2.1 KiB
Dataflow paradigm (working title)
I don't know if there's actually anything to write here.
There is a sort of parallel between the declarative nature of computational graphs in TensorFlow, and functional programming (possibly function-level - think of the J language and how important rank is to its computations).
Apache Spark and TensorFlow are very similar in a lot of ways. The key difference I see is that Spark handles different types of data internally that are more suited to databases, reords, tables, and generally relational data, while TensorFlow is, well, tensors (arbitrary-dimensional arrays).
The interesting part to me with both of these is how they've moved "bulk" computations into first-class objects (ish) and permitted some level of introspection into them before they run, as they run, and after they run. Like I noted in Notes - Paper, 2016-11-13, "One interesting (to me) facet is how the computation process has been split out and instrumented enough to allow some meaningful introspection with it. It hasn't precisely made it a first-class construct, but still, this feature pervades all of Spark's major abstractions (RDD, DataFrame, Dataset)."
Spark does this with a database. TensorFlow does it with numerical calculations. Node-RED does it with irregular, asynchronous data.