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From Michael Miklavcic <michael.miklav...@gmail.com>
Subject Re: [DISCUSS] Generating and Interacting with serialized summary objects
Date Thu, 04 Jan 2018 18:28:49 GMT
I mentioned this earlier, but I'll reiterate that I think this approach
gives us the ability to make specific use cases via a UI, or other
interface should we choose to add one, while keeping the core adaptable and
flexible. This is ideal for middle tier as I think this effectively gives
us the ability to pivot to other use cases very easily while not being so
generic as to be useless. The fact that you were able to create this as
quickly as you did seems to me directly related to the fact we made the
decision to keep the loader somewhat flexible rather than very specific.
The operation ordering and state carry from one phase of processing to the
next would simply have been inscrutable, if not impossible, with a CLI
option-only approach. Sure, it's not as simple as "put infile.txt
outfile.txt", but the alternatives are not that clear either. One might
argue we could split up the processing pieces as in traditional Hadoop, eg
ETL: Sqoop ingest -> HDFS -> mapreduce, pig, hive, or spark transform. But
quite frankly that's going in the *opposite* direction I think we want
here. That's more complex in terms of moving parts. The config approach
with pluggable Stellar insulates users from specific implementations, but
also gives you the ability to pass lower level constructs, eg Spark SQL or
HiveQL, should the need arise.

In summary, my impressions are that at this point the features and level of
abstraction feel appropriate to me. I think it buys us 1) learning from a
starting typosquatting use case, 2) flexibility to change and adapt it
without affecting users, and 3) enough concrete capability to make more
specific use cases easy to deliver with a UI.

Cheers,
Mike

On Jan 4, 2018 9:59 AM, "Casey Stella" <cestella@gmail.com> wrote:

> It also occurs to me that even in this situation, it's not a sufficient
> generalization for just Bloom, but this is a bloom filter of the output of
> the all the typosquatted domains for the domain in each row.  If we wanted
> to hard code, we'd have to hard code specifically the bloom filter *for*
> typosquatting use-case.  Hard coding this would prevent things like bloom
> filters containing malicious IPs from a reference source, for instance.
>
> On Thu, Jan 4, 2018 at 10:46 AM, Casey Stella <cestella@gmail.com> wrote:
>
> > So, there is value outside of just bloom usage.  The most specific
> example
> > of this would be in order to configure a bloom filter, we need to know at
> > least an upper bound of the number of items that are going to be added to
> > the bloom filter.  In order to do that, we need to count the number of
> > typosquatted domains.  Specifically at https://github.com/
> > cestella/incubator-metron/tree/typosquat_merge/use-
> > cases/typosquat_detection#configure-the-bloom-filter you can see how we
> > use the CONSOLE writer with an extractor config to count the number of
> > typosquatted domains in the alexa top 10k dataset so we can size the
> filter
> > appropriately.
> >
> > I'd argue that other types of probabalistic data structures could also
> > make sense here as well, like statistical sketches. Consider, for
> instance,
> > a cheap and dirty DGA indicator where we take the Alexa top 1M and look
> at
> > the distribution of shannon entropy in the domains.  If the shannon
> entropy
> > of a domain going across metron is more than 5 std devs from the mean,
> that
> > could be circumstantial evidence of a malicious attack.  This would
> yield a
> > lot of false positives, but used in conjunction with other indicators it
> > could be valuable.
> >
> > Computing that would be as follows:
> >
> > {
> >   "config" : {
> >     "columns" : {
> >        "rank" : 0,
> >        "domain" : 1
> >     },
> >     "value_transform" : {
> >        "domain" : "DOMAIN_REMOVE_TLD(domain)"
> >     },
> >     "value_filter" : "LENGTH(domain) > 0",
> >     "state_init" : "STATS_INIT()",
> >     "state_update" : {
> >        "state" : "STATS_ADD(state, STRING_ENTROPY(domain))"
> >                      },
> >     "state_merge" : "STATS_MERGE(states)",
> >     "separator" : ","
> >   },
> >   "extractor" : "CSV"
> > }
> >
> > Also, for another example, imagine a situation where we have a SPARK_SQL
> > engine rather than just LOCAL for summarizing.  We could create a general
> > summary of URL lengths in bro data which could be used for determining if
> > someone is trying to send in very large URLs maliciously (see Jon
> Zeolla's
> > concerns in https://issues.apache.org/jira/browse/METRON-517 for a
> > discussion of this).  In order to do that, we could simply execute:
> >
> > $METRON_HOME/bin/flatfile_summarizer.sh -i "select uri from bro" -o
> /tmp/reference/bro_uri_distribution.ser -e ~/uri_length_extractor.json -p
> 5 -om HDFS -m SPARK_SQL
> >
> > with uri_length_extractor.json containing:
> >
> > {
> >   "config" : {
> >     "value_filter" : "LENGTH(uri) > 0",
> >     "state_init" : "STATS_INIT()",
> >     "state_update" : {
> >        "state" : "STATS_ADD(state, LENGTH(uri))"
> >                      },
> >     "state_merge" : "STATS_MERGE(states)",
> >     "separator" : ","
> >   },
> >   "extractor" : "SQL_ROW"
> > }
> >
> >
> > Regarding value filter, that's already around in the extractor config
> > because of the need to transform data in the flatfile loader.  While I
> > definitely see the desire to use unix tools to prep data, there are some
> > things that aren't as easy to do.  For instance, here, removing the TLD
> of
> > a domain is not a trivial task in a shell script and we have existing
> > functions for that in Stellar.  I would see people using both.
> >
> > To address the issue of a more targeted experience to bloom, I think that
> > sort of specialization should best exist in the UI layer.  Having a more
> > complete and expressive backend reused across specific UIs seems to be
> the
> > best of all worlds.  It allows power users to drop down and do more
> complex
> > things and still provides a (mostly) code-free and targeted experience
> for
> > users.  It seems to me that limiting the expressibility in the backend
> > isn't the right way to go since this work just fits in with our existing
> > engine.
> >
> >
> > On Thu, Jan 4, 2018 at 1:40 AM, James Sirota <jsirota@apache.org> wrote:
> >
> >> I just went through these pull requests as well and also agree this is
> >> good work.  I think it's a good first pass.  I would be careful with
> trying
> >> to boil the ocean here.  I think for the initial use case I would only
> >> support loading the bloom filters from HDFS.  If people want to
> pre-process
> >> the CSV file of domains using awk or sed this should be out of scope of
> >> this work.  It's easy enough to do out of band and I would not include
> any
> >> of these functions at all.   I also think that the config could be
> >> considerably simplified.  I think value_filter should be removed (since
> I
> >> believe that preprocessing should be done by the user outside of this
> >> process).  I also have a question about the init, update, and merge
> >> configurations.  Would I ever initialize to anything but an empty bloom
> >> filter?  For the state update would I ever do anything other than add to
> >> the bloom filter?  For the state merge would I ever do anything other
> than
> >> merge the states?  If the answer to these is 'no', then this should
> simply
> >> be hard coded and not externalized into config values.
> >>
> >> 03.01.2018, 14:20, "Michael Miklavcic" <michael.miklavcic@gmail.com>:
> >> > I just finished stepping through the typosquatting use case README in
> >> your
> >> > merge branch. This is really, really good work Casey. I see most of
> our
> >> > previous documentation issues addressed up front, e.g. special
> variables
> >> > are cited, all new fields explained, side effects documented. The use
> >> case
> >> > doc brings it all together soup-to-nuts and I think all the pieces
> make
> >> > sense in a mostly self-contained way. I can't think of anything I had
> to
> >> > sit and think about for more than a few seconds. I'll be making my way
> >> > through your individual PR's in more detail, but my first impressions
> >> are
> >> > that this is excellent.
> >> >
> >> > On Wed, Jan 3, 2018 at 12:43 PM, Michael Miklavcic <
> >> > michael.miklavcic@gmail.com> wrote:
> >> >
> >> >>  I'm liking this design and growth strategy, Casey. I also think Nick
> >> and
> >> >>  Otto have some valid points. I always find there's a natural tension
> >> >>  between too little, just enough, and boiling the ocean and these
> >> discuss
> >> >>  threads really help drive what the short and long term visions
> should
> >> look
> >> >>  like.
> >> >>
> >> >>  On the subject of repositories and strategies, I agree that
> pluggable
> >> >>  repos and strategies for modifying them would be useful. For the
> first
> >> >>  pass, I'd really like to see HDFS with the proposed set of Stellar
> >> >>  functions. This gives us a lot of bang for our buck - we can
> >> capitalize on
> >> >>  a set of powerful features around existence checking earlier without
> >> having
> >> >>  to worry about later interface changes impacting users. With the
> >> primary
> >> >>  interface coming through the JSON config, we are building a nice
> >> facade
> >> >>  that protects users from later implementation abstractions and
> >> >>  improvements, all while providing a stable enough interface on which
> >> we can
> >> >>  develop UI features as desired. I'd be interested to hear more about
> >> what
> >> >>  features could be provided by a repository as time goes by.
> >> Federation,
> >> >>  permissions, governance, metadata management, perhaps?
> >> >>
> >> >>  I also had some concern over duplicating existing Unix features. I
> >> think
> >> >>  where I'm at has been largely addressed by Casey's comments on 1)
> >> scaling,
> >> >>  2) multiple variables, and 3) portability to Hadoop. Providing 2
> >> approaches
> >> >>  - 1 which is config-based and the other a composable set of
> functions
> >> gives
> >> >>  us the ability to provide a core set of features that can later be
> >> easily
> >> >>  expanded by users as the need arises. Here again I think the
> >> prescribed
> >> >>  approach provides a strong first pass that we can then expand on
> >> without
> >> >>  concern of future improvements becoming a hassle for end users.
> >> >>
> >> >>  Best,
> >> >>  Mike
> >> >>
> >> >>  On Wed, Jan 3, 2018 at 10:25 AM, Simon Elliston Ball <
> >> >>  simon@simonellistonball.com> wrote:
> >> >>
> >> >>>  There is some really cool stuff happening here, if only I’d
been
> >> allowed
> >> >>>  to see the lists over Christmas... :)
> >> >>>
> >> >>>  A few thoughts...
> >> >>>
> >> >>>  I like Otto’s generalisation of the problem to include specific
> local
> >> >>>  stellar objects in a cache loaded from a store (HDFS seems a
> >> natural, but
> >> >>>  not only place, maybe even a web service / local microservicey
> object
> >> >>>  provider!?) That said, I suspect that’s a good platform
> optimisation
> >> >>>  approach. Should we look at this as a separate piece of work given
> it
> >> >>>  extends beyond the scope of the summarisation concept and
> ultimately
> >> use it
> >> >>>  as a back-end to feed the summarising engine proposed here for
the
> >> >>>  enrichment loader?
> >> >>>
> >> >>>  On the more specific use case, one think I would comment on is
the
> >> >>>  configuration approach. The iteration loop
> (state_{init|update|merge}
> >> >>>  should be consistent with the way we handle things like the
> profiler
> >> >>>  config, since it’s the same approach to data handling.
> >> >>>
> >> >>>  The other thing that seems to have crept in here is the interface
> to
> >> >>>  something like Spark, which again, I am really very very keen
on
> >> seeing
> >> >>>  happen. That said, not sure how that would happen in this context,
> >> unless
> >> >>>  you’re talking about pushing to something like livy for example
> >> (eminently
> >> >>>  sensible for things like cross instance caching and faster RPC-ish
> >> access
> >> >>>  to an existing spark context which seem to be what Casey is driving
> >> at with
> >> >>>  the spark piece.
> >> >>>
> >> >>>  To address the question of text manipulation in Stellar / metron
> >> >>>  enrichment ingest etc, we already have this outside of the context
> >> of the
> >> >>>  issues here. I would argue that yes, we don’t want too many
paths
> >> for this,
> >> >>>  and that maybe our parser approach might be heavily related to
> >> text-based
> >> >>>  ingest. I would say the scope worth dealing with here though is
not
> >> really
> >> >>>  text manipulation, but summarisation, which is not well served
by
> >> existing
> >> >>>  CLI tools like awk / sed and friends.
> >> >>>
> >> >>>  Simon
> >> >>>
> >> >>>  > On 3 Jan 2018, at 15:48, Nick Allen <nick@nickallen.org>
wrote:
> >> >>>  >
> >> >>>  >> Even with 5 threads, it takes an hour for the full Alexa
1m, so
> I
> >> >>>  think
> >> >>>  > this will impact performance
> >> >>>  >
> >> >>>  > What exactly takes an hour? Adding 1M entries to a bloom
filter?
> >> That
> >> >>>  > seems really high, unless I am not understanding something.
> >> >>>  >
> >> >>>  >
> >> >>>  >
> >> >>>  >
> >> >>>  >
> >> >>>  >
> >> >>>  > On Wed, Jan 3, 2018 at 10:17 AM, Casey Stella <
> cestella@gmail.com>
> >> >>>  wrote:
> >> >>>  >
> >> >>>  >> 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/met
> >> ron/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.
> >> >>>  >>>>>
> >> >>>  >>>>
> >> >>>  >>>
> >> >>>  >>
> >>
> >> -------------------
> >> Thank you,
> >>
> >> James Sirota
> >> PMC- Apache Metron
> >> jsirota AT apache DOT org
> >>
> >
> >
>

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