Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-09-29 00:31 Python csv-read uncompressed, file, nyctaxi_2010-01 1.026 s -0.104494
2021-09-29 00:34 Python dataframe-to-table type_dict 0.012 s 0.957950
2021-09-29 00:38 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 61.742 s -0.584083
2021-09-29 00:56 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.029 s 0.086383
2021-09-29 01:09 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 1.962 s 0.347189
2021-09-29 01:09 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.678 s 0.530261
2021-09-29 00:29 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.097 s -0.925592
2021-09-29 00:31 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.704 s -0.635506
2021-09-29 01:10 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.860 s -1.401300
2021-09-29 01:15 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.135 s 1.315616
2021-09-29 01:18 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.795 s 2.025065
2021-09-29 00:30 Python csv-read gzip, streaming, fanniemae_2016Q4 15.046 s -0.945547
2021-09-29 00:35 Python dataset-filter nyctaxi_2010-01 4.398 s -1.359116
2021-09-29 00:42 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.355 s 3.687117
2021-09-29 00:31 Python csv-read gzip, file, fanniemae_2016Q4 6.034 s -1.097331
2021-09-29 00:34 Python dataframe-to-table type_strings 0.368 s 0.405567
2021-09-29 00:57 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.028 s 0.105536
2021-09-29 01:12 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 7.755 s 1.242849
2021-09-29 01:13 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 7.896 s 0.642374
2021-09-29 01:16 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.473 s 1.405378
2021-09-29 01:18 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.768 s 1.020131
2021-09-29 00:52 Python dataset-read async=True, nyctaxi_multi_ipc_s3 184.206 s 0.412388
2021-09-29 00:30 Python csv-read uncompressed, file, fanniemae_2016Q4 1.180 s -0.149422
2021-09-29 00:34 Python dataframe-to-table type_floats 0.011 s 1.207317
2021-09-29 00:35 Python dataframe-to-table type_simple_features 0.903 s 1.015352
2021-09-29 00:52 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.260 s 0.180173
2021-09-29 01:13 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.181 s -0.810700
2021-09-29 01:14 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.077 s 1.494190
2021-09-29 01:16 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.357 s -0.156076
2021-09-29 00:35 Python dataframe-to-table type_nested 2.953 s 0.083718
2021-09-29 01:10 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.280 s -1.455301
2021-09-29 01:11 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.804 s 0.264904
2021-09-29 01:14 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.423 s 1.155804
2021-09-29 00:57 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.023 s -0.015911
2021-09-29 01:10 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.031 s -1.318728
2021-09-29 01:12 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.035 s 0.126164
2021-09-29 01:17 Python file-write lz4, feather, table, fanniemae_2016Q4 1.167 s -0.544140
2021-09-29 01:09 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.794 s 0.586159
2021-09-29 01:12 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.110 s 0.433561
2021-09-29 01:13 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.001 s 0.790185
2021-09-29 01:17 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.594 s 0.951282
2021-09-29 00:32 Python csv-read gzip, streaming, nyctaxi_2010-01 10.624 s -0.367326
2021-09-29 01:11 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.766 s -1.813714
2021-09-29 00:32 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.616625
2021-09-29 01:11 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.164 s -1.786579
2021-09-29 01:19 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.855 s 2.254701
2021-09-29 01:19 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.367 s -1.104208
2021-09-29 00:34 Python dataframe-to-table chi_traffic_2020_Q1 19.744 s 0.285275
2021-09-29 01:11 Python file-read lz4, feather, table, fanniemae_2016Q4 0.594 s 1.300709
2021-09-29 01:15 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.450 s 1.333962
2021-09-29 00:34 Python dataframe-to-table type_integers 0.011 s -0.046668
2021-09-29 01:11 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.688 s -0.963762
2021-09-29 01:17 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.091 s 1.110241
2021-09-29 01:20 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.297 s 0.548465
2021-09-29 01:11 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.292 s -0.166350
2021-09-29 01:10 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.314 s -2.272450
2021-09-29 01:12 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.027 s 0.532543
2021-09-29 01:13 Python file-read lz4, feather, table, nyctaxi_2010-01 0.661 s 1.682754
2021-09-29 01:20 Python file-write lz4, feather, table, nyctaxi_2010-01 1.802 s 0.423746
2021-09-29 01:20 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.799 s 0.357386
2021-09-29 01:20 Python wide-dataframe use_legacy_dataset=true 0.395 s -0.286218
2021-09-29 01:20 Python wide-dataframe use_legacy_dataset=false 0.621 s -0.829958
2021-09-29 02:01 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 7.936 s -0.170189
2021-09-29 02:21 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.877 s 2.655468
2021-09-29 02:23 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.101 s -0.325111
2021-09-29 02:01 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.290 s -0.827241
2021-09-29 02:00 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 7.939 s -0.203015
2021-09-29 02:03 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 13.177 s -0.494153
2021-09-29 01:59 R dataframe-to-table type_simple_features, R 275.336 s -1.169757
2021-09-29 02:32 JavaScript Parse readBatches, tracks 0.000 s -0.014342
2021-09-29 02:32 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.025 s -0.046117
2021-09-29 01:19 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.787 s 1.153561
2021-09-29 02:00 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.242 s 0.110442
2021-09-29 02:04 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 13.246 s -0.344497
2021-09-29 02:05 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.516 s -0.094786
2021-09-29 02:10 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.756 s 1.343373
2021-09-29 02:13 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.247 s 0.257019
2021-09-29 02:18 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.668 s 2.622772
2021-09-29 01:36 R dataframe-to-table type_nested, R 0.539 s -0.674765
2021-09-29 02:32 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.682 s -0.327945
2021-09-29 02:32 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -1.588580
2021-09-29 02:11 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.828 s 0.533981
2021-09-29 02:21 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.583 s 2.593737
2021-09-29 02:33 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s -0.569507
2021-09-29 02:01 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.569 s -1.329829
2021-09-29 02:09 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.296 s 1.552776
2021-09-29 02:12 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.595 s 0.609482
2021-09-29 01:35 R dataframe-to-table type_dict, R 0.051 s 0.056127
2021-09-29 02:06 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.844 s 1.573750
2021-09-29 02:32 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.606267
2021-09-29 01:35 R dataframe-to-table type_integers, R 0.085 s -0.670402
2021-09-29 02:05 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.670 s 1.424539
2021-09-29 02:21 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.575 s 3.397318
2021-09-29 02:33 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.305980
2021-09-29 02:01 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.943 s -1.331712
2021-09-29 02:15 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.815 s 2.432246
2021-09-29 02:21 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.192 s 0.118701
2021-09-29 02:02 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.045 s 1.864018
2021-09-29 02:32 JavaScript Parse serialize, tracks 0.005 s 0.407595
2021-09-29 02:32 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.188410
2021-09-29 01:36 R dataframe-to-table type_floats, R 0.109 s 0.099732
2021-09-29 02:22 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.173 s 0.509703
2021-09-29 02:32 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.769 s -0.132599
2021-09-29 02:32 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.955 s -1.141703
2021-09-29 02:33 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.374622
2021-09-29 02:19 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.265 s 0.140497
2021-09-29 02:32 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.510310
2021-09-29 01:35 R dataframe-to-table type_strings, R 0.492 s -0.717462
2021-09-29 02:23 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.604 s 0.486012
2021-09-29 02:23 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.605 s 0.029851
2021-09-29 02:32 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 1.808194
2021-09-29 02:05 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.968 s -0.063288
2021-09-29 02:25 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.207 s 2.757785
2021-09-29 02:32 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.478962
2021-09-29 02:33 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s -0.242962
2021-09-29 02:33 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.541 s -0.478370
2021-09-29 01:59 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.237 s 0.170192
2021-09-29 02:16 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.468 s 2.508596
2021-09-29 02:22 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.518 s -0.237650
2021-09-29 02:24 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.470 s 0.742335
2021-09-29 02:32 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s 0.504293
2021-09-29 01:35 R dataframe-to-table chi_traffic_2020_Q1, R 5.364 s 0.738086
2021-09-29 02:02 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.399 s -1.134045
2021-09-29 02:03 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.142 s -0.859382
2021-09-29 02:04 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.195 s 1.139038
2021-09-29 02:08 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.297 s 1.378528
2021-09-29 02:18 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.289 s -2.403237
2021-09-29 02:32 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.997 s -2.781366
2021-09-29 02:33 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.356230
2021-09-29 02:12 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.396 s 0.965935
2021-09-29 02:14 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.818 s 3.074045
2021-09-29 02:20 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.478 s 2.384802
2021-09-29 02:24 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.349 s 2.886174
2021-09-29 02:25 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.484 s 0.170945
2021-09-29 02:32 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.494149
2021-09-29 02:32 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.088366
2021-09-29 02:32 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.457401
2021-09-29 02:21 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.091 s 0.232926
2021-09-29 02:23 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.957 s 2.998578
2021-09-29 02:32 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.025 s -0.046645
2021-09-29 02:33 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.875844
2021-09-29 02:22 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.599 s 2.999508
2021-09-29 02:24 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.196 s 0.790872
2021-09-29 02:32 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.719 s -0.775285
2021-09-29 02:32 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.590292
2021-09-29 02:32 JavaScript Parse Table.from, tracks 0.000 s 0.172476
2021-09-29 02:32 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.717 s -0.512665
2021-09-29 02:32 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s 0.530898
2021-09-29 02:33 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.925504