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https://issues.apache.org/jira/browse/TIKA-1149?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jukka Zitting updated TIKA-1149:
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Attachment: 0001-TIKA-1149-Improve-parser-lookup-performance.patch
See the attached patch for a quick draft (doesn't pass all tests yet) of what
this could look like.
The patch relies on a new SimpleParser class that would replace AbstractParser
as the base class for all "simple" parsers that support a fixed set of
(normalized) media types regardless of the context. Such an extra constraint
allows CompositeParser to speed up parser lookups as it doesn't need to build
the full parser map on each access.
> Improve parser lookup performance
> ---------------------------------
>
> 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: 0001-TIKA-1149-Improve-parser-lookup-performance.patch,
> 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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