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Editorial Team
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Editorial Team
Asked: May 12, 20262026-05-12T10:14:53+00:00 2026-05-12T10:14:53+00:00

Here is my situation. I am using two java.util.HashMap to store some frequently used

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Here is my situation. I am using two java.util.HashMap to store some frequently used data in a Java web app running on Tomcat. I know the exact number of entries into each Hashmap. The keys will be strings, and ints respectively.

My question is, what is the best way to set the initial capacity and loadfactor?

Should I set the capacity equal to the number of elements it will have and the load capacity to 1.0? I would like the absolute best performance without using too much memory. I am afraid however, that the table would not fill optimally. With a table of the exact size needed, won’t there be key collision, causing a (usually short) scan to find the correct element?

Assuming (and this is a stretch) that the hash function is a simple mod 5 of the integer keys, wouldn’t that mean that keys 5, 10, 15 would hit the same bucket and then cause a seek to fill the buckets next to them? Would a larger initial capacity increase performance?

Also, if there is a better datastructure than a hashmap for this, I am completely open to that as well.

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  1. Editorial Team
    Editorial Team
    2026-05-12T10:14:53+00:00Added an answer on May 12, 2026 at 10:14 am

    In the absence of a perfect hashing function for your data, and assuming that this is really not a micro-optimization of something that really doesn’t matter, I would try the following:

    Assume the default load capacity (.75) used by HashMap is a good value in most situations. That being the case, you can use it, and set the initial capacity of your HashMap based on your own knowledge of how many items it will hold – set it so that initial-capacity x .75 = number of items (round up).

    If it were a larger map, in a situation where high-speed lookup was really critical, I would suggest using some sort of trie rather than a hash map. For long strings, in large maps, you can save space, and some time, by using a more string-oriented data structure, such as a trie.

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