Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-08 02:34 Python csv-read gzip, streaming, fanniemae_2016Q4 14.908 s -0.415678
2021-10-08 02:38 Python dataframe-to-table type_simple_features 0.915 s -0.208262
2021-10-08 03:44 R file-read snappy, parquet, table, nyctaxi_2010-01, R 1.111 s 1.247157
2021-10-08 04:05 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=10, R 1.188 s 0.483348
2021-10-08 03:11 Python file-read uncompressed, parquet, table, fanniemae_2016Q4 1.295 s -0.117068
2021-10-08 03:16 Python file-write uncompressed, parquet, dataframe, fanniemae_2016Q4 13.316 s -0.014341
2021-10-08 02:35 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.501 s 1.057327
2021-10-08 02:38 Python dataframe-to-table type_strings 0.373 s -0.394266
2021-10-08 02:55 Python dataset-select nyctaxi_multi_parquet_s3_repartitioned 1.159 s 0.257819
2021-10-08 02:38 Python dataframe-to-table type_integers 0.011 s 1.010908
2021-10-08 02:34 Python csv-read gzip, file, fanniemae_2016Q4 6.031 s -0.043149
2021-10-08 02:38 Python dataframe-to-table type_dict 0.012 s 0.185306
2021-10-08 03:10 Python dataset-selectivity 1%, chi_traffic_2020_Q1 6.003 s -0.029542
2021-10-08 02:46 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 82.592 s 0.489118
2021-10-08 03:35 R dataframe-to-table chi_traffic_2020_Q1, R 3.396 s 0.252177
2021-10-08 03:10 Python dataset-selectivity 10%, nyctaxi_multi_ipc_s3 2.005 s -0.005969
2021-10-08 03:10 Python dataset-selectivity 100%, nyctaxi_multi_ipc_s3 1.705 s 0.438902
2021-10-08 03:11 Python dataset-selectivity 10%, chi_traffic_2020_Q1 6.248 s -0.051417
2021-10-08 02:35 Python csv-read gzip, streaming, nyctaxi_2010-01 10.485 s 1.121593
2021-10-08 03:18 Python file-write lz4, feather, table, fanniemae_2016Q4 1.175 s -1.003166
2021-10-08 03:35 R dataframe-to-table type_floats, R 0.013 s 3.982421
2021-10-08 03:42 R file-read uncompressed, parquet, table, fanniemae_2016Q4, R 1.244 s 0.256480
2021-10-08 03:42 R file-read uncompressed, parquet, dataframe, fanniemae_2016Q4, R 1.481 s 3.673447
2021-10-08 03:10 Python dataset-selectivity 1%, nyctaxi_multi_ipc_s3 1.883 s 0.035969
2021-10-08 03:42 R file-read snappy, parquet, table, fanniemae_2016Q4, R 1.243 s 0.099130
2021-10-08 03:44 R file-read snappy, parquet, dataframe, nyctaxi_2010-01, R 1.236 s 3.666206
2021-10-08 03:48 R file-write uncompressed, parquet, dataframe, fanniemae_2016Q4, R 12.270 s 0.603871
2021-10-08 04:00 R file-write uncompressed, feather, dataframe, nyctaxi_2010-01, R 2.241 s 0.855766
2021-10-08 03:21 Python file-write uncompressed, feather, dataframe, nyctaxi_2010-01 6.350 s -0.343945
2021-10-08 03:44 R file-read uncompressed, parquet, table, nyctaxi_2010-01, R 1.052 s 0.838965
2021-10-08 02:38 Python dataset-filter nyctaxi_2010-01 4.359 s 0.395125
2021-10-08 02:33 Python csv-read uncompressed, file, fanniemae_2016Q4 1.167 s 0.429059
2021-10-08 02:38 Python dataframe-to-table type_floats 0.011 s 1.288306
2021-10-08 02:38 Python dataframe-to-table type_nested 2.871 s 0.710087
2021-10-08 02:59 Python dataset-selectivity 10%, nyctaxi_multi_parquet_s3 1.031 s 0.091331
2021-10-08 03:12 Python file-read uncompressed, feather, table, fanniemae_2016Q4 2.289 s 0.144644
2021-10-08 03:12 Python file-read lz4, feather, table, fanniemae_2016Q4 0.604 s -0.086471
2021-10-08 02:33 Python csv-read uncompressed, streaming, fanniemae_2016Q4 14.969 s -0.372091
2021-10-08 02:59 Python dataset-selectivity 100%, nyctaxi_multi_parquet_s3 1.019 s 0.053205
2021-10-08 03:12 Python file-read uncompressed, parquet, table, nyctaxi_2010-01 0.992 s 2.308779
2021-10-08 03:13 Python file-read snappy, parquet, dataframe, nyctaxi_2010-01 8.328 s -0.873813
2021-10-08 03:19 Python file-write uncompressed, parquet, table, nyctaxi_2010-01 5.795 s 0.594627
2021-10-08 02:37 Python dataframe-to-table chi_traffic_2020_Q1 19.818 s -0.828394
2021-10-08 03:12 Python file-read snappy, parquet, dataframe, fanniemae_2016Q4 3.812 s -0.901648
2021-10-08 03:13 Python file-read snappy, parquet, table, nyctaxi_2010-01 1.011 s 1.621755
2021-10-08 03:16 Python file-write snappy, parquet, table, fanniemae_2016Q4 8.436 s 0.595459
2021-10-08 03:47 R file-write uncompressed, parquet, table, fanniemae_2016Q4, R 7.856 s 0.486016
2021-10-08 02:55 Python dataset-read async=True, nyctaxi_multi_ipc_s3 187.128 s 0.055553
2021-10-08 03:35 R dataframe-to-table type_dict, R 0.052 s -0.063179
2021-10-08 03:36 R dataframe-to-table type_nested, R 0.542 s 0.198998
2021-10-08 03:42 R file-read uncompressed, feather, table, fanniemae_2016Q4, R 0.288 s 1.073159
2021-10-08 03:43 R file-read uncompressed, feather, dataframe, fanniemae_2016Q4, R 9.952 s -1.805585
2021-10-08 02:59 Python dataset-selectivity 1%, nyctaxi_multi_parquet_s3 0.990 s 0.678640
2021-10-08 03:12 Python file-read lz4, feather, dataframe, fanniemae_2016Q4 3.236 s -0.574022
2021-10-08 03:13 Python file-read uncompressed, parquet, dataframe, nyctaxi_2010-01 8.306 s -0.819172
2021-10-08 03:15 Python file-read lz4, feather, dataframe, nyctaxi_2010-01 7.955 s -0.772973
2021-10-08 03:19 Python file-write uncompressed, parquet, dataframe, nyctaxi_2010-01 9.764 s 0.794575
2021-10-08 03:11 Python dataset-selectivity 100%, chi_traffic_2020_Q1 5.821 s 0.205539
2021-10-08 03:18 Python file-write uncompressed, feather, dataframe, fanniemae_2016Q4 9.734 s -0.067801
2021-10-08 03:57 R file-write snappy, parquet, table, nyctaxi_2010-01, R 6.454 s 0.927458
2021-10-08 03:11 Python file-read snappy, parquet, table, fanniemae_2016Q4 1.155 s -0.441187
2021-10-08 03:12 Python file-read uncompressed, feather, dataframe, fanniemae_2016Q4 4.944 s -0.825372
2021-10-08 02:35 Python csv-read uncompressed, file, nyctaxi_2010-01 0.998 s 1.412320
2021-10-08 03:11 Python file-read uncompressed, parquet, dataframe, fanniemae_2016Q4 3.878 s -0.819372
2021-10-08 03:14 Python file-read uncompressed, feather, dataframe, nyctaxi_2010-01 8.440 s -0.700628
2021-10-08 03:14 Python file-read lz4, feather, table, nyctaxi_2010-01 0.664 s 1.059211
2021-10-08 03:17 Python file-write uncompressed, feather, table, fanniemae_2016Q4 5.605 s
2021-10-08 03:21 Python wide-dataframe use_legacy_dataset=true 0.392 s 1.369211
2021-10-08 03:58 R file-write snappy, parquet, dataframe, nyctaxi_2010-01, R 7.640 s 1.080598
2021-10-08 04:01 R file-write lz4, feather, dataframe, nyctaxi_2010-01, R 2.169 s 0.858298
2021-10-08 03:15 Python file-write uncompressed, parquet, table, fanniemae_2016Q4 8.074 s 0.631320
2021-10-08 03:20 Python file-write snappy, parquet, dataframe, nyctaxi_2010-01 11.834 s 0.336238
2021-10-08 03:21 Python file-write lz4, feather, table, nyctaxi_2010-01 1.857 s -2.748725
2021-10-08 03:44 R file-read uncompressed, parquet, dataframe, nyctaxi_2010-01, R 1.164 s 3.667928
2021-10-08 03:17 Python file-write snappy, parquet, dataframe, fanniemae_2016Q4 13.717 s
2021-10-08 03:20 Python file-write snappy, parquet, table, nyctaxi_2010-01 7.869 s 0.404832
2021-10-08 04:04 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=10, R 2.515 s 1.188708
2021-10-08 03:18 Python file-write lz4, feather, dataframe, fanniemae_2016Q4 6.275 s -0.628453
2021-10-08 03:42 R file-read snappy, parquet, dataframe, fanniemae_2016Q4, R 1.488 s 3.420309
2021-10-08 03:52 R file-write uncompressed, feather, dataframe, fanniemae_2016Q4, R 6.547 s 0.984191
2021-10-08 04:02 R partitioned-dataset-filter payment_type_3, dataset-taxi-parquet, R 1.864 s 0.559341
2021-10-08 04:02 R partitioned-dataset-filter count_rows, dataset-taxi-parquet, R 0.089 s 0.399087
2021-10-08 04:02 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=1, R 0.183 s -0.544736
2021-10-08 03:21 Python file-write lz4, feather, dataframe, nyctaxi_2010-01 5.821 s -0.554539
2021-10-08 03:51 R file-write uncompressed, feather, table, fanniemae_2016Q4, R 2.825 s 1.329860
2021-10-08 03:56 R file-write uncompressed, parquet, dataframe, nyctaxi_2010-01, R 5.784 s 0.957128
2021-10-08 04:05 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=10, R 2.510 s -1.278588
2021-10-08 03:21 Python wide-dataframe use_legacy_dataset=false 0.625 s -0.778788
2021-10-08 04:00 R file-write lz4, feather, table, nyctaxi_2010-01, R 1.485 s 0.997211
2021-10-08 03:35 R dataframe-to-table type_strings, R 0.485 s 0.201164
2021-10-08 03:35 R dataframe-to-table type_integers, R 0.010 s 3.996584
2021-10-08 03:55 R file-write uncompressed, parquet, table, nyctaxi_2010-01, R 4.819 s 0.892946
2021-10-08 04:03 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=10, R 2.900 s 0.518295
2021-10-08 04:04 R tpch arrow, feather, memory_map=False, query_id=6, scale_factor=1, R 0.485 s -1.532087
2021-10-08 03:42 R dataframe-to-table type_simple_features, R 3.390 s 2.508376
2021-10-08 03:44 R file-read lz4, feather, dataframe, fanniemae_2016Q4, R 8.389 s -0.141515
2021-10-08 03:43 R file-read lz4, feather, table, fanniemae_2016Q4, R 0.561 s 0.406647
2021-10-08 03:44 R file-read uncompressed, feather, table, nyctaxi_2010-01, R 0.197 s -1.535120
2021-10-08 03:45 R file-read uncompressed, feather, dataframe, nyctaxi_2010-01, R 13.981 s 0.355085
2021-10-08 03:59 R file-write uncompressed, feather, table, nyctaxi_2010-01, R 1.282 s 0.392993
2021-10-08 04:01 R partitioned-dataset-filter vignette, dataset-taxi-parquet, R 0.586 s 0.456117
2021-10-08 04:02 R tpch arrow, feather, memory_map=False, query_id=1, scale_factor=1, R 0.517 s 0.102202
2021-10-08 03:45 R file-read lz4, feather, table, nyctaxi_2010-01, R 0.685 s -0.021202
2021-10-08 03:49 R file-write snappy, parquet, table, fanniemae_2016Q4, R 8.302 s 0.523492
2021-10-08 03:53 R file-write lz4, feather, table, fanniemae_2016Q4, R 1.400 s 0.350408
2021-10-08 03:46 R file-read lz4, feather, dataframe, nyctaxi_2010-01, R 13.551 s -0.980322
2021-10-08 04:02 R tpch arrow, parquet, memory_map=False, query_id=1, scale_factor=1, R 0.610 s 0.443992
2021-10-08 03:50 R file-write snappy, parquet, dataframe, fanniemae_2016Q4, R 12.732 s 0.581125
2021-10-08 04:05 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=10, R 0.208 s -1.517098
2021-10-08 03:54 R file-write lz4, feather, dataframe, fanniemae_2016Q4, R 5.197 s 0.744042
2021-10-08 04:04 R tpch arrow, parquet, memory_map=False, query_id=6, scale_factor=1, R 0.365 s -0.059224
2021-10-08 04:02 R partitioned-dataset-filter small_no_files, dataset-taxi-parquet, R 0.570 s 0.561726
2021-10-08 04:04 R tpch arrow, native, memory_map=False, query_id=6, scale_factor=1, R 0.113 s -1.892430
2021-10-08 04:12 JavaScript Iterate vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.701 s -0.254774
2021-10-08 04:12 JavaScript Slice toArray vectors lat, 1,000,000, Float32, tracks 0.001 s 0.747528
2021-10-08 04:12 JavaScript DataFrame Filter-Iterate origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.027 s 0.506737
2021-10-08 04:12 JavaScript Get values by index lng, 1,000,000, Float32, tracks 0.034 s -2.913637
2021-10-08 04:12 JavaScript Slice toArray vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.945 s -0.829731
2021-10-08 04:12 JavaScript DataFrame Count By origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s -0.164162
2021-10-08 04:12 JavaScript Parse Table.from, tracks 0.000 s -0.166307
2021-10-08 04:12 JavaScript Slice toArray vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.912 s -0.866326
2021-10-08 04:12 JavaScript Iterate vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 2.777 s -0.173720
2021-10-08 04:12 JavaScript DataFrame Iterate 1,000,000, tracks 0.052 s 0.661637
2021-10-08 04:13 JavaScript DataFrame Direct Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 4.537 s -0.420796
2021-10-08 04:12 JavaScript Iterate vectors lng, 1,000,000, Float32, tracks 0.022 s -0.907669
2021-10-08 04:12 JavaScript Slice toArray vectors lng, 1,000,000, Float32, tracks 0.000 s 0.550979
2021-10-08 04:12 JavaScript DataFrame Filter-Scan Count lng, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.586166
2021-10-08 04:12 JavaScript Parse readBatches, tracks 0.000 s -0.263554
2021-10-08 04:12 JavaScript Get values by index origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.330 s 2.456762
2021-10-08 04:12 JavaScript Iterate vectors lat, 1,000,000, Float32, tracks 0.022 s -0.939193
2021-10-08 04:12 JavaScript Slice vectors lng, 1,000,000, Float32, tracks 0.000 s -0.626801
2021-10-08 04:12 JavaScript DataFrame Filter-Iterate lat, 1,000,000, gt, Float32, 0, tracks 0.046 s 1.184839
2021-10-08 04:12 JavaScript Get values by index lat, 1,000,000, Float32, tracks 0.033 s -2.384288
2021-10-08 04:12 JavaScript Get values by index destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 4.358 s 2.296092
2021-10-08 04:12 JavaScript Parse serialize, tracks 0.005 s -0.240266
2021-10-08 04:12 JavaScript Slice vectors destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.480942
2021-10-08 04:12 JavaScript DataFrame Count By destination, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.004 s 0.015966
2021-10-08 04:12 JavaScript Slice vectors lat, 1,000,000, Float32, tracks 0.000 s -0.553649
2021-10-08 04:12 JavaScript Slice vectors origin, 1,000,000, Dictionary<Int8, Utf8>, tracks 0.000 s 0.477876
2021-10-08 04:12 JavaScript DataFrame Filter-Scan Count lat, 1,000,000, gt, Float32, 0, tracks 0.021 s 1.429943
2021-10-08 04:12 JavaScript DataFrame Direct Count lng, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.969919
2021-10-08 04:12 JavaScript DataFrame Filter-Scan Count origin, 1,000,000, eq, Dictionary<Int8, Utf8>, Seattle, tracks 0.018 s 0.089957
2021-10-08 04:12 JavaScript DataFrame Filter-Iterate lng, 1,000,000, gt, Float32, 0, tracks 0.046 s 0.965626
2021-10-08 04:12 JavaScript DataFrame Direct Count lat, 1,000,000, gt, Float32, 0, tracks 0.009 s -0.962447
2021-10-08 02:36 Python csv-read gzip, file, nyctaxi_2010-01 9.038 s 2.252886
2021-10-08 02:41 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 58.596 s 0.703709
2021-10-08 03:20 Python file-write uncompressed, feather, table, nyctaxi_2010-01 2.328 s 1.187670
2021-10-08 03:14 Python file-read uncompressed, feather, table, nyctaxi_2010-01 1.178 s -0.270651
2021-10-08 04:03 R tpch arrow, native, memory_map=False, query_id=1, scale_factor=10, R 0.616 s -0.732235