There is a "fast" or "less wrong" way to get "dependent" data from Cloud Firestore?
I have two main collections: "users" and "championship".
And a collection named "team" inside every document of a "user".
Every championship is composed of teams, and I'm storing every team in an array of references, like this:
"championship": {
"teams": [
team_1_ref, team_2_ref, ...
]
}
By doing this I can get all teams of a championship, but I also want to get the user owner of the team.
I know that I can create a new reference "attribute" named owner for the team and then get the "data" of every user, but there is another way to do this?
If you're asking if there is a way to perform a "join" type query that spans data from multiple collections, that is not possible. If you have a document with an array, and you want to get all the other documents referred to in that array, you will have to write code to get each one individually. This is normal for nosql type databases, and it's not as bad as you think, in terms of performance.
Related
I'm developing an Android app with Java and using Firestore, It's a social network and I have a collection with all the posts. I'm trying to show only those posts that belong to the followed users, so I make a query to show all the posts ordered by timestamp, but I don't know if I can filtered them by comparing with the collection "followed" inside "User".
The main collection "Users" has documents, each of them is a user, inside every user there is a subcollection "followed" that contains the followed users, every document is a user and the document id is the same that the User ID.
The posts are stored in another main collection called Posts, so I need to compare the id User inside "Posts" documents with the id of the docs in the subcollection "followed". I hope somebody can help me, I spent a lot of time and I can't find anything, thank you.
Firestore does not have the ability to "join" documents in collections as you're describing here. It's relatively straightforward in SQL (if your server has enough memory), but Firestore (and other NoSQL databases) aren't built for this, due to its distributed nature, and the way it needs to scale.
The only way to do what you want is to write code to read every document in every collection that would need a comparison, and also perform that comparison with the documents in memory.
I am writing Couchbase DAO using Java API. I store all documents for one entity in particular bucket. I wonder what is the best way to get all documents from this bucket?
Thanks in advance!
First: do you plan to store each entity type in their own buckets? That will probably not work in the long run, unless you plan to only ever have no more than 10 total entities. Buckets are not made to organize data like that: they are meant to store a variety of different types of data.
Second: do you really want to get all data from a bucket? That seems like a very uncommon use case. It's almost like asking "how do I query all data from all tables in a relational database"
That being said, I could imagine a very specialized situation where you'd want to do this. So, you could:
Create a PRIMARY index and execute a N1QL query like SELECT * FROM mybucket;
Create a very simple map/reduce view index of the data.
Both of these things can be done with the Java SDK.
Supposed I have the following class:
class Example {
List<ObjectA> objectsOfA;
List<ObjectB> objectsOfB;
List<ObjectC> objectsOfC;
....
}
I would usually have these tables:
Example (Id, other-attributes)
ObjectA (some attributes, ExampleId)
ObjectB (some attributes, ExampleId)
ObjectC (some attributes, ExampleId)
If I want to restore an Example-object, I imagine I have two options:
join every table together, resulting in a lot of entries and organizing it in hashmaps to reassemble the objects
loading every Example and for every example doing single requests for the lists of ObjectA, ObjectB, ObjectC.
If the number of Example-entries is low, option2 might be the best. But for every single entry in Example, I need to do x more requests where x is the number of tables in my class.
Otherwise, having everything in a single join requires me to reorganize all the data by myself - creating hashmaps, iterating through data and stuff, which is usually a lot of work in code.
I also get the possibility of lazy loading with option 2.
Do I have other choices? Did I miss something? (Of course I know about ORMs, but I decided to not use them on Android)
If I understood correctly, your question is, which of these ways is better to load data from a main table and related tables:
Load a JOIN of the main table and related tables
Load the data of the main table and related tables separately and join them in Java
Something else
Unless you are extremely constrained for bandwidth,
I'd say option 1 is simple and I would go with that.
It's easier to let the DB do the joining and in Java just map the records to objects.
If saving bandwidth with the database is important,
then option 2 is better,
because every piece of data will be fetched only once without duplication,
as the result of a JOIN in option is essentially denormalized data.
In any case, I recommend to follow Occam's razor: the simplest solution is often the best.
Google Apps Engine offers the Google Datastore as the only NoSQL database (I think it is based on BigTable).
In my application I have a social-like data structure and I want to model it as I would do in a graph database. My application must save heterogeneous objects (users,files,...) and relationships among them (such as user1 OWNS file2, user2 FOLLOWS user3, and so on).
I'm looking for a good way to model this typical situation, and I thought to two families of solutions:
List-based solutions: Any object contains a list of other related objects and the object presence in the list is itself the relationship (as Google said in the JDO part https://developers.google.com/appengine/docs/java/datastore/jdo/relationships).
Graph-based solution: Both nodes and relationships are objects. The objects exist independently from the relationships while each relationship contain a reference to the two (or more) connected objects.
What are strong and weak points of these two approaches?
About approach 1: This is the simpler approach one can think of, and it is also presented in the official documentation but:
Each directed relationship make the object record grow: are there any limitations on the number of the possible relationships given for instance by the object dimension limit?
Is that a JDO feature or also the datastore structure allows that approach to be naturally implemented?
The relationship search time will increase with the list, is this solution suitable for large (million) of relationships?
About approach 2: Each relationship can have a higher level of characterization (it is an object and it can have properties). And I think memory size is not a Google problem, but:
Each relationship requires its own record, so the search time for each related couple will increase as the total number of relationships increase. Is this suitable for large amount of relationships(millions, billions)? I.e. does Google have good tricks to search among records if they are well structured? Or I will be soon in a situation in which if I want to search a friend of User1 called User4 I have to wait seconds?
On the other side each object doesn't increase in dimension as new relationships are added.
Could you help me to find other important points on the two approaches in such a way to chose the best model?
First, the search time in the Datastore does not depend on the number of entities that you store, only on the number of entities that you retrieve. Therefore, if you need to find one relationship object out of a billion, it will take the same time as if you had just one object.
Second, the list approach has a serious limitation called "exploding indexes". You will have to index the property that contains a list to make it searchable. If you ever use a query that references more than just this property, you will run into this issue - google it to understand the implications.
Third, the list approach is much more expensive. Every time you add a new relationship, you will rewrite the entire entity at considerable writing cost. The reading costs will be higher too if you cannot use keys-only queries. With the object approach you can use keys-only queries to find relationships, and such queries are now free.
UPDATE:
If your relationships are directed, you may consider making Relationship entities children of User entities, and using an Object id as an id for a Relationship entity as well. Then your Relationship entity will have no properties at all, which is probably the most cost-efficient solution. You will be able to retrieve all objects owned by a user using keys-only ancestor queries.
I have an AppEngine application and I use both approaches. Which is better depends on two things: the practical limits of how many relationships there can be and how often the relationships change.
NOTE 1: My answer is based on experience with Objectify and heavy use of caching. Mileage may vary with other approaches.
NOTE 2: I've used the term 'id' instead of the proper DataStore term 'name' here. Name would have been confusing and id matches objectify terms better.
Consider users linked to the schools they've attended and vice versa. In this case, you would do both. Link the users to schools with a variation of the 'List' method. Store the list of school ids the user attended as a UserSchoolLinks entity with a different type/kind but with the same id as the user. For example, if the user's id = '6h30n' store a UserSchoolLinks object with id '6h30n'. Load this single entity by key lookup any time you need to get the list of schools for a user.
However, do not do the reverse for the users that attended a school. For that relationship, insert a link entity. Use a combination of the school's id and the user's id for the id of the link entity. Store both id's in the entity as separate properties. For example, the SchoolUserLink for user '6h30n' attending school 'g3g0a3' gets id 'g3g0a3~6h30n' and contains the fields: school=g3g0a3 and user=6h30n. Use a query on the school property to get all the SchoolUserLinks for a school.
Here's why:
Users will see their schools frequently but change them rarely. Using this approach, the user's schools will be cached and won't have to be fetched every time they hit their profile.
Since you will be getting the user's schools via a key lookup, you won't be using a query. Therefore, you won't have to deal with eventual consistency for the user's schools.
Schools may have many users that attended them. By storing this relationship as link entities, we avoid creating a huge single object.
The users that attended a school will change a lot. This way we don't have to write a single, large entity frequently.
By using the id of the User entity as the id for the UserSchoolLinks entity we can fetch the links knowing just the id of the user.
By combining the school id and the user id as the id for the SchoolUser link. We can do a key lookup to see if a user and school are linked. Once again, no need to worry about eventual consistency for that.
By including the user id as a property of the SchoolUserLink we don't need to parse the SchoolUserLink object to get the id of the user. We can also use this field to check consistency between both directions and have a fallback in case somehow people are attending hundreds of schools.
Downsides:
1. This approach violates the DRY principle. Seems like the least of evils here.
2. We still have to use a query to get the users who attended a school. That means dealing with eventual consistency.
Don't forget Update the UserSchoolLinks entity and add/remove the SchoolUserLink entity in a transaction.
You question is too complex but I try explain the best solution (I will answer in Python but same can be done in Java).
class User(db.User):
followers = db.StringListProperty()
Simple add follower.
user = User.get(key)
user.followers.append(str(followerKey))
This allow fast query who is followed and followers
User.all().filter('followers', followerKey) # -> followed
This query i/o costly so you can make it faster but more complicated and costly in i/o writes:
class User(db.User):
followers = db.StringListProperty()
follows = db.StringListProperty()
Whatever this is complicated during changes since delete of Users need update follows so you need 2 writes.
You can also store relationships but it is the worse scenario since it is more complex than second example with followers and follows ... - keep in mind than entity can have 1Mb it is not limit but can be.
It is advisable to store some information(meta-data) about a content in the Id(or key) of that content ?
In other words, I am using a time based UUIDs as the Ids (or key) for some content stored in the database. My application first accesses the list of all such Ids(or keys) of the content (from the database) and then accessed the corresponding content(from the database). These Ids are actually UUIDs(time based). My idea is to store some extra information about the content, in the Ids itself, so that the my software can access this meta-content without accessing the entire content from the database again.
My application context is a website using Java technology and Cassandra database.
So my question is,
whether I should do so ? I am concerned since lots of processing may be required (at the time of presentation of data to user) in order to retrieve the meta data from the ids of the content!! Thus it may be instead better to retrieve it from database then getting it through processing of the Id of that content.
If suggested then , How should I implement that in an efficient manner ? I was thinking of following way :-
Id of a content = 'Timebased UUID' + 'UserId'
where, 'timebasedUUID' is the generated ID based on the timestamp when that content was added by a user & 'userId' represents the Id of the user who put that content.
so my example Id would look something like this:- e4c0b9c0-a633-15a0-ac78-001b38952a49(TimeUUID) -- ff7405dacd2b(UserId)
How should I extract this userId from the above id of the content, in most efficient manner?
Is there a better approach to store meta information in the Ids ?
I hate to say it since you seem to have put a lot of thought into this but I would say this is not advisable. Storing data like this sounds like a good idea at first but ends up causing problems because you will have many unexpected issues reading and saving the data. It's best to keep separate data as separate variables and columns.
If you are really interested in accessing meta-content with out main content I would make two column families. One family has the meta-content and the other the larger main content and both share the same ID key. I don't know much about Cassandra but this seems to be the recommended way to do this sort of thing.
I should note that I don't think that all this will be necessary. Unless the users are storing very large amounts of information their size should be trivial and your retrievals of them should remain quick
I agree with AmaDaden. Mixing IDs and data is the first step on a path that leads to a world of suffering. In particular, you will eventually find a situation where the business logic requires the data part to change and the database logic requires the ID not to change. Off the cuff, in your example, there might suddenly be a requirement for a user to be able to merge two accounts to a single user id. If user id is just data, this should be a trivial update. If it's part of the ID, you need to find and update all references to that id.