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[Bigdata-commit] SF.net SVN: bigdata:[4891] branches
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From: <tho...@us...> - 2011-07-12 14:33:09
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Revision: 4891
http://bigdata.svn.sourceforge.net/bigdata/?rev=4891&view=rev
Author: thompsonbry
Date: 2011-07-12 14:33:03 +0000 (Tue, 12 Jul 2011)
Log Message:
-----------
added release notes for the 1.0.1 dot release.
Added Paths:
-----------
branches/BIGDATA_RELEASE_1_0_0/bigdata/src/releases/RELEASE_1_0_1.txt
branches/TERMS_REFACTOR_BRANCH/bigdata/src/releases/RELEASE_1_0_1.txt
Added: branches/BIGDATA_RELEASE_1_0_0/bigdata/src/releases/RELEASE_1_0_1.txt
===================================================================
--- branches/BIGDATA_RELEASE_1_0_0/bigdata/src/releases/RELEASE_1_0_1.txt (rev 0)
+++ branches/BIGDATA_RELEASE_1_0_0/bigdata/src/releases/RELEASE_1_0_1.txt 2011-07-12 14:33:03 UTC (rev 4891)
@@ -0,0 +1,85 @@
+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/tags/BIGDATA_RELEASE_1_0_1
+
+Bug fixes:
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/349 (TermIdEncoder limits
+ Journal to 2B distinct RDF Values per triple/quad store instance).
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/124 (TermIdEncoder should
+ use more bits for scale-out).
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/107 (Unicode clean schema
+ names in the sparse row store).
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/348 (BigdataValueFactory.asValue()
+ must return new instance when DummyIV is used).
+
+New features in 1.0.x release:
+
+- Single machine data storage to ~50B triples/quads (RWStore);
+- Simple embedded and/or webapp deployment (NanoSparqlServer);
+- 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_Page
+[2] https://sourceforge.net/apps/mediawiki/bigdata/index.php?title=GettingStarted
+[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\xAE
+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\xAE
+RDF database supports RDFS and OWL Lite reasoning, high-level query (SPARQL),
+and datum level provenance.
Added: branches/TERMS_REFACTOR_BRANCH/bigdata/src/releases/RELEASE_1_0_1.txt
===================================================================
--- branches/TERMS_REFACTOR_BRANCH/bigdata/src/releases/RELEASE_1_0_1.txt (rev 0)
+++ branches/TERMS_REFACTOR_BRANCH/bigdata/src/releases/RELEASE_1_0_1.txt 2011-07-12 14:33:03 UTC (rev 4891)
@@ -0,0 +1,85 @@
+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/tags/BIGDATA_RELEASE_1_0_1
+
+Bug fixes:
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/349 (TermIdEncoder limits
+ Journal to 2B distinct RDF Values per triple/quad store instance).
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/124 (TermIdEncoder should
+ use more bits for scale-out).
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/107 (Unicode clean schema
+ names in the sparse row store).
+
+ - https://sourceforge.net/apps/trac/bigdata/ticket/348 (BigdataValueFactory.asValue()
+ must return new instance when DummyIV is used).
+
+New features in 1.0.x release:
+
+- Single machine data storage to ~50B triples/quads (RWStore);
+- Simple embedded and/or webapp deployment (NanoSparqlServer);
+- 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_Page
+[2] https://sourceforge.net/apps/mediawiki/bigdata/index.php?title=GettingStarted
+[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\xAE
+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\xAE
+RDF database supports RDFS and OWL Lite reasoning, high-level query (SPARQL),
+and datum level provenance.
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