mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Pat Ferrel <>
Subject Re: Mahout 1.0: is DRM too file-bound?
Date Fri, 10 Oct 2014 16:09:39 GMT
For what it’s worth key design goals are the use of immutable objects and functional programming.
Adding lazy evaluation allows for an optimizer underneath the DSL and has other benefits.
I wouldn’t call mahout file-bound since files are really import and export. In Hadoop Mapreduce
files were use for every intermediate result and so mahout _was_ file bound. Now it is just
file centric and that is only because someone like you hasn’t stepped up to add support
for DBs.

drmFromHDFS is a package level helper functions, like the coming indexedDatasetDFSRead(src,

You don’t have to use them. There are reader and writer traits parameterized by what you
want to r/w. These are meant to be extended with store specific read write functions since
they only store schema (a HashMap[String, Any]) and a device context. 

The extending class is a reader factory for the object read in. The extending writer is a
trait or class adding write functionality to the object read by the reader. You extend writer
in your class or use an a extending writer trait as a mixin to your class. Either way it adds
a .dfsWrite or in your case a .hbaseWrite. I’ve done this with IndexedDatasets using Spark’s
parallel r/w of text and you may want to go that route only dealing with HBase. Alternatively
you can create a reader for a DRM directly if you want.

I’d be interested in supporting this if you go this route and providing any needed refactoring.

> On Oct 9, 2014, at 10:56 PM, Reinis Vicups <> wrote:
> Guys, thank you very much for your feedback.
> I have already my own vanilla spark-based implementation of row similarity that reads
and writes into NoSQL (in my case HBase).
> My intention is to profit from your effort to abstract algebraic layer from physical
backend because I find it a great idea.
> There is no question that the effort to implement i/o with some NoSql and spark is very
low nowadays.
> My question is more towards understanding your design.
> In particular, why for instance org.apache.mahout.math.drm.DistributedEngine has def
> I do understand argument with "files is most basic and common" and "we had this already
in mahout 0.6 so its for compatibility purposes", but
> why for instance instead of drmFromHDFS() there is no def createDRM() and then some particular
implementation of DistributedEngine (or medium-specific helper) that then decides how DRM
shall be created?
> Admittedly, I do NOT understand your design fully just yet and I am asking these questions
not to criticize this design but to help me understand it.
> Another example is existance of org.apache.mahout.drivers.Schema. It seems that there
is effort to kind of make medium-specific format flexible and abstract it away, but again
the limitation is it is file-centric.
> Thank you for your hints with  drmWrap and IndexedDataset. With this in mind, maybe my
error is that I am trying to reuse classes in org.apache.mahout.drivers, maybe I should just
write my own driver from scratch and with Database in mind.
> Thank you again for your hints and ideas
> reinis
On 10.10.2014 01:00, Pat Ferrel wrote:
> There is also the mahout Reader and Writer traits and classes that currently work with
text delimited file I/O. These were imagined as a general framework to support parallelized
read/write to any format and store using whatever method is expedient, including the ones
Dmitriy mentions. I personally would like to do MongoDB since I have an existing app using
> These are built to support a sort of extended-DRM (IndexedDataset) which maintains external
IDs. These IDs can be anything you can put in a string like Mongo or Cassandra keys or can
be left as human readable external keys. From an IndexedDataset you can get a CheckpointedDRM
and do anything in the DSL with it.
> They are in the spark module but the base traits have been moved to the core “math-scala”
to make the concepts core with implementations in left in the engine specific modules. This
is work about to be put in a PR but you can look at it in the master to see if it helps—expect
some refactoring shortly.
> I’m sure there will be changes needed for DBs but haven’t gotten to that so would
love another set of eyes on the code.
> On Oct 9, 2014, at 3:08 PM, Dmitriy Lyubimov<>  wrote:
> Bottom line, some very smart people decided to do all that work in Spark
> and give us for free. Not sure why, but that did. If the capability already
> found in Spark, there's no need for us to replicate it.
> WRT specifically  NoSql, Spark can read HBase trivially. I also did a bit
> more advanced things with a custom rdd implementation in Spark that was
> able to stream coprocessor outputs into rdd functors. In either case this
> is actually a fairly small effort. I never looked at it closely, but i know
> there are also Cassandra  adapters for spark as well. Chances are, you
> could probably load data from any thinkable distributed data store into
> Spark these days via off the shelf implementations. If not, Spark actually
> makes it very easy to come with one on your own.
> On Thu, Oct 9, 2014 at 2:47 PM, Dmitriy Lyubimov<>  wrote:
>> Matrix defines structure. Not necessarily where it can be imported from.
>> You're right in the sense that framework itself  avoids defining apis for
>> custom partition formation. But you're wrong in implying you cannot do it
>> if you wanted, our that you d have to do anything that complex as you say.
>> As long as you can form your own rdd of keys and row vectors, you can
>> always wrap it into a matrix (drmWrap api). Hdfs drm persistence on the
>> other hand had been around for as long as I remember, not just in 1.0. So
>> naturally those are provided to be interoperable with mahout .9 and before,
>> e g be able to load output from stuff like seq2sparse and such.
>> Note that if you instruct your backend to use some sort off data locality
>> information, it will also be able capitalize on that automatically.
>> There is actually far greater number of concerns of interacting with
>> native engine capabilities than just reading the data. For example, what if
>> we wanted to wrap an output of a shark query into a matrix. Instead of
>> addressing all those individually, we just chose to delegate those to
>> actual capabilities of backend. Chances are they already have (and, in
>> fact, do in case of spark) all that tooling far better than we will ever
>> have on our own.
>> Sent from my phone.
>> On Oct 9, 2014 12:56 PM, "Reinis Vicups"<>  wrote:
>>> Hello,
>>> I am currently looking into the new (DRM) mahout framework.
>>> I find myself wondering why is it so that from one side there is a lot
>>> of thought, effort and design complexity being invested into abstracting
>>> engines, contexts or algebraic operations,
>>> but from the other side, even abstract interfaces, are defined in a way
>>> that everything has to be read or written from files (on HDFS).
>>> I am considering to implement reading/writing to NoSQL database and
>>> initially I assumed it will be enough just to implement own
>>> ReaderWriter, but I am currently realizing that I will have to
>>> re-implement or hack-around by derivating own versions of large(?)
>>> portions of framework including own variant of CheckpointedDrm,
>>> DistributedEngine and what not.
>>> Is it because abstracting away storage type would introduce even more
>>> complexity or because there are aspects of design that absolutely
>>> require to read/write only to (seq)files?
>>> kind regards
>>> reinis

View raw message