概述

前段时间帮同事处理了一个把 CSV 数据导入到 MysqL 的需求。两个很大的 CSV 文件， 分别有 3GB、2100 万条记录和 7GB、3500 万条记录。对于这个量级的数据，用简单的单进程／单线程导入 会耗时很久，最终用了多进程的方式来实现。具体过程不赘述，记录一下几个要点：

具体的代码实现如下：

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import codecs
import csv
import logging
import multiprocessing
import os
import warnings

import click
import MysqLdb
import sqlalchemy

warnings.filterwarnings('ignore',category=MysqLdb.Warning)

# 批量插入的记录数量
BATCH = 5000

DB_URI = 'MysqL://root@localhost:3306/example?charset=utf8'

engine = sqlalchemy.create_engine(DB_URI)


def get_table_cols(table):
  sql = 'SELECT * FROM `{table}` LIMIT 0'.format(table=table)
  res = engine.execute(sql)
  return res.keys()


def insert_many(table,cols,rows,cursor):
  sql = 'INSERT INTO `{table}` ({cols}) VALUES ({marks})'.format(
      table=table,cols=','.join(cols),marks=','.join(['%s'] * len(cols)))
  cursor.execute(sql,*rows)
  logging.info('process %s inserted %s rows into table %s',os.getpid(),len(rows),table)


def insert_worker(table,queue):
  rows = []
  # 每个子进程创建自己的 engine 对象
  cursor = sqlalchemy.create_engine(DB_URI)
  while True:
    row = queue.get()
    if row is None:
      if rows:
        insert_many(table,cursor)
      break

    rows.append(row)
    if len(rows) == BATCH:
      insert_many(table,cursor)
      rows = []


def insert_parallel(table,reader,w=10):
  cols = get_table_cols(table)

  # 数据队列，主进程读文件并往里写数据，worker 进程从队列读数据
  # 注意一下控制队列的大小，避免消费太慢导致堆积太多数据，占用过多内存
  queue = multiprocessing.Queue(maxsize=w*BATCH*2)
  workers = []
  for i in range(w):
    p = multiprocessing.Process(target=insert_worker,args=(table,queue))
    p.start()
    workers.append(p)
    logging.info('starting # %s worker process,pid: %s...',i + 1,p.pid)

  dirty_data_file = './{}_dirty_rows.csv'.format(table)
  xf = open(dirty_data_file,'w')
  writer = csv.writer(xf,delimiter=reader.dialect.delimiter)

  for line in reader:
    # 记录并跳过脏数据: 键值数量不一致
    if len(line) != len(cols):
      writer.writerow(line)
      continue

    # 把 None 值替换为 'NULL'
    clean_line = [None if x == 'NULL' else x for x in line]

    # 往队列里写数据
    queue.put(tuple(clean_line))
    if reader.line_num % 500000 == 0:
      logging.info('put %s tasks into queue.',reader.line_num)

  xf.close()

  # 给每个 worker 发送任务结束的信号
  logging.info('send close signal to worker processes')
  for i in range(w):
    queue.put(None)

  for p in workers:
    p.join()


def convert_file_to_utf8(f,rv_file=None):
  if not rv_file:
    name,ext = os.path.splitext(f)
    if isinstance(name,unicode):
      name = name.encode('utf8')
    rv_file = '{}_utf8{}'.format(name,ext)
  logging.info('start to process file %s',f)
  with open(f) as infd:
    with open(rv_file,'w') as outfd:
      lines = []
      loop = 0
      chunck = 200000
      first_line = infd.readline().strip(codecs.BOM_UTF8).strip() + '\n'
      lines.append(first_line)
      for line in infd:
        clean_line = line.decode('gb18030').encode('utf8')
        clean_line = clean_line.rstrip() + '\n'
        lines.append(clean_line)
        if len(lines) == chunck:
          outfd.writelines(lines)
          lines = []
          loop += 1
          logging.info('processed %s lines.',loop * chunck)

      outfd.writelines(lines)
      logging.info('processed %s lines.',loop * chunck + len(lines))


@click.group()
def cli():
  logging.basicConfig(level=logging.INFO,format='%(asctime)s - %(levelname)s - %(name)s - %(message)s')


@cli.command('gbk_to_utf8')
@click.argument('f')
def convert_gbk_to_utf8(f):
  convert_file_to_utf8(f)


@cli.command('load')
@click.option('-t','--table',required=True,help='表名')
@click.option('-i','--filename',help='输入文件')
@click.option('-w','--workers',default=10,help='worker 数量，默认 10')
def load_fac_day_pro_nos_sal_table(table,filename,workers):
  with open(filename) as fd:
    fd.readline()  # skip header
    reader = csv.reader(fd)
    insert_parallel(table,w=workers)


if __name__ == '__main__':
  cli()

以上就是本文给大家分享的全部没人了，希望大家能够喜欢

总结

以上是编程之家为你收集整理的Python实现 多进程导入CSV数据到 MySQL全部内容，希望文章能够帮你解决Python实现 多进程导入CSV数据到 MySQL所遇到的程序开发问题。

如果您也喜欢它,动动您的小指点个赞吧