I do not understand how (2) will help us reduce the size of the serialized
object. It sounds like we need to serialize an object the way we do today,
and then compress it when it arrives to the server side and gets stored in
some page. This looks like a double whammy.
I do not understand why the approach (3) is difficult. Yes, we will have to
implement a shared compression table, but I am not sure if it will ever
grow too big. As a matter of fact, we can stop adding entries to it, after
it reaches a certain pre-defined size. Also, we already know how to persist
data, so spilling the compression table to disk should not be an issue.
If my assumption about (2) is correct, I would like to ask you to give
another thought to the (3).
On Wed, Aug 9, 2017 at 7:48 AM, Vladimir Ozerov <voze...@gridgain.com>
> 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.