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java lucene Improve Indexing Speed

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 [WWW] writer.ramSizeInBytes() after every added doc then call [WWW] 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 [WWW] maxBufferedDocs
    large enough to prevent the writer from flushing based on document
    count. However, don't set it too large otherwise you may hit [WWW] 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 [WWW] 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 [WWW] setUseCompoundFile(false). Building the compound file format takes time during indexing (7-33% in testing for [WWW] 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 [WWW] 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 [WWW] 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 [WWW] mergeFactor, but not too much.

    Larger [WWW] mergeFactors
    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
    hard drives.

  • 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
    performance point.

  • 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 [WWW] JMP.
    There are many others. Often you will be pleasantly surprised to find
    some silly, unexpected method is taking far too much time.


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