Improve Indexing Speed
How to make indexing faster
Here are some things to try to speed up the indexing speed of your Lucene application. Please see ImproveSearchingSpeed for how to speed up searching.
Be sure you really need to speed things up.
Many of the ideas here are simple to try, but others will
necessarily add some complexity to your application. So be sure your
indexing speed is indeed too slow and the slowness is indeed within
Make sure you are using the latest version of Lucene.
Use a local filesystem.
Remote filesystems are typically quite a bit slower for indexing. If
your index needs to be on the remote fileysystem, consider building it
first on the local filesystem and then copying it up to the remote
Get faster hardware, especially a faster IO system.
Open a single writer and re-use it for the duration of your indexing session.
Flush by RAM usage instead of document count.
Call writer.ramSizeInBytes() after every added doc then call flush()
when it's using too much RAM. This is especially good if you have small
docs or highly variable doc sizes. You need to first set maxBufferedDocs
large enough to prevent the writer from flushing based on document
count. However, don't set it too large otherwise you may hit LUCENE-845. Somewhere around 2-3X your "typical" flush count should be OK.
Use as much RAM as you can afford.
More RAM before flushing means Lucene writes larger segments to begin with which means less merging later. Testing in LUCENE-843 found that around 48 MB is the sweet spot for that content set, but, your application could have a different sweet spot.
Turn off compound file format.
Call setUseCompoundFile(false). Building the compound file format takes time during indexing (7-33% in testing for LUCENE-888).
However, note that doing this will greatly increase the number of file
descriptors used by indexing and by searching, so you could run out of
file descriptors if mergeFactor is also large.
Re-use Document and Field instances
As of Lucene 2.3 (not yet released) there are new setValue(...)
methods that allow you to change the value of a Field. This allows you
to re-use a single Field instance across many added documents, which
can save substantial GC cost.
It's best to create a single Document instance, then add multiple
Field instances to it, but hold onto these Field instances and re-use
them by changing their values for each added document. For example you
might have an idField, bodyField, nameField, storedField1, etc. After
the document is added, you then directly change the Field values
(idField.setValue(...), etc), and then re-add your Document instance.
Note that you cannot re-use a single Field instance within a
Document, and, you should not change a Field's value until the Document
containing that Field has been added to the index. See Field for details.
Re-use a single Token instance in your analyzer
Analyzers often create a new Token for each term in sequence that
needs to be indexed from a Field. You can save substantial GC cost by
re-using a single Token instance instead.
Use the char API in Token instead of the String API to represent token Text
As of Lucene 2.3 (not yet released), a Token can represent its text
as a slice into a char array, which saves the GC cost of new'ing and
then reclaiming String instances. By re-using a single Token instance
and using the char API you can avoid new'ing any objects for each
term. See Token for details.
Use autoCommit=false when you open your IndexWriter
In Lucene 2.3 (not yet released), there are substantial
optimizations for Documents that use stored fields and term vectors, to
save merging of these very large index files. You should see the best
gains by using autoCommit=false for a single long-running session of IndexWriter. Note however that searchers will not see any of the changes flushed by this IndexWriter
until it is closed; if that is important you should stick with
autoCommit=true instead or periodically close and re-open the writer.
Instead of indexing many small text fields, aggregate the
text into a single "contents" field and index only that (you can still
store the other fields).
Increase mergeFactor, but not too much.
defers merging of segments until later, thus speeding up indexing
because merging is a large part of indexing. However, this will slow
down searching, and, you will run out of file descriptors if you make
it too large. Values that are too large may even slow down indexing
since merging more segments at once means much more seeking for the
Turn off any features you are not in fact using.
If you are storing fields but not using them at query time, don't
store them. Likewise for term vectors. If you are indexing many fields,
turning off norms for those fields may help performance.
Use a faster analyzer.
Sometimes analysis of a document takes alot of time. For example,
StandardAnalyzer is quite time consuming, especially in Lucene version
<= 2.2. If you can get by with a simpler analyzer, then try it.
Speed up document construction.
Often the process of retrieving a document from somewhere external
(database, filesystem, crawled from a Web site, etc.) is very time
Don't optimize unless you really need to (for faster searching).
Use multiple threads with one IndexWriter.
Modern hardware is highly concurrent (multi-core CPUs, multi-channel
memory architectures, native command queuing in hard drives, etc.) so
using more than one thread to add documents can give good gains
overall. Even on older machines there is often still concurrency to be
gained between IO and CPU. Test the number of threads to find the best
Index into separate indices then merge.
If you have a very large amount of content to index then you can
break your content into N "silos", index each silo on a separate
machine, then use the writer.addIndexesNoOptimize to merge them all
into one final index.
Run a Java profiler.
If all else fails, profile your application to figure out where the
time is going. I've had success with a very simple profiler called JMP.
There are many others. Often you will be pleasantly surprised to find
some silly, unexpected method is taking far too much time.