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From "Adrien Grand (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (LUCENE-4226) Efficient compression of small to medium stored fields
Date Wed, 03 Oct 2012 22:48:07 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-4226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13468945#comment-13468945
] 

Adrien Grand commented on LUCENE-4226:
--------------------------------------

Oh, I didn't mean THAT shortly :-)
                
> Efficient compression of small to medium stored fields
> ------------------------------------------------------
>
>                 Key: LUCENE-4226
>                 URL: https://issues.apache.org/jira/browse/LUCENE-4226
>             Project: Lucene - Core
>          Issue Type: Improvement
>          Components: core/index
>            Reporter: Adrien Grand
>            Assignee: Adrien Grand
>            Priority: Trivial
>             Fix For: 4.1, 5.0
>
>         Attachments: CompressionBenchmark.java, CompressionBenchmark.java, LUCENE-4226.patch,
LUCENE-4226.patch, LUCENE-4226.patch, LUCENE-4226.patch, LUCENE-4226.patch, SnappyCompressionAlgorithm.java
>
>
> I've been doing some experiments with stored fields lately. It is very common for an
index with stored fields enabled to have most of its space used by the .fdt index file. To
prevent this .fdt file from growing too much, one option is to compress stored fields. Although
compression works rather well for large fields, this is not the case for small fields and
the compression ratio can be very close to 100%, even with efficient compression algorithms.
> In order to improve the compression ratio for small fields, I've written a {{StoredFieldsFormat}}
that compresses several documents in a single chunk of data. To see how it behaves in terms
of document deserialization speed and compression ratio, I've run several tests with different
index compression strategies on 100,000 docs from Mike's 1K Wikipedia articles (title and
text were indexed and stored):
>  - no compression,
>  - docs compressed with deflate (compression level = 1),
>  - docs compressed with deflate (compression level = 9),
>  - docs compressed with Snappy,
>  - using the compressing {{StoredFieldsFormat}} with deflate (level = 1) and chunks of
6 docs,
>  - using the compressing {{StoredFieldsFormat}} with deflate (level = 9) and chunks of
6 docs,
>  - using the compressing {{StoredFieldsFormat}} with Snappy and chunks of 6 docs.
> For those who don't know Snappy, it is compression algorithm from Google which has very
high compression ratios, but compresses and decompresses data very quickly.
> {noformat}
> Format           Compression ratio     IndexReader.document time
> ————————————————————————————————————————————————————————————————
> uncompressed     100%                  100%
> doc/deflate 1     59%                  616%
> doc/deflate 9     58%                  595%
> doc/snappy        80%                  129%
> index/deflate 1   49%                  966%
> index/deflate 9   46%                  938%
> index/snappy      65%                  264%
> {noformat}
> (doc = doc-level compression, index = index-level compression)
> I find it interesting because it allows to trade speed for space (with deflate, the .fdt
file shrinks by a factor of 2, much better than with doc-level compression). One other interesting
thing is that {{index/snappy}} is almost as compact as {{doc/deflate}} while it is more than
2x faster at retrieving documents from disk.
> These tests have been done on a hot OS cache, which is the worst case for compressed
fields (one can expect better results for formats that have a high compression ratio since
they probably require fewer read/write operations from disk).

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