Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-13 18:08 Python dataset-read async=True, nyctaxi_multi_ipc_s3 193.833 s -1.035866
2021-10-13 19:11 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.927 s -0.858068
2021-10-13 19:21 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.852160
2021-10-13 17:50 Python dataframe-to-table type_integers 0.011 s 0.074024
2021-10-13 18:26 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.523 s 1.349809
2021-10-13 18:50 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.321 s -1.249318
2021-10-13 19:02 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.310 s -3.020693
2021-10-13 17:46 Python csv-read gzip, streaming, fanniemae_2016Q4 14.557 s 3.257225
2021-10-13 17:48 Python csv-read gzip, streaming, nyctaxi_2010-01 10.469 s 0.961008
2021-10-13 17:50 Python dataframe-to-table type_floats 0.011 s 0.462261
2021-10-13 18:25 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.843 s -0.202254
2021-10-13 18:27 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.034 s 0.099036
2021-10-13 18:34 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.838 s 0.042819
2021-10-13 18:49 R dataframe-to-table type_dict, R 0.061 s -1.835281
2021-10-13 18:53 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.996 s 0.052863
2021-10-13 18:56 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.747 s -3.748738
2021-10-13 19:03 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.887 s -0.510739
2021-10-13 18:28 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.176 s 0.073537
2021-10-13 18:29 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.344 s -2.936735
2021-10-13 17:47 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.478 s 0.967971
2021-10-13 18:27 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 1.019 s 0.622678
2021-10-13 18:31 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.990 s -1.569594
2021-10-13 17:47 Python csv-read uncompressed, file, nyctaxi_2010-01 1.007 s 0.130321
2021-10-13 17:48 Python csv-read gzip, file, nyctaxi_2010-01 9.042 s 1.054594
2021-10-13 18:27 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.201 s 0.563037
2021-10-13 18:28 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.278 s 1.158209
2021-10-13 18:12 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.079 s -0.260466
2021-10-13 18:35 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.882 s -0.161552
2021-10-13 18:36 Python wide-dataframe use_legacy_dataset=true 0.391 s 0.630113
2021-10-13 18:52 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.212 s 0.652192
2021-10-13 18:12 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 1.054 s -0.211413
2021-10-13 18:25 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.265 s -0.311506
2021-10-13 18:32 Python file-write lz4, feather, table, fanniemae_2016Q4 1.151 s 0.320729
2021-10-13 18:52 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.105 s 0.932834
2021-10-13 18:57 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.554 s -2.670457
2021-10-13 19:08 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.472 s 0.836440
2021-10-13 19:11 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.826 s -3.264446
2021-10-13 18:26 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.302 s 0.013691
2021-10-13 18:28 Python file-read lz4, feather, table, nyctaxi_2010-01 0.668 s 0.164060
2021-10-13 18:51 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 10.058 s -1.050336
2021-10-13 18:08 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.329 s 0.161345
2021-10-13 18:24 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.762 s -0.239483
2021-10-13 18:33 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.878 s -0.757929
2021-10-13 18:49 R dataframe-to-table type_strings, R 0.494 s 0.229638
2021-10-13 18:51 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.557 s 0.901223
2021-10-13 19:05 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.520 s -0.458694
2021-10-13 19:10 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.596 s 0.278585
2021-10-13 19:13 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.485 s 1.688407
2021-10-13 17:45 Python csv-read uncompressed, file, fanniemae_2016Q4 1.154 s 0.569476
2021-10-13 17:50 Python dataframe-to-table type_strings 0.367 s 0.390786
2021-10-13 17:50 Python dataframe-to-table type_nested 2.846 s 1.677076
2021-10-13 18:25 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.041 s -0.807448
2021-10-13 18:27 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.716 s 0.751725
2021-10-13 18:32 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.369 s -0.365840
2021-10-13 18:50 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.222 s 0.433309
2021-10-13 17:45 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.628 s 3.488789
2021-10-13 17:46 Python csv-read gzip, file, fanniemae_2016Q4 6.023 s 1.403681
2021-10-13 18:36 Python wide-dataframe use_legacy_dataset=false 0.616 s 0.622738
2021-10-13 18:52 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.045 s 1.017243
2021-10-13 18:52 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.214 s -0.828810
2021-10-13 19:07 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.274 s 0.970855
2021-10-13 18:29 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.838 s 0.734685
2021-10-13 19:00 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.668 s -2.822805
2021-10-13 19:21 JavaScript Parse readBatches, tracks 0.000 s 0.287228
2021-10-13 18:26 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.289 s 0.134105
2021-10-13 18:26 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.119 s 1.302233
2021-10-13 18:30 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.667 s -1.926202
2021-10-13 18:54 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.526 s 0.131612
2021-10-13 17:51 Python dataset-filter nyctaxi_2010-01 4.372 s -0.805009
2021-10-13 18:24 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.613 s -2.853067
2021-10-13 18:26 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.600 s 1.333862
2021-10-13 18:28 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.175 s 0.865846
2021-10-13 18:53 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.694 s 0.001446
2021-10-13 19:12 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.359 s 1.061033
2021-10-13 19:13 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.215 s -2.547313
2021-10-13 18:27 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 2.995 s 1.327262
2021-10-13 18:31 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.690 s -2.775141
2021-10-13 18:34 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.944 s -0.758756
2021-10-13 18:35 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.324 s 0.581239
2021-10-13 19:09 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.816 s 1.490085
2021-10-13 17:50 Python dataframe-to-table type_dict 0.011 s 1.069291
2021-10-13 18:27 Python file-read lz4, feather, table, fanniemae_2016Q4 0.596 s 0.720060
2021-10-13 18:49 R dataframe-to-table type_integers, R 0.009 s 0.703753
2021-10-13 17:50 Python dataframe-to-table chi_traffic_2020_Q1 19.138 s 1.138147
2021-10-13 17:54 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 63.783 s -0.628762
2021-10-13 17:58 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 81.254 s 1.013100
2021-10-13 18:49 R dataframe-to-table type_floats, R 0.013 s 0.695483
2021-10-13 18:50 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.757 s 0.583505
2021-10-13 18:50 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.449 s 0.655092
2021-10-13 18:52 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.390 s 0.160484
2021-10-13 18:55 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 8.100 s -2.674257
2021-10-13 19:01 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.389 s 0.773520
2021-10-13 19:07 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.268 s -2.231738
2021-10-13 19:09 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.570 s 0.804078
2021-10-13 19:12 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -0.709441
2021-10-13 18:32 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.897 s -0.302838
2021-10-13 18:36 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.795 s 0.560160
2021-10-13 18:59 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.815 s 0.798695
2021-10-13 19:09 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.196 s -2.229418
2021-10-13 19:10 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.576 s 0.042851
2021-10-13 19:12 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.495 s -1.549823
2021-10-13 18:12 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.013 s 0.299099
2021-10-13 18:24 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.776 s 0.610762
2021-10-13 18:33 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.323 s 0.275320
2021-10-13 18:50 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.496 s -2.113719
2021-10-13 19:11 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.595 s -0.007988
2021-10-13 19:13 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.123 s 2.186081
2021-10-13 18:52 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.163 s 0.654596
2021-10-13 19:06 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.770 s -1.664864
2021-10-13 19:10 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.192 s -0.944082
2021-10-13 19:10 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.528 s -0.567176
2021-10-13 18:35 Python file-write lz4, feather, table, nyctaxi_2010-01 1.793 s 0.587597
2021-10-13 18:35 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.365 s -0.675965
2021-10-13 18:49 R dataframe-to-table chi_traffic_2020_Q1, R 3.563 s 0.258811
2021-10-13 18:49 R dataframe-to-table type_nested, R 0.537 s 0.231884
2021-10-13 19:21 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.560825
2021-10-13 19:21 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.021567
2021-10-13 19:21 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s -0.948905
2021-10-13 19:21 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.506354
2021-10-13 19:21 JavaScript Parse Table.from, tracks 0.000 s 0.112277
2021-10-13 19:21 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.024 s 0.098013
2021-10-13 19:21 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.552588
2021-10-13 19:21 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.722200
2021-10-13 19:21 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.024 s 0.094804
2021-10-13 19:21 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -1.023180
2021-10-13 19:21 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.198949
2021-10-13 19:21 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.001 s 0.442499
2021-10-13 19:21 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.065181
2021-10-13 19:21 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.654 s -0.373210
2021-10-13 19:21 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s -0.107253
2021-10-13 19:21 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.663 s 0.469521
2021-10-13 19:21 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.465527
2021-10-13 19:21 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.823 s 1.644507
2021-10-13 19:21 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 5.657 s -0.416915
2021-10-13 19:21 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -1.036620
2021-10-13 19:21 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.850 s 0.821364
2021-10-13 19:21 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.444722
2021-10-13 19:21 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.727929
2021-10-13 19:21 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s 1.205195
2021-10-13 19:21 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.598 s 1.611536
2021-10-13 19:21 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.135907
2021-10-13 19:21 JavaScript Parse serialize, tracks 0.005 s -0.489666
2021-10-13 19:21 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 0.579399
2021-10-13 19:21 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.501 s 0.357197
2021-10-13 18:58 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 13.136 s -3.255936
2021-10-13 19:04 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.905 s -1.584190
2021-10-13 19:10 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.473982