I have a collection with documents like:
{
"_id": "Mongo ObjectID",
"some_prop": "some_value",
"features": [
{ "name": "A", "icon": "01.png" },
{ "name": "B", "icon": "02.png" }
]
}
Another document sample:
{
"_id": "Mongo ObjectID",
"some_prop": "other one",
"features": [
{ "name": "B", "icon": "02.png" },
{ "name": "C", "icon": "03.png" },
{ "name": "D", "icon": "04.png" }
]
}
Notice that in the first document and the second there is the same feature B. This occurs all over many documents.
What I need is to update all features B to a new icon, something like this:
{ "name": "B", "icon": "10.png" }
I need to apply this change for all documents that has a feature with name B.
I already did a very horrible code to get all documents and update one by one in a loop. But my guess is there is a better way to do it, maybe in a single collection.update command? I'm new in MongoDB and so far googling didnt work.
You need to use $positional operator to update the fields inside an array
db.collection.updateMany(
{ "features.name": "B" },
{ "$set": { "features.$.icon": "10.png" }}
)
Let's imagine a mongo collection of - let's say magazines. For some reason, we've ended up storing each issue of the magazine as a separate document. Each article is a subdocument inside an Articles-array, and the authors of each article is represented as a subdocument inside the Writers-array on the Article-subdocument. Only the name and email of the author is stored inside the article, but there is an Writers-array on the magazine level containing more information about each author.
{
"Title": "The Magazine",
"Articles": [
{
"Title": "Mongo Queries 101",
"Summary": ".....",
"Writers": [
{
"Name": "tom",
"Email": "tom#example.com"
},
{
"Name": "anna",
"Email": "anna#example.com"
}
]
},
{
"Title": "Why not SQL instead?",
"Summary": ".....",
"Writers": [
{
"Name": "mike",
"Email": "mike#example.com"
},
{
"Name": "anna",
"Email": "anna#example.com"
}
]
}
],
"Writers": [
{
"Name": "tom",
"Email": "tom#example.com",
"Web": "tom.example.com"
},
{
"Name": "mike",
"Email": "mike#example.com",
"Web": "mike.example.com"
},
{
"Name": "anna",
"Email": "anna#example.com",
"Web": "anna.example.com"
}
]
}
How can one author be completely removed from a magazines?
Finding magazines where the unwanted author exist is quite easy. The problem is pulling the author out of all the sub documents.
MongoDB 3.6 introduces some new placeholder operators, $[] and $[<identity>], and I suspect these could be used with either $pull or $pullAll, but so far, I haven't had any success.
Is it possible to do this in one go? Or at least no more than two? One query for removing the author from all the articles, and one for removing the biography from the magazine?
You can try below query.
db.col.update(
{},
{"$pull":{
"Articles.$[].Writers":{"Name": "tom","Email": "tom#example.com"},
"Writers":{"Name": "tom","Email": "tom#example.com"}
}},
{"multi":true}
);
I have two collections
1)User -> With fields,
name as String
emailId as String
2)
Rating -> With fields,
`userId as String.` (This will be the ID of the user and Foreign Key as per SQL)
comment as String`
I have created a record for the user which looks like
{
"_id": {
"$oid": "565fe1294a27a93449751a9a"
},
"name": "Some name",
"email": "somemail#gmail.com",
"createdAt": {
"$date": "2015-12-03T06:28:57.904Z"
},
"updatedAt": {
"$date": "2015-12-03T06:28:57.904Z"
}
}
I have create a record for the Rating which looks like
{
"_id": {
"$oid": "565fefa30878764428d96be1"
},
"userId": "565fe1294a27a93449751a9a",
"comment": "just a test comment",
"createdAt": {
"$date": "2015-12-03T07:30:43.409Z"
},
"updatedAt": {
"$date": "2015-12-03T07:30:43.409Z"
}
}
Now I want to make a query where all the rating done by the user along with the user document are returned.
If I make a query like
db.user.find({
"userId" :"565fe1294a27a93449751a9a"
})
I get the result like
{
"id": "565fefa30878764428d96be1",
"userId": "565fe1294a27a93449751a9a",
"comment": "just a test comment"
}
But I want the user object as well in it something like.
{
"id": "565fefa30878764428d96be1",
"user": { "name": "Some name",
"email": "somemail#gmail.com",
"id": "565fe1294a27a93449751a9a"
},
"comment": "just a test comment"
}
Or even something like this will work as well
"rating": {
"id": "565fefa30878764428d96be1",
"userId": "565fe1294a27a93449751a9a",
"comment": "just a test comment"
},
"user": {
"id": "565fefa30878764428d96be1",
"userId": "565fe1294a27a93449751a9a",
"comment": "just a test comment"
}
Here you need to change schema. You need to change user type from string to reference. If you add reference of User in rating, then that will be easy. If you add reference of User schema then you can populate user on rating. Then will get user info with rating.
example of reference :
User: {type: mongoose.Schema.ObjectId, ref: 'User'}
Found my exact solution here. http://sailsjs.org/documentation/concepts/models-and-orm/associations/one-to-many
Technically it creates two queries in the backend. Found this out by the response time.
But anyways it solves my problem.
How do I add/attach tags while creating/updating user stories in Rally? I'm using the below JSON script and I'm getting a error "cannot find referenced object". What am I missing?
{
"HierarchicalRequirement": {
"Description": "As a developer to create a user story",
"Name": "User story to be created",
"Notes": "Created via REST Client",
"Project": {
"_ref": "https://rally1.rallydev.com/slm/webservice/v2.0/project/1302421049",
"_refObjectName": "Sample Project",
"_type": "Project"
},
"Tags": {
"_type": "Tag",
"_tagsNameArray": [{
"Name": "My Tag"
}],
"Count": 1
}
}
}
Thanks in advance,
Leo.
You just need to encode your tags a little differently and you'll have it. Tags (and all collections) expect to be specified as an array of objects with _ref properties like so:
"Tags": [
{
"_ref": "/tag/12345"
},
{
"_ref": "/tag/23456"
}
]
I'm in the process of developing Route Tracking/Optimization software for my refuse collection company and would like some feedback on my current data structure/situation.
Here is a simplified version of my MongoDB structure:
Database: data
Collections:
“customers” - data collection containing all customer data.
[
{
"cust_id": "1001",
"name": "Customer 1",
"address": "123 Fake St",
"city": "Boston"
},
{
"cust_id": "1002",
"name": "Customer 2",
"address": "123 Real St",
"city": "Boston"
},
{
"cust_id": "1003",
"name": "Customer 3",
"address": "12 Elm St",
"city": "Boston"
},
{
"cust_id": "1004",
"name": "Customer 4",
"address": "16 Union St",
"city": "Boston"
},
{
"cust_id": "1005",
"name": "Customer 5",
"address": "13 Massachusetts Ave",
"city": "Boston"
}, { ... }, { ... }, ...
]
“trucks” - data collection containing all truck data.
[
{
"truckid": "21",
"type": "Refuse",
"year": "2011",
"make": "Mack",
"model": "TerraPro Cabover",
"body": "Mcneilus Rear Loader XC",
"capacity": "25 cubic yards"
},
{
"truckid": "22",
"type": "Refuse",
"year": "2009",
"make": "Mack",
"model": "TerraPro Cabover",
"body": "Mcneilus Rear Loader XC",
"capacity": "25 cubic yards"
},
{
"truckid": "12",
"type": "Dump",
"year": "2006",
"make": "Chevrolet",
"model": "C3500 HD",
"body": "Rugby Hydraulic Dump",
"capacity": "15 cubic yards"
}
]
“drivers” - data collection containing all driver data.
[
{
"driverid": "1234",
"name": "John Doe"
},
{
"driverid": "4321",
"name": "Jack Smith"
},
{
"driverid": "3421",
"name": "Don Johnson"
}
]
“route-lists” - data collection containing all predetermined route lists.
[
{
"route_name": "monday_1",
"day": "monday",
"truck": "21",
"stops": [
{
"cust_id": "1001"
},
{
"cust_id": "1010"
},
{
"cust_id": "1002"
}
]
},
{
"route_name": "friday_1",
"day": "friday",
"truck": "12",
"stops": [
{
"cust_id": "1003"
},
{
"cust_id": "1004"
},
{
"cust_id": "1012"
}
]
}
]
"routes" - data collections containing data for all active and completed routes.
[
{
"routeid": "1",
"route_name": "monday1",
"start_time": "04:31 AM",
"status": "active",
"stops": [
{
"customerid": "1001",
"status": "complete",
"start_time": "04:45 AM",
"finish_time": "04:48 AM",
"elapsed_time": "3"
},
{
"customerid": "1010",
"status": "complete",
"start_time": "04:50 AM",
"finish_time": "04:52 AM",
"elapsed_time": "2"
},
{
"customerid": "1002",
"status": "incomplete",
"start_time": "",
"finish_time": "",
"elapsed_time": ""
},
{
"customerid": "1005",
"status": "incomplete",
"start_time": "",
"finish_time": "",
"elapsed_time": ""
}
]
}
]
Here is the process thus far:
Each day drivers begin by Starting a New Route. Before starting a new route drivers must first input data:
driverid
date
truck
Once all data is entered correctly the Start a New Route will begin:
Create new object in collection “routes”
Query collection “route-lists” for “day” + “truck” match and return "stops"
Insert “route-lists” data into “routes” collection
As driver proceeds with his daily stops/tasks the “routes” collection will update accordingly.
On completion of all tasks the driver will then have the ability to Complete the Route Process by simply changing “status” field to “active” from “complete” in the "routes" collection.
That about sums it up. Any feedback, opinions, comments, links, optimization tactics are greatly appreciated.
Thanks in advance for your time.
You database schema looks like for me as 'classic' relational database schema. Mongodb good fit for data denormaliztion. I guess when you display routes you loading all related customers, driver, truck.
If you want make your system really fast you may embedd everything in route collection.
So i suggest following modifications of your schema:
customers - as-is
trucks - as-is
drivers - as-is
route-list:
Embedd data about customers inside stops instead of reference. Also embedd truck. In this case schema will be:
{
"route_name": "monday_1",
"day": "monday",
"truck": {
_id = 1,
// here will be all truck data
},
"stops": [{
"customer": {
_id = 1,
//here will be all customer data
}
}, {
"customer": {
_id = 2,
//here will be all customer data
}
}]
}
routes:
When driver starting new route copy route from route-list and in addition embedd driver information:
{
//copy all route-list data (just make new id for the current route and leave reference to routes-list. In this case you will able to sync route with route-list.)
"_id": "1",
route_list_id: 1,
"start_time": "04:31 AM",
"status": "active",
driver: {
//embedd all driver data here
},
"stops": [{
"customer": {
//all customer data
},
"status": "complete",
"start_time": "04:45 AM",
"finish_time": "04:48 AM",
"elapsed_time": "3"
}]
}
I guess you asking yourself what do if driver, customer or other denormalized data changed in main collection. Yeah, you need update all denormalized data within other collections. You will probably need update billions of documents (depends on your system size) and it's okay. You can do it async if it will take much time.
What benfits in above data structure?
Each document contains all data that you may need to display in your application. So, for instance, you no need load related customers, driver, truck when you need display routes.
You can make any difficult queries to your database. For example in your schema you can build query that will return all routes thats contains stops in stop of customer with name = "Bill" (you need load customer by name first, get id, and look by customer id in your current schema).
Probably you asking yourself that your data can be unsynchronized in some cases, but to solve this you just need build a few unit test to ensure that you update your denormolized data correctly.
Hope above will help you to see the world from not relational side, from document database point of view.