I like using MongoDB but can't quite swallow the non-relational aspect of it. As far as I can tell from mongo users and the docs: "It's fine, just duplicate parts of your data".
As I'm worried about scaling, and basically just not remembering to update parts of the code to update the correct parts of the data, it seems like a good trade-off to just do an extra query when my API has to return the data for a user with a summary of posts included:
{
"id": 1,
"name": "Default user",
"posts_summary": [
{
"id": 1,
"name": "I am making a blog post",
"description": "I write about some stuff and there are comments after it",
"tags_count": 3
},
{
"id": 2,
"name": "This is my second post",
"description": "In this one I write some more stuff",
"tags_count": 4
}
]
}
...when the posts data looks like this below:
//db.posts
{
"id": 1,
"owner": 1,
"name": "I am making a blog post",
"description": "I write about some stuff and there are comments after it",
"tags": ["Writing", "Blogs", "Stuff"]
},
{
"id": 2,
"owner": 1,
"name": "This is my second post",
"description": "In this one I write some mores tuff",
"tags": ["Writing", "Blogs", "Stuff", "Whatever"]
}
So behind the API, when the query to get the user succeeds, I am doing an additional query to the posts collection to get the "posts_summary" data I need, and adding it in before the API sends response.
It seems like a good trade-off considering the problems it will solve later. Is this what some mongo users do to get around it not being relational, or have I made a mistake when designing my schema?
You can use schema objects as references to implement relational mapping using mongoose
http://mongoosejs.com/docs/populate.html
using mongoose ur schema would be like:
User:Schema({
_id : Number,
name : String,
owner : String,
Post : [{ type: Schema.Types.ObjectId, ref: 'Post' }]
});
Post:Schema({
_id : Number,
name : String,
owner : String,
description : String,
tags:[String]
})
Related
I am stuck and need help to get data from another collection based array element from another collection.
My collections are as --
programs.dbs --
{
"_id": ObjectId("61c8f42ec63e700b415b4bed"),
"name": "Java",
"description": "this is a dummy information",
"instructor": ["61c8f6d7e690fc413a075e15", "61c8f6d7e690fc413a071e15", "61c8f0d7e690fc413a071e15"]
}
instructor.dbs --
{
"_id": ObjectId("61c8f6d7e690fc413a075e15"),
"name": "Instrctor1",
"description": "this is a dummy Instructor",
},
{
"_id": ObjectId("61c8f6d7e690fc413a071e15"),
"name": "Instrctor2",
"description": "this is a dummy Instructor",
},
{
"_id": ObjectId("61c8f0d7e690fc413a071e15"),
"name": "Instrctor3",
"description": "this is a dummy Instructor",
}
My query is to find one instructor information based on collection A instructor Array element.
I have tried to use this query but getting empty result -
db.getCollection('programs.dbs').aggregate([{$lookup:{from:'instructor.dbs',localField:'instructor',foreignField:"_id",as:'instructors'}}])
Immediate help will be great.
if you are using mongoose maybe the populate method can work.
Your array of instructors should not reference the IDs as strings but as ObjectId(...)
So you would need to have:
"instructor": [ObjectId("61c8f6d7e690fc413a075e15"), ObjectId("61c8f6d7e690fc413a071e15"), ObjectId("61c8f0d7e690fc413a071e15")]
I am trying to query over populated children attributes using mongoose but it straight up doesn't work and will return empty arrays all the time.
even hardcoding right and existing information as values for the query would return empty arrays.
my schema is a business schema with a 1 to 1 relationship with user schema via the attribute createdBy. the user schema has an attribute name which I am trying to query on.
so if I make a query like this :
business.find({'createdBy.name': {$regex:"steve"}}).populate('createdBy')
the above will never return any documents. although, without the find condition, everything works fine.
Can I search by the name inside a populated child or not? all tutorials say this should work fine but it just doesn't.
EDIT : an example of what the record looks like :
{
"_id": "5fddedd00e8a7e069085964f",
"status": 6,
"addInfo": "",
"descProduit": "",
"createdBy": {
"_id": "5f99b1bea9ba194dec3bd6aa",
"status": 1,
"fcmtokens": [
],
"emailVerified": 1,
"phoneVerified": 0,
"userType": "User",
"name": "steve buschemi",
"firstName": "steve",
"lastName": "buschemi",
"tel": "",
"email": "steve#buschemi.com",
"register_token": "747f1e1e8fa1ecd2f1797bb402563198",
"createdAt": "2020-10-28T18:00:30.814Z",
"updatedAt": "2020-12-18T13:52:07.430Z",
"__v": 19,
"business": "5f99b1e101bfff39a8259457",
"credit": 635,
},
"createdAt": "2020-12-19T12:10:57.703Z",
"updatedAt": "2020-12-19T12:11:16.538Z",
"__v": 0,
"nid": "187"
}
It seems there is no way to filter parent documents by conditions on child documents:
From the official documentation:
In general, there is no way to make populate() filter stories based on properties of the story's author. For example, the below query won't return any results, even though author is populated.
const story = await Story.
findOne({ 'author.name': 'Ian Fleming' }).
populate('author').
exec();
story; // null
If you want to filter stories by their author's name, you should use denormalization.
Thank you for your help.
I am scratching my head all day, I don't know I am in the right direction or not.
Problem :
I have a document [Doctor] which contains the reference [doctorSpecialities].
I have to GET ALL DOCTORS who have this id in there doctorSpecialities reference Array
Id : 5ef58dd048cdd203a0c07ba8
JSON Structure
{
"doctorSpecialities": [
"5f00cebc8bcdcd0660c12ce2",
"5ef58dd048cdd203a0c07ba8"
]
"_id": "5ef31ae80399ac05eb23e555",
"email": "signup#gmail.com",
"username": "signup#gmail.com",
"DOB": null,
"zip": null,
"phone": "12657334566",
"PMDC": "7658493",
"isVerified": false,
"aboutMe": "About Me",
"achievements": "Achievements",
"address": "padasdad",
"city": "Lahore",
"gender": "Male",
"managePractice": "Manage Practice",
"practiceGrowth": "Practice Growth",
"qualiflication": "Qualifcation",
"state": "eeeeeeee",
"workExperince": "Work Experince",
"doctorAvailability": [],
"doctorReviews": [],
"degreeCompletionYear": "2019-10-10",
"institute": "institute",
"practiceDate": "2020-10-10",
"services": "Dental"
},
Query tried
await Doctor.find({ doctorSpecialities : req.params.id})
await Doctor.find({ doctorSpecialities :{$in [ req.params.id}})
Specialty Collection
doctorCollection = Doctor.find();
doctorCollection.find({"doctorSpecialities": specialty.id})
This is how I did is it wrong?
I tried to user $Lookup but I don't know how to use it in this requirement
Please let me know if you need more information.
Thank you
If you have to get doctors details then you can use
db.collection.find({"doctorSpecialities":"5ef58dd048cdd203a0c07ba8"})
play
It returns all documents where doctorSpecialities field contains 5ef58dd048cdd203a0c07ba8
I am creating an blogging application in Node.js + MongoDB Database. I have used relational Database like MySQL before but this is my first experience with NoSQL database. So I would like to conform my MongoDB data models before I move further.
I have decided my blogDB to have 3 collections
post_collection - stores information about that article
comment_collection - store information about comments on articles
user_info_collection - contains user inforamtion
PostDB
{
_"id" : ObjectID(...),
"author": "author_name",
"Date": new Date(....),
"tag" : ["politics" , "war"],
"post_title": "My first Article",
"post_content": "Big big article"
"likes": 23
"access": "public"
}
CommentDB
{
"_id" : Objectid(...),
"POST": "My First Article",
"comment_by": "User_name",
"comment": "MY comments"
}
UserInfoDB
{
"_id": ObjectID(...),
"user": "User_name",
"password": "My_password"
}
I would appreciate your comments.
In your place, I would embed the Comments collection into the Posts collection.
The disadvantage of this is if you have many many comments you could reach the limit of 16MB.
The advantage is that you prejoined the data, so the query will be faster. You won't look for a comment without its post anyway.
Another thing is that you could make the "user" field the ID of the UserInfo collection. That way you will have a reference to a unique user in Comments.
I'm trying to model document a hierarchy in CouchDB to use in my system, which is conceptually similar to a blog. Each blog post belongs to at least one category and each category can have many posts. Categories are hierarchical, meaning that if a post belongs to CatB in the hierarchy "CatA->CatB" ("CatB is in CatA)", it belongs also to CatA.
Users must be able to quickly find all post in a category (and all its children).
Solution 1
Each document of the post type contains a "category" array representing its position in the hierarchy (see 2).
{
"_id": "8e7a440862347a22f4a1b2ca7f000e83",
"type": "post",
"author": "dexter",
"title": "Hello",
"category":["OO","Programming","C++"]
}
Solution 2
Each document of the post type contains the "category" string representing its path in the hierarchy (see 4).
{
"_id": "8e7a440862347a22f4a1b2ca7f000e83",
"type": "post",
"author": "dexter",
"title": "Hello",
"category": "OO/Programming/C++"
}
Solution 3
Each document of the post type contains its parent "category" id representing its path in the hierarchy (see 3). A hierarchical category structure is built through linked "category" document types.
{
"_id": "8e7a440862347a22f4a1b2ca7f000e83",
"type": "post",
"author": "dexter",
"title": "Hello",
"category_id": "3"
}
{
"_id": "1",
"type": "category",
"name": "OO"
}
{
"_id": "2",
"type": "category",
"name": "Programming",
"parent": "1"
}
{
"_id": "3",
"type": "category",
"name": "C++",
"parent": "2"
}
Question
What's the best way to store this kind of relationship in CouchDB? What's the most efficient solution in terms of disk space, scalability and retrieval speed?
Can such a relation be modelled to take into account localised category names?
Disclaimer
I know this question has been asked a few times already here on SO, but it seems there's no definitive answer to it nor an answer which deals with the pros and cons of each solution. Sorry for the length of the question :)
Read so far
CouchDB - The Definitive Guide
Storing Hierarchical Data in CouchDB
Retrieving Hierarchical/Nested Data From CouchDB
Using CouchDB group_level for hierarchical data
There's no right answer to this question, hence the lack of a definitive answer. It mostly depends on what kind of usage you want to optimize for.
You state that retrieval speed of documents that belong to a certain category (and their children) is most important. The first two solutions allow you to create a view that emits a blog post multiple times, once for each category in the chain from the leaf to the root. Thus selecting all documents can be done using a single (and thus fast) query. The only difference of second solution to first solution is that you move the parsing of the category "path" into components from the code that inserts the document to the map function of the view. I would prefer the first solution as it's simpler to implement the map function and a bit more flexible (e.g. it allows a category's name to contain a slash character).
In your scenario you probably also want to create a reduced view which counts the number of blog posts for each category. This is very simple with either of these solutions. With a fitting reduction function, the number of post in every category can be retrieved using a single request.
A downside of the first two solutions is that renaming or moving a category from one parent to another requires every document to be updated. The third solution allows that without touching the documents. But from the description of your scenario I assume that retrieval by category is very frequent and category renaming/moving is very rare.
Solution 4 I propose a fourth solution where blog post documents hold references to category documents but still reference all the ancestors of the post's category. This allows categories to be renamed without touching the blog posts and allows you to store additional metadata with a category (e.g. translations of the category name or a description):
{
"_id": "8e7a440862347a22f4a1b2ca7f000e83",
"type": "post",
"author": "dexter",
"title": "Hello",
"category_ids": [3, 2, 1]
}
{
"_id": "1",
"type": "category",
"name": "OO"
}
{
"_id": "2",
"type": "category",
"name": "Programming",
"parent": "1"
}
{
"_id": "3",
"type": "category",
"name": "C++",
"parent": "2"
}
You will still have to store the parents of categories with the categories, which is duplicating data in the posts, to allow categories to be traversed (e.g. for displaying a tree of categories for navigation).
You can extend this solution or any of your solutions to allow a post to be categorized under multiple categories, or a category to have multiple parents. When a post is categorized in multiple categories, you will need to store the union of the ancestors of each category in the post's document while preserving the categories selected by the author to allow them to be displayed with the post or edited later.
Lets assume that there is an additional category named "Ajax" with anchestors "JavaScript", "Programming" and "OO". To simplify the following example, I've chosen the document IDs of the categories to equal the category's name.
{
"_id": "8e7a440862347a22f4a1b2ca7f000e83",
"type": "post",
"author": "dexter",
"title": "Hello",
"category_ids": ["C++", "Ajax"],
"category_anchestor_ids": ["C++", "Programming", "OO", "Ajax", "JavaScript"]
}
To allow a category to have multiple parents, just store multiple parent IDs with a category. You will need to eliminate duplicates while finding all the ancestors of a category.
View for Solution 4 Suppose you want to get all the blog posts for a specific category. We will use a database with the following sample data:
{ "_id": "100", "type": "category", "name": "OO" }
{ "_id": "101", "type": "category", "name": "Programming", "parent_id": "100" }
{ "_id": "102", "type": "category", "name": "C++", "parent_id": "101" }
{ "_id": "103", "type": "category", "name": "JavaScript", "parent_id": "101" }
{ "_id": "104", "type": "category", "name": "AJAX", "parent_id": "103" }
{ "_id": "200", "type": "post", "title": "OO Post", "category_id": "104", "category_anchestor_ids": ["100"] }
{ "_id": "201", "type": "post", "title": "Programming Post", "category_id": "101", "category_anchestor_ids": ["101", "100"] }
{ "_id": "202", "type": "post", "title": "C++ Post", "category_id": "102", "category_anchestor_ids": ["102", "101", "100"] }
{ "_id": "203", "type": "post", "title": "AJAX Post", "category_id": "104", "category_anchestor_ids": ["104", "103", "101", "100"] }
In addition to that, we use a view called posts_by_category in a design document called _design/blog with the the following map function:
function (doc) {
if (doc.type == 'post') {
for (i in doc.category_anchestor_ids) {
emit([doc.category_anchestor_ids[i]], doc)
}
}
}
Then we can get all the posts in the Programming category (which has ID "101") or one of it's subcategories using a GET requests to the following URL.
http://localhost:5984/so/_design/blog/_view/posts_by_category?reduce=false&key=["101"]
This will return a view result with the keys set to the category ID and the values set to the post documents. The same view can also be used to get a summary list of all categories and the number of post in that category and it's children. We add the following reduce function to the view:
function (keys, values, rereduce) {
if (rereduce) {
return sum(values)
} else {
return values.length
}
}
And then we use the following URL:
http://localhost:5984/so/_design/blog/_view/posts_by_category?group_level=1
This will return a reduced view result with the keys again set to the category ID and the values set to the number of posts in each category. In this example, the categories name's would have to be fetched separately but it is possible to create view where each row in the reduced view result already contains the category name.