Speaking as someone who's been doing OO for many years, and recently has been building a largeish project requiring a good deal of speed (a real-time automated options trading system) in Haskell:
- Are there really significantly different usage patterns for data structures between functional and imperative programming?
If you're talking about Haskell, yes, very much so. A large part of this is due to purity, however; the differences are somewhat less in other functional languages where mutable data is more often used. That said, as others have pointed out, recursive code and structures are much more heavily used in all or nearly all functional languages.
- If so, is this a problem?
It hasn't been one for me, aside from having to spend some time to learn the new way of working. In particular, performance has certainly not been an issue: the system I'm working on runs considerably faster than the previous Java implementation, for example.
- What if you really do need a hash table for some application? Do you simply swallow the extra expense incurred for modifications?
Generally the problem is not that "you really do need a hash table," but that you need access to certain data within some given time constraints (which may well be, "as fast as possible on some given hardware."). For that, you look around and do what you need to do. If that includes introducing mutability, I don't see a big problem with that, and you can do it in Haskell, though it might not be as convenient as it is in other languages. But do keep in mind, if you have a problem of this nature, it's certainly not going to be as simple as, "use a generic hash table and you're done." Extremely high performance for particular functionality on a particular hardware platform invariably takes a lot of work, and usually more than a few tricks. Preferring one language implementation over another just because it has some particular thing that works better than it does in other languages is, in my opinion, a rather unsophisticated approach to software engineering that is not likely to consistently produce good results.