azure-functions-host VS SQLAlchemy

True but I was baffled to find out that structured logging simply doesn't work in Azure Functions in Isolated Mode! The solution is to use Serilog.

Investigating and reporting issues with timer triggered functions not firing

As someone whose job involves maintaining uptime of a critical system that's dependent on Cosmos DB this sort of thing is scary. Where there's been other reliability issues with Cosmos before we've not had an understanding customer base, and it feels very out of my control.

I'm finding a lot of the reliability guarantees of Azure PaaS services are overblown or come with big caveats when you start to work with them in a serious way. For example I've had some bad reliability issues with Azure Functions not firing, or the hosts becoming unresponsive. And it seems like there's a large set of outstanding issues with them beyond that https://github.com/Azure/azure-functions-host/issues

I think people need to look more carefully at these PaaS guarantees and look at what that 99.999% reliability Microsoft are claiming actually means.

See the answers of this issue on GitHub.

Before we move on, so what really is a custom handler and how does it work? Custom handlers let your Function app to accept events (eg. HTTP requests) from the Global host (aka Function host - that powers your Function apps) - as long as your chosen language supports HTTP primitives.

Functions can be triggered in multiple ways: HTTP, Queue, Db, Blob Storage Change and many more. More on this can be found here. This was one of the major reasons why I thought Azure Functions are great. I mostly needed the HTTP and Queue trigger. With HTTP functions comes also the requirement for Authentication and Authorization. I'm using Auth0 as an authentication provider. The implementation is usually straightforward. The frontend obtains an access token and the API validates the token and authenticates the request. OpenID connect is well documented and somewhat easy to use in asp.net core for example. Not with Azure Functions I googled for days, opened an issue and tried everything I could think of and came to the conclusion: Microsoft doesn’t provide you with proper SDKs to handle authentication adequately.

ODBC drivers in most (if not all) cases are slower than native libraries. I didn't check what does binding for python uses, you can check their repo if you want to https://github.com/Azure/azure-functions-host

Technically the Azure Function host reaches out to a CDN and installs the function based on some constants hidden in the Azure Functions Host (see https://github.com/Azure/azure-functions-host/blob/9bdb40b2d517f32c5052956ba8c9cf662ea36a9e/src/WebJobs.Script/ScriptConstants.cs => search for ExtensionBundleDefaultSourceUri)

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.

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.

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.

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..

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.

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.

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...

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.

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).