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Search by ObjectId in mongodb with PyMongo – Python

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  • Last Updated : 10 Jun, 2022
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MongoDB is NoSQL Document-oriented database. In MongoDB, the data are stored in a JSON-like document structure instead of storing data in columns and rows manner. MongoDB is flexible for storing semi-structured and unstructured data. MongoDB is used for high-volume data storage and can scale horizontally.

ObjectId: ObjectId class is a 12-byte binary BSON type, in which 4 bytes of the timestamp of creation, 5 bytes of a random value, and 3 bytes of incrementing counter. ObjectId is the default primary key for MongoDB documents and is usually found in “_id” field.

eg : { "_id" : ObjectId("54759eb3c090d83494e2d804") }

PyMongo: PyMongo is a native Python driver for MongoDB. It allows interaction with MongoDB Database through Python.

Step 1: Make sure to start the MongoDB database locally on the default port (27017). Connecting to MongoDB, accessing the database, and collecting objects.

Python3




# importing MongoClient from pymongo
from pymongo import MongoClient
 
# importing ObjectId from bson library
from bson.objectid import ObjectId
 
# Establishing connection with
# mongodb on localhost
client = MongoClient('127.0.0.1', 27017)
 
# Access database object
db = client['database']
 
# Access collection object
collection = db['collection']

Step 2: Querying MongoDB with find_one().

Syntax:

find_one(filter=None, *args, **kwargs)
filter(optional): query filter that selects which documents should be included in the result set.
*args (optional): any additional positional arguments
*kwargs (optional): any additional keyword arguments

Example 1: In this example, we have already stored the ObjectId object instance, then use the stored object instance to search for the document.

Python3




# inserts data into collection and
# returns an object of type objectId
objInstance = collection.insert_one({"name": "sam", "age": 20}).inserted_id
 
# search MongoDB with
# an object of type objectId
collection.find_one(objInstance)

Output:

Example 2: In this example, we have the hex string not an object of ObjectId. We import ObjectId from the bson library to search the document.

Python3




# use the string object id
# and objectId object to
# create object of type ObjectId
id = "5fec2c0b348df9f22156cc07"
objInstance = ObjectId(id)
 
collection.find_one({"_id": objInstance})
 
# below line works same as the above
collection.find_one({"_id": ObjectId(id)})
collection.find_one(ObjectId(id))

Output:

Example 3: In this example, we advance the search by specifying projections that is which field we want to include/exclude in the result set. The below projection will return the entire document without the “_id” field.

Python3




# search document with filter
# first parameter is search query
# second parameter is filter query
# you can as many fields you want
# you can specify either 1 or 0
# in filter fields
# if you specify 0 the field will
# be eliminated form the result
query = {"_id": ObjectId(id)}
 
filter = {"_id": 0}
 
collection.find_one(query, filter)

Output:


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