java程序员的python之路（mongodb高级查询）

最新推荐文章于 2023-07-21 11:02:10 发布

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最新推荐文章于 2023-07-21 11:02:10 发布

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分类专栏： python mongodb python之路文章标签： python mongodb

本文链接：https://blog.youkuaiyun.com/maosijunzi/article/details/76615708

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上一篇文章已经介绍了pymongo的基本操作，这里我们介绍一个pymongo高级一些的操作，比如内嵌域和数组的查询，条件符号查询以及查询结果的排序。聚集操作和map-reduce。废话就不多说了，直接看代码：

查询和排序

import pymongo
from pymongo import MongoClient


#连接字符串   mongodb://[username:password@]host1[:port1]]][/[database][?options]]
client = MongoClient("mongodb://test:test@192.168.1.235:27017/test?authMechanism=SCRAM-SHA-1") #MONGODB-CR
db = client["test"]

#插入
db.restaurants.remove()
db.restaurants.insert([
    {
        "address": { "zipcode": "10075", },
        "borough": "Manhattan",
        "cuisine": "Italian",
        "grades": [ {"grade": "A", "score":30}, 
                    {"grade": "B", "score":15}],
    },
    {
        "address": { "zipcode": "10075", },
        "borough": "Queens",
        "cuisine": "Brazilian",
        "grades": [ {"grade": "A", "score":30},
                    {"grade": "B", "score":15} ],
    },
])


#顶级属性的查询
cursor = db.restaurants.find({"name":"Vella"})
for doc in cursor:
    print(doc)


#内嵌属性的查询
cursor = db.restaurants.find({"address.zipcode":"10075"})
for doc in cursor:
    print(doc)


#数组的查询
cursor = db.restaurants.find({"grades.grade": "B"})
for doc in cursor:
    print(doc)


#条件符号查询
cursor = db.restaurants.find({"grades.score": {"$gt": 30}})#gt
for doc in cursor:
    print(doc)
cursor = db.restaurants.find({"grades.score": {"$lt": 10}})#lt
for doc in cursor:
    print(doc)
cursor = db.restaurants.find({"cuisine": "Italian", "address.zipcode": "10075"})#and
for doc in cursor:
    print(doc)
cursor = db.restaurants.find({"$or": [{"cuisine": "Italian"}, {"address.zipcode": "10075"}]})#or
for doc in cursor:
    print(doc)


#查询结果排序
cursor = db.restaurants.find().sort([("borough", pymongo.ASCENDING), ("address.zipcode", pymongo.ASCENDING) ])
for doc in cursor:
    print("sort:",doc)

client.close()

输出结果如下：

{'address': {'zipcode': '10075'}, 'cuisine': 'Italian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1d'), 'borough': 'Manhattan'}
{'address': {'zipcode': '10075'}, 'cuisine': 'Brazilian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1e'), 'borough': 'Queens'}
{'address': {'zipcode': '10075'}, 'cuisine': 'Italian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1d'), 'borough': 'Manhattan'}
{'address': {'zipcode': '10075'}, 'cuisine': 'Brazilian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1e'), 'borough': 'Queens'}
{'address': {'zipcode': '10075'}, 'cuisine': 'Italian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1d'), 'borough': 'Manhattan'}
{'address': {'zipcode': '10075'}, 'cuisine': 'Italian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1d'), 'borough': 'Manhattan'}
{'address': {'zipcode': '10075'}, 'cuisine': 'Brazilian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1e'), 'borough': 'Queens'}
sort: {'address': {'zipcode': '10075'}, 'cuisine': 'Italian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1d'), 'borough': 'Manhattan'}
sort: {'address': {'zipcode': '10075'}, 'cuisine': 'Brazilian', 'grades': [{'grade': 'A', 'score': 30}, {'grade': 'B', 'score': 15}], '_id': ObjectId('59829dd5762c3126d4957e1e'), 'borough': 'Queens'}

聚集操作

import pymongo
from pymongo import MongoClient


#连接字符串   mongodb://[username:password@]host1[:port1]]][/[database][?options]]
client = MongoClient("mongodb://test:test@192.168.1.235:27017/test?authMechanism=SCRAM-SHA-1") #MONGODB-CR
db = client["test"]

#插入
db.restaurants.remove()
db.restaurants.insert([
    {
        "address": { "zipcode": "10075", },
        "borough": "Manhattan",
        "cuisine": "Italian",
        "grades": [ {"grade": "A", "score":30}, 
                    {"grade": "B", "score":15}],
    },
    {
        "address": { "zipcode": "10075", },
        "borough": "Queens",
        "cuisine": "Brazilian",
        "grades": [ {"grade": "A", "score":30},
                    {"grade": "B", "score":15} ],
    },
])

#聚集操作

#根据属性分组并计算数量
cursor = db.restaurants.aggregate([{"$group": {"_id": "$borough", "count": {"$sum": 1}}}] )
for doc in cursor:
    print("aggregate:",doc)


#先过滤再分组
cursor = db.restaurants.aggregate(
    [
        {"$match": {"borough": "Queens", "cuisine": "Brazilian"}},
        {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}}
    ]
)
for doc in cursor:
    print("aggregate:",doc)

输出结果如下：

aggregate: {'count': 1, '_id': 'Queens'}
aggregate: {'count': 1, '_id': 'Manhattan'}
aggregate: {'count': 1, '_id': '10075'}

map-reduce

from pymongo import MongoClient


#连接字符串   mongodb://[username:password@]host1[:port1]]][/[database][?options]]
client = MongoClient("mongodb://test:test@192.168.1.235:27017/test?authMechanism=SCRAM-SHA-1") #MONGODB-CR
db = client["test"]

#map-reduce操作,此操作一般用来做更灵活的聚合操作，使用js脚本来编写map方法和reduce方法，结果会产生一个新的集合。
#比如当前需求如下，统计某小区各种宠物的数量
from bson.code import Code

db.animals.remove()
db.animals.insert({"name": "aaa", "animals": ["dog", "cat"]})  
db.animals.insert({"name": "bbb", "animals": ["cat"]})  
db.animals.insert({"name": "ccc", "animals": ["mouse", "cat", "dog"]})  
db.animals.insert({"name": "ddd", "animals": []})

mapfun = Code("function () {"
              "    this.animals.forEach(function(z) {emit(z, 1);});"#emit函数把结果传递给reduce函数
              "}")

reducefun = Code("function (key, values) {"  
                 "  var total = 0;"  
                 "  for (var i = 0; i < values.length; i++) {"  
                 "    total += values[i];"  
                 "  }"  
                 "  return total;"  
                 "}")


result = db.animals.map_reduce(mapfun,reducefun,"results")#会产生一个新的集合，名字叫results。可以在mongo中使用show collections查看。
for doc in result.find():
    print("map-reduce",doc)




#再比如统计各班各科成绩的总和

db.students.remove()

#学生数据如下
db.students.insert({"id":"1","sex":"boy","class":"181","scores":{"math":80,"english":60,"chinese":90}})
db.students.insert({"id":"2","sex":"gril","class":"182","scores":{"math":100,"english":90,"chinese":80}})
db.students.insert({"id":"3","sex":"boy","class":"182","scores":{"math":60,"english":50,"chinese":88}})
db.students.insert({"id":"4","sex":"gril","class":"182","scores":{"math":79,"english":87,"chinese":78}})
db.students.insert({"id":"5","sex":"gril","class":"181","scores":{"math":79,"english":87,"chinese":78}})


mapfun = Code("function(){"
              "    emit(this.class,this.scores);"  #emit函数把结果传递给reduce函数
              "}")

reducefun = Code("function(key,values){"
                 "    var result = {math:0,english:0,chinese:0};"
                 "    for(var i = 0;i < values.length;i++){"
                 "        result.math += values[i].math;"
                 "        result.english += values[i].english;"
                 "        result.chinese += values[i].chinese;"
                 "    }"
                 "    return result;"
                 "}")

result = db.students.map_reduce(mapfun,reducefun,"class_scores")
for doc in result.find():
    print("map-reduce1",doc)



client.close()

输出结果如下：

map-reduce {'value': 3.0, '_id': 'cat'}
map-reduce {'value': 2.0, '_id': 'dog'}
map-reduce {'value': 1.0, '_id': 'mouse'}
map-reduce1 {'value': {'chinese': 168.0, 'english': 147.0, 'math': 159.0}, '_id': '181'}
map-reduce1 {'value': {'chinese': 246.0, 'english': 227.0, 'math': 239.0}, '_id': '182'}