Advanced use of hash codes in Java: duplicate elimination with a BitSet
On the previous page, we introduced the problem of duplicate elimination when we had a fairly large number of elements: for example, writing some data to a database table for a given client only if we hadn't previously logged the data for that client's IP address. A possible solution,
but a memory- and object-hungry one, was to store a the previously-seen IP addresses as strings, or better
their hash codes. in a HashSet. On this page, we'll consider that a more efficient structure
could be a BitSet.
Now let's think again about the
statistics of hash codes, and about what our actual
purpose is here. For a given IP address, we only want to store a "true" or "false" value: i.e.
one bit of information. Now let's suppose for a minute that we had a hash table with
a million buckets but which only allowed one hash code per bucket. The process would
be as follows:
- have a BitSet with a million bits;
- for a given item IP address, calculate the hash code;
- for that hash code, calculate the bucket number n, as the hash code modulo a million
(hashCode % noBuckets);
- we consider that IP address to be present if the nth bit in the BitSet is set
(and set it to indicate that it's present).
Now, because we're not dealing with collisions, that will mean that in some cases, we'll get
some "false positives": where one IP address is incorrectly eliminated because we previously
set its bit based on another IP address whose hash code modulo a million happened to result in the
same bit index. But we can calculate how often we expect this to happen, and the error rate
may be acceptable for the purposes of our logging.
Recall the approximation:
After 50,000 distinct IP addresses and a million buckets, the number of expected collisions
will be approximately 50,0002/2 million, or about 1250. In other words, we will
incorrectly discard about 1 in 50 clients because we think we've already logged them. For our
purposes, that may be an acceptable error rate. And if it's not, we can increase the number of
bits to give a better error rate (doubling the number of bits roughly halves the error rate, as
indicated by the formula).
A million bits is about a megabyte; our equivalent hash set would require
several megabytes and the creation of tens of thousands of objects.
As the number of distinct items increases, this technique could be the difference between
storing the set in memory and having to hit the database every time...
Using a better hash function
A proviso of the above scheme is that the stated error rate holds for an "ideal"
hash function. We know that the standard Java hash functions, while not bad,
are far from ideal. For optimal results, we would therefore consider using a better
hash function, such as the 64-bit "strong" hash code
presented elsewhere in this section.
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Editorial page content written by Neil Coffey. Copyright © Javamex UK 2021. All rights reserved.