Scala & Spark Bindings:

Bringing algebraic semantics

What is Scala & Spark Bindings?

In short, Scala & Spark Bindings for Mahout is Scala DSL and algebraic optimizer of something like this (actual formula from (d)spca)


bound to in-core and distributed computations (currently, on Apache Spark).

Mahout Scala & Spark Bindings expression of the above:

    val g = bt.t %*% bt - c - c.t + (s_q cross s_q) * (xi dot xi)

The main idea is that a scientist writing algebraic expressions cannot care less of distributed operation plans and works entirely on the logical level just like he or she would do with R.

Another idea is decoupling logical expression from distributed back-end. As more back-ends are added, this implies “write once, run everywhere”.

The linear algebra side works with scalars, in-core vectors and matrices, and Mahout Distributed Row Matrices (DRMs).

The ecosystem of operators is built in the R’s image, i.e. it follows R naming such as %*%, colSums, nrow, length operating over vectors or matices.

Important part of Spark Bindings is expression optimizer. It looks at expression as a whole and figures out how it can be simplified, and which physical operators should be picked. For example, there are currently about 5 different physical operators performing DRM-DRM multiplication picked based on matrix geometry, distributed dataset partitioning, orientation etc. If we count in DRM by in-core combinations, that would be another 4, i.e. 9 total – all of it for just simple x %*% y logical notation.

Please refer to the documentation for details.


This environment addresses mostly R-like Linear Algebra optmizations for Spark, Flink and H20.


Distributed methods and solvers using Bindings

  • In-core (ssvd) and Distributed (dssvd) Stochastic SVD – guinea pigs – see the bindings manual
  • In-core (spca) and Distributed (dspca) Stochastic PCA – guinea pigs – see the bindings manual
  • Distributed thin QR decomposition (dqrThin) – guinea pig – see the bindings manual
  • Current list of algorithms

Reading RDDs and DataFrames into DRMs


TODO: Do we still want this? (I don’t think so…)

  • CLI and Driver for Spark version of item similarity – MAHOUT-1541
  • Command line interface for generalizable Spark pipelines – MAHOUT-1569
  • Cooccurrence Analysis / Item-based Recommendation – MAHOUT-1464
  • Spark Bindings – MAHOUT-1346
  • Scala Bindings – MAHOUT-1297
  • Interactive Scala & Spark Bindings Shell & Script processor – MAHOUT-1489
  • OLS tutorial using Mahout shell – MAHOUT-1542
  • Full abstraction of DRM apis and algorithms from a distributed engine – MAHOUT-1529
  • Port Naive Bayes – MAHOUT-1493

Work in progress

  • Text-delimited files for input and output – MAHOUT-1568

  • Your issue here!