I am currently looking into my first application using Firebase as the backend.
I have 2 models, School and User. Each user can sign up for a date to attend the school, so I also need a Date.
A SQL table would look like this:
schools: id, name
users: id, name, email
schools_users: id, school_id, user_id, date
What would be the proper way of designing this data structure in Firebase?
Since you don't specify any requirements, I suggest starting with the most naive mapping at first:
root
schools
1: "name of school1"
2: "name of school2"
users:
1: { "name": "Maeh", "email": "2523229#stackoverflow.com" }
2: { "name": "Frank", "email": "209103#stackoverflow.com" }
schools_users:
1_1: "20141031"
1_2: "20130102"
Related
I am trying to create a database for gym workout plans. This would have each member's workout plan, which comprises multiple exercises.
My question is: given that one member might have 5 exercises, another member 3, another 6, and so on, how can I store this in the database?
My thought process has been around hardcoding a set number of exercises in the table, and give the possibility of them being null (for exemple 10 exercises, but if you only use 5 exercises, everything else can be stored with nothing). But this doesn't feel very correct.
I am using MySQL for this.
Like #Sami pointed out, you are going to work on whats called a relational database.
Below is a simple database structure that will help you get started.
Members table
Table Name: tbl_members
Table Structure:
|member_id|member_name|member_contact|
Exercise or schedule table
Table Name: tbl_member_schedule
Table Structure:
|schedule_id|member_id|assigned_date|Schedule
In the tbl_member_schedule you can store the entire schedule as a JSON structure or you can further normalize. Since this is an example I am storing the entire schedule in JSON format inside the Schedule field.
Now when you want to retrieve the schedule of a specific member (member id: 1) you can query the database as follows:
select
tbl_members.member_id, tbl_members.member_name,
tbl_member_schedule.Schedule
from
tbl_members join
tbl_member_schedule on tbl_members.member_id = tbl_member_schedule.member_id
where
tbl_members.member_id = 1;
So you JSON structure for your schedule could be
{
day1: [
{exerciseName: 'dumbbell curls', reps: '8 x 4'},
{exerciseName: 'leg extension', reps: '8 x 3'}
],
day2: [
{exerciseName: 'dumbbell curls', reps: '8 x 4'},
{exerciseName: 'leg extension', reps: '8 x 3'}
]
}
I have a Postgres statement that returns extracts/iterates over a json blob in the value column of a table. I am able to get a count one level deep using the query below but I can't count any deeper. I was using:
select jsonb_array_length(value -> 'team') as team_count
This returns the proper count but I cant seem to leverage this to count the names under each team.
In a perfect world I would my results to return 4 lines of results like this(title and a matching count of names):
Product Owner, 2
Technical Product Manager, 2
Data Modeler, 0
Engineer, 0
How would I go about amending this query to give me the count of names under team? I tried all sorts of stuff but nothing that got me close.
Sample Json is below.
"team":[
{
"title":"Product Owner",
"names":[
"John Smith",
"Jane Doe"
]
},
{
"title":"Technical Project Manager",
"names":[
"Fred Flintstone",
"Barney Rubble"
]
},
{
"title":"Data Modeler"
},
{
"title":"Engineer"
}
You seem to be looking for
SELECT
role -> 'title' AS team_role,
jsonb_array_length(role -> 'names') AS member_count
FROM jsonb_array_elements(value -> 'team') AS team(role)
I am working on a simple app for Android. I am having some trouble using the Firebase database since it uses JSON objects and I am used to relational databases.
My data will consists of two users that share a value. In relational databases this would be represented in a table like this:
**uname1** **uname2** shared_value
In which the usernames are the keys. If I wanted the all the values user Bob shares with other users, I could do a simple union statement that would return the rows where:
uname1 == Bob or unname == Bob
However, in JSON databases, there seems to be a tree-like hierarchy in the data, which is complicated since I would not be able to search for users at the top level. I am looking for help in how to do this or how to structure my database for best efficiency if my most common search will be one similar to the one above.
In case this is not enough information, I will elaborate: My database would be structured like this:
{
'username': 'Bob'
{
'username2': 'Alice'
{
'shared_value' = 2
}
}
'username': 'Cece'
{
'username2': 'Bob'
{
'shared_value' = 4
}
}
As you can see from the example, Bob is included in two relationships, but looking into Bobs node doesn't show that information. (The relationship is commutative, so who is "first" cannot be predicted).
The most intuitive way to fix this would be duplicate all data. For example, when we add Bob->Alice->2, also add Alice->Bob->2. In my experience with relational databases, duplication could be a big problem, which is why I haven't done this already. Also, duplication seems like an inefficient fix.
Is there a reason why you don't invert this? How about a collection like:
{ "_id": 2, "usernames":[ "Bob", "Alice"]}
{ "_id": 4, "usernames":[ "Bob", "Cece"]}
If you need all the values for "Bob", then index on "usernames".
EDIT:
If you need the two usernames to be a unique key, then do something like this:
{ "_id": {"uname1":"Bob", "uname2":"Alice"}, "value": 2 }
But this would still permit the creation of:
{ "_id": {"uname1":"Alice", "uname2":"Bob"}, "value": 78 }
(This issue is also present in your as-is relational model, btw. How do you handle it there?)
In general, I think implementing an array by creating multiple columns with names like "attr1", "attr2", "attr3", etc. and then having to search them all for a possible value is an artifact of relational table modeling, which does not support array values. If you are converting to a document-oriented storage, these really should be an embedded list of values, and you should use the document paradigm and model them as such, instead of just reimplementing your table rows as documents.
You can still have old structure:
[
{ username: 'Bob', username2: 'Alice', value: 2 },
{ username: 'Cece', username2: 'Bob', value: 4 },
]
You may want to create indexes on 'username' and 'username2' for performance. And then just do the same union.
To create a tree-like structure, the best way is to create an "ancestors" array that stores all the ancestors of a particular entry. That way you can query for either ancestors or descendants and all documents that are related to a particular value in the tree. Using your example, you would be able to search for all descendants of Bob's, or any of his ancestors (and related documents).
The answer above suggest:
{ "_id": {"uname1":"Bob", "uname2":"Alice"}, "value": 2 }
That is correct. But you don't get to see the relationship between Bob and Cece with this design. My suggestion, which is from Mongo, is to store ancestor keys in an ancestor array.
{ "_id": {"uname1":"Bob", "uname2":"Alice"}, "value": 2 , "ancestors": [{uname: "Cece"}]}
With this design you still get duplicates, which is something that you do not want. I would design it like this:
{"username": "Bob", "ancestors": [{"username": "Cece", "shared_value": 4}]}
{"username": "Alice", "ancestors": [{"username": "Bob", "shared_value": 2}, {"username": "Cece"}]}
I'm fairly new to couchbase and have tried to find the answer to a particular query I'm trying to create with not much success so far.
I've debated between using a view or N1QL for this particular case and settled with N1QL but haven't managed to get it to work so maybe a view is better after all.
Basically I have the document key (Group_1) for the following document:
Group_1
{
"cbType": "group",
"ID": 1,
"Name": "Group Atlas 3",
"StoreList": [
2,
4,
6
]
}
I also have 'store' documents, their keys are listed in this document's storelist. (Store_2, Store_4, Store_6 and they have a storeID value that is 2, 4 and 6) I basically want to obtain all 3 documents listed.
What I do have that works is I obtain this document with its id by doing:
var result = CouchbaseManager.Bucket.Get<dynamic>(couchbaseKey);
mygroup = JsonConvert.DeserializeObject<Group> (result.ToString());
I can then loop through it's storelist and obtain all it's stores in the same manner, but i don't need anything else from the group, all i want are the stores and would have prefered to do this in a single operation.
Does anyone know how to do a N1QL directly unto a specified document value?
Something like (and this is total imaginary non working code I'm just trying to clearly illustrate what I'm trying to get at):
SELECT * FROM mycouchbase WHERE documentkey IN
Group_1.StoreList
Thanks
UPDATE:
So Nic's solution does not work;
This is the closest I get to what I need atm:
SELECT b from DataBoard c USE KEYS ["Group_X"] UNNEST c.StoreList b;
"results":[{"b":2},{"b":4},{"b":6}]
Which returns the list of IDs of the Stores I want for any given group (Group_X) - I haven't found a way to get the full Stores instead of just the ID in the same statement yet.
Once I have, I'll post the full solution as well as all the speed bumps I've encountered in the process.
I apologize if I have a misunderstanding of your question, but I'm going to give it my best shot. If I misunderstood, please let me know and we'll work from there.
Let's use the following scenario:
group_1
{
"cbType": "group",
"ID": 1,
"Name": "Group Atlas 3",
"StoreList": [
2,
4,
6
]
}
store_2
{
"cbType": "store",
"ID": 2,
"name": "some store name"
}
store_4
{
"cbType": "store",
"ID": 4,
"name": "another store name"
}
store_6
{
"cbType": "store",
"ID": 6,
"name": "last store name"
}
Now lets say you wan't to query the stores from a particular group (group_1), but include no other information about the group. You essentially want to use N1QL's UNNEST and JOIN operators.
This might leave you with a query like so:
SELECT
stores.name
FROM `bucket-name-here` AS groups
UNNEST groups.StoreList AS groupstore
JOIN `bucket-name-here` AS stores ON KEYS ("store_" || groupstore.ID)
WHERE
META(groups).id = 'group_1';
A few assumptions are made in this. Both your documents exist in the same bucket and you only want to select from group_1. Of course you could use a LIKE and switch the group id to a percent wildcard.
Let me know if something doesn't make sense.
Best,
Try this query:
select Name
from buketname a join bucketname b ON KEYS a.StoreList
where Name="Group Atlas 3"
Based on your update, you can do the following:
SELECT b, s
FROM DataBoard c USE KEYS ["Group_X"]
UNNEST c.StoreList b
JOIN store_bucket s ON KEYS "Store_" || TO_STRING(b);
I have a similar requirement and I got what I needed with a query like this:
SELECT store
FROM `bucket-name-here` group
JOIN `bucket-name-here` store ON KEYS group.StoreList
WHERE group.cbType = 'group'
AND group.ID = 1
I am trying to learn mongodb. Suppose there are two tables and they are related. For example like this -
1st table has
First name- Fred, last name- Zhang, age- 20, id- s1234
2nd table has
id- s1234, course- COSC2406, semester- 1
id- s1234, course- COSC1127, semester- 1
id- s1234, course- COSC2110, semester- 1
how to insert data in the mongo db? I wrote it like this, not sure is it correct or not -
db.users.insert({
given_name: 'Fred',
family_name: 'Zhang',
Age: 20,
student_number: 's1234',
Course: ['COSC2406', 'COSC1127', 'COSC2110'],
Semester: 1
});
Thank you in advance
This would be a assuming that what you want to model has the "student_number" and the "Semester" as what is basically a unique identifier for the entries. But there would be a way to do this without accumulating the array contents in code.
You can make use of the upsert functionality in the .update() method, with the help of of few other operators in the statement.
I am going to assume you are going this inside a loop of sorts, so everything on the right side values is actually a variable:
db.users.update(
{
"student_number": student_number,
"Semester": semester
},
{
"$setOnInsert": {
"given_name": given_name,
"family_name": family_name,
"Age": age
},
"$addToSet": { "courses": course }
},
{ "upsert": true }
)
What this does in an "upsert" operation is first looks for a document that may exist in your collection that matches the query criteria given. In this case a "student_number" with the current "Semester" value.
When that match is found, the document is merely "updated". So what is being done here is using the $addToSet operator in order to "update" only unique values into the "courses" array element. This would seem to make sense to have unique courses but if that is not your case then of course you can simply use the $push operator instead. So that is the operation you want to happen every time, whether the document was "matched" or not.
In the case where no "matching" document is found, a new document will then be inserted into the collection. This is where the $setOnInsert operator comes in.
So the point of that section is that it will only be called when a new document is created as there is no need to update those fields with the same information every time. In addition to this, the fields you specified in the query criteria have explicit values, so the behavior of the "upsert" is to automatically create those fields with those values in the newly created document.
After a new document is created, then the next "upsert" statement that uses the same criteria will of course only "update" the now existing document, and as such only your new course information would be added.
Overall working like this allows you to "pre-join" the two tables from your source with an appropriate query. Then you are just looping the results without needing to write code for trying to group the correct entries together and simply letting MongoDB do the accumulation work for you.
Of course you can always just write the code to do this yourself and it would result in fewer "trips" to the database in order to insert your already accumulated records if that would suit your needs.
As a final note, though it does require some additional complexity, you can get better performance out of the operation as shown by using the newly introduced "batch updates" functionality.For this your MongoDB server version will need to be 2.6 or higher. But that is one way of still reducing the logic while maintaining fewer actual "over the wire" writes to the database.
You can either have two separate collections - one with student details and other with courses and link them with "id".
Else you can have a single document with courses as inner document in form of array as below:
{
"FirstName": "Fred",
"LastName": "Zhang",
"age": 20,
"id": "s1234",
"Courses": [
{
"courseId": "COSC2406",
"semester": 1
},
{
"courseId": "COSC1127",
"semester": 1
},
{
"courseId": "COSC2110",
"semester": 1
},
{
"courseId": "COSC2110",
"semester": 2
}
]
}