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From Ali Akhtar <ali.rac...@gmail.com>
Subject Re: Architecture recommendations for a tricky use case
Date Thu, 29 Sep 2016 15:35:40 GMT
My concern with Postgres / Cassandra is only scalability. I will look
further into Postgres horizontal scaling, thanks.

Writes could be idempotent if done as upserts, otherwise updates will be
idempotent but not inserts.

Data should not be lost. The system should be as fault tolerant as possible.

What's the advantage of using Spark for reading Kafka instead of direct
Kafka consumers?

On Thu, Sep 29, 2016 at 8:28 PM, Cody Koeninger <cody@koeninger.org> wrote:

> I wouldn't give up the flexibility and maturity of a relational
> database, unless you have a very specific use case.  I'm not trashing
> cassandra, I've used cassandra, but if all I know is that you're doing
> analytics, I wouldn't want to give up the ability to easily do ad-hoc
> aggregations without a lot of forethought.  If you're worried about
> scaling, there are several options for horizontally scaling Postgres
> in particular.  One of the current best from what I've worked with is
> Citus.
>
> On Thu, Sep 29, 2016 at 10:15 AM, Deepak Sharma <deepakmca05@gmail.com>
> wrote:
> > Hi Cody
> > Spark direct stream is just fine for this use case.
> > But why postgres and not cassandra?
> > Is there anything specific here that i may not be aware?
> >
> > Thanks
> > Deepak
> >
> > On Thu, Sep 29, 2016 at 8:41 PM, Cody Koeninger <cody@koeninger.org>
> wrote:
> >>
> >> How are you going to handle etl failures?  Do you care about lost /
> >> duplicated data?  Are your writes idempotent?
> >>
> >> Absent any other information about the problem, I'd stay away from
> >> cassandra/flume/hdfs/hbase/whatever, and use a spark direct stream
> >> feeding postgres.
> >>
> >> On Thu, Sep 29, 2016 at 10:04 AM, Ali Akhtar <ali.rac200@gmail.com>
> wrote:
> >> > Is there an advantage to that vs directly consuming from Kafka?
> Nothing
> >> > is
> >> > being done to the data except some light ETL and then storing it in
> >> > Cassandra
> >> >
> >> > On Thu, Sep 29, 2016 at 7:58 PM, Deepak Sharma <deepakmca05@gmail.com
> >
> >> > wrote:
> >> >>
> >> >> Its better you use spark's direct stream to ingest from kafka.
> >> >>
> >> >> On Thu, Sep 29, 2016 at 8:24 PM, Ali Akhtar <ali.rac200@gmail.com>
> >> >> wrote:
> >> >>>
> >> >>> I don't think I need a different speed storage and batch storage.
> Just
> >> >>> taking in raw data from Kafka, standardizing, and storing it
> somewhere
> >> >>> where
> >> >>> the web UI can query it, seems like it will be enough.
> >> >>>
> >> >>> I'm thinking about:
> >> >>>
> >> >>> - Reading data from Kafka via Spark Streaming
> >> >>> - Standardizing, then storing it in Cassandra
> >> >>> - Querying Cassandra from the web ui
> >> >>>
> >> >>> That seems like it will work. My question now is whether to use
> Spark
> >> >>> Streaming to read Kafka, or use Kafka consumers directly.
> >> >>>
> >> >>>
> >> >>> On Thu, Sep 29, 2016 at 7:41 PM, Mich Talebzadeh
> >> >>> <mich.talebzadeh@gmail.com> wrote:
> >> >>>>
> >> >>>> - Spark Streaming to read data from Kafka
> >> >>>> - Storing the data on HDFS using Flume
> >> >>>>
> >> >>>> You don't need Spark streaming to read data from Kafka and
store on
> >> >>>> HDFS. It is a waste of resources.
> >> >>>>
> >> >>>> Couple Flume to use Kafka as source and HDFS as sink directly
> >> >>>>
> >> >>>> KafkaAgent.sources = kafka-sources
> >> >>>> KafkaAgent.sinks.hdfs-sinks.type = hdfs
> >> >>>>
> >> >>>> That will be for your batch layer. To analyse you can directly
read
> >> >>>> from
> >> >>>> hdfs files with Spark or simply store data in a database of
your
> >> >>>> choice via
> >> >>>> cron or something. Do not mix your batch layer with speed layer.
> >> >>>>
> >> >>>> Your speed layer will ingest the same data directly from Kafka
into
> >> >>>> spark streaming and that will be  online or near real time
(defined
> >> >>>> by your
> >> >>>> window).
> >> >>>>
> >> >>>> Then you have a a serving layer to present data from both speed
> (the
> >> >>>> one from SS) and batch layer.
> >> >>>>
> >> >>>> HTH
> >> >>>>
> >> >>>>
> >> >>>>
> >> >>>>
> >> >>>> Dr Mich Talebzadeh
> >> >>>>
> >> >>>>
> >> >>>>
> >> >>>> LinkedIn
> >> >>>>
> >> >>>> https://www.linkedin.com/profile/view?id=
> AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> >> >>>>
> >> >>>>
> >> >>>>
> >> >>>> http://talebzadehmich.wordpress.com
> >> >>>>
> >> >>>>
> >> >>>> Disclaimer: Use it at your own risk. Any and all responsibility
for
> >> >>>> any
> >> >>>> loss, damage or destruction of data or any other property which
may
> >> >>>> arise
> >> >>>> from relying on this email's technical content is explicitly
> >> >>>> disclaimed. The
> >> >>>> author will in no case be liable for any monetary damages arising
> >> >>>> from such
> >> >>>> loss, damage or destruction.
> >> >>>>
> >> >>>>
> >> >>>>
> >> >>>>
> >> >>>> On 29 September 2016 at 15:15, Ali Akhtar <ali.rac200@gmail.com>
> >> >>>> wrote:
> >> >>>>>
> >> >>>>> The web UI is actually the speed layer, it needs to be
able to
> query
> >> >>>>> the data online, and show the results in real-time.
> >> >>>>>
> >> >>>>> It also needs a custom front-end, so a system like Tableau
can't
> be
> >> >>>>> used, it must have a custom backend + front-end.
> >> >>>>>
> >> >>>>> Thanks for the recommendation of Flume. Do you think this
will
> work:
> >> >>>>>
> >> >>>>> - Spark Streaming to read data from Kafka
> >> >>>>> - Storing the data on HDFS using Flume
> >> >>>>> - Using Spark to query the data in the backend of the web
UI?
> >> >>>>>
> >> >>>>>
> >> >>>>>
> >> >>>>> On Thu, Sep 29, 2016 at 7:08 PM, Mich Talebzadeh
> >> >>>>> <mich.talebzadeh@gmail.com> wrote:
> >> >>>>>>
> >> >>>>>> You need a batch layer and a speed layer. Data from
Kafka can be
> >> >>>>>> stored on HDFS using flume.
> >> >>>>>>
> >> >>>>>> -  Query this data to generate reports / analytics
(There will
> be a
> >> >>>>>> web UI which will be the front-end to the data, and
will show the
> >> >>>>>> reports)
> >> >>>>>>
> >> >>>>>> This is basically batch layer and you need something
like Tableau
> >> >>>>>> or
> >> >>>>>> Zeppelin to query data
> >> >>>>>>
> >> >>>>>> You will also need spark streaming to query data online
for speed
> >> >>>>>> layer. That data could be stored in some transient
fabric like
> >> >>>>>> ignite or
> >> >>>>>> even druid.
> >> >>>>>>
> >> >>>>>> HTH
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> Dr Mich Talebzadeh
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> LinkedIn
> >> >>>>>>
> >> >>>>>> https://www.linkedin.com/profile/view?id=
> AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> http://talebzadehmich.wordpress.com
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> Disclaimer: Use it at your own risk. Any and all responsibility
> for
> >> >>>>>> any loss, damage or destruction of data or any other
property
> which
> >> >>>>>> may
> >> >>>>>> arise from relying on this email's technical content
is
> explicitly
> >> >>>>>> disclaimed. The author will in no case be liable for
any monetary
> >> >>>>>> damages
> >> >>>>>> arising from such loss, damage or destruction.
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>>
> >> >>>>>> On 29 September 2016 at 15:01, Ali Akhtar <ali.rac200@gmail.com>
> >> >>>>>> wrote:
> >> >>>>>>>
> >> >>>>>>> It needs to be able to scale to a very large amount
of data,
> yes.
> >> >>>>>>>
> >> >>>>>>> On Thu, Sep 29, 2016 at 7:00 PM, Deepak Sharma
> >> >>>>>>> <deepakmca05@gmail.com> wrote:
> >> >>>>>>>>
> >> >>>>>>>> What is the message inflow ?
> >> >>>>>>>> If it's really high , definitely spark will
be of great use .
> >> >>>>>>>>
> >> >>>>>>>> Thanks
> >> >>>>>>>> Deepak
> >> >>>>>>>>
> >> >>>>>>>>
> >> >>>>>>>> On Sep 29, 2016 19:24, "Ali Akhtar" <ali.rac200@gmail.com>
> wrote:
> >> >>>>>>>>>
> >> >>>>>>>>> I have a somewhat tricky use case, and
I'm looking for ideas.
> >> >>>>>>>>>
> >> >>>>>>>>> I have 5-6 Kafka producers, reading various
APIs, and writing
> >> >>>>>>>>> their
> >> >>>>>>>>> raw data into Kafka.
> >> >>>>>>>>>
> >> >>>>>>>>> I need to:
> >> >>>>>>>>>
> >> >>>>>>>>> - Do ETL on the data, and standardize it.
> >> >>>>>>>>>
> >> >>>>>>>>> - Store the standardized data somewhere
(HBase / Cassandra /
> Raw
> >> >>>>>>>>> HDFS / ElasticSearch / Postgres)
> >> >>>>>>>>>
> >> >>>>>>>>> - Query this data to generate reports /
analytics (There will
> be
> >> >>>>>>>>> a
> >> >>>>>>>>> web UI which will be the front-end to the
data, and will show
> >> >>>>>>>>> the reports)
> >> >>>>>>>>>
> >> >>>>>>>>> Java is being used as the backend language
for everything
> >> >>>>>>>>> (backend
> >> >>>>>>>>> of the web UI, as well as the ETL layer)
> >> >>>>>>>>>
> >> >>>>>>>>> I'm considering:
> >> >>>>>>>>>
> >> >>>>>>>>> - Using raw Kafka consumers, or Spark Streaming,
as the ETL
> >> >>>>>>>>> layer
> >> >>>>>>>>> (receive raw data from Kafka, standardize
& store it)
> >> >>>>>>>>>
> >> >>>>>>>>> - Using Cassandra, HBase, or raw HDFS,
for storing the
> >> >>>>>>>>> standardized
> >> >>>>>>>>> data, and to allow queries
> >> >>>>>>>>>
> >> >>>>>>>>> - In the backend of the web UI, I could
either use Spark to
> run
> >> >>>>>>>>> queries across the data (mostly filters),
or directly run
> >> >>>>>>>>> queries against
> >> >>>>>>>>> Cassandra / HBase
> >> >>>>>>>>>
> >> >>>>>>>>> I'd appreciate some thoughts / suggestions
on which of these
> >> >>>>>>>>> alternatives I should go with (e.g, using
raw Kafka consumers
> vs
> >> >>>>>>>>> Spark for
> >> >>>>>>>>> ETL, which persistent data store to use,
and how to query that
> >> >>>>>>>>> data store in
> >> >>>>>>>>> the backend of the web UI, for displaying
the reports).
> >> >>>>>>>>>
> >> >>>>>>>>>
> >> >>>>>>>>> Thanks.
> >> >>>>>>>
> >> >>>>>>>
> >> >>>>>>
> >> >>>>>
> >> >>>>
> >> >>>
> >> >>
> >> >>
> >> >>
> >> >> --
> >> >> Thanks
> >> >> Deepak
> >> >> www.bigdatabig.com
> >> >> www.keosha.net
> >> >
> >> >
> >>
> >> ---------------------------------------------------------------------
> >> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
> >>
> >
> >
> >
> > --
> > Thanks
> > Deepak
> > www.bigdatabig.com
> > www.keosha.net
>

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