There were several problems with rbt hashtable implementation:

1. Our internal hashing function returns uint64_t value, but it was
   silently truncated to unsigned int in dns_name_hash() and
   dns_name_fullhash() functions.  As the SipHash 2-4 higher bits are
   more random, we need to use the upper half of the return value.

2. The hashtable implementation in rbt.c was using modulo to pick the
   slot number for the hash table.  This has several problems because
   modulo is: a) slow, b) oblivious to patterns in the input data.  This
   could lead to very uneven distribution of the hashed data in the
   hashtable.  Combined with the single-linked lists we use, it could
   really hog-down the lookup and removal of the nodes from the rbt
   tree[a].  The Fibonacci Hashing is much better fit for the hashtable
   function here.  For longer description, read "Fibonacci Hashing: The
   Optimization that the World Forgot"[b] or just look at the Linux
   kernel.  Also this will make Diego very happy :).

3. The hashtable would rehash every time the number of nodes in the rbt
   tree would exceed 3 * (hashtable size).  The overcommit will make the
   uneven distribution in the hashtable even worse, but the main problem
   lies in the rehashing - every time the database grows beyond the
   limit, each subsequent rehashing will be much slower.  The mitigation
   here is letting the rbt know how big the cache can grown and
   pre-allocate the hashtable to be big enough to actually never need to
   rehash.  This will consume more memory at the start, but since the
   size of the hashtable is capped to `1 << 32` (e.g. 4 mio entries), it
   will only consume maximum of 32GB of memory for hashtable in the
   worst case (and max-cache-size would need to be set to more than
   4TB).  Calling the dns_db_adjusthashsize() will also cap the maximum
   size of the hashtable to the pre-computed number of bits, so it won't
   try to consume more gigabytes of memory than available for the
   database.

   FIXME: What is the average size of the rbt node that gets hashed?  I
   chose the pagesize (4k) as initial value to precompute the size of
   the hashtable, but the value is based on feeling and not any real
   data.

For future work, there are more places where we use result of the hash
value modulo some small number and that would benefit from Fibonacci
Hashing to get better distribution.

Notes:
a. A doubly linked list should be used here to speedup the removal of
   the entries from the hashtable.
b. https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/