cattrs
SQLAlchemy
cattrs | SQLAlchemy | |
---|---|---|
7 | 124 | |
756 | 8,807 | |
0.8% | 2.2% | |
8.8 | 9.7 | |
17 days ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
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cattrs
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Writing Python like it’s Rust
I'd suggest you look at my cattrs (https://catt.rs) library as a good serde lookalike in Python (sum type support present and getting better), and to use attrs instead of dataclasses in general.
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Starlite updates March '22 | 2.0 is coming
Pydantic is by far not the only library of its kind, with prominent members of the same class being attrs, cattrs or even plain dataclasses for some use cases.
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Noob question on saving objects in YAML files
That being said, data serialization is a very common thing to do, so naturally there are tons of libraries that automate it for you. Personally, using dataclasses and cattrs is my goto way for doing such things.
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Taking JSON input for "posts", "tags" etc. How to escape '\' charecter or detect carefully?
I'm fond of attrs and cattrs myself, attrs make creating data classes a snap, writing all of the stupid code python requires to have a dataclass. Note the new built in dataclass is actually a limited copy of attrs. https://www.attrs.org/en/stable/ and https://github.com/python-attrs/cattrs
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apischema v0.17 - I've developed the fastest typed JSON (de)serialization library, and you can also build your GraphQL schema with it
This month, I've released version 0.17, and it's now blazing fast; there is in fact no more comparison with Pydantic, which more than 5x slower (up to 30x in serialization). It's also faster than alternatives like mashumaro or cattrs. (See the quick benchmark result in documentation, and the code)
- cattrs – an open source Python library for structuring and unstructuring data
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I use attrs instead of pydantic
```
Cattrs has some problems with generics [1] [2]. Dacite and marshmallow-dataclasses don't support generics well either, with some issues around Union types.
They do work well for simple python types but what I'd like to see is guarantee that the serialisation operation is completely reversible and if not raise warning/exception.
[1] https://github.com/Tinche/cattrs/issues/149
SQLAlchemy
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Python: A SQLAlchemy Wrapper Component That Works With Both Flask and FastAPI Frameworks
In SQLAlchemy, models representing database tables typically subclass sqlalchemy.orm.DeclarativeBase (this class supersedes the sqlalchemy.orm.declarative_base function). Accordingly, the abstract base class in this database wrapper component is a sqlalchemy.orm.DeclarativeBase subclass, accompanied by another custom base class providing additional dunder methods.
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Xz/liblzma: Bash-stage Obfuscation Explained
OK -
can we start considering binary files committed to a repo, even as data for tests, to be a huge red flag, and that the binary files themselves should instead be generated at testing time by source code that's stated as reviewable cleartext. This would make it much harder (though of course we can never really say "impossible") to embed a substantial payload in this way.
when binary files are part of a test suite, they are typically trying to illustrate some element of the program being tested, in this case a file that was incorrectly xz-encoded. Binary files like these weren't typed by hand, they will always ultimately come from something plaintext source.
Here's an example! My own SQLAlchemy repository has a few binary files in it! https://github.com/sqlalchemy/sqlalchemy/blob/main/test/bina... oh noes. Why are those files there? well in this case I just wanted to test that I can send large binary BLOBs into the database driver and I was lazy. This is actually pretty dumb, the two binary files here add 35K of useless crap to the source, and I could just as easily generate this binary data on the fly using a two liner that spits out random bytes. Anyone could see that two liner and know that it isn't embedding a malicious payload.
If I wanted to generate a poorly formed .xz file, I'd illustrate source code that generates random data, runs it through .xz, then applies "corruption" to it, like zeroing out the high bit of every byte. The process by which this occurs would be all reviewable in source code.
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Introducing Flama for Robust Machine Learning APIs
Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.
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Alembic with Async SQLAlchemy
Alembic is a lightweight database migration tool for usage with SQLAlchemy. The term migration can be a little misleading, because in this context it doesn't mean to migrate to a different database in the sense of using a different version or a different type of database. In this context, migration refers to changes to the database schema: add a new column to a table, modify the type of an existing column, create a new index, etc..
- Imperative vs. Declarative mapping style in Domain Driven Design project
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Unlocking efficient authZ with Cerbos’ Query Plan
To simplify this process, Cerbos developers have come up with adapters for popular Object-Relational Mapping (ORM) frameworks. You can check out for more details on the query plan repo - which also contains adapters for Prisma and SQLAlchemy - as well as a fully functioning application using Mongoose as its ORM.
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Python: Just Write SQL
That above pattern is one I've seen people do even recently, using the "select().c" attribute which from very early versions of SQLAlchemy is defined as "the columns from a subquery of the SELECT" ; this usage began raising deprecation warnings in 1.4 and is fully removed in 2.0 as it was a remnant of a much earlier version of SQLAlchemy. it will do exactly as you say, "make a subquery for each filter condition".
the moment you see SQLAlchemy doing something you see that seems "asinine", send an example to https://github.com/sqlalchemy/sqlalchemy/discussions and I will clarify what's going on, correct the usage so that the query you have is what you expect, and quite often we will add new warnings or documentation when we see people doing things we didn't anticipate.
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A steering council note about making the global
The creator and lead maintainer of SQLAlchemy, one of the most popular and most used Python library for accessing databases (who doesn't?) gave a rather interesting response to PEP703.
If this doesn't ring any alarm bells I don't know what will.
> Basically for the moment the GIL-less idea would likely be burdensome for us and the fact that it's only an "option" seems to strongly imply major compatibility issues that we would not prefer.
https://github.com/sqlalchemy/sqlalchemy/discussions/10002#d...
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More public SQL-queryable databases?
Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.
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How useful is Python in accounting and auditing?
When using python with sql databases like postgres or mariadb or SQLite you would use SQLAlchemy or another ORM of if you're feeling brave, you code it by hand. With ORMs you provide the address of your database and it connects for you, letting you use abstractions instead of writing all the SQL yourself (kind of analogous to using vlookups or index match instead of manually entering data).
What are some alternatives?
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
tortoise-orm - Familiar asyncio ORM for python, built with relations in mind
pydantic - Data validation using Python type hints
PonyORM - Pony Object Relational Mapper
Fast JSON schema for Python - Fast JSON schema validator for Python.
Peewee - a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb
serpy - ridiculously fast object serialization
Orator - The Orator ORM provides a simple yet beautiful ActiveRecord implementation.
datamodel-code-generator - Pydantic model and dataclasses.dataclass generator for easy conversion of JSON, OpenAPI, JSON Schema, and YAML data sources.
prisma-client-py - Prisma Client Python is an auto-generated and fully type-safe database client designed for ease of use
lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)
pyDAL - A pure Python Database Abstraction Layer