PostgreSQL有一个关于如何最好地初始填充数据库的指南，他们建议使用COPY命令批量加载行。该指南还提供了其他一些关于如何加快处理速度的好技巧，比如在加载数据之前删除索引和外键(然后再将它们添加回来)。

正如其他人所注意到的，在将数据导入Postgres时，会因为Postgres为您设计的检查而减慢速度。此外，您经常需要以某种方式操作数据，以使其适合使用。任何可以在Postgres进程之外完成的操作都意味着您可以使用COPY协议进行导入。

For my use I regularly import data from the httparchive.org project using pgloader. As the source files are created by MySQL you need to be able to handle some MySQL oddities such as the use of \N for an empty value and along with encoding problems. The files are also so large that, at least on my machine, using FDW runs out of memory. pgloader makes it easy to create a pipeline that lets you select the fields you want, cast to the relevant data types and any additional work before it goes into your main database so that index updates, etc. are minimal.

我刚刚遇到了这个问题，建议将csvsql(发行版)批量导入到Postgres。要执行批量插入，只需创建b，然后使用csvsql，它连接到数据库，并为整个csv文件夹创建单独的表。

$ createdb test 
$ csvsql --db postgresql:///test --insert examples/*.csv

May be I'm late already. But, there is a Java library called pgbulkinsert by Bytefish. Me and my team were able to bulk insert 1 Million records in 15 seconds. Of course, there were some other operations that we performed like, reading 1M+ records from a file sitting on Minio, do couple of processing on the top of 1M+ records, filter down records if duplicates, and then finally insert 1M records into the Postgres Database. And all these processes were completed within 15 seconds. I don't remember exactly how much time it took to do the DB operation, but I think it was around less then 5 seconds. Find more details from https://www.bytefish.de/blog/pgbulkinsert_bulkprocessor.html

这主要取决于数据库中的(其他)活动。这样的操作会有效地冻结其他会话的整个数据库。另一个需要考虑的问题是数据模型和约束、触发器等的存在。

我的第一种方法总是:创建一个(临时)表，其结构与目标表类似(创建表tmp AS select * from target where 1=0)，并从将文件读入临时表开始。 然后我检查哪些是可以检查的:重复项，目标中已经存在的键，等等。

然后执行do insert到target select * from tmp或类似的操作。

如果失败了，或者花费了太长时间，我将中止它并考虑其他方法(暂时删除索引/约束等)

你可以使用COPY表TO…使用二进制，它“比文本和CSV格式略快”。只有当您有数百万行要插入，并且您对二进制数据感到满意时才这样做。

下面是一个使用psycopg2和二进制输入的Python食谱示例。