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by strbean
1439 days ago
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How is the maturity/stability of Poetry these days? I despise Pipenv, and was hoping to push for a switch to Poetry at my place of work a couple years ago, but I ran into blocking bugs across multiple versions (latest N versions affected by bug A, prior M versions affected by bug B). Had to chock it up to "not yet mature enough" and resign myself to the absurd lock times and countless terrible behaviors of Pipenv. |
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1. Poetry's default assumption on packages respecting semver simply does not hold up in reality. There are very few packages actually sticking to semver. Thus the `^x.y.z` default version range is quite often too loose. I've found that using `~x.y.z` for most packages is far more stable.
2. Imho, `poetry update` is a footgun. Without specifier, it will attempt to update the entire dependency tree. Not only is this slow, together with 1) it's all too likely one ends up with dependencies that actually are incompatible at runtime. I'd much rather have a `poetry update --all` flag instead for the rare instance I do want to update everything. The default behaviour should be to require a list of packages to update.
3. There are some common packages that cause very long resolution times if they are not restricted. Case in point: boto3. Even if one doesn't use boto3 oneself, it's very likely a transient dependency. Many packages simply specify `'*'` as their version dependency (they shouldn't, but it's the unfortunate reality many do). This will cause poetry to consider every possible boto3 version. With hundreds of versions - boto3 has a release every other day - this gets unwieldy fast. So I often end up specifying boto3 myself with some sensible range in my toml file, even when it's not a strict dependency of my own project.
4. The datascience ecosystem needs particular attention. Best to simply pin those, as every pandas update is guaranteed to break something. ABI changes to numpy are a particular nightmare. This is again due to too many packages simply specifying `'*'` for their numpy dependency. Which is further complicated by the fact that most don't distinguish between build-time dependencies and run-time dependencies. The numpy ABI is only forward compatible. Hence one should build with the oldest supported numpy[0].
[0]: https://pypi.org/project/oldest-supported-numpy/