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From Casey Stella <ceste...@gmail.com>
Subject Re: [DISCUSS] Generating and Interacting with serialized summary objects
Date Wed, 03 Jan 2018 15:17:25 GMT
Thanks for the feedback, Nick.

Regarding "IMHO, I'd rather not reinvent the wheel for text manipulation."

I would argue that we are not reinventing the wheel for text manipulation
as the extractor config exists already and we are doing a similar thing in
the flatfile loader (in fact, the code is reused and merely extended).
Transformation operations are already supported in our codebase in the
extractor config, this PR has just added some hooks for stateful operations.

Furthermore, we will need a configuration object to pass to the REST call
if we are ever to create a UI around importing data into hbase or creating
these summary objects.

Regarding your example:
$ cat top-1m.csv | awk -F, '{print $2}' | sed '/^$/d' | stellar -i
'DOMAIN_REMOVE_TLD(_)' | stellar -i 'BLOOM_ADD(_)'

I'm very sympathetic to this type of extension, but it has some issues:

   1. This implies a single-threaded addition to the bloom filter.
      1. Even with 5 threads, it takes an hour for the full alexa 1m, so I
      think this will impact performance
      2. There's not a way to specify how to merge across threads if we do
      make a multithread command line option
   2. This restricts these kinds of operations to roles with heavy unix CLI
   knowledge, which isn't often the types of people who would be doing this
   type of operation
   3. What if we need two variables passed to stellar?
   4. This approach will be harder to move to Hadoop.  Eventually we will
   want to support data on HDFS being processed by Hadoop (similar to flatfile
   loader), so instead of -m LOCAL being passed for the flatfile summarizer
   you'd pass -m SPARK and the processing would happen on the cluster
      1. This is particularly relevant in this case as it's a
      embarrassingly parallel problem in general

In summary, while this a CLI approach is attractive, I prefer the extractor
config solution because it is the solution with the smallest iteration that:

   1. Reuses existing metron extraction infrastructure
   2. Provides the most solid base for the extensions that will be sorely
   needed soon (and will keep it in parity with the flatfile loader)
   3. Provides the most solid base for a future UI extension in the
   management UI to support both summarization and loading




On Tue, Dec 26, 2017 at 11:27 AM, Nick Allen <nick@nickallen.org> wrote:

> First off, I really do like the typosquatting use case and a lot of what
> you have described.
>
> > We need a way to generate the summary sketches from flat data for this to
> > work.
> > ​..​
> >
>
> I took this quote directly from your use case.  Above is the point that I'd
> like to discuss and what your proposed solutions center on.  This is what I
> think you are trying to do, at least with PR #879
> <https://github.com/apache/metron/pull/879>...
>
> (Q) Can we repurpose Stellar functions so that they can operate on text
> stored in a file system?
>
>
> Whether we use the (1) Configuration or the (2) Function-based approach
> that you described, fundamentally we are introducing new ways to perform
> text manipulation inside of Stellar.
>
> IMHO, I'd rather not reinvent the wheel for text manipulation.  It would be
> painful to implement and maintain a bunch of Stellar functions for text
> manipulation.  People already have a large number of tools available to do
> this and everyone has their favorites.  People are resistant to learning
> something new when they already are familiar with another way to do the
> same thing.
>
> So then the question is, how else can we do this?  My suggestion is that
> rather than introducing text manipulation tools inside of Stellar, we allow
> people to use the text manipulation tools they already know, but with the
> Stellar functions that we already have.  And the obvious way to tie those
> two things together is the Unix pipeline.
>
> A quick, albeit horribly incomplete, example to flesh this out a bit more
> based on the example you have in PR #879
> <https://github.com/apache/metron/pull/879>.  This would allow me to
> integrate Stellar with whatever external tools that I want.
>
> $ cat top-1m.csv | awk -F, '{print $2}' | sed '/^$/d' | stellar -i
> 'DOMAIN_REMOVE_TLD(_)' | stellar -i 'BLOOM_ADD(_)'
>
>
>
>
>
>
>
>
> On Sun, Dec 24, 2017 at 8:28 PM, Casey Stella <cestella@gmail.com> wrote:
>
> > I'll start this discussion off with my idea around a 2nd step that is
> more
> > adaptable.  I propose the following set of stellar functions backed by
> > Spark in the metron-management project:
> >
> >    - CSV_PARSE(location, separator?, columns?) : Constructs a Spark
> >    Dataframe for reading the flatfile
> >    - SQL_TRANSFORM(dataframe, spark sql statement): Transforms the
> > dataframe
> >    - SUMMARIZE(state_init, state_update, state_merge): Summarize the
> >    dataframe using the lambda functions:
> >       - state_init - executed once per worker to initialize the state
> >       - state_update - executed once per row
> >       - state_merge - Merge the worker states into one worker state
> >    - OBJECT_SAVE(obj, output_path) : Save the object obj to the path
> >    output_path on HDFS.
> >
> > This would enable more flexibility and composibility than the
> > configuration-based approach that we have in the flatfile loader.
> > My concern with this approach, and the reason I didn't do it initially,
> was
> > that I think that users will want at least 2 ways to summarize data (or
> > load data):
> >
> >    - A configuration based approach, which enables a UI
> >    - A set of stellar functions via the scriptable REPL
> >
> > I would argue that both have a place and I started with the configuration
> > based approach as it was a more natural extension of what we already had.
> > I'd love to hear thoughts about this idea too.
> >
> >
> > On Sun, Dec 24, 2017 at 8:20 PM, Casey Stella <cestella@gmail.com>
> wrote:
> >
> > > Hi all,
> > >
> > > I wanted to get some feedback on a sensible plan for something.  It
> > > occurred to me the other day when considering the use-case of detecting
> > > typosquatted domains, that one approach was to generate the set of
> > > typosquatted domains for some set of reference domains and compare
> > domains
> > > as they flow through.
> > >
> > > One way we could do this would be to generate this data and import the
> > > typosquatted domains into HBase.  I thought, however, that another
> > approach
> > > which may trade-off accuracy to remove the network hop and potential
> disk
> > > seek by constructing a bloom filter that includes the set of
> typosquatted
> > > domains.
> > >
> > > The challenge was that we don't have a way to do this currently.  We
> do,
> > > however, have a loading infrastructure (e.g. the flatfile_loader) and
> > > configuration (see https://github.com/apache/
> metron/tree/master/metron-
> > > platform/metron-data-management#common-extractor-properties)  which
> > > handles:
> > >
> > >    - parsing flat files
> > >    - transforming the rows
> > >    - filtering the rows
> > >
> > > To enable the new use-case of generating a summary object (e.g. a bloom
> > > filter), in METRON-1378 (https://github.com/apache/metron/pull/879) I
> > > propose that we create a new utility that uses the same extractor
> config
> > > add the ability to:
> > >
> > >    - initialize a state object
> > >    - update the object for every row
> > >    - merge the state objects (in the case of multiple threads, in the
> > >    case of one thread it's not needed).
> > >
> > > I think this is a sensible decision because:
> > >
> > >    - It's a minimal movement from the flat file loader
> > >       - Uses the same configs
> > >       - Abstracts and reuses the existing infrastructure
> > >    - Having one extractor config means that it should be easier to
> > >    generate a UI around this to simplify the experience
> > >
> > > All that being said, our extractor config is..shall we say...daunting
> :).
> > > I am sensitive to the fact that this adds to an existing difficult
> > config.
> > > I propose that this is an initial step forward to support the use-case
> > and
> > > we can enable something more composable going forward.  My concern in
> > > considering this as the first step was that it felt that the composable
> > > units for data transformation and manipulation suddenly takes us into a
> > > place where Stellar starts to look like Pig or Spark RDD API.  I wasn't
> > > ready for that without a lot more discussion.
> > >
> > > To summarize, what I'd like to get from the community is, after
> reviewing
> > > the entire use-case at https://github.com/cestella/
> > incubator-metron/tree/
> > > typosquat_merge/use-cases/typosquat_detection:
> > >
> > >    - Is this so confusing that it does not belong in Metron even as a
> > >    first-step?
> > >    - Is there a way to extend the extractor config in a less confusing
> > >    way to enable this?
> > >
> > > I apologize for making the discuss thread *after* the JIRAs, but I felt
> > > this one might bear having some working code to consider.
> > >
> >
>

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