Why is this simple test case inserting 100,000 rows 25 times slower with SQLAlchemy than it is using the sqlite3 driver directly? I have seen similar slowdowns in real-world applications. Am I doing something wrong?
Is there a way to make SQLAlchemy generate a query with a custom column that is a subquery that correlates with current row:
I am giving Pylons a try with SQLAlchemy, and I love it, there is just one thing, is it possible to print out the raw SQL CREATE TABLE data generated from Table().create() before it’s executed?
I have 3 tables: User, Community, community_members (for relationship many2many of users and community).
I want to upload a huge number of entries (~600k) into a simple table in a PostgreSQL DB, with one foreign key, a timestamp and 3 float per each entry. However, it takes 60 ms per each entry to execute the core bulk insert described here, thus the whole execution would take 10 h. I have found out, that it is a performance issue of executemany() method, however it has been solved with the execute_values() method in psycopg2 2.7.
I want to make a Database Application Programming Interface written in Python and using SQLAlchemy (or any other database connectors if it is told that using SQLAlchemy for this kind of task is not the good way to go). The setup is a MySQL server running on Linux or BSD and a the Python software running on a Linux or BSD machine (Either foreign or local).
Does anyone have experience profiling a Python/SQLAlchemy app? And what are the best way to find bottlenecks and design flaws?
We have a need to create SQLAlchemy classes to access multiple external data sources that will increase in number over time. We use the declarative base for our core ORM models and I know we can manually specify new ORM classes using the autoload=True to auto generate the mapping.