I know there are ways of storing data/tables from one server to another, such as the instruction provided here. However, due to I use python to scrape, create, and store data, I am wondering that whether I could fulfill this process by directly using SQLAlchemy. More precisely, after I store the scraped data in the database I create through SQLAlchemy in my own computer, can I simultaneously store.copy those database/tables to another computer/server directly through SQLAlchemy? Can anyone help? Thanks so much.
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I'm querying an existing read-only database with SQLAlchemy, and wonder if there is a way to cache the queried table object locally (in an automatic way) so that I can use it later. The main reason of this need is not to lock the database while my script is running (e.g. I have to keep a session connected to wait for user's response), and the database is read-only so I really don't need the modified data to be synchronized back.
Right now I'm working through a solution to convert the queried results into pd.DataFrame, but it would be nice to keep the advantage of SQLAlchemy where the queried result can retain it's structure rather than being converted to a flat table in pd.DataFrame.
I'm new to SQLAlchemy and still learning. Any suggestions about the solution or if I miss some major features already provided in SQLAlchemy package would really be appreciated!
I have a bigquery table about 200 rows, i need to insert,delete and update values in this through a web interface(the table cannot be migrated to any other relational or non-relational database).
The web application will be deployed in google-cloud on app-engine and the user who acts as admin and owner privileges on Bigquery will be able to create and delete records and the other users with view permissions on the dataset in bigquery will be able to view records only.
I am planning to use the scripting language as python,
server(django or flask or any other)-> not sure which one is better
The web application should be displayed as a data-grid like appearance with buttons create,delete or view visiblility according to their roles.
I have not done anything like this in python,bigquery and django. I am already familiar with calling bigquery from python-client but to call in a web interface and in a transactional way, i am totally new.
I am seeing examples only related to django with their inbuilt model and not with big-query.
Can anyone please help me and clarify whether this is possible to implement and how?
I was able to achieve all of "C R U D" on Bigquery with the help of SQLAlchemy, though I had make a lot of concessions like if i use sqlalchemy class i needed to use a false primary key as Bigquery does not use any primary key and for storing sessions i needed to use file based session On Django for updates and create sqlalchemy does not allow without primary key, so i used raw sql part of SqlAlchemy. Thanks to the #mhawke who provided the hint for me to carry out this exericse
No, at most you could achieve the "R" of "CRUD." BigQuery isn't a transactional database, it's for querying vast amounts of data and preparing the results as an immutable view.
It doesn't provide a method to modify the source data directly and even if you did you'd need to run the query again. Also important to note are that queries are asynchronous and require much longer to perform than traditional databases.
The only reasonable solution would be to export the table data to GCS and then import it into a normal database for querying. Alternatively if you can't use another database and since you said there are only 1,000 rows you could perform your CRUD actions directly on that exported CSV.
I am not sure exactly right place to ask but I need any single infrmation about it.
I am going to create a dashboard with Klipfolio and I want to make data pre processing with Python and integrate in klipfolio but unfortunately Klipfoli does not have any specific place to do it.
Is anyone used Klipfolio, data pre processing with Python for Klipfolio.
While Klipfolio does not have any Python integrations, Klipfolio does connect to various types of SQL databases. One work around is to dump your processed data from Python into a SQL database and then connect that SQL database with Klipfolio to make data sources to build the visualization.
You can either directly connect to the database, or if you are running python on a server, you can user "Rest/URL" method in Klipfolio to directly connect to your python code and integrate the output into your dashboard.
I'm updating from an ancient language to Django. I want to keep the data from the old project into the new.
But old project is mySQL. And I'm currently using SQLite3 in dev mode. But read that postgreSQL is most capable. So first question is: Is it better to set up postgreSQL while in development. Or is it an easy transition to postgreSQL from SQLite3?
And for the data in the old project. I am bumping up the table structure from the old mySQL structure. Since it got many relation db's. And this is handled internally with foreignkey and manytomany in SQLite3 (same in postgreSQL I guess).
So I'm thinking about how to transfer the data. It's not really much data. Maybe 3-5.000 rows.
Problem is that I don't want to have same table structure. So a import would be a terrible idea. I want to have the sweet functionality provided by SQLite3/postgreSQL.
One idea I had was to join all the data and create a nested json for each post. And then define into what table so the relations are kept.
But this is just my guessing. So I'm asking you if there is a proper way to do this?
Thanks!
better create the postgres database. write down the python script which take the data from the mysql database and import in postgres database.
I'm completely new to managing data using databases so I hope my question is not too stupid but I did not find anything related using the title keywords...
I want to setup a SQL database to store computation results; these are performed using a python library. My idea was to use a python ORM like SQLAlchemy or peewee to store the results to a database.
However, the computations are done by several people on many different machines, including some that are not directly connected to internet: it is therefore impossible to simply use one common database.
What would be useful to me would be a way of saving the data in the ORM's format to be able to read it again directly once I transfer the data to a machine where the main database can be accessed.
To summarize, I want to do:
On the 1st machine: Python data -> ORM object -> ORM.fileformat
After transfer on a connected machine: ORM.fileformat -> ORM object -> SQL database
Would anyone know if existing ORMs offer that kind of feature?
Is there a reason why some of the machine cannot be connected to the internet?
If you really can't, what I would do is setup a database and the Python app on each machine where data is collected/generated. Have each machine use the app to store into its own local database and then later you can create a dump of each database from each machine and import those results into one database.
Not the ideal solution but it will work.
Ok,
thanks to MAhsan's and Padraic's answers I was able to find the how this can be done: the CSV format is indeed easy to use for import/export from a database.
Here are examples for SQLAlchemy (import 1, import 2, and export) and peewee