validation
from dotenv import load_dotenv , find_dotenv
import os
import pprint
from pymongo import MongoClient,errors
from bson.objectid import ObjectId
from datetime import datetime
load_dotenv(find_dotenv())
password = os.environ.get("MONGODB_PWD")
connection_string = f"mongodb+srv://blueaves15:{password}@trainingclustor.nl44poy.mongodb.net/?retryWrites=true&w=majority&authSource=admin"
client = MongoClient(connection_string)
production = client.production
def create_book_collection():
book_validator = {
"$jsonSchema": {
"bsonType": "object",
# "title": "Book Object Validation",
"required": [ "title", "authors", "publish_date", "type", "copies" ],
"properties": {
"title": {
"bsonType": "string",
"description": "must be a string and is required"
},
"authors": {
"bsonType": "array",
"items":{
"bsonType": "objectId",
"description": "must be a objectId and is required"
}
},
"publish_date": {
"bsonType": "date",
"description": "must be a string and is required"
},
"type": {
"enum": ["Fiction", "Non-Fiction"],
"description": "must be a enum and is required"
},
"copies": {
"bsonType": "int",
"minimum": 0,
"description": "must be a integer grater than 0 and is required"
},
}
}
}
try:
production.create_collection("book")
except errors.CollectionInvalid as er:
print(er)
except errors.OperationFailure as OP:
print(OP)
production.command("collMod", "book", validator = book_validator)
create_book_collection()
def create_author_collection():
author_validator = {
"$jsonSchema": {
"bsonType": "object",
"title": "author Object Validation",
"required": [ "first_name", "last_name", "date_of_birth" ],
"properties": {
"first_name": {
"bsonType": "string",
"description": "must be a string and is required"
},
"last_name": {
"bsonType": "string",
"description": "must be a string and is required"
},
"date_of_birth": {
"bsonType": "date",
"description": "must be a date and is required"
},
}}
}
try:
production.create_collection("author")
except errors.CollectionInvalid as er:
print(er)
except errors.OperationFailure as OP:
print(OP)
production.command("collMod", "author", validator = author_validator)
# create_author_collection()
def create_data():
authors = [
{
"first_name":"ashish",
"last_name": "bindra",
"date_of_birth": datetime(1998,7,20)
}, {
"first_name":"sonu",
"last_name": "bindra",
"date_of_birth": datetime(1995,7,10)
}, {
"first_name":"sershti",
"last_name": "bindra",
"date_of_birth": datetime(1996,7,2)
},
{
"first_name":"sapna",
"last_name": "bindra",
"date_of_birth": datetime(1850,2,20)
}, {
"first_name":"sarvesh",
"last_name": "bindra",
"date_of_birth": datetime(1940,3,20)
},
]
author_collection = production.author
authors_ids = author_collection.insert_many(authors).inserted_ids
print(authors_ids)
books = [
{
"title": "MongoDB Advance",
"authors": [authors_ids[0]],
"publish_date": datetime.today(),
"type": "Non-Fiction",
"copies":5
}, {
"title": "python Advance",
"authors": [authors_ids[1]],
"publish_date": datetime.today(),
"type": "Non-Fiction",
"copies":5
},
# {
# "title": "Java Advance",
# "authors": [authors_ids[3]],
# "publish_date": datetime.today(),
# "type": "Non-Fiction",
# "copies":5
# },
{
"title": "Ninteen Advance",
"authors": [authors_ids[4]],
"publish_date": datetime(1949,3,5),
"type": "Fiction",
"copies":5
}, {
"title": "pandas Advance",
"authors": [authors_ids[1]],
"publish_date": datetime.today(),
"type": "Fiction",
"copies":5
}, {
"title": "Marry",
"authors": [authors_ids[3]],
"publish_date": datetime(2012,2,14),
"type": "Fiction",
"copies":5
},
{
"title": "Moby Dick",
"authors": [authors_ids[2]],
"publish_date": datetime(1800,9,23),
"type": "Fiction",
"copies":5
},
]
book_collection = production.book
book_collection.insert_many(books)
# create_data()
printer = pprint.PrettyPrinter()
# book_containing_a = production.book.find({"title": {"$regex": "a{1}"}}) #atleast one a matched
# printer.pprint(list(book_containing_a))
# authors_and_books = production.author.aggregate([{
# "$lookup": {
# "from": "book",
# "localField":"_id",
# "foreignField": "authors",
# "as":"boos"
# }
# }])
# printer.pprint(list(authors_and_books))
authors_and_books_count = production.author.aggregate([
{
"$lookup": {
"from": "book",
"localField":"_id",
"foreignField": "authors",
"as":"books"
},
},
{
"$addFields": {
"total_books": {"$size": "$books"}
}
},
{
"$project": {"first_name":1, "last_name": 1,"total_books":1, "_id":0} # should be in last
}
])
# printer.pprint(list(authors_and_books_count))
# find age of the author with in 50 to 150 years
authors_and_books = production.book.aggregate([{
"$lookup": {
"from": "author",
"localField":"authors",
"foreignField": "_id",
"as":"authors"
}
} ,
{
"$set":{
"authors": {
"$map":{
"input":"$authors",
"in":{
"age": {
"$dateDiff":{
"startDate": "$$this.date_of_birth",
"endDate": "$$NOW",
"unit": "year"
}
},
"first_name": "$$this.first_name",
"last_name": "$$this.last_name",
}
}
}
}
}
,{
"$match":{
"$and":[
{"authors.age": {"$gte":50}},
{"authors.age": {"$lte":150}},
]
}
},{
"$sort":{
"age": 1
}
}
])
printer.pprint(list(authors_and_books))
from dotenv import load_dotenv , find_dotenv
import os
import pprint
from pymongo import MongoClient
from bson.objectid import ObjectId
load_dotenv(find_dotenv())
password = os.environ.get("MONGODB_PWD")
connection_string = f"mongodb+srv://blueaves15:{password}@trainingclustor.nl44poy.mongodb.net/?retryWrites=true&w=majority"
client = MongoClient(connection_string)
dbs = client.list_database_names()
print("list od dbs",dbs)
test_db = client.test
collections = test_db.list_collection_names()
print("list of collections",collections)
def insert_test_doc():
"""
get test collection from test database
"""
collection = test_db.test_db
test_document = {
"name": "ashish",
"type": "Test",
}
inserted_id = collection.insert_one(test_document).inserted_id
print(inserted_id) # Bson Object ID
# insert_test_doc()
production = client.production
person_collection = production.person_collection
def create_document():
first_names = ["sunny","bunny","cunny","vunny","munny"]
last_names = ["imaggnry","imaggnry","imaggnry","imaggnry","imaggnry"]
ages = [11,22,33,44,55]
docs = []
for first_name, last_name, age in zip(first_names, last_names, ages):
doc = {"first_name": first_name,
"last_name": last_name,
"age":age}
docs.append(doc)
# persion_collection.insert_one(doc)
person_collection.insert_many(docs)
# create_document()
printer = pprint.PrettyPrinter()
def find_all_people():
people = person_collection.find()
for person in people:
# print(person)
printer.pprint(person)
# find_all_people()
def find_name(name):
name_is = person_collection.find_one({"first_name":name})
printer.pprint(name_is)
# find_name("cunny")
def count_people():
count = person_collection.count_documents(filter={})
# or
# count = person_collection.find().count() # number of element return by the find , we hav not to all the element ,
# count() is debrecated
print("Number of people",count)
# count_people()
def get_person_by_id(persion_id):
_id = ObjectId(persion_id)
persion = person_collection.find_one({"_id": _id})
printer.pprint(persion)
# get_person_by_id("65c88c0529d602cdfab899cf")
def get_age_range(min_age, max_age):
query = {
"$and": [
{"age": {"$gte": min_age}},
{"age": {"$lte": max_age}}
]
}
people = person_collection.find(query).sort("age")
for person in people:
printer.pprint(person)
# get_age_range(20,35)
def project_columns():
columns = {"_id":0, "first_name": 1, "last_name": 1}
people = person_collection.find({}, columns)
for person in people:
printer.pprint(person)
# project_columns()
def update_person_by_id(persion_id):
_id = ObjectId(persion_id)
all_updates = {
"$set": {"new_field": True}, # if field currently exisited we would orride that field
"$inc": {"age": 1},
"$rename": {"first_name": "first", "last_name": "last"}
}
# person_collection.update_one({"_id": _id}, all_updates)
person_collection.update_one({"_id": _id}, {"$unset": {"new_field": ""}})
# update_person_by_id("65c88c0529d602cdfab899d2")
# print("update")
def replace_one(persion_id):
_id = ObjectId(persion_id)
new_doc = {
"first_name": "ashish",
"last_name": "bindra",
"age":100
}
person_collection.replace_one({"_id": _id}, new_doc)
# replace_one("65c88c0529d602cdfab899cf")
def delete_doc_by_id(persion_id):
_id = ObjectId(persion_id)
person_collection.delete_one({"_id": _id})
# delete_doc_by_id("65c88c0529d602cdfab899cf")
def add_address_embed(persion_id, address):
_id = ObjectId(persion_id)
person_collection.update_one(
{"_id": _id},{"$addToSet": {'addresses': address}}
)
address={
# "_id":"1",
"street":"13",
"number": 2456,
"city":"patiala",
"country": "india",
}
# add_address_embed("65c88c0529d602cdfab899d0",address=address)
def add_address_relationship(person_id, address):
_id = ObjectId(person_id)
address = address.copy()
address["owner_id"] = person_id
address_collection = production.address
address_collection.insert_one(address)
add_address_relationship("65c88c0529d602cdfab899d2",address)
- first install pymongo
pip install pymongo[srv] pip install python-dotenv
No sql database like mongodb actually stores unstructured data and typically in json format - Abiltiy to perform frequent changes to struce of the data - Highly Flexible () - Faster quiery - saclable
document model
database -> collections -> documents
Bson type satore
find({})
allow to insert a query object which is different properties that we are tyring to match when we are looking for elemnt or document. -if we live empty it just find all document in collection
a curser is sothing that we can iterate over
oprator
$set change the value of specific field create a new field to be a value, can use as overrite maily for new field - $inc => we can increament value - $rename => we can rename the field - $unset => to remove a field - $addToSet => add as an array - $addFields => to add a new field - $regex => regular expression - $lookup => left outter join - $projection => field to display - $this => referance of cuurent elemants - $dateDiff => to find the date differences - $map => to itrate the elements
- replace_one()-> to replace a document without replacing and id
schema validation
way to create a strucure in mongodb allow to insert what king of what type document we want
error
- 12632064:error:10000438:SSL routines:OPENSSL_internal:TLSV1_ALERT_INTERNAL_ERROR:....\third_party\boringssl\src\ssl\tls_record.cc:592:SSL alert number 80 --> solve using add ip address
- bson.errors.InvalidDocument: cannot encode object: datetime.date(2000, 7, 20), of type:
data modeling( relationships and embeded) - When we have one - to - one relationships is totaly fine to store as embedeed inside store - with multiple relationships we can store seprate coollection, if we chnge so we havent change in wvery documents
subset
it can be time consuming if we are grabing every single document for a specific document