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> you can't be fully sure that upgrading everything to the latest version is not going to break anything. Well, it's still what you do every time you create a

> you can't be fully sure that upgrading everything to the latest version is not going to break anything. Well, it's still what you do every time you create a new project. > It is mandatory in quite a lot of package managers. Ok but many packages won't be installable at all anymore, because people pin different versions instead of maintaining their packages by continuously integrating upstream releases. > You can't live at latest release with software coming from wildly different developers, with wildly different policies on compatibility, versioning, breaking changes, bugs... Well I didn't know can't so I did. > I can't just take the latest numpy and patch it for Python 3.6. Instead, you should be upgrading your code to support the newer numpy version. Seriously, try it out, you'll be spending less effort at the end of the year. > You also spend time doing maintenance and debugging on new installations to debug and fix compatibility issues, when those upgrades might not bring any value to the product. I spend less time and more spread over the year, bringing value to the whole ecosystem of packages which are bringing value to my product, and bringing value to myself as I develop new products with the dependencies I like and support as such. > In my case, I do that debugging and fixing in a controlled environment I wait for CI or users to report that a new release isn't compatible so I can fix it as soon as possible, in which case we temporarily pin versions, instead of piling up tech debt until it's so huge the whole project becomes trash. That's what I call taking responsibility with the dependencies that you include in your product.

gjulianm 5y ago
> Well, it's still what you do every time you create a new project. Yep, and it is a pain in the ass when the package you want to use creates a dependency conflict. Luckily, at that stage I can still search for an alternative with little to no cost. > Ok but many packages won't be installable at all anymore, because people pin different versions instead of maintaining their packages by continuously integrating upstream releases. And some packages won't be installable anymore because they use deprecated APIs, or were only compatible with Python 2, or expected certain files/folders/programs to be present on the system and they aren't anymore. Packages go into abandonware all the time, removing version pinning doesn't fix that. > Instead, you should be upgrading your code to support the newer numpy version. Seriously, try it out, you'll be spending less effort at the end of the year. Thank you for telling me this, I will be relaying to my clients that they need to upgrade their systems to newer Python versions to get new features, surely that will go well. > I wait for CI or users to report that a new release isn't compatible so I can fix it as soon as possible, in which case we temporarily pin versions, instead of piling up tech debt until it's so huge the whole project becomes trash. Well, I prefer to not have my program crashing in client environments because someone decided to update a package and break things. > piling up tech debt until it's so huge the whole project becomes trash. Waiting to release on new dependency versions until those versions are tested is not tech debt. I mean, semantic versioning was done with this purpose precisely: to signal which upgrades are just bug fixes and you should upgrade ASAP, which ones are new features so you can upgrade without too many problems, and which upgrades break compatibility and you should test it thoroughly in case something broke. If living at latest release works for you and dealing with dependency issues on new installations is not a problem, then go. But other systems will require that a certain release installs dependencies on a consistent manner. Some of my dependencies are automatically upgraded and tested before release, others are pinned because they always break things and I only upgrade manually. But once a version of a package is released, that one goes with version pinning because I prefer to avoid the risk of a client installing the software and having to explain them than "oh, it's just that this dependency released a new version that broke things and we didn't specify the version that our software needed".
- 1337shadow 5y ago
  > Packages go into abandonware all the time, removing version pinning doesn't fix that. Abandoned dependencies should be treated like dropped features: re-implement them in your own code or create another library. > Thank you for telling me this, I will be relaying to my clients that they need to upgrade their systems to newer Python versions to get new features, surely that will go well. Very funny, but seriously they should have an upgrade path, or install their machine once and then never do a single upgrade, but again I'd say this is just tech debt piling. > Waiting to release on new dependency versions until those versions are tested is not tech debt. Not having your own tests is tech debt thought. I wouldn't rely on tests made by others. > I mean, semantic versioning was done with this purpose precisely: to signal which upgrades are just bug fixes and you should upgrade ASAP Of course I agree, the problem is that maintainers tend to not upgrade as often as they should, like until they really have too (another dependency upgrades a shared dependency). Again I see this as a maintainance problem rather than a package manager problem. > If living at latest release works for you and dealing with dependency issues on new installations is not a problem, then go. I'm not saying I never had a problem of course I do, I'm saying that having these problems has a better cost/benefit ratio at the end of the year, because: 0. they are smaller, dealing with one BC break at the time, and 1. they are spread over time because I integrate upstream releases continuously instead of waiting for the day I want to upgrade everything. > "oh, it's just that this dependency released a new version that broke things and we didn't specify the version that our software needed" I'd rather say "oh, it's just that this dependency released a new version that we are implementing support for as we speak, here's the command you can run to fix it meanwhile: ...". But if you don't want to have that problem, just run your CI periodically, make sure the first thing you do in the morning is checking that nightly did actually pass tests, as you're talking about paid software maintenance, I'm talking about both paid and volunteer so that's why I'm including "wait for a user to report" but that doesn't apply for paid maintenance of course. For me we're seeing the discussion between two fundamentally opposite approaches, one being defensive and the other being offensive / actual continuous integration. From my experience, the offensive strategy offers a better cost/benefit ratio at the end of the year.

gjulianm 5y ago

> Well, it's still what you do every time you create a new project. Yep, and it is a pain in the ass when the package you want to use creates a dependency conflict. Luckily, at that stage I can still search for an alternative with little to no cost. > Ok but many packages won't be installable at all anymore, because people pin different versions instead of maintaining their packages by continuously integrating upstream releases. And some packages won't be installable anymore because they use deprecated APIs, or were only compatible with Python 2, or expected certain files/folders/programs to be present on the system and they aren't anymore. Packages go into abandonware all the time, removing version pinning doesn't fix that. > Instead, you should be upgrading your code to support the newer numpy version. Seriously, try it out, you'll be spending less effort at the end of the year. Thank you for telling me this, I will be relaying to my clients that they need to upgrade their systems to newer Python versions to get new features, surely that will go well. > I wait for CI or users to report that a new release isn't compatible so I can fix it as soon as possible, in which case we temporarily pin versions, instead of piling up tech debt until it's so huge the whole project becomes trash. Well, I prefer to not have my program crashing in client environments because someone decided to update a package and break things. > piling up tech debt until it's so huge the whole project becomes trash. Waiting to release on new dependency versions until those versions are tested is not tech debt. I mean, semantic versioning was done with this purpose precisely: to signal which upgrades are just bug fixes and you should upgrade ASAP, which ones are new features so you can upgrade without too many problems, and which upgrades break compatibility and you should test it thoroughly in case something broke. If living at latest release works for you and dealing with dependency issues on new installations is not a problem, then go. But other systems will require that a certain release installs dependencies on a consistent manner. Some of my dependencies are automatically upgraded and tested before release, others are pinned because they always break things and I only upgrade manually. But once a version of a package is released, that one goes with version pinning because I prefer to avoid the risk of a client installing the software and having to explain them than "oh, it's just that this dependency released a new version that broke things and we didn't specify the version that our software needed".

1337shadow 5y ago
> Packages go into abandonware all the time, removing version pinning doesn't fix that. Abandoned dependencies should be treated like dropped features: re-implement them in your own code or create another library. > Thank you for telling me this, I will be relaying to my clients that they need to upgrade their systems to newer Python versions to get new features, surely that will go well. Very funny, but seriously they should have an upgrade path, or install their machine once and then never do a single upgrade, but again I'd say this is just tech debt piling. > Waiting to release on new dependency versions until those versions are tested is not tech debt. Not having your own tests is tech debt thought. I wouldn't rely on tests made by others. > I mean, semantic versioning was done with this purpose precisely: to signal which upgrades are just bug fixes and you should upgrade ASAP Of course I agree, the problem is that maintainers tend to not upgrade as often as they should, like until they really have too (another dependency upgrades a shared dependency). Again I see this as a maintainance problem rather than a package manager problem. > If living at latest release works for you and dealing with dependency issues on new installations is not a problem, then go. I'm not saying I never had a problem of course I do, I'm saying that having these problems has a better cost/benefit ratio at the end of the year, because: 0. they are smaller, dealing with one BC break at the time, and 1. they are spread over time because I integrate upstream releases continuously instead of waiting for the day I want to upgrade everything. > "oh, it's just that this dependency released a new version that broke things and we didn't specify the version that our software needed" I'd rather say "oh, it's just that this dependency released a new version that we are implementing support for as we speak, here's the command you can run to fix it meanwhile: ...". But if you don't want to have that problem, just run your CI periodically, make sure the first thing you do in the morning is checking that nightly did actually pass tests, as you're talking about paid software maintenance, I'm talking about both paid and volunteer so that's why I'm including "wait for a user to report" but that doesn't apply for paid maintenance of course. For me we're seeing the discussion between two fundamentally opposite approaches, one being defensive and the other being offensive / actual continuous integration. From my experience, the offensive strategy offers a better cost/benefit ratio at the end of the year.