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https://issues.apache.org/jira/browse/JENA-624?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15031105#comment-15031105
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A. Soroka commented on JENA-624:
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It's definitely two operations (maybe {{size}} and {{numGraphs}}) but it's not
so hard to figure out. I will send a PR for this sometime next week. I'm
starting to think that it may be worthwhile to equip {{TripleTable}} and
{{QuadTable}} with some "quasi-reflective" methods so they can self-describe
the ordering of slots they each represent. Or maybe that can happen at
{{TupleTable}}. It might clarify some of the other code there, and help
generify the places (like here and {{listgraphNodes}} where we penetrate
abstractions for the sake of performance.
> Develop a new in-memory RDF Dataset implementation
> --------------------------------------------------
>
> Key: JENA-624
> URL: https://issues.apache.org/jira/browse/JENA-624
> Project: Apache Jena
> Issue Type: Improvement
> Reporter: Andy Seaborne
> Assignee: A. Soroka
> Labels: java, linked_data, rdf
>
> The current (Jan 2014) Jena in-memory dataset uses a general purpose
> container that works for any storage technology for graphs together with
> in-memory graphs.
> This project would develop a new implementation design specifically for RDF
> datasets (triples and quads) and efficient SPARQL execution, for example,
> using multi-core parallel operations and/or multi-version concurrent
> datastructures to maximise true parallel operation.
> This is a system project suitable for someone interested in datatbase
> implementation, datastructure design and implementation, operating systems or
> distributed systems.
> Note that TDB can operate in-memory using a simulated disk with
> copy-in/copy-out semantics for disk-level operations. It is for faithful
> testing TDB infrastructure and is not designed performance, general in-memory
> use or use at scale. While lesson may be learnt from that system, TDB
> in-memory is not the answer here.
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