| I’m torn. Practically Mercurial feels like it should be the winner. The commands are more uniform and predictable. That’s not all it has going for it either. Mercurial has a concept of commit stages to make history rewriting safer. It has a commit model that enables you to work on and manipulate branches of commits seamlessly, without needing named branches. It has not just a tree of commits but also each commit tracks its own history through rewrites. You get cool commands like absorb and evolve. It’s easier to extend than Git. The only downside to modern Mercurial I can think of is it’s still slower than Git, by at least a bit. But it can scale incredibly far with its extensibility. For example, what Facebook did: https://code.fb.com/core-data/scaling-mercurial-at-facebook/ So why does it never seem to get consideration? I guess it’s because of the insane proliferation of Github and Linux, which is definitely more a blessing than a curse. But it’s weird. Back in the CVS and SVN days, it didn’t seem like there was ever going to be a ‘network effect’ for source control like there is today. I kind of wish Gitlab would implement Mercurial support. I bet it would help Mercurial gain more adoption within teams working on closed source projects. I know Bitbucket does, but to be honest that doesn’t really appeal to me much. |
I think that's true if you're coming from something like Perforce or Subversion and have some notion of what the expectations are around those environments. It's much easier to translate that model into Mercurial commands.
But in the end it's deceptive; git, ugly as it is, reflects the underlying data model. Once you understand that data model, and how the basic commands manipulate that model, then it becomes a very natural language for manipulating the commit chain.
If there's a downside, it's that git doesn't let you get away for too long without learning the data model, while Mercurial lets you live in blessed ignorance until the first time it doesn't work like svn does, and then you're stuck because its data model is not as cleanly exposed, so you need to gently massage it into a good state rather than just surgically moving it to where you want it to be.