4 ms·

Binary searching is quite slow and should be used sparingly but not because of branch misprediction necessarily but because of memory stalls - you're almost alw

Binary searching is quite slow and should be used sparingly but not because of branch misprediction necessarily but because of memory stalls - you're almost always guaranteed to have a cache miss during the search. Similarly for B-trees it's going to be memory stalls that you're probably more focused on addressing, not branch mispredicts.

emn13 2y ago
This probably depends on the size of the area to be searched, and just how hot that region is. After all, if it's fairly small, there won't be any cache misses, and the data structure does use less memory than a typical hash table, which is itself an advantage.
- orf 2y ago
  If the size of the data is small, a linear search through a contiguous array is going to be far faster than anything more complex.
  - emn13 2y ago
    Yep; though we'd have to test a few cases to figure out what the cutoffs are here, and if there's any middle ground left for a divide-and-conquer strategy. It's also definitely going to depend on the cost of the hash function and comparison function - for something like strings, where those can be quite expensive, binary search probably has a better chance of applicability than for guid's say.
    - kllrnohj 2y ago
      For things with non-trivial comparison functions you're all but certainly better off with those in something else like a hashmap. After all, the more expensive the compare, the more expensive the sort & reordering that a binary search requires. And then for trivial comparison objects, binary searching is still slower even at "huge" sizes like 10,000. It's really hard to find a good use of binary searching an array on modern CPUs.
  - eru 2y ago
    Well, it depends on how expensive your comparison is compared to a cache miss.
barrkel 2y ago
B-trees are cache friendly, spending more time doing linear scans at each level to keep the tree shallow, and thus indirection less frequent. They're designed for high latency indirection - loading pages from spinning rust.

emn13 2y ago

This probably depends on the size of the area to be searched, and just how hot that region is. After all, if it's fairly small, there won't be any cache misses, and the data structure does use less memory than a typical hash table, which is itself an advantage.

orf 2y ago
If the size of the data is small, a linear search through a contiguous array is going to be far faster than anything more complex.
- emn13 2y ago
  Yep; though we'd have to test a few cases to figure out what the cutoffs are here, and if there's any middle ground left for a divide-and-conquer strategy. It's also definitely going to depend on the cost of the hash function and comparison function - for something like strings, where those can be quite expensive, binary search probably has a better chance of applicability than for guid's say.
  - kllrnohj 2y ago
    For things with non-trivial comparison functions you're all but certainly better off with those in something else like a hashmap. After all, the more expensive the compare, the more expensive the sort & reordering that a binary search requires. And then for trivial comparison objects, binary searching is still slower even at "huge" sizes like 10,000. It's really hard to find a good use of binary searching an array on modern CPUs.
- eru 2y ago
  Well, it depends on how expensive your comparison is compared to a cache miss.

barrkel 2y ago

B-trees are cache friendly, spending more time doing linear scans at each level to keep the tree shallow, and thus indirection less frequent. They're designed for high latency indirection - loading pages from spinning rust.