将MongoDB(3.0)集合的子集保存到Python中的另一个集合 [英] Save a subset of MongoDB(3.0) collection to another collection in Python

问题描述

我找到了这个答案-答案链接

db.full_set.aggregate([ { $match: { date: "20120105" } }, { $out: "subset" } ]);

我想做同样的事情，但集合中有前15000个文档，我找不到如何将限制应用于此类查询(我尝试使用$limit : 15000，但无法识别$ limit)

I want do same thing but with first 15000 documents in collection, I couldn't find how to apply limit to such query (I tried using $limit : 15000, but it doesn't recognize $limit)

也在尝试时-

db.subset.insert(db.full_set.find({}).limit(15000).toArray())

对于输出类型cursor没有功能toArray().

指导我如何做到?

there is no function toArray() for output type cursor.

Guide me how can I accomplish it?

推荐答案

嗯，
在python中，这就是工作原理-$limit需要包装在""，
中 并且您需要创建管道以将其作为命令执行.

Well,
in python, this is how things work - $limit needs to be wrapped in "",
and you need to create a pipeline to execute it as a command.

在我的代码中-

    pipeline = [{ '$limit': 15000 },{'$out': "destination_collection"}]
    db.command('aggregate', "source_collection", pipeline=pipeline)

您需要将所有内容都用双引号引起来，包括源和目标集合. 在db.command中，db是数据库的对象(即dbclient.database_name)

You need to wrap everything in double quotes, including your source and destination collection. And in db.command db is the object of your database (ie dbclient.database_name)

根据此答案-

至少在我看来，它的速度比forEach快约100倍.这是因为整个聚合管道都在mongod进程中运行，而基于find()和insert()的解决方案必须将所有文档从服务器发送到客户端，然后再发送回客户端.即使服务器和客户端在同一台计算机上，这也会降低性能.

It works about 100 times faster than forEach at least in my case. This is because the entire aggregation pipeline runs in the mongod process, whereas a solution based on find() and insert() has to send all of the documents from the server to the client and then back. This has a performance penalty, even if the server and client are on the same machine.

真正帮助我找出答案的人-参考1
还有官方文档

The one that really helped me figure this answer out - Reference 1
And official documentation

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