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I am developing an app for keeping attendance records for the students and I am confused about using either SQFlite or Hive for the local database. What are the pros and cons of both? How does each perform in apps with smaller and more complex databases?
I am currently using Hive and it seems like a hassle regarding code readability.
if you want the saved data to be fetched instantly and you won't need complex queries or have relations among the saved entities then go ahead and use Hive since it loads its boxes (saved data) in the memory for instant fetching
otherwise use SQFlite as it's more readable, scalable, and customizable.
in your case and regarding the project you are working on, I believe you should go with SQFlite, maybe in the future you will add some other features not just attendance, and might need to make complex queries.
Both of them work great, but for both proper data modelling is essential.
However, I wonder whether your app can work without storing data externally. In that case, neither is a good option.
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I have an app built with flutter and firestore as the DB. It works fine the way I need, it's pretty fast (does not have much data), but since a few days ago I started wondering if adding GraphQL in this App would bring any benefits to it?
To give some basic idea, let's say the app is a Uber eats like app, with several restaurants and multiple users making requests. I achieved this by with plain Flutter + Firestore. I'm afraid that at some point let's say, the app has lots of users and start to get slow, would GQL help on this aspect?
I found some Flutter packages like Firegraph that looks promising and easy to use.
Hope someone can share some knowledge on this area.
Fetching the data from firebase has a completely different approach compared to graphql or REST. Its developed in such a way that there's 0 delay. If you are using firestore then using graphQl will make no much difference in terms of performance. Also by checking the source of this package i see that it's fetching a document, saving it in cache and iterating though each document fields. So it's basically adding another layer to fetch data which i think isn't a good idea
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I'm new to Swift and I'm building on a simple app that relies on login/sign up functions for each user. When I wanted to learn about databases in Swift, I came across Core Data and from what I understood it's like cookies/cache that stores data in the user's phone. If that's so, what's the best way to connect the app to a regular database like SQL?
Core Data is an ORM (object-relational mapping) system. Overly simplyfied you build your data model entities and and relationships between them and Core Data cares about the rest. Under the hood it uses an SQLite database by default, but it also can handle other types of storage (e.g. binary or XML).
To answer your question, for Login/Signup purposes you should look into storing user data such as auth tokens etc. in the Keychain.
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I have to decide on a NoSQL database for a web app that should keep track of the user input and update the corresponding record as frequent as possible. To think about the frequency: let's say a blank record is generated on start and it should update on every key-up event coming from the user.
The methods I have seen for this kind of work are:
Write-Ahead-Logging/Journaling for the user data (not like the internal data consistency methods like Journaling of MongoDB or Write-ahead logging of CouchDb): I don't know if it is even implemented for the user data or the current methods can be utilized for this purpose.
Versioning for MongoDB or a less implicit cell versioning way of doing it in Cassandra
I tended to use Cassandra at the beginning, but I want to know the best fit methods* to achieve that kind of a scenario.
In Cassandra frequent updates on a cell can (but must not) lead to problems with compactions (to be more specific, when updated data is flushed from memtables to sstables because of too many concurrent updates.
If you do not need this data persisted an in memory solution (or in addition to a database) could help, I used Hazelcast for this.
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I have just started using neo4j to store data in a graph, i.e. friends of friends etc..
But now I need to store standard application configuration data. This isn't graph data really, its settings that the application will use to help it run.
Normally I would store this in a table in a RDMS or in a document in MongoDB.
Can I still store this data in neo4j ? or should I use another database to store it?
Is it beneficial to have 2 databases i.e. neo4j and mongodb ?? Pros and Cons?
Anybody done anything like this?
Thanks in advance
It's perfectly fine to store it in Neo4j, if it is not large binary data.
Then I'd store it in a blob storage and just store references to it.
If you have nested structures you can either decompose them into multiple nodes, use property prefixes or serialize them to a string (e.g. JSON).
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I am trying to learn NoSQL, and am implementing that in a project I am working on now as a means to pick it up. I understand there are no hard rules around it. But I'd be happy to read on some of the following:
Guidelines on how to structure a NoSQL document.
Moving from a RDBMS to a NoSQL thinking.
Difference between storing data in a NoSQL to that from RDBMS
Thanks!
I do have previous experience in RDBMS, and have been working with them for years.
Every concept will require to learn new thinking. Your question is to general for a specific
answer.
You will structure and work with CouchDB documents in another way as with MongoDB documents. In CouchDB you will do queries with MapReduce. In MongoDB you have a flexible query interface similar to a RDBMS.
A Key-Value store requires a completely new way of thinking. You have to know your query patterns before you are able to structure your content the right way. You have no index, so you have to build your own structure.
One blog that gives a lot of NoSql information is http://nosql.mypopescu.com
Update
The Riak people have some interresting questions too:
Will my access pattern be read-heavy, write-heavy, or balanced?
Which datasets churn the most? Which ones require more sophisticated conflict resolution?
How will I find this particular type of data? Which method is most efficient?
How independent/interrelated is this type of data with this other type of data? Do they belong together?
How much will I need to do online queries on this data? How quickly do I need them to return results?