I hope this question is not considered too basic for this forum, but we'll see. I'm wondering how to refactor some code for better performance that is getting run a bunch of times.
Say I'm creating a word frequency list, using a Map (probably a HashMap), where each key is a String with the word that's being counted and the value is an Integer that's incremented each time a token of the word is found.
In Perl, incrementing such a value would be trivially easy:
$map{$word}++;
But in Java, it's much more complicated. Here the way I'm currently doing it:
int count = map.containsKey(word) ? map.get(word) : 0; map.put(word, count + 1);
Which of course relies on the autoboxing feature in the newer Java versions. I wonder if you can suggest a more efficient way of incrementing such a value. Are there even good performance reasons for eschewing the Collections framework and using a something else instead?
Update: I've done a test of several of the answers. See below.
Map<String, Integer> map = new HashMap<>(); String key = "a random key"; int count = map. getOrDefault(key, 0); // ensure count will be one of 0,1,2,3,... map. put(key, count + 1);
There is no standard small implementation of Map in Java. HashMap is one of the best and most flexible Map implementations around, and is hard to beat.
instead of using putIfAbsent on main map, just use compute function (and do everything inside that function, even the inner map stuff). Whatever you do inside the function will be thread-safe if using ConcurrentHashMap in the root map; no need for synchronization block if you do it this way.
I've gotten a lot of good answers to this question--thanks folks--so I decided to run some tests and figure out which method is actually fastest. The five methods I tested are these:
Here's what I did...
I'll present the results first and the code below for those who are interested.
The ContainsKey method was, as expected, the slowest, so I'll give the speed of each method in comparison to the speed of that method.
It would appear that only the MutableInt method and the Trove method are significantly faster, in that only they give a performance boost of more than 10%. However, if threading is an issue, AtomicLong might be more attractive than the others (I'm not really sure). I also ran TestForNull with final
variables, but the difference was negligible.
Note that I haven't profiled memory usage in the different scenarios. I'd be happy to hear from anybody who has good insights into how the MutableInt and Trove methods would be likely to affect memory usage.
Personally, I find the MutableInt method the most attractive, since it doesn't require loading any third-party classes. So unless I discover problems with it, that's the way I'm most likely to go.
Here is the crucial code from each method.
import java.util.HashMap; import java.util.Map; ... Map<String, Integer> freq = new HashMap<String, Integer>(); ... int count = freq.containsKey(word) ? freq.get(word) : 0; freq.put(word, count + 1);
import java.util.HashMap; import java.util.Map; ... Map<String, Integer> freq = new HashMap<String, Integer>(); ... Integer count = freq.get(word); if (count == null) { freq.put(word, 1); } else { freq.put(word, count + 1); }
import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.ConcurrentMap; import java.util.concurrent.atomic.AtomicLong; ... final ConcurrentMap<String, AtomicLong> map = new ConcurrentHashMap<String, AtomicLong>(); ... map.putIfAbsent(word, new AtomicLong(0)); map.get(word).incrementAndGet();
import gnu.trove.TObjectIntHashMap; ... TObjectIntHashMap<String> freq = new TObjectIntHashMap<String>(); ... freq.adjustOrPutValue(word, 1, 1);
import java.util.HashMap; import java.util.Map; ... class MutableInt { int value = 1; // note that we start at 1 since we're counting public void increment () { ++value; } public int get () { return value; } } ... Map<String, MutableInt> freq = new HashMap<String, MutableInt>(); ... MutableInt count = freq.get(word); if (count == null) { freq.put(word, new MutableInt()); } else { count.increment(); }
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