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https://issues.apache.org/jira/browse/LUCENE-4226?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13443962#comment-13443962
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Robert Muir commented on LUCENE-4226:
-------------------------------------
{quote}
I was also thinking that some codecs such as this kind of per-field
compression, but maybe even the bloom, memory, direct and pulsing postings
formats might deserve a separate "codecs" module where we could put these
non-default "expert" codecs.
{quote}
We have to do something about this soon!
Do you want to open a separate issue for that (it need not block this issue)?
I think we would try to get everything concrete we can out of core immediately
(maybe saving only the default codec for that release), but use the other
ones for testing. Still we should think about it.
> 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
> Priority: Trivial
> Attachments: CompressionBenchmark.java, CompressionBenchmark.java,
> 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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