Python 使用pymongo操作mongodb库

Python 使用pymongo操作mongodb库

2016-12-31 21:55 1115人阅读 评论(0) 收藏 举报

 分类：

- - - Python（10） 

版权声明：本文为博主原创文章，未经博主允许不得转载。

 

目录(?)[+]

 

1，安装python3.5

如果Python还没有安装，可以直接用yum安装，

# 不过安装的是2.6 version
yum install -y python

源码安装3.5

wget https://www.python.org/ftp/python/3.5.0/Python-3.5.0.tgz
tar -xvf Python-3.5.0.tgz
cd Python-3.5.0
./configure --prefix=/usr/local--enable-shared
make
make install
ln -s /usr/local/bin/python3 /usr/bin/python3

运行python之前需要配置库

echo /usr/local/lib >> /etc/ld.so.conf.d/local.conf

ldconfig

运行演示

python3 --version

部分执行过程：

[root@03_sdwm Python-3.5.0]# echo/usr/local/lib >> /etc/ld.so.conf.d/local.conf
[root@03_sdwm Python-3.5.0]# ldconfig
[root@03_sdwm Python-3.5.0]#
[root@03_sdwm Python-3.5.0]#
[root@03_sdwm Python-3.5.0]# python3--version
Python 3.5.0
[root@03_sdwm Python-3.5.0]#

2，安装pymongo

安装方法有2种，分别是Installing with pip和Installing with easy_install，这里采用Installing witheasy_install参考官方文章：

http://api.mongodb.com/python/current/installation.html#installing-with-easy-install，

安装python pymongo

[root@03_sdwm ~]# python3 -m easy_install pymongo
Searching for pymongo
Reading http://pypi.python.org/simple/pymongo/
Best match: pymongo 3.4.0
Downloading https://pypi.python.org/packages/82/26/f45f95841de5164c48e2e03aff7f0702e22cef2336238d212d8f93e91ea8/pymongo-3.4.0.tar.gz#md5=aa77f88e51e281c9f328cea701bb6f3e
Processing pymongo-3.4.0.tar.gz
Running pymongo-3.4.0/setup.py -q bdist_egg --dist-dir /tmp/easy_install-ZZv1Ig/pymongo-3.4.0/egg-dist-tmp-LRDmoy
zip_safe flag not set; analyzing archive contents...
Adding pymongo 3.4.0 to easy-install.pth file

Installed /usr/lib/python2.6/site-packages/pymongo-3.4.0-py2.6-linux-x86_64.egg
Processing dependencies for pymongo
Finished processing dependencies for pymongo
[root@03_sdwm ~]#

 

3，使用pymongo操作mongodb

进行一些简单的对mongodb库的操作

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

import pymongo
import datetime


def get_db():
    # 建立连接
    client = pymongo.MongoClient(host="10.244.25.180", port=27017)
    db = client['example']
    #或者 db = client.example
    return db


def get_collection(db):
    # 选择集合（mongo中collection和database都是延时创建的）
    coll = db['informations']
    print db.collection_names()
    return coll


def insert_one_doc(db):
    # 插入一个document
    coll = db['informations']
    information = {"name": "quyang", "age": "25"}
    information_id = coll.insert(information)
    print information_id


def insert_multi_docs(db):
    # 批量插入documents,插入一个数组
    coll = db['informations']
    information = [{"name": "xiaoming", "age": "25"}, {"name": "xiaoqiang", "age": "24"}]
    information_id = coll.insert(information)
    print information_id


def get_one_doc(db):
    # 有就返回一个，没有就返回None
    coll = db['informations']
    print coll.find_one()  # 返回第一条记录
    print coll.find_one({"name": "quyang"})
    print coll.find_one({"name": "none"})


def get_one_by_id(db):
    # 通过objectid来查找一个doc
    coll = db['informations']
    obj = coll.find_one()
    obj_id = obj["_id"]
    print "_id 为ObjectId类型，obj_id:" + str(obj_id)

    print coll.find_one({"_id": obj_id})
    # 需要注意这里的obj_id是一个对象，不是一个str，使用str类型作为_id的值无法找到记录
    print "_id 为str类型 "
    print coll.find_one({"_id": str(obj_id)})
    # 可以通过ObjectId方法把str转成ObjectId类型
    from bson.objectid import ObjectId

    print "_id 转换成ObjectId类型"
    print coll.find_one({"_id": ObjectId(str(obj_id))})


def get_many_docs(db):
    # mongo中提供了过滤查找的方法，可以通过各种条件筛选来获取数据集，还可以对数据进行计数，排序等处理
    coll = db['informations']
    #ASCENDING = 1 升序;DESCENDING = -1降序;default is ASCENDING
    for item in coll.find().sort("age", pymongo.DESCENDING):
        print item

    count = coll.count()
    print "集合中所有数据 %s个" % int(count)

    #条件查询
    count = coll.find({"name":"quyang"}).count()
    print "quyang: %s"%count

def clear_all_datas(db):
    #清空一个集合中的所有数据
    db["informations"].remove()

if __name__ == '__main__':
    db = get_db()
    my_collection = get_collection(db)
    post = {"author": "Mike", "text": "My first blog post!", "tags": ["mongodb", "python", "pymongo"],
            "date": datetime.datetime.utcnow()}
    # 插入记录
    my_collection.insert(post)
    insert_one_doc(db)
    # 条件查询
    print my_collection.find_one({"x": "10"})
    # 查询表中所有的数据
    for iii in my_collection.find():
        print iii
    print my_collection.count()
    my_collection.update({"author": "Mike"},
                         {"author": "quyang", "text": "My first blog post!", "tags": ["mongodb", "python", "pymongo"],
                          "date": datetime.datetime.utcnow()})
    for jjj in my_collection.find():
        print jjj
    get_one_doc(db)
    get_one_by_id(db)
    get_many_docs(db)
    # clear_all_datas(db)

  

mysql> show profile for query 4;
+--------------------+----------+
| Status             | Duration |
+--------------------+----------+
| executing          | 0.000017 |
| Sending data       | 0.018048 |
| executing          | 0.000028 |
| Sending data       | 0.018125 |
| executing          | 0.000022 |
| Sending data       | 0.015749 |
| executing          | 0.000017 |
| Sending data       | 0.015633 |
| executing          | 0.000017 |
| Sending data       | 0.015382 |
| executing          | 0.000015 |
| Sending data       | 0.015707 |
| executing          | 0.000023 |
| Sending data       | 0.015890 |
| executing          | 0.000022 |
| Sending data       | 0.015908 |
| executing          | 0.000017 |
| Sending data       | 0.015761 |
| executing          | 0.000022 |
| Sending data       | 0.015542 |
| executing          | 0.000014 |
| Sending data       | 0.015561 |
| executing          | 0.000016 |
| Sending data       | 0.015546 |
| executing          | 0.000037 |
| Sending data       | 0.015555 |
| executing          | 0.000015 |
| Sending data       | 0.015779 |
| executing          | 0.000026 |
| Sending data       | 0.015815 |
| executing          | 0.000015 |
| Sending data       | 0.015468 |
| executing          | 0.000015 |
| Sending data       | 0.015457 |
| executing          | 0.000015 |
| Sending data       | 0.015457 |
| executing          | 0.000014 |
| Sending data       | 0.015500 |
| executing          | 0.000014 |
| Sending data       | 0.015557 |
| executing          | 0.000015 |
| Sending data       | 0.015537 |
| executing          | 0.000014 |
| Sending data       | 0.015395 |
| executing          | 0.000021 |
| Sending data       | 0.015416 |
| executing          | 0.000014 |
| Sending data       | 0.015416 |
| executing          | 0.000014 |
| Sending data       | 0.015399 |
| executing          | 0.000023 |
| Sending data       | 0.015407 |
| executing          | 0.000014 |
| Sending data       | 0.015585 |
| executing          | 0.000014 |
| Sending data       | 0.015385 |
| executing          | 0.000014 |
| Sending data       | 0.015412 |
| executing          | 0.000014 |
| Sending data       | 0.015408 |
| executing          | 0.000014 |
| Sending data       | 0.015753 |
| executing          | 0.000014 |
| Sending data       | 0.015376 |
| executing          | 0.000014 |
| Sending data       | 0.015416 |
| executing          | 0.000019 |
| Sending data       | 0.015368 |
| executing          | 0.000014 |
| Sending data       | 0.015481 |
| executing          | 0.000015 |
| Sending data       | 0.015619 |
| executing          | 0.000015 |
| Sending data       | 0.015662 |
| executing          | 0.000016 |
| Sending data       | 0.015574 |
| executing          | 0.000015 |
| Sending data       | 0.015566 |
| executing          | 0.000015 |
| Sending data       | 0.015488 |
| executing          | 0.000013 |
| Sending data       | 0.015493 |
| executing          | 0.000015 |
| Sending data       | 0.015386 |
| executing          | 0.000015 |
| Sending data       | 0.015485 |
| executing          | 0.000018 |
| Sending data       | 0.015760 |
| executing          | 0.000014 |
| Sending data       | 0.015386 |
| executing          | 0.000015 |
| Sending data       | 0.015418 |
| executing          | 0.000014 |
| Sending data       | 0.015458 |
| end                | 0.000016 |
| query end          | 0.000019 |
| closing tables     | 0.000018 |
| freeing items      | 0.000825 |
| logging slow query | 0.000067 |
| cleaning up        | 0.000025 |
+--------------------+----------+
100 rows in set, 1 warning (0.00 sec)

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