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From Austin Leahy <Aus...@digitalminion.com>
Subject Re: [Discuss] - Future plans for Spot-ingest
Date Wed, 19 Apr 2017 22:39:17 GMT
The advantage of flume or a flume Kafka hybrid is that the team doesn't
have to build sinks for any new source types added to the project just
create configs pointing to the landing pad
On Wed, Apr 19, 2017 at 3:31 PM kant kodali <kanth909@gmail.com> wrote:

> What kind of benchmarks are we looking for? just throughput? since I am
> assuming this is for ingestion. I haven't seen anything faster than Kafka
> and that is because of its simplicity after all publisher appends message
> to a file(so called the partition in kafka) and clients just do sequential
> reads from a file so its a matter of disk throughput. The benchmark numbers
> I have for Kafka is at very least 75K messages/sec where each message is
> 1KB on m4.xlarge which by default has EBS storage (EBS is network-attached
> SSD disk). The network attached disk has a max throughput of
> 125MB/s(m4.xlarge has 1Gigabit) but if we were deploy it on ephemeral
> storage (local-SSD) and on a 10 Gigabit Network we would easily get 5-10X
> more.
>
> No idea about flume.
>
> Finally, not trying to pitch for Kafka however it is fastest I have seen
> but if someone has better numbers for flume then we should use that. Also I
> would suspect there are benchmarks for Kafka vs Flume available online
> already or we can try it with our own datasets.
>
> Thanks!
>
> On Wed, Apr 19, 2017 at 3:09 PM, Austin Leahy <Austin@digitalminion.com>
> wrote:
>
> > I am happy to create and test a flume source... #intelteam would need to
> > create the benchmark by deploying it and pointing a data source at it...
> > since I don't have good enough volume of source data handy
> > On Wed, Apr 19, 2017 at 3:04 PM Ross, Alan D <alan.d.ross@intel.com>
> > wrote:
> >
> > > We discussed this in our staff meeting a bit today.  I would like to
> see
> > > some benchmarking of different approaches (kafka, flume, etc) to see
> what
> > > the numbers look like. Is anyone in the community willing to volunteer
> on
> > > this work?
> > >
> > > -----Original Message-----
> > > From: Austin Leahy [mailto:Austin@digitalminion.com]
> > > Sent: Wednesday, April 19, 2017 1:05 PM
> > > To: dev@spot.incubator.apache.org
> > > Subject: Re: [Discuss] - Future plans for Spot-ingest
> > >
> > > I think Kafka is probably a red herring. It's an industry goto in the
> > > application world because because of redundancy but the type and
> volumes
> > of
> > > network telemetry that we are talking about here will bog kafka down
> > unless
> > > you dedicate really serious hardware to just the kafka implementation.
> > It's
> > > essentially the next level of the problem that the team was already
> > running
> > > into when rabbitMQ was queueing in data.
> > >
> > > On Wed, Apr 19, 2017 at 12:33 PM Mark Grover <mark@apache.org> wrote:
> > >
> > > > On Wed, Apr 19, 2017 at 10:19 AM, Smith, Nathanael P <
> > > > nathanael.p.smith@intel.com> wrote:
> > > >
> > > > > Mark,
> > > > >
> > > > > just digesting the below.
> > > > >
> > > > > Backing up in my thought process, I was thinking that the ingest
> > > > > master (first point of entry into the system) would want to put the
> > > > > data into a standard serializable format. I was thinking that
> > > > > libraries (such as pyarrow in this case) could help by writing the
> > > > > data in parquet format early in the process. You are probably
> > > > > correct that at this point in time it might not be worth the time
> and
> > > can be kept in the backlog.
> > > > > That being said, I still think the master should produce data in a
> > > > > standard format, what in your opinion (and I open this up of course
> > > > > to
> > > > > others) would be the most logical format?
> > > > > the most basic would be to just keep it as a .csv.
> > > > >
> > > > > The master will likely write data to a staging directory in HDFS
> > > > > where
> > > > the
> > > > > spark streaming job will pick it up for normalization/writing to
> > > > > parquet
> > > > in
> > > > > the correct block sizes and partitions.
> > > > >
> > > >
> > > > Hi Nate,
> > > > Avro is usually preferred for such a standard format - because it
> > > > asserts a schema (types, etc.) which CSV doesn't and it allows for
> > > > schema evolution which depending on the type of evolution, CSV may or
> > > may not support.
> > > > And, that's something I have seen being done very commonly.
> > > >
> > > > Now, if the data were in Kafka before it gets to master, one could
> > > > argue that the master could just send metadata to the workers (topic
> > > > name, partition number, offset start and end) and the workers could
> > > > read from Kafka directly. I do understand that'd be a much different
> > > > architecture than the current one, but if you think it's a good idea
> > > > too, we could document that, say in a JIRA, and (de-)prioritize it
> > > > (and in line with the rest of the discussion on this thread, it's not
> > > the top-most priority).
> > > > Thoughts?
> > > >
> > > > - Nathanael
> > > > >
> > > > >
> > > > >
> > > > > > On Apr 17, 2017, at 1:12 PM, Mark Grover <mark@apache.org>
> wrote:
> > > > > >
> > > > > > Thanks all your opinion.
> > > > > >
> > > > > > I think it's good to consider two things:
> > > > > > 1. What do (we think) users care about?
> > > > > > 2. What's the cost of changing things?
> > > > > >
> > > > > > About #1, I think users care more about what format data is
> > > > > > written
> > > > than
> > > > > > how the data is written. I'd argue whether that uses Hive, MR, or
> > > > > > a
> > > > > custom
> > > > > > Parquet writer is not as important to them as long as we maintain
> > > > > > data/format compatibility.
> > > > > > About #2, having worked on several projects, I find that it's
> > > > > > rather difficult to keep up with Parquet. Even in Spark, there
> are
> > > > > > a few
> > > > > different
> > > > > > ways to write to Parquet - there's a regular mode, and a legacy
> > > > > > mode <https://github.com/apache/spark/blob/master/sql/core/
> > > > > src/main/scala/org/apache/spark/sql/execution/datasources/parquet/
> > > > > ParquetWriteSupport.scala#L44>
> > > > > > which
> > > > > > continues to cause confusion
> > > > > > <https://issues.apache.org/jira/browse/SPARK-20297> till date.
> > > > > > Parquet itself is pretty dependent on Hadoop
> > > > > > <https://github.com/Parquet/parquet-mr/search?l=Maven+POM&
> > > > > q=hadoop&type=&utf8=%E2%9C%93>
> > > > > > and,
> > > > > > just integrating it with systems with a lot of developers (like
> > > > > > Spark <
> https://www.google.com/webhp?sourceid=chrome-instant&ion=1&
> > > > > espv=2&ie=UTF-8#q=spark+parquet+jiras>)
> > > > > > is still a lot of work.
> > > > > >
> > > > > > I personally think we should leverage higher level tools like
> > > > > > Hive, or Spark to write data in widespread formats (Parquet,
> being
> > > > > > a very good
> > > > > > example) but I personally wouldn't encourage us to manage the
> > > > > > writers ourselves.
> > > > > >
> > > > > > Thoughts?
> > > > > > Mark
> > > > > >
> > > > > > On Mon, Apr 17, 2017 at 11:44 AM, Michael Ridley
> > > > > > <mridley@cloudera.com
> > > > >
> > > > > > wrote:
> > > > > >
> > > > > >> Without having given it too terribly much thought, that seems
> > > > > >> like an
> > > > OK
> > > > > >> approach.
> > > > > >>
> > > > > >> Michael
> > > > > >>
> > > > > >> On Mon, Apr 17, 2017 at 2:33 PM, Nathanael Smith <
> > > > nathanael@apache.org>
> > > > > >> wrote:
> > > > > >>
> > > > > >>> I think the question is rather we can write the data
> generically
> > > > > >>> to
> > > > > HDFS
> > > > > >>> as parquet without the use of hive/impala?
> > > > > >>>
> > > > > >>> Today we write parquet data using the hive/mapreduce method.
> > > > > >>> As part of the redesign i’d like to use libraries for this as
> > > > > >>> opposed
> > > > > to
> > > > > >> a
> > > > > >>> hadoop dependency.
> > > > > >>> I think it would be preferred to use the python master to write
> > > > > >>> the
> > > > > data
> > > > > >>> into the format we want, then do normalization of the data in
> > > > > >>> spark streaming.
> > > > > >>> Any thoughts?
> > > > > >>>
> > > > > >>> - Nathanael
> > > > > >>>
> > > > > >>>
> > > > > >>>
> > > > > >>>> On Apr 17, 2017, at 11:08 AM, Michael Ridley
> > > > > >>>> <mridley@cloudera.com>
> > > > > >>> wrote:
> > > > > >>>>
> > > > > >>>> I had thought that the plan was to write the data in Parquet
> in
> > > > > >>>> HDFS ultimately.
> > > > > >>>>
> > > > > >>>> Michael
> > > > > >>>>
> > > > > >>>> On Sun, Apr 16, 2017 at 11:55 AM, kant kodali
> > > > > >>>> <kanth909@gmail.com>
> > > > > >>> wrote:
> > > > > >>>>
> > > > > >>>>> Hi Mark,
> > > > > >>>>>
> > > > > >>>>> Thank you so much for hearing my argument. And I definetly
> > > > understand
> > > > > >>> that
> > > > > >>>>> you guys have bunch of things to do. My only concern is that
> I
> > > > > >>>>> hope
> > > > > it
> > > > > >>>>> doesn't take too long too support other backends. For example
> > > > > @Kenneth
> > > > > >>> had
> > > > > >>>>> given an example of LAMP stack had not moved away from mysql
> > > > > >>>>> yet
> > > > > which
> > > > > >>>>> essentially means its probably a decade ? I see that in the
> > > > > >>>>> current architecture the results from with python
> > > > > >>>>> multiprocessing or Spark Streaming are written back to HDFS
> > > > > >>>>> and  If so, can we write them in
> > > > > >>> parquet
> > > > > >>>>> format ? such that users should be able to plug in any query
> > > > > >>>>> engine
> > > > > >> but
> > > > > >>>>> again I am not pushing you guys to do this right away or
> > > > > >>>>> anything
> > > > > just
> > > > > >>>>> seeing if there a way for me to get started in parallel and
> if
> > > > > >>>>> not feasible, its fine I just wanted to share my 2 cents and
> I
> > > > > >>>>> am glad
> > > > my
> > > > > >>>>> argument is heard!
> > > > > >>>>>
> > > > > >>>>> Thanks much!
> > > > > >>>>>
> > > > > >>>>> On Fri, Apr 14, 2017 at 1:38 PM, Mark Grover <
> mark@apache.org>
> > > > > wrote:
> > > > > >>>>>
> > > > > >>>>>> Hi Kant,
> > > > > >>>>>> Just wanted to make sure you don't feel like we are ignoring
> > > > > >>>>>> your
> > > > > >>>>>> comment:-) I hear you about pluggability.
> > > > > >>>>>>
> > > > > >>>>>> The design can and should be pluggable but the project has
> > > > > >>>>>> one
> > > > stack
> > > > > >> it
> > > > > >>>>>> ships out of the box with, one stack that's the default
> stack
> > > > > >>>>>> in
> > > > the
> > > > > >>>>> sense
> > > > > >>>>>> that it's the most tested and so on. And, for us, that's our
> > > > current
> > > > > >>>>> stack.
> > > > > >>>>>> If we were to take Apache Hive as an example, it shipped
> (and
> > > > ships)
> > > > > >>> with
> > > > > >>>>>> MapReduce as the default configuration engine. At some
> point,
> > > > Apache
> > > > > >>> Tez
> > > > > >>>>>> came along and wanted Hive to run on Tez, so they made a
> > > > > >>>>>> bunch of
> > > > > >>> things
> > > > > >>>>>> pluggable to run Hive on Tez (instead of the only option
> > > > > >>>>>> up-until
> > > > > >> then:
> > > > > >>>>>> Hive-on-MR) and then Apache Spark came and re-used some of
> > > > > >>>>>> that pluggability and even added some more so Hive-on-Spark
> > > > > >>>>>> could
> > > > become
> > > > > a
> > > > > >>>>>> reality. In the same way, I don't think anyone disagrees
> here
> > > > > >>>>>> that pluggabilty is a good thing but it's hard to do
> > > > > >>>>>> pluggability
> > > > right,
> > > > > >> and
> > > > > >>>>> at
> > > > > >>>>>> the right level, unless on has a clear use-case in mind.
> > > > > >>>>>>
> > > > > >>>>>> As a project, we have many things to do and I personally
> > > > > >>>>>> think the
> > > > > >>>>> biggest
> > > > > >>>>>> bang for the buck for us in making Spot a really solid and
> > > > > >>>>>> the
> > > > best
> > > > > >>> cyber
> > > > > >>>>>> security solution isn't pluggability but the things we are
> > > > > >>>>>> working
> > > > > on
> > > > > >>> - a
> > > > > >>>>>> better user interface, a common/unified approach to storing
> > > > > >>>>>> and
> > > > > >>> modeling
> > > > > >>>>>> data, etc.
> > > > > >>>>>>
> > > > > >>>>>> Having said that, we are open, if it's important to you or
> > > > > >>>>>> someone
> > > > > >>> else,
> > > > > >>>>>> we'd be happy to receive and review those patches.
> > > > > >>>>>>
> > > > > >>>>>> Thanks!
> > > > > >>>>>> Mark
> > > > > >>>>>>
> > > > > >>>>>> On Fri, Apr 14, 2017 at 10:14 AM, kant kodali
> > > > > >>>>>> <kanth909@gmail.com
> > > > >
> > > > > >>>>> wrote:
> > > > > >>>>>>
> > > > > >>>>>>> Thanks Ross! and yes option C sounds good to me as well
> > > > > >>>>>>> however I
> > > > > >> just
> > > > > >>>>>>> think Distributed Sql query engine  and the resource
> manager
> > > > should
> > > > > >> be
> > > > > >>>>>>> pluggable.
> > > > > >>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>> On Fri, Apr 14, 2017 at 9:55 AM, Ross, Alan D <
> > > > > >> alan.d.ross@intel.com>
> > > > > >>>>>>> wrote:
> > > > > >>>>>>>
> > > > > >>>>>>>> Option C is to use python on the front end of ingest
> > > > > >>>>>>>> pipeline
> > > > and
> > > > > >>>>>>>> spark/scala on the back end.
> > > > > >>>>>>>>
> > > > > >>>>>>>> Option A uses python workers on the backend
> > > > > >>>>>>>>
> > > > > >>>>>>>> Option B uses all scala.
> > > > > >>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>> -----Original Message-----
> > > > > >>>>>>>> From: kant kodali [mailto:kanth909@gmail.com]
> > > > > >>>>>>>> Sent: Friday, April 14, 2017 9:53 AM
> > > > > >>>>>>>> To: dev@spot.incubator.apache.org
> > > > > >>>>>>>> Subject: Re: [Discuss] - Future plans for Spot-ingest
> > > > > >>>>>>>>
> > > > > >>>>>>>> What is option C ? am I missing an email or something?
> > > > > >>>>>>>>
> > > > > >>>>>>>> On Fri, Apr 14, 2017 at 9:15 AM, Chokha Palayamkottai <
> > > > > >>>>>>>> chokha@integralops.com> wrote:
> > > > > >>>>>>>>
> > > > > >>>>>>>>> +1 for Python 3.x
> > > > > >>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>> On 4/14/2017 11:59 AM, Austin Leahy wrote:
> > > > > >>>>>>>>>
> > > > > >>>>>>>>>> I think that C is the strong solution, getting the
> ingest
> > > > really
> > > > > >>>>>>>>>> strong is going to lower barriers to adoption. Doing it
> > > > > >>>>>>>>>> in
> > > > > Python
> > > > > >>>>>>>>>> will open up the ingest portion of the project to
> include
> > > > > >>>>>>>>>> many
> > > > > >>>>> more
> > > > > >>>>>>>> developers.
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> Before it comes up I would like to throw the following
> on
> > > > > >>>>>>>>>> the
> > > > > >>>>>> pile...
> > > > > >>>>>>>>>> Major
> > > > > >>>>>>>>>> python projects django/flash, others are dropping 2.x
> > > > > >>>>>>>>>> support
> > > > in
> > > > > >>>>>>>>>> releases scheduled in the next 6 to 8 months. Hadoop
> > > > > >>>>>>>>>> projects
> > > > in
> > > > > >>>>>>>>>> general tend to lag in modern python support, lets
> please
> > > > build
> > > > > >>>>> this
> > > > > >>>>>>>>>> in 3.5 so that we don't have to immediately expect a
> > > > > >>>>>>>>>> rebuild
> > > > in
> > > > > >>>>> the
> > > > > >>>>>>>>>> pipeline.
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> -Vote C
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> Thanks Nate
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> Austin
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> On Fri, Apr 14, 2017 at 8:52 AM Alan Ross
> > > > > >>>>>>>>>> <alan@apache.org>
> > > > > >>>>> wrote:
> > > > > >>>>>>>>>>
> > > > > >>>>>>>>>> I really like option C because it gives a lot of
> > > > > >>>>>>>>>> flexibility
> > > > for
> > > > > >>>>>>>>>> ingest
> > > > > >>>>>>>>>>> (python vs scala) but still has the robust spark
> > > > > >>>>>>>>>>> streaming
> > > > > >>>>> backend
> > > > > >>>>>>>>>>> for performance.
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> Thanks for putting this together Nate.
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> Alan
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> On Fri, Apr 14, 2017 at 8:44 AM, Chokha Palayamkottai <
> > > > > >>>>>>>>>>> chokha@integralops.com> wrote:
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>> I agree. We should continue making the existing stack
> > > > > >>>>>>>>>>> more
> > > > > >> mature
> > > > > >>>>>> at
> > > > > >>>>>>>>>>>> this point. Maybe if we have enough community support
> > > > > >>>>>>>>>>>> we can
> > > > > >> add
> > > > > >>>>>>>>>>>> additional datastores.
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>> Chokha.
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>> On 4/14/17 11:10 AM, kenneth@floss.cat wrote:
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Hi Kant,
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> YARN is the standard scheduler in Hadoop. If you're
> > > > > >>>>>>>>>>>>> using
> > > > > >>>>>>>>>>>>> Hive+Spark, then sure you'll have YARN.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Haven't seen any HIVE on Mesos so far. As said, Spot
> > > > > >>>>>>>>>>>>> is
> > > > based
> > > > > >>>>> on
> > > > > >>>>>> a
> > > > > >>>>>>>>>>>>> quite standard Hadoop stack and I wouldn't switch too
> > > > > >>>>>>>>>>>>> many
> > > > > >>>>> pieces
> > > > > >>>>>>>> yet.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> In most Opensource projects you start relying on a
> > > > well-known
> > > > > >>>>>>>>>>>>> stack and then you begin to support other DB backends
> > > > > >>>>>>>>>>>>> once
> > > > > >> it's
> > > > > >>>>>>>>>>>>> quite mature. Think in the loads of LAMP apps which
> > > > > >>>>>>>>>>>>> haven't
> > > > > >>>>> been
> > > > > >>>>>>>>>>>>> ported away from MySQL yet.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> In any case, you'll need a high performance SQL +
> > > > > >>>>>>>>>>>>> Massive
> > > > > >>>>> Storage
> > > > > >>>>>>>>>>>>> + Machine Learning + Massive Ingestion, and... ATM,
> > > > > >>>>>>>>>>>>> + that
> > > > can
> > > > > >> be
> > > > > >>>>>>>>>>>>> only provided by Hadoop.
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Regards!
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> Kenneth
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> A 2017-04-14 12:56, kant kodali escrigué:
> > > > > >>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> Hi Kenneth,
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> Thanks for the response.  I think you made a case
> for
> > > > > >>>>>>>>>>>>>> HDFS however users may want to use S3 or some other
> > > > > >>>>>>>>>>>>>> FS in which
> > > > > >>>>> case
> > > > > >>>>>>>>>>>>>> they can use Auxilio (hoping that there are no
> > > > > >>>>>>>>>>>>>> changes
> > > > > needed
> > > > > >>>>>>>>>>>>>> within Spot in which case I
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> can
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> agree to that). for example, Netflix stores all there
> > > > > >>>>>>>>>>>> data
> > > > > into
> > > > > >>>>> S3
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> The distributed sql query engine I would say should
> > > > > >>>>>>>>>>>>>> be
> > > > > >>>>> pluggable
> > > > > >>>>>>>>>>>>>> with whatever user may want to use and there a bunch
> > > > > >>>>>>>>>>>>>> of
> > > > them
> > > > > >>>>> out
> > > > > >>>>>>>> there.
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> sure
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> Impala is better than hive but what if users are
> > > > > >>>>>>>>>>>> already
> > > > using
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> something
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> else like Drill or Presto?
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> Me personally, would not assume that users are
> > > > > >>>>>>>>>>>>>> willing to
> > > > > >>>>> deploy
> > > > > >>>>>>>>>>>>>> all
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> of
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> that and make their existing stack more complicated at
> > > > > >>>>>>>>>>>> very
> > > > > >>>>> least
> > > > > >>>>>> I
> > > > > >>>>>>>>>>>>>> would
> > > > > >>>>>>>>>>>>>> say it is a uphill battle. Things have been changing
> > > > rapidly
> > > > > >>>>> in
> > > > > >>>>>>>>>>>>>> Big
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> data
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> space so whatever we think is standard won't be
> > > > > >>>>>>>>>>>> standard
> > > > > >> anymore
> > > > > >>>>>>>>>>>> but
> > > > > >>>>>>>>>>>>>> importantly there shouldn't be any reason why we
> > > > > >>>>>>>>>>>>>> shouldn't
> > > > > be
> > > > > >>>>>>>>>>>>>> flexible right.
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> Also I am not sure why only YARN? why not make that
> > > > > >>>>>>>>>>>>>> also
> > > > > more
> > > > > >>>>>>>>>>>>>> flexible so users can pick Mesos or standalone.
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> I think Flexibility is a key for a wide adoption
> > > > > >>>>>>>>>>>>>> rather
> > > > than
> > > > > >>>>> the
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>> tightly
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> coupled architecture.
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> Thanks!
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> On Fri, Apr 14, 2017 at 3:12 AM, Kenneth Peiruza
> > > > > >>>>>>>>>>>>>> <kenneth@floss.cat>
> > > > > >>>>>>>>>>>>>> wrote:
> > > > > >>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> PS: you need a big data platform to be able to
> > > > > >>>>>>>>>>>>>> collect all
> > > > > >>>>> those
> > > > > >>>>>>>>>>>>>>> netflows
> > > > > >>>>>>>>>>>>>>> and logs.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Spot isn't intended for SMBs, that's clear, then
> you
> > > > > >>>>>>>>>>>>>>> need
> > > > > >>>>> loads
> > > > > >>>>>>>>>>>>>>> of data to get ML working properly, and somewhere
> to
> > > > > >>>>>>>>>>>>>>> run
> > > > > >>>>> those
> > > > > >>>>>>>>>>>>>>> algorithms. That
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> is
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> Hadoop.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Regards!
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Kenneth
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Sent from my Mi phone On kant kodali
> > > > > >>>>>>>>>>>>>>> <kanth909@gmail.com>, Apr 14, 2017 4:04
> > > > AM
> > > > > >>>>>> wrote:
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Hi,
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Thanks for starting this thread. Here is my
> feedback.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> I somehow think the architecture is too complicated
> > > > > >>>>>>>>>>>>>>> for
> > > > > wide
> > > > > >>>>>>>>>>>>>>> adoption since it requires to install the
> following.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> HDFS.
> > > > > >>>>>>>>>>>>>>> HIVE.
> > > > > >>>>>>>>>>>>>>> IMPALA.
> > > > > >>>>>>>>>>>>>>> KAFKA.
> > > > > >>>>>>>>>>>>>>> SPARK (YARN).
> > > > > >>>>>>>>>>>>>>> YARN.
> > > > > >>>>>>>>>>>>>>> Zookeeper.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Currently there are way too many dependencies that
> > > > > >>>>> discourages
> > > > > >>>>>>>>>>>>>>> lot of users from using it because they have to go
> > > > through
> > > > > >>>>>>>>>>>>>>> deployment of all that required software. I think
> > > > > >>>>>>>>>>>>>>> for
> > > > wide
> > > > > >>>>>>>>>>>>>>> option we should minimize the dependencies and have
> > > > > >>>>>>>>>>>>>>> more pluggable architecture. for example I am
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> not
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> sure why HIVE & IMPALA both are required? why not just
> > > > > >>>>>>>>>>>> use
> > > > > >> Spark
> > > > > >>>>>>>>>>>> SQL
> > > > > >>>>>>>>>>>>>>> since
> > > > > >>>>>>>>>>>>>>> its already dependency or say users may want to use
> > > > > >>>>>>>>>>>>>>> their
> > > > > >> own
> > > > > >>>>>>>>>>>>>>> distributed query engine they like such as Apache
> > > > > >>>>>>>>>>>>>>> Drill
> > > > or
> > > > > >>>>>>>>>>>>>>> something else. we should be flexible enough to
> > > > > >>>>>>>>>>>>>>> provide
> > > > > that
> > > > > >>>>>>>>>>>>>>> option
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Also, I see that HDFS is used such that collectors
> > > > > >>>>>>>>>>>>>>> can
> > > > > >>>>> receive
> > > > > >>>>>>>>>>>>>>> file path's through Kafka and be able to read a
> > > > > >>>>>>>>>>>>>>> file. How
> > > > > >> big
> > > > > >>>>>>>>>>>>>>> are these files ?
> > > > > >>>>>>>>>>>>>>> Do we
> > > > > >>>>>>>>>>>>>>> really need HDFS for this? Why not provide more
> ways
> > > > > >>>>>>>>>>>>>>> to
> > > > > send
> > > > > >>>>>>>>>>>>>>> data such as sending data directly through Kafka or
> > > > > >>>>>>>>>>>>>>> say
> > > > > just
> > > > > >>>>>>>>>>>>>>> leaving up to the user to specify the file location
> > > > > >>>>>>>>>>>>>>> as an argument to collector process
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Finally, I learnt that to generate Net flow data
> one
> > > > would
> > > > > >>>>>>>>>>>>>>> require a specific hardware. This really means
> > > > > >>>>>>>>>>>>>>> Apache
> > > > Spot
> > > > > >> is
> > > > > >>>>>>>>>>>>>>> not meant for everyone.
> > > > > >>>>>>>>>>>>>>> I thought Apache Spot can be used to analyze the
> > > > > >>>>>>>>>>>>>>> network
> > > > > >>>>>> traffic
> > > > > >>>>>>>>>>>>>>> of
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>> any
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> machine but if it requires a specific hard then I
> think
> > > > > >>>>>>>>>>>> it
> > > > is
> > > > > >>>>>>>>>>>>>>> targeted for
> > > > > >>>>>>>>>>>>>>> specific group of people.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> The real strength of Apache Spot should mainly be
> > > > > >>>>>>>>>>>>>>> just
> > > > > >>>>>> analyzing
> > > > > >>>>>>>>>>>>>>> network traffic through ML.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Thanks!
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> On Thu, Apr 13, 2017 at 4:28 PM, Segerlind, Nathan
> L
> > > > > >>>>>>>>>>>>>>> < nathan.l.segerlind@intel.com> wrote:
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> Thanks, Nate,
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Nate.
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> -----Original Message-----
> > > > > >>>>>>>>>>>>>>>> From: Nate Smith [mailto:natedogs911@gmail.com]
> > > > > >>>>>>>>>>>>>>>> Sent: Thursday, April 13, 2017 4:26 PM
> > > > > >>>>>>>>>>>>>>>> To: user@spot.incubator.apache.org
> > > > > >>>>>>>>>>>>>>>> Cc: dev@spot.incubator.apache.org;
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> private@spot.incubator.apache.org
> > > > > >>>>>>>>>>>
> > > > > >>>>>>>>>>>> Subject: Re: [Discuss] - Future plans for Spot-ingest
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> I was really hoping it came through ok, Oh well :)
> > > > Here’s
> > > > > >> an
> > > > > >>>>>>>>>>>>>>>> image form:
> > > > > >>>>>>>>>>>>>>>> http://imgur.com/a/DUDsD
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> On Apr 13, 2017, at 4:05 PM, Segerlind, Nathan L <
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> nathan.l.segerlind@intel.com> wrote:
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> The diagram became garbled in the text format.
> > > > > >>>>>>>>>>>>>>>>> Could you resend it as a pdf?
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> Thanks,
> > > > > >>>>>>>>>>>>>>>>> Nate
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> -----Original Message-----
> > > > > >>>>>>>>>>>>>>>>> From: Nathanael Smith
> > > > > >>>>>>>>>>>>>>>>> [mailto:nathanael@apache.org]
> > > > > >>>>>>>>>>>>>>>>> Sent: Thursday, April 13, 2017 4:01 PM
> > > > > >>>>>>>>>>>>>>>>> To: private@spot.incubator.apache.org;
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> dev@spot.incubator.apache.org;
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> user@spot.incubator.apache.org
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> Subject: [Discuss] - Future plans for Spot-ingest
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> How would you like to see Spot-ingest change?
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> A. continue development on the Python
> > > > > >>>>>>>>>>>>>>>>> Master/Worker
> > > > with
> > > > > >>>>>> focus
> > > > > >>>>>>>>>>>>>>>>> on
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> performance / error handling / logging B. Develop
> > > > > >>>>>>>>>>>>>>>> Scala
> > > > > >>>>> based
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>> ingest to
> > > > > >>>>>>>>>>>>>>> be
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> inline with code base from ingest, ml, to OA (UI
> to
> > > > > >> continue
> > > > > >>>>>>>>>>>>>>>> being
> > > > > >>>>>>>>>>>>>>>> ipython/JS) C. Python ingest Worker with Scala
> > > > > >>>>>>>>>>>>>>>> based
> > > > Spark
> > > > > >>>>>> code
> > > > > >>>>>>>>>>>>>>>> for normalization and input into DB
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> Including the high level diagram:
> > > > > >>>>>>>>>>>>>>>>> +-----------------------------
> > > > > >>>>> ------------------------------
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> -------------------------------+
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | +--------------------------+
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +-----------------+        |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | | Master                   |  A. B. C.
> > > > > >>>>>>> |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Worker          |        |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |    A. Python             +---------------+
> > > A.
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> |   A.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Python     |        |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |    B. Scala              |               |
> > > > > >>>>>>> +------------->
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>         +----+   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |    C. Python             |               |
>   |
> > > > > >>>>>>> |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>         |    |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | +---^------+---------------+               |
>   |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +-----------------+    |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |      |                               |
>   |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>              |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |      |                               |
>   |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>              |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |     +Note--------------+             |
>   |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +-----------------+    |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |     |Running on a      |             |
>   |
> > > > > >>>>>>> |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Spark
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Streaming |    |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |     |worker node in    |             |
>   |
> > > > > >> B.
> > > > > >>>>>> C.
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> | B.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Scala        |    |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |     |the Hadoop cluster|             |
>   |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +--------> C.
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Scala        +-+  |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |     |     +------------------+             |
>   |
> > > > |
> > > > > >>>>>>> |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>         | |  |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |   A.|                                      |
>   |
> > > > |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +-----------------+ |  |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |   B.|                                      |
>   |
> > > > |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>            |  |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> |   C.|                                      |
>   |
> > > > |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>            |  |   |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | +----------------------+
> > > > > +-v------+----+----+-+
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +--------------v--v-+ |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |                      |          |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> |           |
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>                 | |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |   Local FS:          |          |    hdfs
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> |           |
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> Hive / Impala    | |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |  - Binary/Text       |          |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> |           |
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> - Parquet -     | |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |    Log files -       |          |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> |           |
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>                 | |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | |                      |          |
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> |           |
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>                 | |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> | +----------------------+
> > > > > +--------------------+
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> +-------------------+ |
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> +-----------------------------
> > > > > >>>>> ------------------------------
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>> -------------------------------+
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> Please let me know your thoughts,
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>> - Nathanael
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>>>>
> > > > > >>>>>>>>>>>>
> > > > > >>>>>>>>>
> > > > > >>>>>>>>
> > > > > >>>>>>>
> > > > > >>>>>>
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>>
> > > > > >>>>
> > > > > >>>> --
> > > > > >>>> Michael Ridley <mridley@cloudera.com>
> > > > > >>>> office: (650) 352-1337
> > > > > >>>> mobile: (571) 438-2420
> > > > > >>>> Senior Solutions Architect
> > > > > >>>> Cloudera, Inc.
> > > > > >>>
> > > > > >>>
> > > > > >>
> > > > > >>
> > > > > >> --
> > > > > >> Michael Ridley <mridley@cloudera.com>
> > > > > >> office: (650) 352-1337
> > > > > >> mobile: (571) 438-2420
> > > > > >> Senior Solutions Architect
> > > > > >> Cloudera, Inc.
> > > > > >>
> > > > >
> > > > >
> > > >
> > >
> >
>

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