The first is Flask-SQLAlchemy, an extension that provides a Flask-friendly wrapper to the popular SQLAlchemy package, which is an Object Relational Mapper or ORM. In this chapter I'm going to use two more. In Chapter 3 I showed you a first Flask extension. This application, like most others, can be implemented using either type of database, but for the reasons stated above, I'm going to go with a relational database. While there are great database products in both groups, my opinion is that relational databases are a better match for applications that have structured data such as lists of users, blog posts, etc., while NoSQL databases tend to be better for data that has a less defined structure. The latter group is often called NoSQL, indicating that they do not implement the popular relational query language SQL. The databases can be separated into two big groups, those that follow the relational model, and those that do not. There are great choices for databases in Python, many of them with Flask extensions that make a better integration with the application. This is one of the many areas in which Flask is intentionally not opinionated, which is great, because you have the freedom to choose the database that best fits your application instead of being forced to adapt to one. Databases in FlaskĪs I'm sure you have heard already, Flask does not support databases natively. The GitHub links for this chapter are: Browse, Zip, Diff. For most applications, there is going to be a need to maintain persistent data that can be retrieved efficiently, and this is exactly what databases are made for. The topic of this chapter is extremely important. Chapter 23: Application Programming Interfaces (APIs).Chapter 19: Deployment on Docker Containers.Chapter 15: A Better Application Structure.I did not have the case of a circular dependency, I guess you can suspend temporarily the key checking if that is the case.This is the fourth installment of the Flask Mega-Tutorial series, in which I'm going to tell you how to work with databases.įor your reference, below is a list of the articles in this series. Now you can try to load the resulting csv with PostgreSQL (even graphically with the admin tool), with the only caveat that you must load the tables with foreign keys after you have loaded the tables with the corresponding source keys. This works like a charm, is easy to write, read and debug each function, unlike (for me) the regular expressions. #df = other_transform(df, other_column_name)ĭf.to_csv(table_name + '.csv'), sep=',', header=False, index=False) Suppose you have a table with a bool field (which is 0/1 in sqlite, but must be t/f in PostgreSQL) def int_to_strbool(df, column):ĭf = pd.read_sql(f'select * from ', conn)ĭf = int_to_strbool(df, bool_column_name) I have tried editing/regexping the sqlite dump so PostgreSQL accepts it, it is tedious and prone to error.įirst recreate the schema on PostgreSQL without any data, either editing the dump or if you were using an ORM you may be lucky and it talks to both back-ends (sqlalchemy, peewee. To prove the concept I dumped this testdb and imported into a development environment on a production server and the data transferred over nicely. I know if I had tried to run one of these scripts or do the stepwise conversion mentioned herein, I would have spent much more time. I then created a testdb with createdb:Īfter some queries to check the data, it appears it worked quite well. Set work_mem to '16MB', maintenance_work_mem to '512 MB' With include drop, create tables, create indexes, reset sequences I installed from the *.deb and created a command file like this in a test directory: load database You can convert the flat SQLite file into a usable PostgreSQL database. Pretty cool application and it's relatively easy to use. I looked up the wiki docs:Īnd discovered pgloader. I started looking into the solutions here and realized that I was looking for a more automated method. Even though this post has an accepted answer (and a good one at that +1), I think adding this is important. I came across this post when searching for a way to convert an SQLite dump to PostgreSQL. Importing a big pile of data through SQL INSERTs might take a while but it'll work. The syntax in the SQLite dump file appears to be mostly compatible with PostgreSQL so you can patch a few things and feed it to psql. While SQLite defaults null values to '', PostgreSQL requires them to be set as NULL. You should be able to feed that dump file straight into psql: /path/to/psql -d database -U username -W 0)
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