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From Ivan Rakov <ivan.glu...@gmail.com>
Subject Re: Data compression in Ignite
Date Wed, 09 Aug 2017 15:37:47 GMT
+1 for per-page approach (2).
I'm not sure if dictionary-based encoding within page is really 
efficient. Compression quality grows with increasing size of sample 
(it's pageSize in our case - not so big), and we can't encode "words" on 
the border of two entries. Also, we'll have to "garbage-collect" page 
dictionary from time to time (as set of entries may change and some 
"words" may become redundant).
However, if Oracle implemented it in profitable way, we can do it as well :)

I have a concern about (3) that it will add significant latency/network 
overhead. Accessing distributed caсhe with dictionary at every put is 
not free.

Also, I'd like to propose one more approach (let it be (4)) - local 
per-partition dictionary. We can store set of dictionary "words" in B+ 
tree or any other structure in pages - it will be automatically 
persisted. Efficiency of encoding will be higher than in per-page case. 
The open question for per-partition dictionary is how to 
"garbage-collect" it. In per-page case we can do it under page write 
lock, but here we have to do it in more tricky way.

Best Regards,
Ivan Rakov

On 09.08.2017 17:48, Vladimir Ozerov wrote:
> Igniters,
> I had several private talks with Igniters about data compression and would
> like to share the summary with ... Igniters :-)
> Currently all Ignite's data is uncompressed. It leads to excessive network
> traffic, GC pressure and disk IO (in case of persistence). Most modern
> databases are able to compress data, what gives them 2-4x size reduction on
> typical workloads. We need compression in Ignite.
> There are several options I'd like to discuss. The main difference between
> them - on what "level" to compress: per-entry, per-data-page or per-cache.
> *1) Per-entry compression*
> Apache Geode uses this approach. Every cache entry is compressed using
> Snappy. This is very easy to implement, but every entry access (e.g.
> reading single field) require full decompression or even re-compression,
> what could lead to higher CPU consumption and worse performance.
> *2) Per-data-page compression*
> Oracle and DB2 use this approach. Pages are compressed with
> dictionary-based approach (e.g. LZV). It is important, that they do not
> compress the whole page. Instead, only actual data is compressed, while
> page structure remains intact. Dictionary is placed within the page. This
> way it is possible to work with individual entries and even individual
> fields without full page decompression. Another important thing - it is not
> necessary to re-compress the page on each write. Instead, data is stored in
> uncompressed form first, and compressed even after certain threshold is
> reached. So negative CPU impact is minimal. Typical compression rate would
> be higher than in per-entry case, because the more data you have, the
> better it can be compressed.
> *3) Per-cache compression*
> Suggested by Alex Goncharuk. We could have a dictionary for the whole
> cache. This way we could achieve the highest compression rate possible. The
> downside is complex implementation - we would have to develop an algorithm
> of sharing the dictionary within the cluster. At some point the dictionary
> could become too huge to fit in-memory, so we should either control it's
> size or spill it to disk.
> I propose to use per-data-page approach as both gives nice compression rate
> and relatively easy to implement.
> Please share your thoughts.
> Vladimir.

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