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：

批量插入而不是逐条插入

为了加快插入速度，先不要建索引

生产者和消费者模型，主进程读文件，多个 worker 进程执行插入

注意控制 worker 的数量，避免对 MySQL 造成太大的压力

注意处理脏数据导致的异常

原始数据是 GBK 编码，所以还要注意转换成 UTF-8

用 click 封装命令行工具

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, cols, 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, cols, rows, cursor)

break

rows.append(row)

if len(rows) == BATCH:

insert_many(table, cols, rows, 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, cols, 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', required=True, 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, reader, w=workers)

if __name__ == '__main__':

cli()

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