Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 13:03 Python csv-read uncompressed, file, fanniemae_2016Q4 1.158 s 0.217708
2021-09-29 13:09 Python dataset-filter nyctaxi_2010-01 4.403 s -1.164740
2021-09-29 13:12 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 60.613 s -0.189022
2021-09-29 13:08 Python dataframe-to-table chi_traffic_2020_Q1 19.462 s 1.694647
2021-09-29 13:05 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.654 s -0.346832
2021-09-29 13:26 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.136 s 0.868402
2021-09-29 13:06 Python csv-read gzip, file, nyctaxi_2010-01 9.050 s -1.285975
2021-09-29 13:08 Python dataframe-to-table type_nested 2.884 s 1.972784
2021-09-29 13:26 Python dataset-read async=True, nyctaxi_multi_ipc_s3 196.265 s -0.869609
2021-09-29 13:30 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.105 s -1.240691
2021-09-29 13:08 Python dataframe-to-table type_floats 0.012 s -0.986339
2021-09-29 13:06 Python csv-read gzip, streaming, nyctaxi_2010-01 10.652 s -0.388033
2021-09-29 13:03 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.075 s -0.807096
2021-09-29 13:30 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.053 s -0.297290
2021-09-29 13:08 Python dataframe-to-table type_strings 0.366 s 0.653492
2021-09-29 13:08 Python dataframe-to-table type_dict 0.012 s 0.759917
2021-09-29 13:09 Python dataframe-to-table type_simple_features 0.929 s -1.819400
2021-09-29 13:04 Python csv-read gzip, file, fanniemae_2016Q4 6.038 s -1.657769
2021-09-29 13:04 Python csv-read gzip, streaming, fanniemae_2016Q4 15.020 s -0.821125
2021-09-29 13:05 Python csv-read uncompressed, file, nyctaxi_2010-01 1.021 s -0.044428
2021-09-29 13:08 Python dataframe-to-table type_integers 0.011 s -1.339055
2021-09-29 13:16 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.530 s 1.891629
2021-09-29 13:30 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.032 s 0.028968
2021-09-29 13:42 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.979 s 0.216401
2021-09-29 13:42 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.873 s 0.189117
2021-09-29 13:43 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.167 s -1.354387
2021-09-29 13:42 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.035 s -1.111666
2021-09-29 13:42 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.766 s 0.126997
2021-09-29 13:44 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.297 s -1.058721
2021-09-29 14:36 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.115595
2021-09-29 13:44 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.781 s -2.137223
2021-09-29 13:43 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.861 s -1.111398
2021-09-29 13:43 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.287 s -1.332879
2021-09-29 13:43 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.310 s -0.970387
2021-09-29 13:43 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.856 s -2.550873
2021-09-29 13:44 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.922 s -3.496047
2021-09-29 13:45 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.045 s -0.033425
2021-09-29 13:46 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.127 s 0.180572
2021-09-29 13:50 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.172 s 0.377770
2021-09-29 13:52 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.307 s 2.684461
2021-09-29 14:31 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.256 s -0.043872
2021-09-29 13:44 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.030 s 0.466585
2021-09-29 13:45 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.031 s 0.328798
2021-09-29 15:04 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.516 s -0.078982
2021-09-29 13:47 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.079 s 1.184037
2021-09-29 13:51 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.846 s 1.705168
2021-09-29 13:44 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.212 s -2.754389
2021-09-29 13:52 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.812 s 0.858727
2021-09-29 14:06 R dataframe-to-table chi_traffic_2020_Q1, R 5.400 s 0.056971
2021-09-29 15:04 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.873864
2021-09-29 13:44 Python file-read lz4, feather, table, fanniemae_2016Q4 0.610 s -1.593970
2021-09-29 13:46 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.632 s 0.179441
2021-09-29 13:53 Python wide-dataframe use_legacy_dataset=false 0.619 s -0.207054
2021-09-29 14:07 R dataframe-to-table type_dict, R 0.043 s 0.619422
2021-09-29 14:33 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.563 s 0.087834
2021-09-29 13:53 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.477842
2021-09-29 14:07 R dataframe-to-table type_integers, R 0.082 s 1.889853
2021-09-29 14:54 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.563522
2021-09-29 15:04 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.889826
2021-09-29 15:04 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.644661
2021-09-29 13:53 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.790 s 0.412669
2021-09-29 13:48 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.140495
2021-09-29 14:07 R dataframe-to-table type_strings, R 0.493 s -0.815159
2021-09-29 14:34 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.175 s -0.159815
2021-09-29 14:38 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.844 s 1.145157
2021-09-29 14:47 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.807 s 1.596161
2021-09-29 14:53 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.591 s 1.612700
2021-09-29 15:04 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.698 s 0.281828
2021-09-29 15:04 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.934 s -0.716069
2021-09-29 15:04 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.326133
2021-09-29 13:48 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.201 s 0.848992
2021-09-29 13:51 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.795 s 0.706850
2021-09-29 14:34 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.151 s -1.622217
2021-09-29 14:53 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.525 s -1.267106
2021-09-29 15:04 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.674951
2021-09-29 15:04 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.890 s -0.129564
2021-09-29 15:04 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.401763
2021-09-29 13:53 Python file-write lz4, feather, table, nyctaxi_2010-01 1.812 s 0.001155
2021-09-29 14:32 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 8.037 s -1.233088
2021-09-29 15:04 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.513673
2021-09-29 15:04 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.507265
2021-09-29 13:46 Python file-read lz4, feather, table, nyctaxi_2010-01 0.667 s 0.454352
2021-09-29 14:33 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.056 s -0.016854
2021-09-29 14:49 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.664 s 1.733559
2021-09-29 14:51 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.491 s -0.100821
2021-09-29 14:52 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.880 s 1.663887
2021-09-29 15:04 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.497361
2021-09-29 13:50 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.595 s 0.922757
2021-09-29 14:35 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.267 s -1.328625
2021-09-29 14:41 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.737 s 1.082594
2021-09-29 14:43 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.628 s -0.233698
2021-09-29 15:04 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.755824
2021-09-29 14:07 R dataframe-to-table type_floats, R 0.110 s -0.656935
2021-09-29 14:32 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.289 s 0.055434
2021-09-29 14:33 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.385 s -0.295008
2021-09-29 14:53 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.603 s 1.363247
2021-09-29 15:04 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.650 s -0.325439
2021-09-29 13:49 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.368 s -0.232983
2021-09-29 14:31 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.910 s 0.120787
2021-09-29 14:36 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.982 s -0.700045
2021-09-29 14:45 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.225 s 0.603927
2021-09-29 14:50 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.264 s 0.199410
2021-09-29 15:04 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.458086
2021-09-29 15:04 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-29 13:50 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.808 s 1.215297
2021-09-29 13:53 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.327 s 0.292451
2021-09-29 14:53 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.391269
2021-09-29 15:04 JavaScript Parse readBatches, tracks 0.000 s -0.356085
2021-09-29 15:04 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.709138
2021-09-29 15:04 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.248172
2021-09-29 13:45 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.034 s 0.049587
2021-09-29 13:45 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.147239
2021-09-29 13:49 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.535 s 0.973399
2021-09-29 13:50 Python file-write lz4, feather, table, fanniemae_2016Q4 1.170 s -0.982995
2021-09-29 14:07 R dataframe-to-table type_nested, R 0.538 s -0.312170
2021-09-29 14:35 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.413685
2021-09-29 14:53 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.216196
2021-09-29 15:04 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.436223
2021-09-29 15:04 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.596823
2021-09-29 14:30 R dataframe-to-table type_simple_features, R 275.062 s -0.425458
2021-09-29 14:31 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.235 s 0.191521
2021-09-29 14:37 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.534 s -0.702541
2021-09-29 14:44 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.395 s 1.224352
2021-09-29 14:52 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.191 s 0.246218
2021-09-29 14:54 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.947 s 1.387503
2021-09-29 14:55 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.472 s 0.351147
2021-09-29 14:56 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.746514
2021-09-29 15:04 JavaScript Parse Table.from, tracks 0.000 s -0.142285
2021-09-29 15:04 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.640 s -0.207342
2021-09-29 15:04 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.147573
2021-09-29 15:04 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.215758
2021-09-29 15:04 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.059106
2021-09-29 14:33 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.924 s -0.386929
2021-09-29 14:40 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.299 s 1.111729
2021-09-29 14:42 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.836 s -1.052027
2021-09-29 14:48 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.467 s 1.654474
2021-09-29 14:52 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.663274
2021-09-29 14:55 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -1.478901
2021-09-29 14:56 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.150 s 1.406301
2021-09-29 15:04 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.700 s -0.336882
2021-09-29 14:56 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.475 s 0.169359
2021-09-29 15:04 JavaScript Parse serialize, tracks 0.005 s -0.187614
2021-09-29 15:04 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.046126
2021-09-29 14:39 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.281 s 1.079909
2021-09-29 14:55 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.574 s 0.379144
2021-09-29 14:55 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 1.337186
2021-09-29 14:46 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.834 s 1.511116
2021-09-29 14:50 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.574536
2021-09-29 15:04 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334