I think you guys have created a system that demonstrates a new field of mathematics, unless I am missed something in my research on the topic. If we define the system as being an N-dimensional space of R-dimensional subspaces, does this mean we need to use a real 3D matrix to model the algebra implicit in the implementation? Here's the catch, I don't mean a 2D matrix modeling 3 dimensions (square matrix), I mean a 3D matrix with matrix elements E(x)(y)(z). The dimensionality of the (z) elements of the matrix would be R, while (x) and (y) would be the number of partitions in a square matrix. What is the definition of a determinant and such in a matrix that is a volume of elements? Like with a 4D Voronoi diagram, with the ability to project 3D "surfaces" out of the 4D diagram, a 3D matrix (vatrix?) could project 2D matrices.
So what are off plane elements doing and what operations can be performed with a 3D vatrix? That's neat, thank you for Kafka! Rob > On Friday, July 18, 2014 at 7:22 PM, Robert Withers > <robert.w.with...@gmail.com> wrote: > > I had some time to consider my suggestion that it be viewed > as a relativistic frame of reference. Consider the model > where each dimension of the frame of reference for each > consumer is each partition, actually a sub-space with the > dimensionality of the replication factor, but with a single > leader election, so consider it 1 dimension. The total > dimensionality of the consumers frame of reference is the > number of partitions, but only assigned partitions are open > to a given consumers viewpoint. The offset is the partition > dimension's coordinate and only consumers with an open > dimension can translate the offset. A rebalance opens or > closes a dimension for a given consumer and can be viewed as > a rotation. Could Kafka consumption and rebalance (and ISR > leader election) be reduced to matrix operations?
