Operation Python MongoDB document database

1.Pymongo installation

安装pymongo：

pip install pymongo

• PyMongo is the driver, so that the program can be used python Mongodb database, written in python;

2.Pymongo method

• insert_one(): Inserting a record;
• insert(): Inserting a plurality of records;
• find_one(): Query a record, without any parameters returns the first record, with parameters press criteria to return;
• find(): Query multiple records, with no parameters returns all records, with parameters according to criteria to return;
• count(): View the total number of records;
• create_index(): Create an index;
• update_one(): Updated to match the first data;
• update(): Update all data to match;
• remove(): Delete a record, with no parameters means to delete all records, with participation by means drop condition;
• delete_one(): Delete a single record;
• delete_many(): Delete multiple records;

The operation 3.Pymongo

• View database

from pymongo import MongoClient

connect = MongoClient(host='localhost', port=27017, username="root", password="123456")
connect = MongoClient('mongodb://localhost:27017/', username="root", password="123456") print(connect.list_database_names())

• Access to the database instance

test_db = connect['test']

• Examples of acquired collection

collection = test_db['students']

• Insert document row, row query document, the document's line takes a value

from pymongo import MongoClient
from datetime import datetime

connect = MongoClient(host='localhost', port=27017, username="root", password="123456",) # 获取db test_db = connect['test'] # 获取collection collection = test_db['students'] # 构建document document = {"author": "Mike", "text": "My first blog post!", "tags": ["mongodb", "python", "pymongo"], "date": datetime.now()} # 插入document one_insert = collection.insert_one(document=document) print(one_insert.inserted_id) # 通过条件过滤出一条document one_result = collection.find_one({"author": "Mike"}) # 解析document字段 print(one_result, type(one_result)) print(one_result['_id']) print(one_result['author']) 注意：如果需要通过id查询一行document，需要将id包装为ObjectId类的实例对象 from bson.objectid import ObjectId collection.find_one({'_id': ObjectId('5c2b18dedea5818bbd73b94c')})

• Insert multiple rows documents, multi-line query document, see how many rows document collections

from pymongo import MongoClient
from datetime import datetime
connect = MongoClient(host='localhost', port=27017, username="root", password="123456",) # 获取db test_db = connect['test'] # 获取collection collection = test_db['students'] documents = [{"author": "Mike","text": "Another post!","tags": ["bulk", "insert"], "date": datetime(2009, 11, 12, 11, 14)}, {"author": "Eliot", "title": "MongoDB is fun", "text": "and pretty easy too!", "date": datetime(2009, 11, 10, 10, 45)}] collection.insert_many(documents=documents) # 通过条件过滤出多条document documents = collection.find({"author": "Mike"}) # 解析document字段 print(documents, type(documents)) print('*'*300) for document in documents: print(document) print('*'*300) result = collection.count_documents({'author': 'Mike'}) print(result)

• Compare range query

from pymongo import MongoClient
from datetime import datetime

connect = MongoClient(host='localhost', port=27017, username="root", password="123456",) # 获取db test_db = connect['test'] # 获取collection collection = test_db['students'] # 通过条件过滤时间小于datetime(2019, 1,1,15,40,3) 的document documents = collection.find({"date": {"$lt": datetime(2019, 1,1,15,40,3)}}).sort('date') # 解析document字段 print(documents, type(documents)) print('*'*300) for document in documents: print(document)

• Creating an index

from pymongo import MongoClient
import pymongo
from datetime import datetime connect = MongoClient(host='localhost', port=27017, username="root", password="123456",) # 获取db test_db = connect['test'] # 获取collection collection = test_db['students'] # 创建字段索引 collection.create_index(keys=[("name", pymongo.DESCENDING)], unique=True) # 查询索引 result = sorted(list(collection.index_information())) print(result)

• document modification

from pymongo import MongoClient
connect = MongoClient(host='localhost', port=27017, username="root", password="123456",) # 获取db test_db = connect['test'] # 获取collection collection = test_db['students'] result = collection.update({'name': 'robby'}, {'$set': {"name": "Petter"}}) print(result) 注意：还有update_many()方法

• Delete document

from pymongo import MongoClient
connect = MongoClient(host='localhost', port=27017, username="root", password="123456",) # 获取db test_db = connect['test'] # 获取collection collection = test_db['students'] result = collection.delete_one({'name': 'Petter'}) print(result.deleted_count) 注意：还有delete_many()方法

4.MongoDB ODM Detailed

• MongoDB ODM using a method analogous to Django ORM;
• MongoEngine mapper is a target document, written in Python, MongoDB for processing;
• Abstract MongoEngine provides is class-based, like all models are created;

# 安装mongoengine
pip install mongoengine

• Field of the type used mongoengine

BinaryField
BooleanField
ComplexDateTimeField
DateTimeField
DecimalField
DictField
DynamicField
EmailField
EmbeddedDocumentField
EmbeddedDocumentListField
FileField
FloatField
GenericEmbeddedDocumentField
GenericReferenceField
GenericLazyReferenceField
GeoPointField
ImageField
IntField
ListField:可以将自定义的文档类型嵌套
MapField
ObjectIdField
ReferenceField
LazyReferenceField
SequenceField
SortedListField
StringField
URLField
UUIDField
PointField
LineStringField
PolygonField
MultiPointField
MultiLineStringField
MultiPolygonField

5. Create a database connection using mongoengine

from mongoengine import connect

conn = connect(db='test', host='localhost', port=27017, username='root', password='123456', authentication_source='admin')
print(conn)

connect（db = None，alias ='default'，** kwargs ）；

• db: Name of the database to be used for compatibility with connect;
• host : Host name mongod instance you want to connect;
• port : Port run mongod instance;
• username: Used to authenticate the user name;
• password: For password authentication;
• authentication_source : To authenticate database;

Construction of model documents, insert data

from mongoengine import connect, \
                        Document, \
                        StringField,\
                        IntField, \ FloatField,\ ListField, \ EmbeddedDocumentField,\ DateTimeField, \ EmbeddedDocument from datetime import datetime # 嵌套文档 class Score(EmbeddedDocument): name = StringField(max_length=50, required=True) value = FloatField(required=True) class Students(Document): choice = (('F', 'female'), ('M', 'male'),) name = StringField(max_length=100, required=True, unique=True) age = IntField(required=True) hobby = StringField(max_length=100, required=True, ) gender = StringField(choices=choice, required=True) # 这里使用到了嵌套文档，这个列表中的每一个元素都是一个字典，因此使用嵌套类型的字段 score = ListField(EmbeddedDocumentField(Score)) time = DateTimeField(default=datetime.now()) if __name__ == '__main__': connect(db='test', host='localhost', port=27017, username='root', password='123456', authentication_source='admin') math_score = Score(name='math', value=94) chinese_score = Score(name='chinese', value=100) python_score = Score(name='python', value=99) for i in range(10): students = Students(name='robby{}'.format(i), age=int('{}'.format(i)), hobby='read', gender='M', score=[math_score, chinese_score, python_score]) students.save()

Query data

from mongoengine import connect, \
                        Document, \
                        StringField,\
                        IntField, \
                        FloatField,\
                        ListField, \
                        EmbeddedDocumentField,\
                        DateTimeField, \
                        EmbeddedDocument
from datetime import datetime

# 嵌套文档 class Score(EmbeddedDocument): name = StringField(max_length=50, required=True) value = FloatField(required=True) class Students(Document): choice = (('F', 'female'), ('M', 'male'),) name = StringField(max_length=100, required=True, unique=True) age = IntField(required=True) hobby = StringField(max_length=100, required=True, ) gender = StringField(choices=choice, required=True) # 这里使用到了嵌套文档，这个列表中的每一个元素都是一个字典，因此使用嵌套类型的字段 score = ListField(EmbeddedDocumentField(Score)) time = DateTimeField(default=datetime.now()) if __name__ == '__main__': connect(db='test', host='localhost', port=27017, username='root', password='123456', authentication_source='admin') first_document = Students.objects.first() all_document = Students.objects.all() # 如果只有一条，也可以使用get specific_document = Students.objects.filter(name='robby3') print(first_document.name, first_document.age, first_document.time) for document in all_document: print(document.name) for document in specific_document: print(document.name, document.age)

Modify, update, delete data

from mongoengine import connect, \
                        Document, \
                        StringField,\
                        IntField, \ FloatField,\ ListField, \ EmbeddedDocumentField,\ DateTimeField, \ EmbeddedDocument from datetime import datetime # 嵌套文档 class Score(EmbeddedDocument): name = StringField(max_length=50, required=True) value = FloatField(required=True) class Students(Document): choice = (('F', 'female'), ('M', 'male'),) name = StringField(max_length=100, required=True, unique=True) age = IntField(required=True) hobby = StringField(max_length=100, required=True, ) gender = StringField(choices=choice, required=True) # 这里使用到了嵌套文档，这个列表中的每一个元素都是一个字典，因此使用嵌套类型的字段 score = ListField(EmbeddedDocumentField(Score)) time = DateTimeField(default=datetime.now()) if __name__ == '__main__': connect(db='test', host='localhost', port=27017, username='root', password='123456', authentication_source='admin') specific_document = Students.objects.filter(name='robby3') specific_document.update(set__age=100) specific_document.update_one(set__age=100) for document in specific_document: document.name = 'ROBBY100' document.save() for document in specific_document: document.delete()

• all(): Return all documents;
• all_fields(): Includes all fields;
• as_pymongo(): Document instance but not returned pymongo value;
• average(): Average value exceeds a specified value of the field;
• batch_size(): Limiting the number of documents returned by a single batch;
• clone(): Creates a copy of the current query set;
• comment(): Add comments in the query;
• count(): Calculate the selected element in the query;
• create(): Create a new object, return to save the object instance;
• delete(): Delete query matching documents;
• distinct(): Returns the field value different lists;

Embedded document query method

• count(): The number of embedded documents in the list, the length of the list;
• create(): Create a new embedded document and save it to the database;
• delete(): Deleted from the database embedded in the document;
• exclude(** kwargs ): To filter the list by using the given keyword arguments to exclude embedded document;
• first(): Back in the first embedded documents;
• get(): Embedded document retrieval determined by a given keyword arguments;
• save(): Save ancestors document;
• update(): Replacing the given value updating embedded document;