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From Michael McCandless <luc...@mikemccandless.com>
Subject Re: Performance of never optimizing
Date Tue, 04 Nov 2008 17:36:45 GMT

If possible, you should try to use a larger corpus (eg Wikipedia)  
rather than multiply Reuters by N, which creates unnatural term  
frequency distribution.

The graphs are hard to read because of the spline interpolation.   
Maybe you could overlay X's where there is a real datapoint?

After the 6 rounds at each doc count, how do you then derive the  
number to put on the graph?

It's best to use a real query log, if possible, to run the queries.   
If you are expecting your production machines to have plenty of RAM to  
hold the index, then you should definitely run the queries through  
once, discard it, to get all stuff loaded in RAM including the OS  
caching all required data in its IO cache.

Not opening/closing a reader per search should change the graphs quite  
a bit (for the better) and hopefully change some of the odd things you  
are seeing (in the questions below).

Mike

Justus Pendleton wrote:

> Howdy,
>
> I have a couple of questions regarding some Lucene benchmarking and  
> what the results mean[3]. (Skip to the numbered list at the end if  
> you don't want to read the lengthy exegesis :)
>
> I'm a developer for JIRA[1]. We are currently trying to get a better  
> understanding of Lucene, and our use of it, to cope with the needs  
> of our larger customers. These "large" indexes are only a couple  
> hundred thousand documents but our problem is compounded by the fact  
> that they have a relatively high rate of modification (=delete 
> +insert of new document) and our users expect these modification to  
> show up in query results pretty much instantly.
>
> Our current default behaviour is a merge factor of 4. We perform an  
> optimization on the index every 4000 additions. We also perform an  
> optimize at midnight. Our fundamental problem is that these  
> optimizations are locking the index for unacceptably long periods of  
> time, something that we want to resolve for our next major release,  
> hopefully without undermining search performance too badly.
>
> In the Lucene javadoc there is a comment, and a link to a mailing  
> list discussion[2], that suggests applications such as JIRA should  
> never perform optimize but should instead set their merge factor  
> very low.
>
> In an attempt to understand the impact of a) lowering the merge  
> factor from 4 to 2 and b) never, ever optimizing on an index (over  
> the course of years and millions of additions/updates) I wanted to  
> try to benchmark Lucene.
>
> I used the contrib/benchmark framework and wrote a small algorithm  
> that adds documents to an index (using the Reuters doc generator),  
> does a search, does an optimize, then does another search. All the  
> pretty pictures can be seen at:
>
>  http://confluence.atlassian.com/display/JIRACOM/Lucene+graphs
>
> I have several questions, hopefully they aren't overwhelming in  
> their quantity :-/
>
> 1. Why does the merge factor of 4 appear to be faster than the merge  
> factor of 2?
>
> 2. Why does non-optimized searching appear to be faster than  
> optimized searching once the index hits ~500,000 documents?
>
> 3. There appears to be a fairly sizable performance drop across the  
> board around 450,000 documents. Why is that?
>
> 4. Searching performance appears to decrease towards a fairly  
> pessimistic 20 searches per second (for a relatively simple search).  
> Is this really what we should expect long-term from Lucene?
>
> 5. Does my benchmark even make sense? I am far from an expert on  
> benchmarking so it is possible I'm not measuring what I think I am  
> measuring.
>
> Thanks in advance for any insight you can provide. This is an area  
> that we very much want to understand better as Lucene is a key part  
> of JIRA's success,
>
> Cheers,
> Justus
> JIRA Developer
>
> [1]: http://www.atlassian.com
> [2]: http://www.gossamer-threads.com/lists/lucene/java-dev/47895
> [3]: http://confluence.atlassian.com/display/JIRACOM/Lucene+graphs
>
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