> red-black trees...are often a better option than hash tables
Why is this?
Trees have O(n*log(n)) element lookup time as opposed to O(1) for hashtable (assuming table size is allocated appropriately or grows with the data, and you have an effective hash function). If data structure accesses are on the hot path, a hash table is much faster.
Many times, when you need ordered data, you can actually get away with using either a heap or a sorted list, rather than a red-black tree. Both of these are much easier to implement than red-black trees, and have the advantage of no extra per-node storage for child pointers. Which can make a real difference in memory usage if your objects are very small, and a real time difference if it means the difference between CPU cache/main memory or main memory/swap.
Red-black trees can be good if you can easily write a comparator for your objects but not a hash, really need capabilities like quickly determining subranges, aren't on a performance-critical path, or aren't concerned about CPU efficiency because the available hardware greatly exceeds your problem's requirements.
As with most data structure questions, there are no hard-and-fast rules; the data structure you choose is going to depend on your particular situation. I personally probably use each of the four solutions I mentioned (hashtable, red-black tree, heap, sorted list) less than fifty percent of the time.
As with most data structure questions, there are no hard-and-fast rules
As in everything in programming, there are always going to be trade-offs. At least C++11 gives you options: <map> is generally an RB Tree, <unordered_map> is generally a hash table.
Trees have O(n log(n)) element lookup time as opposed to O(1) for hashtable
According to [1], <map> (And <set> and <multimap> and <multiset>) lookups have O(log n) complexity, while <unordered_map> lookups have O(1) amortized and O(n) worst-case complexity.
Again, lots of trade-offs to consider, which is why the C++11 stdlib, IMHO, does a great job, whereas other popular languages which provide "one true data structure" of each type are really not that great without third party libraries, as they don't give you the option to choose the right data structure for the job. Eg, in python, if you need a dict, you use a dict without thinking about if its implemented as a tree or a hash table or a heap or a... similarly, if you need a list, you use a list without worrying if its a linked list, a skip list, a dynamic array, something else entirely. Usually this is a good thing - you can just get on with your life, but sometimes you really do want to make sure that operation X will be O(1) or O(log n) or whatever, and if the built-in data structure does not guarantee this, you must use third party libraries. At least in C++11, the stdlib gives you some options (and usually makes it clear what the complexity of operations are). I really like that about C++11.
For a lot of languages, "the right data structure for the right job" means a choice between lists, vectors, dictionaries (however they are implemented), sets etc. In reality, that's only the first tier - once you decide that lists are the right tool, you still need to decide what kind of list, and a lot of languages don't give you that choice by default. (Though to be fair, a lot of people don't need that choice - premature optimization and all that)
Why is this?
Trees have O(n*log(n)) element lookup time as opposed to O(1) for hashtable (assuming table size is allocated appropriately or grows with the data, and you have an effective hash function). If data structure accesses are on the hot path, a hash table is much faster.
Many times, when you need ordered data, you can actually get away with using either a heap or a sorted list, rather than a red-black tree. Both of these are much easier to implement than red-black trees, and have the advantage of no extra per-node storage for child pointers. Which can make a real difference in memory usage if your objects are very small, and a real time difference if it means the difference between CPU cache/main memory or main memory/swap.
Red-black trees can be good if you can easily write a comparator for your objects but not a hash, really need capabilities like quickly determining subranges, aren't on a performance-critical path, or aren't concerned about CPU efficiency because the available hardware greatly exceeds your problem's requirements.
As with most data structure questions, there are no hard-and-fast rules; the data structure you choose is going to depend on your particular situation. I personally probably use each of the four solutions I mentioned (hashtable, red-black tree, heap, sorted list) less than fifty percent of the time.