That does not mean that b-trees are unequivocally better than binary search trees. There are some applications, like concurrency or persistence, where comparison count is not as important.
For instance, fewer values per node means less information to copy when copying the node. Locking is more granular with fewer values per node because fewer values are locked at a time.
The binary structure also makes it possible to support randomized balancing and self-adjusting strategies like splay trees.
I am disappointed to still see frequent mention of red-black trees – I see no reason for red-black trees to ever be the best choice in practice, or in theory, or in school. LBST's (logarithmic binary search trees) [1] are generally simpler, more intuitive, and more efficient [2] than red-black trees. They also support positional access inherently, so the balancing information is useful (subtree size).
For instance, fewer values per node means less information to copy when copying the node. Locking is more granular with fewer values per node because fewer values are locked at a time.
The binary structure also makes it possible to support randomized balancing and self-adjusting strategies like splay trees.
I am disappointed to still see frequent mention of red-black trees – I see no reason for red-black trees to ever be the best choice in practice, or in theory, or in school. LBST's (logarithmic binary search trees) [1] are generally simpler, more intuitive, and more efficient [2] than red-black trees. They also support positional access inherently, so the balancing information is useful (subtree size).
[1] https://www.semanticscholar.org/paper/A-New-Method-for-Balan... [2] https://rtheunissen.github.io/bst