Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 22:50 Python dataframe-to-table type_floats 0.012 s -0.957501
2021-09-30 22:47 Python csv-read uncompressed, file, nyctaxi_2010-01 1.005 s 0.238816
2021-09-30 22:50 Python dataframe-to-table type_dict 0.012 s -0.752258
2021-09-30 22:46 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.836 s -1.057792
2021-09-30 22:50 Python dataframe-to-table type_integers 0.011 s -1.499426
2021-09-30 22:50 Python dataframe-to-table type_nested 2.850 s 2.689236
2021-09-30 23:07 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.354 s -0.271263
2021-09-30 22:45 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.820 s -0.482834
2021-09-30 22:49 Python dataframe-to-table chi_traffic_2020_Q1 19.246 s 2.801844
2021-09-30 22:50 Python dataframe-to-table type_strings 0.365 s 0.723778
2021-09-30 22:58 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.928 s 1.721942
2021-09-30 22:47 Python csv-read gzip, streaming, nyctaxi_2010-01 10.829 s -1.075560
2021-09-30 22:50 Python dataset-filter nyctaxi_2010-01 4.404 s -1.144375
2021-09-30 22:45 Python csv-read uncompressed, file, fanniemae_2016Q4 1.152 s 0.312964
2021-09-30 22:46 Python csv-read gzip, streaming, fanniemae_2016Q4 14.769 s -0.505386
2021-09-30 22:50 Python dataframe-to-table type_simple_features 0.934 s -2.127301
2021-09-30 22:48 Python csv-read gzip, file, nyctaxi_2010-01 9.044 s 0.284787
2021-09-30 22:46 Python csv-read gzip, file, fanniemae_2016Q4 6.029 s 0.188468
2021-09-30 22:54 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.662 s -0.817042
2021-09-30 23:07 Python dataset-read async=True, nyctaxi_multi_ipc_s3 183.904 s 0.513791
2021-09-30 23:11 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.040 s -0.095728
2021-09-30 23:11 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.021 s 0.013306
2021-09-30 23:11 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.023 s 0.158133
2021-09-30 23:22 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.873 s -1.336442
2021-09-30 23:28 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.612 s 0.587206
2021-09-30 23:29 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.826 s -0.918754
2021-09-30 23:30 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.792 s 1.461179
2021-09-30 23:47 R dataframe-to-table type_dict, R 0.053 s -0.371408
2021-10-01 00:44 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.632092
2021-09-30 23:23 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.870 s -2.438156
2021-09-30 23:23 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.769 s -1.673074
2021-09-30 23:21 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.622 s -4.385820
2021-09-30 23:22 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.291 s -1.326750
2021-09-30 23:23 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.166 s -1.256005
2021-09-30 23:24 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.213 s -2.408983
2021-09-30 23:32 Python file-write lz4, feather, table, nyctaxi_2010-01 1.833 s -1.126993
2021-09-30 23:23 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.281 s 1.459104
2021-09-30 23:29 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.366 s -0.200791
2021-09-30 23:33 Python wide-dataframe use_legacy_dataset=true 0.394 s -0.152714
2021-10-01 00:33 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.887 s 1.545649
2021-10-01 00:44 JavaScript Parse readBatches, tracks 0.000 s -0.078324
2021-10-01 00:44 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.670 s -0.300938
2021-10-01 00:44 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.660 s 0.380133
2021-10-01 00:44 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.043810
2021-10-01 00:44 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.018969
2021-09-30 23:26 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.646 s 0.085689
2021-09-30 23:28 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.439 s 1.080810
2021-09-30 23:30 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.244 s -0.269287
2021-09-30 23:31 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.825 s 0.478080
2021-09-30 23:33 Python wide-dataframe use_legacy_dataset=false 0.619 s -0.124505
2021-10-01 00:11 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.259 s -0.082744
2021-10-01 00:23 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.586 s 0.602087
2021-09-30 23:24 Python file-read lz4, feather, table, fanniemae_2016Q4 0.611 s -1.689693
2021-09-30 23:24 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.051 s -0.849945
2021-09-30 23:47 R dataframe-to-table type_floats, R 0.108 s 0.567430
2021-10-01 00:11 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.242 s 0.111187
2021-09-30 23:21 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.861 s 0.237799
2021-09-30 23:24 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.087 s -1.610226
2021-09-30 23:25 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.103 s 0.266464
2021-09-30 23:33 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.875 s -0.302703
2021-10-01 00:20 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.434 s 0.059676
2021-09-30 23:25 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.012 s 0.073715
2021-09-30 23:47 R dataframe-to-table type_integers, R 0.083 s 1.576941
2021-09-30 23:47 R dataframe-to-table type_nested, R 0.540 s -0.983457
2021-10-01 00:13 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.558 s 0.900566
2021-10-01 00:26 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.825 s 1.604824
2021-10-01 00:33 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 1.662795
2021-10-01 00:44 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.856 s 0.669695
2021-10-01 00:44 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -2.057364
2021-10-01 00:44 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.371599
2021-09-30 23:22 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.059 s -1.653519
2021-10-01 00:13 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.065 s -1.466435
2021-10-01 00:14 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.106 s 1.816293
2021-10-01 00:44 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.022 s 0.891061
2021-10-01 00:44 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.216533
2021-09-30 23:26 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.150665
2021-10-01 00:18 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.844 s 1.081809
2021-10-01 00:21 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.832 s 0.471984
2021-10-01 00:44 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.562212
2021-10-01 00:44 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.061361
2021-10-01 00:11 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.912 s 0.102791
2021-10-01 00:13 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.390 s -0.510145
2021-10-01 00:15 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.196 s 0.267086
2021-10-01 00:44 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.845 s 1.204347
2021-10-01 00:44 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s -1.989446
2021-10-01 00:44 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.142554
2021-09-30 23:32 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.357 s -0.438518
2021-09-30 23:47 R dataframe-to-table type_strings, R 0.488 s 1.040546
2021-10-01 00:19 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.308 s 0.866313
2021-10-01 00:27 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.806 s 1.495413
2021-10-01 00:31 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.267 s -0.060448
2021-10-01 00:32 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.200 s -0.515558
2021-10-01 00:35 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.804976
2021-09-30 23:23 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.322 s -1.465786
2021-09-30 23:27 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.294 s 0.387711
2021-09-30 23:31 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.855 s 1.461714
2021-10-01 00:17 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.521 s -0.024613
2021-10-01 00:36 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.199 s -0.860879
2021-10-01 00:44 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.184480
2021-10-01 00:29 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.665 s 1.578050
2021-10-01 00:44 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.022 s 0.841624
2021-10-01 00:44 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.026 s 1.198151
2021-09-30 23:21 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.709 s 0.364540
2021-09-30 23:24 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.900 s -2.304588
2021-09-30 23:24 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.075 s -0.157231
2021-09-30 23:29 Python file-write lz4, feather, table, fanniemae_2016Q4 1.197 s -3.587746
2021-10-01 00:32 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.580 s 1.528892
2021-10-01 00:33 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.260143
2021-09-30 23:25 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.171 s 1.099089
2021-10-01 00:10 R dataframe-to-table type_simple_features, R 275.206 s -0.660373
2021-10-01 00:16 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.957 s 0.649047
2021-10-01 00:30 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.283 s -0.309189
2021-10-01 00:35 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.471 s 0.734106
2021-10-01 00:44 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.704 s 0.241554
2021-10-01 00:44 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -1.034246
2021-09-30 23:32 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.828 s 0.718299
2021-09-30 23:32 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.361 s 0.007770
2021-09-30 23:46 R dataframe-to-table chi_traffic_2020_Q1, R 5.401 s 0.008663
2021-10-01 00:25 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.232 s 0.413582
2021-10-01 00:28 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.466 s 1.565170
2021-10-01 00:31 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.498 s -1.546458
2021-10-01 00:34 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.195624
2021-10-01 00:44 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.596940
2021-10-01 00:44 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.927512
2021-10-01 00:44 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.525 s -0.259359
2021-10-01 00:33 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.223736
2021-10-01 00:44 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.642 s -0.314537
2021-10-01 00:44 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.605426
2021-10-01 00:13 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.921 s -0.179364
2021-10-01 00:14 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.171 s 0.065899
2021-10-01 00:16 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.676 s 0.116029
2021-10-01 00:22 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.831 s -0.029291
2021-10-01 00:36 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.164 s 1.266319
2021-10-01 00:44 JavaScript Parse Table.from, tracks 0.000 s 0.125376
2021-10-01 00:44 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-10-01 00:12 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.911 s 0.138028
2021-10-01 00:15 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.274 s -1.651924
2021-10-01 00:24 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.407 s -1.082053
2021-10-01 00:33 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.517 s -0.070705
2021-10-01 00:34 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.962 s 1.284379
2021-10-01 00:36 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.486 s 0.140719
2021-10-01 00:44 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.614412
2021-10-01 00:44 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.919135
2021-09-30 23:26 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.111 s 0.877689
2021-10-01 00:12 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.543212
2021-10-01 00:33 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.602 s 1.276058
2021-10-01 00:35 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.514 s 1.236303
2021-10-01 00:35 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 1.345272
2021-10-01 00:44 JavaScript Parse serialize, tracks 0.005 s -0.915113