Stephan Richter wrote:
> On Friday 13 November 2009, Roché Compaan wrote:
>> We had such an opportunity about 2 years ago and although the client
>> never reached (and probably will never) reach the membership they
>> dreamed about, they did pay us to develop a storage for members that
>> could scale to more than a 100 million members. We implemented a data
>> partitioning strategy at application level. If I had another shot at it,
>> I would try and develop a distributed ZODB storage, because it would be
>> a lot simpler compared to what we had to do at application level.
> Note that Shane developed a sharding solution a year ago with me. It provides 
> container-level partitioning.

Thanks for the reminder. :-)

> This in combination with the encryption work that we did for the ZODB makes 
> the ZODB actually be a lot more advanced than some of the new comers.
> I am very intrigued now to setup an EC2 cluster and install a z3c.sharding 
> based solution demonstrating 100M users with some data. Mmmh...

I've been studying how to build an enormous database based on what I 
know.  There are an incredible number of distributed databases these 
days, but all of them concern me in one way or another.  I'm wondering 
if ZODB might actually have a fighting chance in the distributed 
database realm.  With z3c.sharding or something like it, I think I would 
set things up as follows:

- In-memory ZODB caches would probably be pointlessly painful at that 
scale, so I would set the ZODB cache size for all partitions to 0.  A 
cache size of 0 allows ZODB to cache for the duration of a request, but 
flushes all objects out of the cache at transaction boundaries.

- With the cache size set to 0, we can disable cache invalidation, which 
will probably be a major win.

- I would rely heavily on memcached to provide the pickles.  I would try 
to use the cache checkpointing algorithm I recently added to RelStorage.

- I would aim to read or write only a small number of objects per 
request from partitions.


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