Java – Get term frequencies in Lucene

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Is there a fast and easy way of getting term frequencies from a Lucene index, without doing it through the TermVectorFrequencies class, since that takes an awful lot of time for large collections?

What I mean is, is there something like TermEnum which has not just the document frequency but term frequency as well?

UPDATE:
Using TermDocs is way too slow.

Best Solution

Use TermDocs to get the term frequency for a given document. Like the document frequency, you get the term documents from an IndexReader, using the term of interest.

You won't find a faster method than TermDocs without losing some generality. TermDocs reads directly from the ".frq" file in an index segment, where each term frequency is listed in document order.

If that's "too slow", make sure that you've optimized your index to merge multiple segments into a single segment. Iterate over the documents in order (skips are alright, but you can't jump back and forth in the document list efficiently).

Your next step might be additional processing to create an even more specialized file structure that leaves out the SkipData. Personally I would look for a better algorithm to achieve my objective, or provide better hardware—lots of memory, either to hold a RAMDirectory, or to give to the OS for use on its own file-caching system.

Some test results

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:

the "ContainsKey" method that I presented in the question
the "TestForNull" method suggested by Aleksandar Dimitrov
the "AtomicLong" method suggested by Hank Gay
the "Trove" method suggested by jrudolph
the "MutableInt" method suggested by phax.myopenid.com

Method

Here's what I did...

created five classes that were identical except for the differences shown below. Each class had to perform an operation typical of the scenario I presented: opening a 10MB file and reading it in, then performing a frequency count of all the word tokens in the file. Since this took an average of only 3 seconds, I had it perform the frequency count (not the I/O) 10 times.
timed the loop of 10 iterations but not the I/O operation and recorded the total time taken (in clock seconds) essentially using Ian Darwin's method in the Java Cookbook.
performed all five tests in series, and then did this another three times.
averaged the four results for each method.

Results

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.

ContainsKey: 30.654 seconds (baseline)
AtomicLong: 29.780 seconds (1.03 times as fast)
TestForNull: 28.804 seconds (1.06 times as fast)
Trove: 26.313 seconds (1.16 times as fast)
MutableInt: 25.747 seconds (1.19 times as fast)

Conclusions

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.

The code

Here is the crucial code from each method.

ContainsKey

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);

TestForNull

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);
}

AtomicLong

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();

Trove

import gnu.trove.TObjectIntHashMap;
...
TObjectIntHashMap<String> freq = new TObjectIntHashMap<String>();
...
freq.adjustOrPutValue(word, 1, 1);

MutableInt

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();
}