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 codecsimport csvimport loggingimport multiprocessingimport osimport warningsimport clickimport MySQLdbimport sqlalchemywarnings.filterwarnings('ignore', category=MySQLdb.Warning)# 批量插入的记录数量BATCH = 5000DB_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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