我的机器上的目录中有几千个CSV文件，需要根据我制定的正则表达式进行验证。path_to_validator指向一个 Scala 脚本，该脚本通过命令行上的 Windows .bat 文件运行。它读取正则表达式和 csv 文件，并为其提供通过/失败等级，该等级打印到输出.txt。
约束是这个 Scala 脚本将目录作为参数，而不是 Python 列表，因此我无法如此轻松地在进程之间拆分工作负载。我可以将每个进程的文件移动到临时目录，但项目的细节使得理想情况下，我的部署程序不需要对 CSV 文件的写入权限。
这是代码：
with open("output.txt", 'w') as output:
for filename in os.listdir(path_to_csv_folder):
print("Processing file " + str(current_file_count) + "/" + str(TOTAL_FILE_COUNT), end='r')
output.write(filename + ': ')
validator = subprocess.Popen([path_to_validator, path_to_csv_folder + filename, path_to_csv_schema, "-x",
CSV_ENCODING, "-y", CSV_SCHEMA_ENCODING], stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
result = validator.stdout.read()
output.write(result.decode('windows-1252'))
current_file_count += 1
问题是它需要 1 小时 30 分钟+，而只占用大约 20% 的 CPU。这应该是并行化加速的明显候选者。该目录有 5000+ CSV 文件，它们都需要处理。如何将工作负载拆分为 4 个不同的进程以利用所有 CPU 能力？
这是我实际制作的代码：
"""
Command line API to CSV validator using Scala implementation from:
http://digital-preservation.github.io/csv-validator/#toc7
"""
PATH_TO_VALIDATOR = r"C:progcsvcsv-validator-cmd-1.2-RC2binvalidate.bat"
PATH_TO_CSV_FOLDER = r"C:progcsvCSVFiles"
PATH_TO_CSV_SCHEMA = r"C:progcsvocr-schema.csvs"
# Set defaults
CSV_ENCODING = "windows-1252"
CSV_SCHEMA_ENCODING = "UTF-8"
def open_csv(CSV_LIST):
import subprocess
# To be used to display a simple progress indicator
TOTAL_FILE_COUNT = len(CSV_LIST)
current_file_count = 1
with open("output.txt", 'w') as output:
for filename in CSV_LIST:
print("Processing file " + str(current_file_count) + "/" + str(TOTAL_FILE_COUNT))
output.write(filename + ': ')
validator = subprocess.Popen(
[PATH_TO_VALIDATOR, PATH_TO_CSV_FOLDER + "/" + filename, PATH_TO_CSV_SCHEMA, "--csv-encoding",
CSV_ENCODING, "--csv-schema-encoding", CSV_SCHEMA_ENCODING, '--fail-fast', 'true'], stdout=subprocess.PIPE)
result = validator.stdout.read()
output.write(result.decode('windows-1252'))
current_file_count += 1
# Split a list into n sublists of roughly equal size
def split_list(alist, wanted_parts=1):
length = len(alist)
return [alist[i * length // wanted_parts: (i + 1) * length // wanted_parts]
for i in range(wanted_parts)]
if __name__ == '__main__':
import argparse
import multiprocessing
import os
parser = argparse.ArgumentParser(description="Command line API to Scala CSV validator")
parser.add_argument('-pv', '--PATH_TO_VALIDATOR', help="Specify the path to csv-validator-cmd/bin/validator.bat",
required=True)
parser.add_argument('-pf', '--PATH_TO_CSV_FOLDER', help="Specify the path to the folder containing the csv files "
"you want to validate", required=True)
parser.add_argument('-ps', '--PATH_TO_CSV_SCHEMA', help="Specify the path to CSV schema you want to use to "
"validate the given files", required=True)
parser.add_argument('-cenc', '--CSV_ENCODING', help="Optional parameter to specify the encoding used by the CSV "
"files. Choose UTF-8 or windows-1252. Default windows-1252")
parser.add_argument('-csenc', '--CSV_SCHEMA_ENCODING', help="Optional parameter to specify the encoding used by "
"the CSV Schema. Choose UTF-8 or windows-1252. "
"Default UTF-8")
args = vars(parser.parse_args())
if args['CSV_ENCODING'] is not None:
CSV_ENCODING = args['CSV_ENCODING']
if args['CSV_SCHEMA_ENCODING'] is not None:
CSV_SCHEMA_ENCODING = args['CSV_SCHEMA_ENCODING']
PATH_TO_VALIDATOR = args["PATH_TO_VALIDATOR"]
PATH_TO_CSV_SCHEMA = args["PATH_TO_CSV_SCHEMA"]
PATH_TO_CSV_FOLDER = args["PATH_TO_CSV_FOLDER"]
CPU_COUNT = multiprocessing.cpu_count()
split_csv_directory = split_list(os.listdir(args["PATH_TO_CSV_FOLDER"]), wanted_parts=CPU_COUNT)
# Spawn a Process for each CPU on the system
for csv_list in split_csv_directory:
p = multiprocessing.Process(target=open_csv, args=(csv_list,))
p.start()
请让我知道我的代码中的任何陷阱。
看看这个多处理包的介绍。
例如，尝试：
import multiprocessing as mp
import os
def process_csv(csv):
% process the csv
return {csv: collected_debug_information}
pool = mp.Pool(processes=4)
results = pool.map(process_csv, os.listdir(path_to_csv_folder))
使用返回的字典，您可以查看结果以评估一些解析错误左右。它将是一个以 csv 名称作为键的字典列表。
还有一个很好的包是joblib，也看看它，在引擎盖下它使用多处理包。