Comments (1)
Based on performance and memory tests for primitive maps and sets, we have consistently seen that 0.5 is a good load factor.
The memory footprint of the map per entry is given by the formula: (size of pay load) * 1.5/L + C / (# of entries)
where L
is load factor and C
is a constant. The 1.5 / L
term comes from the fact that the cost per entry is 1 / L
just before resizing and 2 / L
just after resizing so (1/L + 2/L) * 1/2
is the average cost per entry.
In the large entry limit, the final term (C / # of entries)
is ignorable, which leads us to (size of payload) * 1.5/L
. This is 12 bytes per int in an IntHashSet
, which compares very favorably with say, java.util.HashSet<Integer>
, which has an average entry size of 56 bytes.
In the small entry limit, which is where your concern is coming from, the second term dominates. So changing L wonโt help.
In the small limit, there are two possible solutions to achieving lower memory footprint.
- One is to create custom small sets, similar to the work already done on the object side of the API. See
SingletonSet
,DoubletonSet
, etc. These are instantiated via factory methods. - The second solution would be to have a different internal representation in the same set when the set size is small. This is a harder solution, but itโs nicer from an API perspective.
You can implement the first solution yourself (with a little elbow grease). The second solution would require core changes to the existing classes.
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