[elephant-devel] using one store with mutiple lisp instances

[Ian Eslick]

How can you guarantee that the postgres cache is coherent across machines?  

Also, BDB does caching in shared memory regions across multiple processes, so I'm not sure how much extra benefit you'll get from the Postgres cache.

Sent from my iPhone

On May 7, 2011, at 9:20 AM, Alex Mizrahi <alex.mizrahi at gmail.com> wrote:

>> I am curious why the postgres backend is so unpopular.
> 
> I would not recommend postgres backend as a default choice because it 
> has its own "peculiarities". Particularly, it requires a discipline with 
> use of types, as only strings and (64-bit) integers are properly sorted. 
> Another major drawback is that there is no GC for blobs so it requires 
> export/import to reclaim space from time to time.
> 
> But if using multiple machines is a requirement then it looks like 
> postgres is the only option, as Oracle confirms that use of BDB DB on 
> remote filesystem doesn't work for multiple clients:
> 
> http://download.oracle.com/docs/cd/E17076_02/html/programmer_reference/env_remote.html
> 
>> As far as I
>> know there is no real data to support that it is slow in "real world"
>> applications.
> 
> There is certain overhead when working with blobs, so it is slower than 
> it might be in theory. But whether it is "slow" is another question. DB 
> can bottleneck in multiple places (disk operations, concurrency locks, 
> network...) so there is no simple answer.
> 
> To get a good performance with postgres backend one needs to understand 
> that each DB-related operation means a DB query (which potentially goes 
> over the network). So it is a good idea to structure code in a way that 
> minimizes number of slot reads/writes, range queries etc.
> 
>> It also has caching mechanisms that
>> work in multi-process applications.
> 
> Yeah, this gives some freedom with slot reads (although still has some 
> overhead comparing to CLOS slot reads).
> 
> If we implement cache for range queries as well we can get to a 
> situation where almost nothing is read from DB as long as there is 
> enough memory for cache. This would have a huge impact of DB 
> performance, I think.
> 
> Another possible improvement is to reimplement cache synchronization 
> with notifications instead of DB tables, as this might drastically 
> reduce overhead of cache synchronization. (Update notifications won't be 
> written to disk.)
> 
>> why should berkeley db have an edge in that case?
> 
> With improved caching probably postgres will have an edge for common 
> applications.
> 
> 
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