What the question said.
Basically can you do something like
db = pony_magic(filename)
And get the structure in db. For example have db.entities?
Pony cannot, but pony-inspect can.
Related
I would like to know if there is a way to output to a MongoDB in Luigi. I see in the documentation they support files (local FS, HDFS), S3, PostgreSQL but not MongoDB. If not, could someone explain me why not? Maybe it is a bad idea to have it? I would like to store the data in a database because then I can explore it by querying it. However I am using mongodb and I would not like to install another database. I do not need a relational database as I am using the database only to store and query ( NoSql ) without relationships, so the best option is mongodb.
Basically I need a task to read the data and save it in the database. Then the next task take this output and process the data.
Any recommendation, suggestion or clarification is more than welcome. Thanks!
You can try using mortar-luigi.
Check out this link for MongoDB tasks and this example.
I'm new to data migrations, so I'm just wondering what the best way would be to go about migrating all of the data from the Big Table (NDB) over to Django Models (Postgres).
On the one hand, I have plenty of 'tables' that have plenty of relations (KeyProperties) and on the other, I must maintain those relations as well as port some over to general relations (GFK).
I'm not even sure how to go about doing this. I know how to create a Postgres Django DB, just not how to maintain things like, KeyProperties linking to image Blogs. How do I copy those images over and also maintain this 'FK' relation? I have quite a bit of data and would really like to maintain the structure of it.
Is there any good documents on database migrations and how its ideally done?
Any help would be appreciated!!!
Create a Postgres table just for the images (using BLOB or bytea types) and use FK relations to it.
The general question of doing database migrations is too broad to answer, please ask a more specific question. You are going to have to write custom code to split apart each entity's properties and convert them into Postgres data types.
We have a large application with hundreds of classes/enums, and we want to use MongoDB to store some of these.
The situation is that there is a current system whereby we binary serialize the .NET object into a field in a SQL database, then deserialize on demand. What we want is put the object into Mongo in a way that will allow us to query the object's properties directly (ie. without having to load the object into memory, deserialize, etc.). This is so we can start to get some analytics from the historic data without having to drastically change the code base.
My question is, is this something that easily possible? are there in built serializers in the C# driver to do this?
I'm also open to answers that propose a better way to do this if what I'm trying to do is inherently wrong.
Update: to be clear, what I'm trying to do is take an object that has been loaded using NHibernate, and insert it into Mongo as a Queryable object. Ultimately, I'll want to load it back into memory at some point too.
MongoDB is basically a store of JSON documents, so if you can serialize your objects in a JSON way, you should be ok to store it in MongoDB, and I assume there are lots of JSON serializers for .NET, so should be easy to find one.
Once everything is stored as JSON in MongoDB you will be able to query it without any more tools that the ones to query the database directly.
Regards,
You can use Simple.Data.MongoDB a lightweight, dynamic data access .NET component for MongoDB
One thing I have in mind is, that datasets in Core Data (or lets say: managed objects) have no ID like known from other databases such as MySQL. Also, they're not in a specific guaranteed order.
What else makes Core Data much more "special" compared to working with a relational database like MySQL? Besides the whole object graph persisting and ORM stuff?
This is a good article explaining the main differences. The biggy for me is
'Core Data cannot operate on data
without loading the data into memory'
This alone makes core-data and MySQL suited to totally different tasks.
The big difference I would say is that Core Data is built on an ORM, an Object Relational Mapping, while MySQL is just a relational database. You could actually host CoreData on MySQL if Apple wanted to let you. Instead they use a different embedded SQLight solution or an XML representation depending on what you want for your backing store.
Does anyone have any advice on using a datastore with mapkit to provide a database of locations (Restaurants) that are query-able by location?
I would like to use Core data but importing the information into it seems like a project in itself. If anyone has good advice on converting an existing sqlite/cvs file to a coredata sqlite file that would be appreciated.
Is old-fashioned sqlite better than using core data for the task, or is it a case that I should create a web service for the job?
I would like to be able to query the locations based on the map zoom also.
Thanks if you have any advice on the matter.
If you write your object model correctly, you can just point it at an existing sqLite database and it will read it as if core data generated it in the first place.
For example, suppose you have an existing sqlite db of people with columns like firstName, lastName, phone# etc. You just create a core data model with a entity with attributes of firstName, lastName, phone# etc. Spell them the same and make sure they have the right type and then point the NSPersistentStoreCoordinator at the existing database. It will read it in fine.
Core data is always the way to go for any larger data management task. It makes everything so much easier once you learn it.
Edit01:
Never mind the above. I was thinking of Enterprise Objects. Core data won't easily import most existing SQL.
Instead, I would export the sqlite to csv and then use something like cCSVParse to convert to plist. Then you can read it in easily to an array or dictionary and use that to populate the core data db.
That will work easily for db's that don't depend on complex relationships. I think the future benefits of having core data will eventually easily pay for the few man hours spent converting.