13 08:25 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.146 s 1.290863
2021-10-13 08:49 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.201135
2021-10-13 08:49 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.107 s 0.856281
2021-10-13 08:49 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.217 s -1.304791
2021-10-13 09:18 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.987411
2021-10-13 08:23 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.612 s 1.410364
2021-10-13 08:24 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.588 s 0.995997
2021-10-13 08:25 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.039 s -0.017987
2021-10-13 08:31 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.981 s -1.253982
2021-10-13 08:46 R dataframe-to-table type_dict, R 0.052 s -0.074652
2021-10-13 08:47 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.320 s -1.341118
2021-10-13 08:58 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.392 s 0.436038
2021-10-13 09:08 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.530 s -0.942443