Streaming analytics in Scala using ZIO and Apache DataSketches (part 1)

First let's look at a trivial example of streaming analytics using ZIO Streams 2.0.9 (on Scala 2.13).

https://scastie.scala-lang.org/6AHfPh02S6m0k7aN6bkacA

When run, this Scastie just averages some random numbers (gaussian) in a single input stream.  That code is in the trait AvgFun.

But we also see a trait called ParAvgFun with the code for rather painless parallel implementation, using ZIO chunks, via ZStream's  grouped, zipWithIndex, and groupByKey.  Across these two traits, the clean reusability of ZSinks in different pipelines shows a nice design win for the ZIO Streams API.  

The code in ParAvgFun.parAvgUsingChunks demonstrates a terse and reasonably efficient design for accumulating results using parallel ZStreams (within a single process).  It is applicable to  simple cases where the stream elements are lightweight and similar, and the underlying operations are uniformly easy (like summing numbers).  When instead there is great variability in the cost of processing the records (suppose some are under 1KB while others are over 10MB), then we may want to consider a more elaborate pipelining approach using some of the other parallel primitives available in ZStream.

The following two images show a preview of the same code.  When run, the Scastie should print a result as shown (the dark background is console output), which reminds us that we are running only the single-streamed average here.  I left the parallel job triggering out to avoid overusing the resources of the friendly Scastie service.

In a followup post we will go deeper into streaming analytics, using this ZIO code pattern together with the Apache DataSketches library.

Popular posts from this blog

Overview of AxLam model : Nouns, Types, Morphisms

2023-Feb-03 picking up from tweet thread of 220909  AxLam model: Noun (space), Type (paint), Morphism (arrow) AxLam is for writing models using [concepts, types, and morphisms].  Our concepts may be in the form of some data, or less specifically as things we choose to name or refer to in a model.  Over this concept space, we use types and morphisms to define an adjustable descriptive model that directs and regulates processing of the concept data (or other manifestation).  Alternatively and  more approachably we may write in terms of [nouns,  formal classes, and mappings], and this framing is mostly equivalent for informal purposes. Nouns / Concepts / Terms / Data Nouns (i.e. concepts) describe anything we wish to refer to, and need not be explicit or named.  We think of nouns as the space of things we might try to describe, name or refer to.  "Terms" in a type-theoretic sense are nouns.  Real things in the human world that someone cares abo...
Read more

AxioMagic under Spark on cloud (AWS, Azure, ...)

AxioMagic under Spark on ${ cloud  } AxioMagic relies on functional composition over immutable inputs, thus matching modern evaluation frameworks like Apache Spark, which defines a structured approach to managing data contexts of functional evaluation over data frames.  Spark embodies a refinement of both  ETL and MapReduce methodology.  State machines like Spark require computation and storage resources which may come from any cluster, but today are commonly provided from cloud environments, yielding high volume functional computation at predictable usage-based costs.  We especially value Spark for open scientific dataset analysis, where it is common to work with large data files in interchange formats. Essential features of Spark distributed evaluation Spark assembles batch pipelines to pump datasets of structured records through functional tasks (which may  invoke side-effects, with all necessary stipulations).  This pipeline serves to evaluate...
Read more