Вот пример использования apache commons-fileupload:
// apache commons-fileupload to handle file upload
DiskFileItemFactory factory = new DiskFileItemFactory();
factory.setRepository(new File(DataSources.TORRENTS_DIR()));
ServletFileUpload fileUpload = new ServletFileUpload(factory);
List<FileItem> items = fileUpload.parseRequest(req.raw());
FileItem item = items.stream()
.filter(e ->
"the_upload_name".equals(e.getFieldName()))
.findFirst().get();
String fileName = item.getName();
item.write(new File(dir, fileName));
log.info(fileName);
Я не знаю pyspark, но если вы используете панд, это работает. (и pyspark может быть похожим):
1: создавать данные
import pandas as pd
import numpy as np
data = pd.DataFrame({
'id':[1, 2],
'start_tm': pd.date_range('2019-01-01 00:00', periods=2, freq='D'),
'end_dt': pd.date_range('2019-01-01 00:30', periods=2, freq='D')})
# pandas dataframe is similar to the data in pyspark
выводить
id start_tm end_dt
1 2019-01-01 2019-01-01 00:30:00
2 2019-01-02 2019-01-02 00:30:00
2: разбивать столбцы
period = np.timedelta64(5, 'm') # 5 minutes
idx = (data['end_dt'] - data['start_tm']) > period
while idx.any():
new_data = data[idx].copy()
new_data['start_tm'] = new_data['start_tm'] + period
data.loc[idx, 'end_dt'] = (data[idx]['start_tm'] + period).values
data = pd.concat([data, new_data], axis=0)
idx = (data['end_dt'] - data['start_tm']) > period
выход
id start_tm end_dt
1 2019-01-01 00:00:00 2019-01-01 00:05:00
2 2019-01-02 00:00:00 2019-01-02 00:05:00
1 2019-01-01 00:05:00 2019-01-01 00:10:00
2 2019-01-02 00:05:00 2019-01-02 00:10:00
1 2019-01-01 00:10:00 2019-01-01 00:15:00
2 2019-01-02 00:10:00 2019-01-02 00:15:00
1 2019-01-01 00:15:00 2019-01-01 00:20:00
2 2019-01-02 00:15:00 2019-01-02 00:20:00
1 2019-01-01 00:20:00 2019-01-01 00:25:00
2 2019-01-02 00:20:00 2019-01-02 00:25:00
1 2019-01-01 00:25:00 2019-01-01 00:30:00
2 2019-01-02 00:25:00 2019-01-02 00:30:00