Benchmarks
Date Language Batch Benchmark Mean Z-Score
2021-10-05 23:09 Python csv-read uncompressed, streaming, nyctaxi_2010-01 10.582 s 0.705491
2021-10-05 23:12 Python dataframe-to-table type_strings 0.367 s 0.571670
2021-10-05 23:12 Python dataframe-to-table chi_traffic_2020_Q1 19.556 s 0.560094
2021-10-05 23:13 Python dataframe-to-table type_simple_features 0.909 s 0.380032
2021-10-05 23:10 Python csv-read gzip, streaming, nyctaxi_2010-01 10.547 s 0.872901
2021-10-05 23:08 Python csv-read gzip, streaming, fanniemae_2016Q4 14.883 s -0.321498
2021-10-05 23:16 Python dataset-read async=True, pre_buffer=true, nyctaxi_multi_parquet_s3 67.407 s -1.544439
2021-10-05 23:07 Python csv-read uncompressed, streaming, fanniemae_2016Q4 15.019 s -0.775760
2021-10-05 23:09 Python csv-read gzip, file, fanniemae_2016Q4 6.030 s 0.179976
2021-10-05 23:12 Python dataframe-to-table type_integers 0.011 s 1.251688
2021-10-05 23:09 Python csv-read uncompressed, file, nyctaxi_2010-01 1.009 s 0.377219
2021-10-05 23:12 Python dataframe-to-table type_dict 0.012 s 0.661978
2021-10-05 23:12 Python dataframe-to-table type_floats 0.011 s 1.620216
2021-10-05 23:10 Python csv-read gzip, file, nyctaxi_2010-01 9.047 s -0.654643
2021-10-05 23:21 Python dataset-read async=True, pre_buffer=false, nyctaxi_multi_parquet_s3 83.623 s 0.830682
2021-10-05 23:07 Python csv-read uncompressed, file, fanniemae_2016Q4 1.156 s 1.053262
2021-10-05 23:13 Python dataset-filter nyctaxi_2010-01 4.358 s 0.330912
2021-10-05 23:13 Python dataframe-to-table type_nested 2.873 s 1.019603