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From "Luca Della Toffola (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-1149) 12% performance improvement by caching in CompositeParser
Date Mon, 22 Jul 2013 21:18:48 GMT

    [ https://issues.apache.org/jira/browse/TIKA-1149?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13715675#comment-13715675
] 

Luca Della Toffola commented on TIKA-1149:
------------------------------------------

First of all, thanks for the very fast response!
Tomorrow I will take some time to make few experiments with the optimization that you suggested.

                
> 12% performance improvement by caching in CompositeParser
> ---------------------------------------------------------
>
>                 Key: TIKA-1149
>                 URL: https://issues.apache.org/jira/browse/TIKA-1149
>             Project: Tika
>          Issue Type: Improvement
>          Components: parser
>    Affects Versions: 1.3, 1.4
>            Reporter: Luca Della Toffola
>            Priority: Minor
>              Labels: performance
>         Attachments: CompositeParser.patch, ParseContext.patch
>
>
> We found an easy way to improve Tika's performance. The idea is to avoid recomputing
parsers map over and over 
> in CompositeParser.getParsers(...) if the context is empty and to cache the returned
value instead. 
> This can be done safely even under the assumption that the media-registry and the list
of component parsers do change while Tika is executing, by invalidating the cache in the case.
> Our attached patch computes the parsers map once per instance of CompositeParser.
> The patch checks for the case where the context is empty and invalidates the cache if
both media-registry and the list of component parsers change in the corresponding setters.
> For example, when running Tika 1.3 on a set of large (~50k classes) JAR files (i.e.,
Java class library + Tika app + other apps), the patch reduces the running time
> from 32 seconds to 29 seconds -- i.e., a speedup of ~12%. Speedups of the same order
of magnitude are found also for smaller workloads.

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