A mostly professional code monkey, to contact me translate the following "direct from blog to email" at the website ominian.net. Replace spaces with underscores or dashes, whatever is valid. A script will pick up your email, scrub it against a white list, and if your not a spammer I will get an email.
Finally, a basic example might be something like MyTable.query.filter(extract('year', MyTable.date_field) == 2022) which would produce something like SELECT ...hell of a lot of columns... FROM MyTable WHERE STRFTIME("%y", MyTable.date_field);
I’ve written several web frameworks in my life and while I don’t have the desire to keep up with current technology trends, I still like to dabble. book.py is the best example of what this does versus page.py which shows more advanced use cases.
I have a lot of Flask apps running in the background on my home server to do various tasks (home wiki, some CRM stuff, etc) and I end up making the same structure over and over so I figured I would simplify the process and make a repo with just the skeleton of an app.
A few benefits:
as long as you use relative imports . and .. (eg from .. import app) your web application is name agnostic.
The flask application instance of Flask() can be accessed from anywhere in the web application without a risk of circular import problems.
It’s entirely possible to copy and paste web application modules (eg models) into another web application and it will mostly just work (baring configuration needs).
The reason for having the imports for lib models views settings in create_app is to prevent a circular import and allow sub modules like views to do from .. import app to access the Flask application instance.
I needed to optimize an unruly table filled with floats but I also didn’t want to lose my data. Unfortunately the documentation on the alembic website doesn’t mention anything or give any hints on how to do a data migration versus just a schema migration.
Fortunately I was able to run a symbolic debugger against alembic and figured out that all of the op.<method>`calls are atomic. If you have an add_column call, it adds the column when it executes that method. So that opened the door to data migrations.
One note before I pasted the code. You don’t need to specify all of the columns of the source table when used in a data migration scope. This makes your code a lot cleaner as the working model code is specific to what data you plan on using.
Alright, no more babbling, here is the example code.
A while back I downloaded my google location and history data and ran into these strange lat7 and long7 columns (paraphrasing as I don’t remember their exact names). The data were these large integer numbers that I couldn’t figure out how to decode. Suddenly it became obvious when I noticed all of the latitude fields started with 35 and the longitude started with -104. 35, -104 is approximately a few hundred miles from where I live. By doing lat7 / 10000000 (10e7 or 10**7) I was able to get floating point GPS coordinates.
Since then, when it comes time to optimize database schemas I’ve always started with figuring out if I can shift the percentage out and use integers instead. If using sqlite3, a Float is actually a varchar and that’s huge in comparison to using a byte or two of signed integers. Throw a million records on and it can get up to 30-40% of wasted diskspace.
Anyway where was I. Since I wanted to get rid of all of the floats and replace the real fields with @hybrid_propertyand @hybrid_property.expression I renamed latitude to _latitude, shifted out the percent, and used the aforementioned decorators to transform the integers back to floats on demand.
I am working on a pet project to compress a terabyte of video into a slimmer format. While I have been able to automate working with ffmpeg, I didn’t like the fact that I couldn’t follow along with the subprocess running ffmpeg.
I tried a few different ideas of how to watch ffmpeg but also avoid the script from blocking because I wanted to be able to time and monitor it’s progress
process = subprocess.pOpen
stdout, stderr = process communicate blocks until the process is finished
subprocess.stdout.readline() and subprocess.stderr.readline() will both block until there is sufficient data. In ffmpeg’s case there is never stdout output so it will block indefinitely.
By using threading Queue and constantly polling the process, I can watch the output as fast as it can come in but not worry about the main process blocking, just the threads.
A further improvement on the idea would be to have two threads (for stdout and stderr respectively) with the queue items put with sentinels like queue.put((STDERR, line_from_stderr)) and a sentinel for STDOUT.
To use -
r = Runner(["some_long_running_process", "-arg1", "arg1 value"])
for stdout, stderr in r.start():
I went into writing DCDB with little or no plan besides building it around dataclasses. The result is a bit rough and precarious.
That said I think I am going to progress onward with making a DCDB2 library that will change a few things. The first would be to completely separate the DCDB tables themselves from the SQL processing logic in a way similar to sqlalchemy’s session system. I do have some other changes in mind, notably a better separation between the ORM domain classes and business logic as well as changes to how relationship’s work.
On the subject of relationship handling. That one would be a bit more complicated as the DCDB2 design idea I had was to use placeholders for the relationship (what does it connect too and in what way), then have the real instrumented handlers created and assigned to a constructed domain class. That last sentence is a bit painful to read which tells me I need to mull that one over a bit more. Regardless, the hack I put together in DCDB was just way too fragile.
Inside of my “tests” directory I added a “db” directory. Given the logic above, it spawns an entire new database for each test function so that I can go back and verify my database. For someone elses code, you just need to swap out “sal2” with the module name holding your sqlalchemy base and associated model classes. The only thing I wonder about is the issue with create_all. I remember there is a way to bind the metadata object without create_all but damn if I can remember it right now.
While I do use sqlalchemy and to some extent peewee for my projects, I slowly got tired of having to relearn how to write SQL when I’ve known SQL since the mid-90’s.
DCDB’s design is also aiming for simplicity and minimal behind the scenes automagical behaviors. Instead complexity should be added voluntarily and in such a way that it can be traced back.
import dataclasses as dcs
db = dcdb.DBConnection(":memory:") # alternatively this can be a file path
Bind doesn't change Foo in the local scope but instead
it creates a new class DCDB_Foo which is stored to the DBConnection in it's
Behind the scenes, a table `Foo` is created to the connected database. No changes to the name are made (eg pluralization). How you wrote your bound dataclasses is almost exactly how it is stored in the sqlite database.
An exception is that a .id instance property along with DB methods like: update/save, Create, Get, and Select are added to the class definition.
record = db.t.Foo(name="Bob", age="44")
assert record.name == "Bob"
same_record = db.t.Foo.Get("name=?", "Bob")
assert record.age == 44
assert record.id == same_record.id
record.age = 32
same_record = db.t.Foo.Get("age=?", 32)
assert record.id == same_record.id
assert same_record.age == 32
Note it is important to notice that currently same_record and
record have the same .id # property but they are different
instances and copies of the same record with no shared reference.
Changes to one copy will not reflect with the other.