Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-30 12:19 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 62.568 s -0.604010
2021-09-30 12:12 Python csv-read gzip, streaming, nyctaxi_2010-01 10.814 s -1.068284
2021-09-30 12:12 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.833 s -1.098497
2021-09-30 12:15 Python dataframe-to-table type_strings 0.366 s 0.630565
2021-09-30 12:23 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.901 s 2.046967
2021-09-30 12:15 Python dataset-filter nyctaxi_2010-01 4.403 s -1.183417
2021-09-30 12:10 Python csv-read uncompressed, file, fanniemae_2016Q4 1.136 s 0.572578
2021-09-30 12:11 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -0.835485
2021-09-30 12:15 Python dataframe-to-table type_nested 2.853 s 3.562717
2021-09-30 12:14 Python dataframe-to-table chi_traffic_2020_Q1 19.627 s 0.833882
2021-09-30 12:32 Python dataset-read async=True, nyctaxi_multi_ipc_s3 189.783 s -0.136314
2021-09-30 12:11 Python csv-read gzip, streaming, fanniemae_2016Q4 14.744 s -0.530238
2021-09-30 12:15 Python dataframe-to-table type_integers 0.012 s -4.133165
2021-09-30 12:13 Python csv-read gzip, file, nyctaxi_2010-01 9.049 s -1.035743
2021-09-30 12:15 Python dataframe-to-table type_simple_features 0.938 s -3.125922
2021-09-30 12:10 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.820 s -0.535673
2021-09-30 12:15 Python dataframe-to-table type_dict 0.012 s -0.715067
2021-09-30 12:12 Python csv-read uncompressed, file, nyctaxi_2010-01 1.013 s 0.106904
2021-09-30 12:15 Python dataframe-to-table type_floats 0.011 s 0.233613
2021-09-30 12:32 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.300 s -0.026825
2021-09-30 12:36 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.041 s -0.112895
2021-09-30 12:36 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.031 s -0.140267
2021-09-30 12:36 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.029 s 0.076514
2021-09-30 14:10 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.382074
2021-09-30 12:49 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.856 s -1.017202
2021-09-30 12:50 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.041 s 0.046790
2021-09-30 13:37 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.913 s 0.081397
2021-09-30 13:40 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.126 s 0.295812
2021-09-30 14:00 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.606 s 0.232130
2021-09-30 14:02 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.489 s 0.141684
2021-09-30 14:09 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.613 s -0.222377
2021-09-30 13:37 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.246 s 0.071758
2021-09-30 13:59 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.174 s 0.353521
2021-09-30 12:49 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.166 s -1.395799
2021-09-30 12:50 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.290 s 0.041915
2021-09-30 12:47 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.821 s 0.437005
2021-09-30 13:38 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.925 s -0.424897
2021-09-30 13:45 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.273 s 1.194469
2021-09-30 14:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.200 s -1.339422
2021-09-30 14:10 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -1.061112
2021-09-30 14:10 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.967342
2021-09-30 12:48 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.962 s 0.346940
2021-09-30 12:50 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.899 s -3.316328
2021-09-30 12:51 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.981 s 0.264796
2021-09-30 13:38 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 0.599444
2021-09-30 12:51 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.017 s 0.083102
2021-09-30 13:13 R dataframe-to-table type_dict, R 0.027 s 2.418450
2021-09-30 13:42 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.689 s -2.410285
2021-09-30 13:47 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.741 s 1.132014
2021-09-30 13:53 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.800 s 1.906113
2021-09-30 13:55 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.672 s 1.693465
2021-09-30 13:56 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.274 s -0.550487
2021-09-30 14:10 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.910 s -0.556128
2021-09-30 12:49 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.874 s -3.491578
2021-09-30 12:59 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.820 s 0.162768
2021-09-30 12:51 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.044 s -0.409920
2021-09-30 12:58 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.853 s 0.587579
2021-09-30 13:38 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.914 s 0.073573
2021-09-30 13:55 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.281 s 0.591982
2021-09-30 14:09 JavaScript Parse Table.from, tracks 0.000 s -0.118889
2021-09-30 12:49 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.785 s -2.500855
2021-09-30 12:54 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.260 s 0.589873
2021-09-30 12:56 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.716 s -0.037812
2021-09-30 12:58 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.374 s -0.102659
2021-09-30 13:12 R dataframe-to-table chi_traffic_2020_Q1, R 5.403 s 0.016004
2021-09-30 13:42 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.974 s -0.322811
2021-09-30 13:43 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.537 s -0.849782
2021-09-30 13:48 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.827 s 0.792872
2021-09-30 13:54 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.464 s 1.822376
2021-09-30 14:01 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.347 s 1.568742
2021-09-30 14:10 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.400835
2021-09-30 14:09 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.050229
2021-09-30 14:10 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.452286
2021-09-30 14:10 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.594461
2021-09-30 12:48 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.031 s -1.056745
2021-09-30 12:51 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.172 s 0.952750
2021-09-30 13:13 R dataframe-to-table type_integers, R 0.084 s 0.853435
2021-09-30 14:00 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.516 s 1.269096
2021-09-30 14:10 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.529902
2021-09-30 12:50 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.223 s -3.871552
2021-09-30 12:52 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.143 s 0.105428
2021-09-30 12:53 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.084 s 1.179381
2021-09-30 12:58 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.346 s 0.284252
2021-09-30 13:13 R dataframe-to-table type_strings, R 0.489 s 0.544386
2021-09-30 13:58 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 2.039625
2021-09-30 14:01 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.470 s 0.911560
2021-09-30 14:09 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.577 s -0.024679
2021-09-30 13:13 R dataframe-to-table type_floats, R 0.111 s -0.784273
2021-09-30 13:36 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.240 s 0.131452
2021-09-30 13:51 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.819 s 1.982560
2021-09-30 12:48 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.295 s -1.657882
2021-09-30 12:49 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.315 s -1.659336
2021-09-30 12:52 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.660 s 0.054116
2021-09-30 12:57 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.856 s 1.600764
2021-09-30 13:13 R dataframe-to-table type_nested, R 0.533 s 1.296227
2021-09-30 13:36 R dataframe-to-table type_simple_features, R 274.653 s 0.344331
2021-09-30 13:58 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.193 s 0.089433
2021-09-30 14:09 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.026007
2021-09-30 14:10 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.215277
2021-09-30 14:10 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.513673
2021-09-30 14:10 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.216216
2021-09-30 12:50 Python file-read lz4, feather, table, fanniemae_2016Q4 0.607 s -1.157807
2021-09-30 12:57 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.801 s 0.686823
2021-09-30 12:59 Python wide-dataframe use_legacy_dataset=true 0.397 s -0.575038
2021-09-30 13:51 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.221 s 0.728658
2021-09-30 14:10 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.616653
2021-09-30 12:54 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.427 s 1.272113
2021-09-30 12:56 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.817 s 1.104884
2021-09-30 12:59 Python file-write lz4, feather, table, nyctaxi_2010-01 1.806 s 0.273829
2021-09-30 13:39 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.413 s -1.752261
2021-09-30 13:40 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.175 s -0.148934
2021-09-30 13:49 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.404 s -0.312267
2021-09-30 14:09 JavaScript Parse readBatches, tracks 0.000 s 0.362709
2021-09-30 14:10 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -2.063556
2021-09-30 12:52 Python file-read lz4, feather, table, nyctaxi_2010-01 0.670 s -0.107455
2021-09-30 12:55 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.647 s 0.477854
2021-09-30 13:41 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.246 s -0.325726
2021-09-30 13:43 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.855 s 1.131784
2021-09-30 13:57 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.889955
2021-09-30 13:59 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.090 s 0.470188
2021-09-30 13:59 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.594 s 1.541316
2021-09-30 14:09 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.064958
2021-09-30 12:55 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.345 s -0.020120
2021-09-30 12:59 Python wide-dataframe use_legacy_dataset=false 0.622 s -0.895451
2021-09-30 13:39 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.578 s -3.099669
2021-09-30 14:02 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.168 s 1.469012
2021-09-30 14:09 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.021777
2021-09-30 12:48 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.699 s 0.428823
2021-09-30 12:56 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.287 s -0.651792
2021-09-30 13:39 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.054 s 0.372346
2021-09-30 13:58 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.874 s 1.798676
2021-09-30 14:10 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.503 s 0.119731
2021-09-30 12:56 Python file-write lz4, feather, table, fanniemae_2016Q4 1.152 s 0.774303
2021-09-30 13:41 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.109990
2021-09-30 13:49 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.580 s 0.790387
2021-09-30 14:09 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.673 s 0.192188
2021-09-30 14:10 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.578334
2021-09-30 14:10 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.645129
2021-09-30 14:09 JavaScript Parse serialize, tracks 0.005 s -0.695861
2021-09-30 14:09 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.726 s 0.158409
2021-09-30 14:10 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.925 s -0.446177
2021-09-30 14:10 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.146171
2021-09-30 13:46 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 1.155714
2021-09-30 13:58 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.581 s 1.720943
2021-09-30 13:59 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.520 s -0.562569
2021-09-30 14:01 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.102 s -1.464842
2021-09-30 14:10 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.047 s -0.132778
2021-09-30 14:10 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 0.973889
2021-09-30 14:00 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.974 s 1.498183