This paper on succinct graph data structures will be presented at PACT (http://www.pactconf.org) next month.
To put it in the broader context of relational database programming and why it's significant now for software developers in general, here are some thoughts from a thread I posted yesterday [0] on the convergence of ideas that have been in the works for the last 10+ years on the graph database front and the forces that have aligned in hardware and other fronts to bring about the architectural paradigm shift that's to come...
This upcoming paper [...] is more data model than database, but I think you'll find it interesting.
For something more database specific, check out the new RedisGraph module [1] implemented with GraphBLAS [2] by the venerable Tim Davis [3] himself, who as you know implements the underlying sparse matrix algos used in everything from MATLAB to Google Maps...
RedisGraph in the Language of Linear Algebra with GraphBLAS, co-presented by RedisLabs and Tim Davis [video]
GraphBLAS is not Redis-specific, but the Redis data structures and new modular design made it an ideal candidate for the first popular database implementation.
This is significant because GraphBLAS is more than 10 years in the making, the culmination of the initial D4M matrix model design by Jeremy Kepner [4] and his team at MIT Lincoln Laboratory Supercomputing Center.
And for the last ~5 years or so the software model has been designed in collaboration with hardware teams at Intel, NVIDIA, IBM, and the labs [5] to make chips and architectures optimized for these new matrix models and capable of exascale.
It all came together this summer with the release of GraphBLAS 1.0 [6]. Now that GPU and TPU accelerators are populating the data centers and linear algebra has come en vogue, hopefully enough software engineers will be ready with the background understanding and a working mental model for the underpinning architectural paradigm shift [7].
Sadly, RegEx has evolved far away from the original regular expression we learnt in school, and it is certainly less NFA like. This make it harder to execute a faster speed, e.g. backtracing makes it more context sensitive etc.
To put it in the broader context of relational database programming and why it's significant now for software developers in general, here are some thoughts from a thread I posted yesterday [0] on the convergence of ideas that have been in the works for the last 10+ years on the graph database front and the forces that have aligned in hardware and other fronts to bring about the architectural paradigm shift that's to come...
[0] The design and implementation of modern column-oriented database systems https://news.ycombinator.com/item?id=18076547
This upcoming paper [...] is more data model than database, but I think you'll find it interesting.For something more database specific, check out the new RedisGraph module [1] implemented with GraphBLAS [2] by the venerable Tim Davis [3] himself, who as you know implements the underlying sparse matrix algos used in everything from MATLAB to Google Maps...
RedisGraph in the Language of Linear Algebra with GraphBLAS, co-presented by RedisLabs and Tim Davis [video]
https://www.youtube.com/watch?v=xnez6tloNSQ
GraphBLAS is not Redis-specific, but the Redis data structures and new modular design made it an ideal candidate for the first popular database implementation.
This is significant because GraphBLAS is more than 10 years in the making, the culmination of the initial D4M matrix model design by Jeremy Kepner [4] and his team at MIT Lincoln Laboratory Supercomputing Center.
And for the last ~5 years or so the software model has been designed in collaboration with hardware teams at Intel, NVIDIA, IBM, and the labs [5] to make chips and architectures optimized for these new matrix models and capable of exascale.
It all came together this summer with the release of GraphBLAS 1.0 [6]. Now that GPU and TPU accelerators are populating the data centers and linear algebra has come en vogue, hopefully enough software engineers will be ready with the background understanding and a working mental model for the underpinning architectural paradigm shift [7].
[1] RedisGraph https://oss.redislabs.com/redisgraph/
[2] GraphBLAS http://graphblas.org
[3] Tim Davis http://faculty.cse.tamu.edu/davis/
[4] Jeremy Kepner http://www.mit.edu/~kepner/
[5] GraphBLAS: Building Blocks For High Performance Graph Analytics https://crd.lbl.gov/news-and-publications/news/2017/graphbla...
[6] Graph algorithms via SuiteSparse:GraphBLAS: triangle counting and K-truss [pdf] http://faculty.cse.tamu.edu/davis/GraphBLAS/HPEC18/Davis_HPE...
[7] David Patterson Says It’s Time for New Computer Architectures and Languages https://news.ycombinator.com/item?id=18009581