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From "Eks Dev (JIRA)" <>
Subject [jira] [Commented] (LUCENE-4226) Efficient compression of small to medium stored fields
Date Wed, 29 Aug 2012 08:14:09 GMT


Eks Dev commented on LUCENE-4226:

bq. but I removed the ability to select the compression algorithm on a per-field basis in
order to make the patch simpler and to handle cross-field compression.

Maybe it is worth to keep it there for really short fields. Those general compression algorithms
are great for bigger amounts of data, but for really short fields there is nothing like per
field compression.   
Thinking about database usage, e.g. fields with low cardinality, or fields with restricted
symbol set (only digits in long UID field for example).  Say zip code, product color...  is
perfectly compressed using something with static dictionary approach (static huffman coder
with escape symbol-s, at bit level, or plain vanilla dictionary lookup), and both of them
are insanely fast and compress heavily. 

Even trivial utility for users is easily doable, index data without compression, get the frequencies
from the term dictionary-> estimate e.g. static Huffman code table and reindex with this

> Efficient compression of small to medium stored fields
> ------------------------------------------------------
>                 Key: LUCENE-4226
>                 URL:
>             Project: Lucene - Core
>          Issue Type: Improvement
>          Components: core/index
>            Reporter: Adrien Grand
>            Priority: Trivial
>         Attachments:,, LUCENE-4226.patch,
> 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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