Implicit preferences in OR dependencies
Debian packages commonly use or dependencies of the form “a | b” to mean that a or b should be installed, while preferring option a over b. In general, for resolving an or dependency, we will try all options from the left to the right, preferring the left-most option. We also prefer real packages over virtual ones. If one of the alternatives is already installed we use that.
def solve_or(or): best_real = None best_virtual = None for dep in or: for target in dep: if target.name == dep.name and best_real is None: best_real = target if target.name != dep.name and best_virtual is None: best_virtual = target if target.is_installed(): return target return best_real if best_real is not None else best_virtual
Now, this way of solving dependencies is slightly problematic. Let us consider a package that depends on: a | b, b. APT will likely choose to install ‘a’ to satisfy the first dependency and ‘b’ to satisfy the second. I currently have draft code around for a future version of APT that will cause it to later on revert unneeded changes, which means that APT will then only install ‘b’. This result closely matches the CUDF solvers and cupt’s solver.
On the topic of solving algorithms, we also have the problem that optimizing solvers like the ones used with apt-cudf do not respect the order of dependencies, rather choosing to minimise the number of packages installed. This causes such a solver to often do stuff like selecting an sqlite database as backend for some service rather then a larger SQL server, as that installs fewer packages.
To make such solvers aware of the implicit preferences, we can introduce a new type of dependency category: Weak conflicts, also known as Recommends-Not. If a package P defines a Recommends-Not dependency against a package Q, then this means that Q should not be installed if P is installed. Now, if we have a dependency like:
Depends: a | b | c
we can encode this as:
Recommends-Not: c, c, b
Causing the solver to prefer a, then b, and then c. This should be representable as a pseudo-boolean optimization problem, as is common for the dependency problem, although I have not looked at that yet — it should work by taking the standard representation of conflicts, adding a relaxation variable and then minimising [or maximising] the number of relaxation variables.