Portable/reusable flask app skeleton

Data migration with SQLAlchemy and Alembic

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.

Non-blocking python subprocess

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

import subprocess

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.

https://gist.github.com/devdave/9b8553d63e24ef19eea7e56f7cb95c78

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():
    print("STDOUT", stdout)
    print("STDERR", stderr)

Slimmed down twisted compatible reloader script

Working on txweb again, I decided to give it a real flask/django style reloader script. Directly inspired by <a href=”
https://blog.elsdoerfer.name/2010/03/09/twisted-twistd-autoreload/ “>this</a> blog post I decided to cut down on the extraneous bits and also change what files it watched.

Twisted Twistd Autoreload

Linked to blog post

https://gist.github.com/devdave/05de2ed2fa2aa0a09ba931db36314e3e

DCDB post-mortem

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.

Unit-testing sqlalchemy with pytest

@pytest.fixture(scope="function")
def conn(request):
    ts = int(time.time())
    db_path_name = "db"
    db_name = f"{request.function.__name__}.sqlite3"
    filepath = pathlib.Path(__file__).parent / db_path_name / db_name

    LOG.debug(f"Test DB @ {filepath}")
    engine = create_engine(f"sqlite:///{filepath}")
    connection = engine.connect()

    sal2.Base.metadata.drop_all(bind=engine)
    sal2.Base.metadata.create_all(bind=engine)
    factory = scoped_session(sessionmaker(bind=engine))
    sal2.Base.query = factory.query_property()
    session = factory()

    yield ConnResult(connection, session, engine)

    connection.close()
    engine.dispose()

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.

Python dataclass database (DCDB)

Why

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.

Example

import dataclasses as dcs
import dcdb 

@dcs.dataclass()
class Foo:
    name:str
    age:int

db = dcdb.DBConnection(":memory:") # alternatively this can be a file path
db.bind(Foo)
"""
   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 
   table registry.

   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
record.save()

same_record = db.t.Foo.Get("age=?", 32)
assert record.id == same_record.id
assert same_record.age == 32

same_record.delete()

"""
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.

"""

Github DCDB

Hacking around a hack

So google friendly search terms

Pexpect, subprocess.POpen. How to suppress quotes from Popen with Windows. Answer: You cannot BUT you can hack around it.

Note I am using delegator.py BUT it is just a very nice wrapper around PExpect which in turn is a really nice wrapper around Python’s subprocess.Popen which is a really nice wrapper around a ball of shit called CreateProcess. Pedants asside, you can’t make a ball of shit shine no matter how much abstraction you throw at it.

Solution:

fix = f"some_exec -arg abc -arg 123 -arg3=foo"
cmd_line = ["cmd", "/c", fix ]
result = delegator.run(cmd_line)

This looks goofy and it is. The problem I ran into is with a POSIX compliant app that wasn’t prepared for Windows compliant shenanigans https://docs.python.org/3/library/subprocess.html#converting-argument-sequence

Illustration

$some_exec -arg abc -arg2 123 -arg3=foo

That’s what I expected of crafting a subprocess call with an argument like

delegator.run(["some_exec", "-arg abc", "-arg 123", "-arg3=foo"])

What I got was

"some_exec", "-arg abc", "-arg 123", "-arg3=foo"

Which is hilarious because `some_exec` was so not prepared for that and wasn’t able to parse the command line arguments. How we go from a list of command arguments to a quoted text string is all thanks to this https://github.com/python/cpython/blob/3.7/Lib/subprocess.py#L485

Unless you want to write your own standard library version of subprocess.py just so you can work around one single (likely many) “non-complaint” Windows console executables, you need to abuse the cmd.exe shell’s own quirks.

To demonstrate what I mean

    fix = f"some_exec -arg abc -arg 123 -arg3=foo"
    cmd_line = ["echo", "cmd", "/c",  fix ]
    result = delegator.run(cmd_line)

Outputs `cmd /c “some_exec -arg abc -arg2 123 -arg3=foo` which is in turn executed by cmd.exe without quoting every single argument to `some_exec`.

I walked downward from delegator.py’s run() through pexpect to get to subprocess.py’s POpen and this is it, there wasn’t a cheaper fix then this.

For more info on why this works – tldr “cmd /c argument” is run inside of its own temporary shell and all of that sanitizing code in subprocess is skipped because it see’s the `fix` part of the sequence as one gigantic argument to `cmd`. If you try it any other way, say you try to skip out on cmd, you will get `some_exec “-arg abc -arg2 123 -arg3=foo”` which put me right BACK to having my some_exec blow up as it doesn’t know to strip the quotes out (or even to look inside for the arguments).

Summary: I didn’t log a bug report to some_exec’s maintainer because I needed this to work right the hell now and I didn’t have the time or energy to push through a bug report, wait for a fix, and I don’t have time to even wade into some_exec’s code base to figure out what to patch. Nevermind if it is written in C or C++ which I could do but I haven’t used either language in about a year so what I did write would be some ghastly buffer overflow exploit cesspool. I don’t have time to do that. Besides honestly if I was the developer/maintainer of some_exec I would be skeptical if I even wanted to deal with this bullshit on top of everything else that is more pressing.

WSL GNU/Debian Python 3.6.5

Relevant date: 2018 May 30th
This works today but there is no guarantee this is the right way in a month and especially a year later.

After install WSL and then Debian linux, the first thing I did was something recommended from the reviews:

sudo apt update && sudo apt full-upgrade && sudo apt install aptitude
sudo aptitude install ~pstandard ~prequired ~pimportant -F%

Googling led to some goofy answers like using a PPA (which isn’t a bad thing) but you can screw up your environment if not careful

So….. time to go old school and build from source.

sudo apt-get install -y make build-essential libssl-dev zlib1g-dev   
sudo apt-get install -y libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm 
sudo apt-get install -y libncurses5-dev  libncursesw5-dev xz-utils tk-dev

Those are the basic requirements BUT I am fairly certain somethings like lxml (for Beautiful Soup) will be missing. Not an issue if you don’t intend to use lxml AND they are not required for building the python 3.6 interpreter.

wget https://www.python.org/ftp/python/3.6.5/Python-3.6.5.tgz
tar xvf Python-3.6.5.tgz
cd Python-3.6.5
./configure --enable-optimizations --with-ensurepip=install
make
sudo make altinstall

NOTE! you can do

make -j8

for a faster build time BUT ideally using

-j# where the # is the number of cores on your CPU

is better

NOTE! if you have a faster computer, the actual compile process is fairly quick but than it runs a post build unit testing suite that is absolutely massive because of the `–enable-optimizations` flag. If you need speed for a commercial environment AND you are 100% certain your environment across all machines is the same, just retar up the source directory with the pre-built binaries, send it to each machine, and then run `make altinstall`.

From there you can run

user@machine:~/$python3.6.5

some people recommend fiddling with

alternatives

so you can just do

user@machine:~/$python3.6.5

but generally I use virtualenv and explicitly make a new environment with my version of python to keep me sane.

Python 3 – abusing annotations

Since my retirement a few years ago my habit of trying out sometimes useless or convoluted ideas has gone up a few notches.

Latest discovery is that `inspect.signature()` passes parameter annotations straight through. With a bit of function decorator hackery, you can get positional/keyword instrumented transformers.


@magic_params
def some_func(always_str:str):
   print(f"always_str is {type(always_str)} with a value of {repr(always_str)}")

>>some_func(123)
always_str is  with a value of "'123'"

another example


def reversed_str(raw):
    if not isinstance(raw, str):
        raw = str(raw)

    return raw[::-1]

@magic_decorator
def goofy_func(bizarro:reversed_str):
    return bizarro

assert goofy_func("Hello World") == "dlroW olleH"

A working proof of concept

In one of my pet projects, I have a method with a signature like `def process_request(self, action:SomeClass.FromDict)` which takes a dictionary for the `action` parameter and passes that to SomeClass.FromDict which then returns a instance of `SomeClass`.

In another case, when dealing with Twisted in Python3 and that all strings are type `` I used something like the magic_decorator above and a transformer `SafeStr` (ex. def do_something(name:SafeStr)` to ensure that the name parameter is ALWAYS of type str. Anecdotally Python3 blows up if you try to do something like .startswith()`.

Grand scheme I think this is an interesting quirk but if my comments and wording isn’t clear, I would prescribe caution if using this in revenue generating code (or code intended to make you wealthy or at least provide money for pizza & beer).