Re: [Bigdata-developers] bigdata 1.0.0 release announcement

[Bryan T. <br...@sy...>]

We have people who deploy against NAS.  Or are you speaking about a consumer level device? Those often do not offer a full time connection. Bryan 

> -----Original Message-----
> From: Jack Park [mailto:jac...@gm...] 
> Sent: Tuesday, July 05, 2011 1:32 PM
> To: big...@li...
> Subject: Re: [Bigdata-developers] bigdata 1.0.0 release announcement
> 
> When one chooses the path to the journal, has anyone had any 
> success choosing a path to one of those RAID networked 
> terabyte stores?
> 
> Jack
> 
> On Tue, Jul 5, 2011 at 10:26 AM, Bryan Thompson 
> <br...@sy...> wrote:
> > This is a bigdata (R) release.  This release is capable of 
> loading 1B 
> > triples in under one hour on a 15 node cluster.  JDK 1.6 is 
> required.
> >
> > Bigdata(R) is a horizontally scaled open source architecture for 
> > indexed data with an emphasis on semantic web data architectures.  
> > Bigdata operates in both a single machine mode (Journal) 
> and a cluster 
> > mode (Federation).  The Journal provides fast scalable ACID indexed 
> > storage for very large data sets.  The federation provides fast 
> > scalable shard-wise parallel indexed storage using dynamic sharding 
> > and shard-wise ACID updates.  Both platforms support fully 
> concurrent readers with snapshot isolation.
> >
> > Distributed processing offers greater throughput but does 
> not reduce 
> > query or update latency.  Choose the Journal when the anticipated 
> > scale and throughput requirements permit.  Choose the 
> Federation when 
> > the administrative and machine overhead associated with operating a 
> > cluster is an acceptable tradeoff to have essentially 
> unlimited data scaling and throughput.
> >
> > See [1,2,8] for instructions on installing bigdata(R), [4] for the 
> > javadoc, and [3,5,6] for news, questions, and the latest 
> developments. 
> > For more information about SYSTAP, LLC and bigdata, see [7].
> >
> > Starting with this release, we offer a WAR artifact [8] for easy 
> > installation of the Journal mode database.  For custom 
> development and 
> > cluster installations we recommend checking out the code from SVN 
> > using the tag for this release.  The code will build automatically 
> > under eclipse.  You can also build the code using the ant 
> script.  The 
> > cluster installer requires the use of the ant script.  You 
> can checkout this release from the following URL:
> >
> > 
> https://bigdata.svn.sourceforge.net/svnroot/bigdata/branches/BIGDATA_R
> > ELEASE_1_0_0
> >
> > New features:
> >
> > - Single machine data storage to ~50B triples/quads (RWStore);
> > - Simple embedded and/or webapp deployment (NanoSparqlServer);
> > - Fast 100% native SPARQL 1.0 evaluation with lots of query 
> > optimizations;
> >
> > Feature summary:
> >
> > - Triples, quads, or triples with provenance (SIDs);
> > - Fast RDFS+ inference and truth maintenance;
> > - Clustered data storage is essentially unlimited;
> > - Fast statement level provenance mode (SIDs).
> >
> > The road map [3] for the next releases includes:
> >
> > - High-volume analytic query and SPARQL 1.1 query, including 
> > aggregations;
> > - Simplified deployment, configuration, and administration for 
> > clusters; and
> > - High availability for the journal and the cluster.
> >
> > For more information, please see the following links:
> >
> > [1] 
> > 
> https://sourceforge.net/apps/mediawiki/bigdata/index.php?title=Main_Pa
> > ge [2] 
> > 
> https://sourceforge.net/apps/mediawiki/bigdata/index.php?title=Getting
> > Started [3] 
> > 
> https://sourceforge.net/apps/mediawiki/bigdata/index.php?title=Roadmap
> > [4] http://www.bigdata.com/bigdata/docs/api/
> > [5] http://sourceforge.net/projects/bigdata/
> > [6] http://www.bigdata.com/blog
> > [7] http://www.systap.com/bigdata.htm
> > [8] https://sourceforge.net/projects/bigdata/files/bigdata/
> >
> > About bigdata:
> >
> > Bigdata(r) is a horizontally-scaled, general purpose storage and 
> > computing fabric for ordered data (B+Trees), designed to operate on 
> > either a single server or a cluster of commodity hardware. 
> Bigdata(r) 
> > uses dynamically partitioned key-range shards in order to 
> remove any 
> > realistic scaling limits - in principle, bigdata(r) may be 
> deployed on 
> > 10s, 100s, or even thousands of machines and new capacity 
> may be added 
> > incrementally without requiring the full reload of all data. The 
> > bigdata(r) RDF database supports RDFS and OWL Lite 
> reasoning, high-level query (SPARQL), and datum level provenance.
> 
> --------------------------------------------------------------
> ----------------
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