Re: OLAP query using spark dataframe with cassandra

[danielcsant]

You can also evaluate Stratio Sparkta. It is a real time aggregation tool
based on Spark Streaming. 
It is able to write in Cassandra and in other databases like MongoDB,
Elasticsearch,... It is prepared to deploy this aggregations in Mesos so
maybe it fits your necessities.

There is no a query layer that could abstract the analytics part in OLAP but
it is on the roadmap.

Disclaimer: I work in this product 



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Re: OLAP query using spark dataframe with cassandra

[tsh]

Hi,

I'm in the same position right now: we are going to implement something 
like OLAP BI + Machine Learning explorations on the same cluster.
Well, the question is quite ambivalent: from one hand, we have terabytes 
of versatile data and the necessity to make something like cubes (Hive 
and Hive on HBase are unsatisfactory). From the other, our users get 
accustomed to Tableau + Vertica.

So, right now I consider the following choices:
1) Platfora (not free, I don't know price right now) + Spark
2) AtScale + Tableau(not free, I don't know price right now) + Spark
3) Apache Kylin (young project?) + Spark on YARN + Kafka + Flume + some 
storage
4) Apache Phoenix + Apache HBase + Mondrian + Spark on YARN + Kafka + 
Flume (has somebody use it in production?)

5) Spark + Tableau  (cubes?)

For myself, I decided not to dive into Mesos. Cassandra is hardly 
configurable, you'll have to dedicate special employee to support it.


I'll be glad to hear other ideas & propositions as we are at the 
beginning of the process too.


Sincerely yours, Tim Shenkao

On 11/09/2015 09:46 AM, fightf...@163.com wrote:

Hi,

Thanks for suggesting. Actually we are now evaluating and stressing 
the spark sql on cassandra, while


trying to define business models. FWIW, the solution mentioned here is 
different from traditional OLAP


cube engine, right ? So we are hesitating on the common sense or 
direction choice of olap architecture.


And we are happy to hear more use case from this community.

Best,
Sun.


fightf...@163.com

*From:* Jörn Franke <mailto:jornfra...@gmail.com>
*Date:* 2015-11-09 14:40
*To:* fightf...@163.com <mailto:fightf...@163.com>
*CC:* user <mailto:u...@spark.apache.org>; dev
<mailto:dev@spark.apache.org>
    *Subject:* Re: OLAP query using spark dataframe with cassandra

Is there any distributor supporting these software components in
combination? If no and your core business is not software then you
may want to look for something else, because it might not make
sense to build up internal know-how in all of these areas.

In any case - it depends all highly on your data and queries. You
will have to do your own experiments.

On 09 Nov 2015, at 07:02, "fightf...@163.com
<mailto:fightf...@163.com>" <fightf...@163.com
<mailto:fightf...@163.com>> wrote:


Hi, community

We are specially interested about this featural integration
according to some slides from [1]. The
SMACK(Spark+Mesos+Akka+Cassandra+Kafka)

seems good implementation for lambda architecure in the
open-source world, especially non-hadoop based cluster
environment. As we can see,

the advantages obviously consist of :

1 the feasibility and scalability of spark datafram api, which
can also make a perfect complement for Apache Cassandra native
cql feature.

2 both streaming and batch process availability using the
ALL-STACK thing, cool.

3 we can both achieve compacity and usability for spark with
cassandra, including seemlessly integrating with job scheduling
and resource management.

Only one concern goes to the OLAP query performance issue, which
mainly caused by frequent aggregation work between daily
increased large tables, for

both spark sql and cassandra. I can see that the [1] use case
facilitates FiloDB to achieve columnar storage and query
performance, but we had nothing more

knowledge.

Question is : Any guy had such use case for now, especially using
in your production environment ? Would be interested in your
architeture for designing this

OLAP engine using spark +  cassandra. What do you think the
comparison between the scenario with traditional OLAP cube
design? Like Apache Kylin or

pentaho mondrian ?

Best Regards,

Sun.


[1]

http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark


fightf...@163.com <mailto:fightf...@163.com>

Re: OLAP query using spark dataframe with cassandra

[Luke Han]

Some friends refer me this thread about OLAP/Kylin and Spark...

Here's my 2 cents..

If you are trying to setup OLAP, Apache Kylin should be one good idea for
you to evaluate.

The project has developed more than 2 years and going to graduate to Apache
Top Level Project [1].
There are many deployments on production already include eBay, Exponential,
JD.com, VIP.com and others, refer to powered by page [2].

Apache Kylin's spark engine also on the way, there's discussion about
turning the performance [3].

There are variety clients are available to interactive with Kylin with ANSI
SQL, including Tableau, Zeppelin, Pentaho/mondrian, Saiku/mondrian, and the
Excel/PowerBI support will roll out this week.

Apache Kylin is young but mature with huge case validation (one biggest
cube in eBay contains 85+B rows, 90%ile production platform's query latency
in few seconds).

StreamingOLAP is coming in Kylin v2.0 with plug-able architecture, there's
already one real case on production inside eBay, please refer to our design
deck [4]

We are really welcome everyone to join and contribute to Kylin as OLAP
engine for Big Data:-)

Please feel free to contact our community or me for any question.

Thanks.

1. http://s.apache.org/bah
2. http://kylin.incubator.apache.org/community/poweredby.html
3. http://s.apache.org/lHA
4.
http://www.slideshare.net/lukehan/1-apache-kylin-deep-dive-streaming-and-plugin-architecture-apache-kylin-meetup-shanghai
5. http://kylin.io


Best Regards!
-

Luke Han

On Tue, Nov 10, 2015 at 2:56 AM, tsh <t...@timshenkao.su> wrote:

> Hi,
>
> I'm in the same position right now: we are going to implement something
> like OLAP BI + Machine Learning explorations on the same cluster.
> Well, the question is quite ambivalent: from one hand, we have terabytes
> of versatile data and the necessity to make something like cubes (Hive and
> Hive on HBase are unsatisfactory). From the other, our users get accustomed
> to Tableau + Vertica.
> So, right now I consider the following choices:
> 1) Platfora (not free, I don't know price right now) + Spark
> 2) AtScale + Tableau(not free, I don't know price right now) + Spark
> 3) Apache Kylin (young project?) + Spark on YARN + Kafka + Flume + some
> storage
> 4) Apache Phoenix + Apache HBase + Mondrian + Spark on YARN + Kafka +
> Flume (has somebody use it in production?)
> 5) Spark + Tableau  (cubes?)
>
> For myself, I decided not to dive into Mesos. Cassandra is hardly
> configurable, you'll have to dedicate special employee to support it.
>
> I'll be glad to hear other ideas & propositions as we are at the beginning
> of the process too.
>
> Sincerely yours, Tim Shenkao
>
>
> On 11/09/2015 09:46 AM, fightf...@163.com wrote:
>
> Hi,
>
> Thanks for suggesting. Actually we are now evaluating and stressing the
> spark sql on cassandra, while
>
> trying to define business models. FWIW, the solution mentioned here is
> different from traditional OLAP
>
> cube engine, right ? So we are hesitating on the common sense or direction
> choice of olap architecture.
>
> And we are happy to hear more use case from this community.
>
> Best,
> Sun.
>
> --
> fightf...@163.com
>
>
> *From:* Jörn Franke <jornfra...@gmail.com>
> *Date:* 2015-11-09 14:40
> *To:* fightf...@163.com
> *CC:* user <u...@spark.apache.org>; dev <dev@spark.apache.org>
> *Subject:* Re: OLAP query using spark dataframe with cassandra
>
> Is there any distributor supporting these software components in
> combination? If no and your core business is not software then you may want
> to look for something else, because it might not make sense to build up
> internal know-how in all of these areas.
>
> In any case - it depends all highly on your data and queries. You will
> have to do your own experiments.
>
> On 09 Nov 2015, at 07:02, "fightf...@163.com" <fightf...@163.com> wrote:
>
> Hi, community
>
> We are specially interested about this featural integration according to
> some slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)
>
> seems good implementation for lambda architecure in the open-source world,
> especially non-hadoop based cluster environment. As we can see,
>
> the advantages obviously consist of :
>
> 1 the feasibility and scalability of spark datafram api, which can also
> make a perfect complement for Apache Cassandra native cql feature.
>
> 2 both streaming and batch process availability using the ALL-STACK thing,
> cool.
>
> 3 we can both achieve compacity and usability for spark with cassandra,
> including seemlessly integrating with job scheduling and resource
> management.
>
> Only one concern goes to the OLAP query performance issue, whic

Re: Re: OLAP query using spark dataframe with cassandra

[fightf...@163.com]

Hi,

According to my experience, I would recommend option 3) using Apache Kylin for 
your requirements. 

This is a suggestion based on the open-source world. 

For the per cassandra thing, I accept your advice for the special support 
thing. But the community is very

open and convinient for prompt response. 



fightf...@163.com
 
From: tsh
Date: 2015-11-10 02:56
To: fightf...@163.com; user; dev
Subject: Re: OLAP query using spark dataframe with cassandra
Hi,

I'm in the same position right now: we are going to implement something like 
OLAP BI + Machine Learning explorations on the same cluster.
Well, the question is quite ambivalent: from one hand, we have terabytes of 
versatile data and the necessity to make something like cubes (Hive and Hive on 
HBase are unsatisfactory). From the other, our users get accustomed to Tableau 
+ Vertica. 
So, right now I consider the following choices:
1) Platfora (not free, I don't know price right now) + Spark
2) AtScale + Tableau(not free, I don't know price right now) + Spark
3) Apache Kylin (young project?) + Spark on YARN + Kafka + Flume + some storage
4) Apache Phoenix + Apache HBase + Mondrian + Spark on YARN + Kafka + Flume 
(has somebody use it in production?)
5) Spark + Tableau  (cubes?)

For myself, I decided not to dive into Mesos. Cassandra is hardly configurable, 
you'll have to dedicate special employee to support it.

I'll be glad to hear other ideas & propositions as we are at the beginning of 
the process too.

Sincerely yours, Tim Shenkao

On 11/09/2015 09:46 AM, fightf...@163.com wrote:
Hi, 

Thanks for suggesting. Actually we are now evaluating and stressing the spark 
sql on cassandra, while

trying to define business models. FWIW, the solution mentioned here is 
different from traditional OLAP

cube engine, right ? So we are hesitating on the common sense or direction 
choice of olap architecture. 

And we are happy to hear more use case from this community. 

Best,
Sun. 



fightf...@163.com
 
From: Jörn Franke
Date: 2015-11-09 14:40
To: fightf...@163.com
CC: user; dev
Subject: Re: OLAP query using spark dataframe with cassandra

Is there any distributor supporting these software components in combination? 
If no and your core business is not software then you may want to look for 
something else, because it might not make sense to build up internal know-how 
in all of these areas.

In any case - it depends all highly on your data and queries. You will have to 
do your own experiments.

On 09 Nov 2015, at 07:02, "fightf...@163.com" <fightf...@163.com> wrote:

Hi, community

We are specially interested about this featural integration according to some 
slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)

seems good implementation for lambda architecure in the open-source world, 
especially non-hadoop based cluster environment. As we can see, 

the advantages obviously consist of :

1 the feasibility and scalability of spark datafram api, which can also make a 
perfect complement for Apache Cassandra native cql feature.

2 both streaming and batch process availability using the ALL-STACK thing, cool.

3 we can both achieve compacity and usability for spark with cassandra, 
including seemlessly integrating with job scheduling and resource management.

Only one concern goes to the OLAP query performance issue, which mainly caused 
by frequent aggregation work between daily increased large tables, for 

both spark sql and cassandra. I can see that the [1] use case facilitates 
FiloDB to achieve columnar storage and query performance, but we had nothing 
more 

knowledge. 

Question is : Any guy had such use case for now, especially using in your 
production environment ? Would be interested in your architeture for designing 
this 

OLAP engine using spark +  cassandra. What do you think the comparison between 
the scenario with traditional OLAP cube design? Like Apache Kylin or 

pentaho mondrian ? 

Best Regards,

Sun.


[1]  
http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark



fightf...@163.com

Re: Re: OLAP query using spark dataframe with cassandra

[fightf...@163.com]

Hi, 

Have you ever considered cassandra as a replacement ? We are now almost the 
seem usage as your engine, e.g. using mysql to store 

initial aggregated data. Can you share more about your kind of Cube queries ? 
We are very interested in that arch too : )

Best,
Sun.


fightf...@163.com
 
From: Andrés Ivaldi
Date: 2015-11-10 07:03
To: tsh
CC: fightf...@163.com; user; dev
Subject: Re: OLAP query using spark dataframe with cassandra
Hi,
I'm also considering something similar, Spark plain is too slow for my case, a 
possible solution is use Spark as Multiple Source connector and basic 
transformation layer, then persist the information (actually is a RDBM), after 
that with our engine we build a kind of Cube queries, and the result is 
processed again by Spark adding Machine Learning.
Our Missing part is reemplace the RDBM with something more suitable and 
scalable than RDBM, dont care about pre processing information if after pre 
processing the queries are fast.

Regards

On Mon, Nov 9, 2015 at 3:56 PM, tsh <t...@timshenkao.su> wrote:
Hi,

I'm in the same position right now: we are going to implement something like 
OLAP BI + Machine Learning explorations on the same cluster.
Well, the question is quite ambivalent: from one hand, we have terabytes of 
versatile data and the necessity to make something like cubes (Hive and Hive on 
HBase are unsatisfactory). From the other, our users get accustomed to Tableau 
+ Vertica. 
So, right now I consider the following choices:
1) Platfora (not free, I don't know price right now) + Spark
2) AtScale + Tableau(not free, I don't know price right now) + Spark
3) Apache Kylin (young project?) + Spark on YARN + Kafka + Flume + some storage
4) Apache Phoenix + Apache HBase + Mondrian + Spark on YARN + Kafka + Flume 
(has somebody use it in production?)
5) Spark + Tableau  (cubes?)

For myself, I decided not to dive into Mesos. Cassandra is hardly configurable, 
you'll have to dedicate special employee to support it.

I'll be glad to hear other ideas & propositions as we are at the beginning of 
the process too.

Sincerely yours, Tim Shenkao


On 11/09/2015 09:46 AM, fightf...@163.com wrote:
Hi, 

Thanks for suggesting. Actually we are now evaluating and stressing the spark 
sql on cassandra, while

trying to define business models. FWIW, the solution mentioned here is 
different from traditional OLAP

cube engine, right ? So we are hesitating on the common sense or direction 
choice of olap architecture. 

And we are happy to hear more use case from this community. 

Best,
Sun. 



fightf...@163.com
 
From: Jörn Franke
Date: 2015-11-09 14:40
To: fightf...@163.com
CC: user; dev
Subject: Re: OLAP query using spark dataframe with cassandra

Is there any distributor supporting these software components in combination? 
If no and your core business is not software then you may want to look for 
something else, because it might not make sense to build up internal know-how 
in all of these areas.

In any case - it depends all highly on your data and queries. You will have to 
do your own experiments.

On 09 Nov 2015, at 07:02, "fightf...@163.com" <fightf...@163.com> wrote:

Hi, community

We are specially interested about this featural integration according to some 
slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)

seems good implementation for lambda architecure in the open-source world, 
especially non-hadoop based cluster environment. As we can see, 

the advantages obviously consist of :

1 the feasibility and scalability of spark datafram api, which can also make a 
perfect complement for Apache Cassandra native cql feature.

2 both streaming and batch process availability using the ALL-STACK thing, cool.

3 we can both achieve compacity and usability for spark with cassandra, 
including seemlessly integrating with job scheduling and resource management.

Only one concern goes to the OLAP query performance issue, which mainly caused 
by frequent aggregation work between daily increased large tables, for 

both spark sql and cassandra. I can see that the [1] use case facilitates 
FiloDB to achieve columnar storage and query performance, but we had nothing 
more 

knowledge. 

Question is : Any guy had such use case for now, especially using in your 
production environment ? Would be interested in your architeture for designing 
this 

OLAP engine using spark +  cassandra. What do you think the comparison between 
the scenario with traditional OLAP cube design? Like Apache Kylin or 

pentaho mondrian ? 

Best Regards,

Sun.


[1]  
http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark



fightf...@163.com




-- 
Ing. Ivaldi Andres

Re: OLAP query using spark dataframe with cassandra

[Ted Yu]

Please consider using NoSQL engine such as hbase. 

Cheers

> On Nov 9, 2015, at 3:03 PM, Andrés Ivaldi <iaiva...@gmail.com> wrote:
> 
> Hi,
> I'm also considering something similar, Spark plain is too slow for my case, 
> a possible solution is use Spark as Multiple Source connector and basic 
> transformation layer, then persist the information (actually is a RDBM), 
> after that with our engine we build a kind of Cube queries, and the result is 
> processed again by Spark adding Machine Learning.
> Our Missing part is reemplace the RDBM with something more suitable and 
> scalable than RDBM, dont care about pre processing information if after pre 
> processing the queries are fast.
> 
> Regards
> 
>> On Mon, Nov 9, 2015 at 3:56 PM, tsh <t...@timshenkao.su> wrote:
>> Hi,
>> 
>> I'm in the same position right now: we are going to implement something like 
>> OLAP BI + Machine Learning explorations on the same cluster.
>> Well, the question is quite ambivalent: from one hand, we have terabytes 
>> of versatile data and the necessity to make something like cubes (Hive and 
>> Hive on HBase are unsatisfactory). From the other, our users get accustomed 
>> to Tableau + Vertica. 
>> So, right now I consider the following choices:
>> 1) Platfora (not free, I don't know price right now) + Spark
>> 2) AtScale + Tableau(not free, I don't know price right now) + Spark
>> 3) Apache Kylin (young project?) + Spark on YARN + Kafka + Flume + some 
>> storage
>> 4) Apache Phoenix + Apache HBase + Mondrian + Spark on YARN + Kafka + Flume 
>> (has somebody use it in production?)
>> 5) Spark + Tableau  (cubes?)
>> 
>> For myself, I decided not to dive into Mesos. Cassandra is hardly 
>> configurable, you'll have to dedicate special employee to support it.
>> 
>> I'll be glad to hear other ideas & propositions as we are at the beginning 
>> of the process too.
>> 
>> Sincerely yours, Tim Shenkao
>> 
>> 
>>> On 11/09/2015 09:46 AM, fightf...@163.com wrote:
>>> Hi, 
>>> 
>>> Thanks for suggesting. Actually we are now evaluating and stressing the 
>>> spark sql on cassandra, while
>>> 
>>> trying to define business models. FWIW, the solution mentioned here is 
>>> different from traditional OLAP
>>> 
>>> cube engine, right ? So we are hesitating on the common sense or direction 
>>> choice of olap architecture. 
>>> 
>>> And we are happy to hear more use case from this community. 
>>> 
>>> Best,
>>> Sun. 
>>> 
>>> fightf...@163.com
>>>  
>>> From: Jörn Franke
>>> Date: 2015-11-09 14:40
>>> To: fightf...@163.com
>>> CC: user; dev
>>> Subject: Re: OLAP query using spark dataframe with cassandra
>>> 
>>> Is there any distributor supporting these software components in 
>>> combination? If no and your core business is not software then you may want 
>>> to look for something else, because it might not make sense to build up 
>>> internal know-how in all of these areas.
>>> 
>>> In any case - it depends all highly on your data and queries. You will have 
>>> to do your own experiments.
>>> 
>>> On 09 Nov 2015, at 07:02, "fightf...@163.com" <fightf...@163.com> wrote:
>>> 
>>>> Hi, community
>>>> 
>>>> We are specially interested about this featural integration according to 
>>>> some slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)
>>>> 
>>>> seems good implementation for lambda architecure in the open-source world, 
>>>> especially non-hadoop based cluster environment. As we can see, 
>>>> 
>>>> the advantages obviously consist of :
>>>> 
>>>> 1 the feasibility and scalability of spark datafram api, which can also 
>>>> make a perfect complement for Apache Cassandra native cql feature.
>>>> 
>>>> 2 both streaming and batch process availability using the ALL-STACK thing, 
>>>> cool.
>>>> 
>>>> 3 we can both achieve compacity and usability for spark with cassandra, 
>>>> including seemlessly integrating with job scheduling and resource 
>>>> management.
>>>> 
>>>> Only one concern goes to the OLAP query performance issue, which mainly 
>>>> caused by frequent aggregation work between daily increased large tables, 
>>>> for 
>>>> 
>>>> both spark sql and cassandra. I can see that the [1] use case facilitates 
>>>> FiloDB to achieve columnar storage and query performance, but we had 
>>>> nothing more 
>>>> 
>>>> knowledge. 
>>>> 
>>>> Question is : Any guy had such use case for now, especially using in your 
>>>> production environment ? Would be interested in your architeture for 
>>>> designing this 
>>>> 
>>>> OLAP engine using spark +  cassandra. What do you think the comparison 
>>>> between the scenario with traditional OLAP cube design? Like Apache Kylin 
>>>> or 
>>>> 
>>>> pentaho mondrian ? 
>>>> 
>>>> Best Regards,
>>>> 
>>>> Sun.
>>>> 
>>>> 
>>>> [1]  
>>>> http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark
>>>> 
>>>> fightf...@163.com
> 
> 
> 
> -- 
> Ing. Ivaldi Andres

Re: OLAP query using spark dataframe with cassandra

[Jörn Franke]

Is there any distributor supporting these software components in combination? 
If no and your core business is not software then you may want to look for 
something else, because it might not make sense to build up internal know-how 
in all of these areas.

In any case - it depends all highly on your data and queries. You will have to 
do your own experiments.

> On 09 Nov 2015, at 07:02, "fightf...@163.com"  wrote:
> 
> Hi, community
> 
> We are specially interested about this featural integration according to some 
> slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)
> 
> seems good implementation for lambda architecure in the open-source world, 
> especially non-hadoop based cluster environment. As we can see, 
> 
> the advantages obviously consist of :
> 
> 1 the feasibility and scalability of spark datafram api, which can also make 
> a perfect complement for Apache Cassandra native cql feature.
> 
> 2 both streaming and batch process availability using the ALL-STACK thing, 
> cool.
> 
> 3 we can both achieve compacity and usability for spark with cassandra, 
> including seemlessly integrating with job scheduling and resource management.
> 
> Only one concern goes to the OLAP query performance issue, which mainly 
> caused by frequent aggregation work between daily increased large tables, for 
> 
> both spark sql and cassandra. I can see that the [1] use case facilitates 
> FiloDB to achieve columnar storage and query performance, but we had nothing 
> more 
> 
> knowledge. 
> 
> Question is : Any guy had such use case for now, especially using in your 
> production environment ? Would be interested in your architeture for 
> designing this 
> 
> OLAP engine using spark +  cassandra. What do you think the comparison 
> between the scenario with traditional OLAP cube design? Like Apache Kylin or 
> 
> pentaho mondrian ? 
> 
> Best Regards,
> 
> Sun.
> 
> 
> [1]  
> http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark
> 
> fightf...@163.com

Re: Re: OLAP query using spark dataframe with cassandra

[fightf...@163.com]

Hi, 

Thanks for suggesting. Actually we are now evaluating and stressing the spark 
sql on cassandra, while

trying to define business models. FWIW, the solution mentioned here is 
different from traditional OLAP

cube engine, right ? So we are hesitating on the common sense or direction 
choice of olap architecture. 

And we are happy to hear more use case from this community. 

Best,
Sun. 



fightf...@163.com
 
From: Jörn Franke
Date: 2015-11-09 14:40
To: fightf...@163.com
CC: user; dev
Subject: Re: OLAP query using spark dataframe with cassandra

Is there any distributor supporting these software components in combination? 
If no and your core business is not software then you may want to look for 
something else, because it might not make sense to build up internal know-how 
in all of these areas.

In any case - it depends all highly on your data and queries. You will have to 
do your own experiments.

On 09 Nov 2015, at 07:02, "fightf...@163.com" <fightf...@163.com> wrote:

Hi, community

We are specially interested about this featural integration according to some 
slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)

seems good implementation for lambda architecure in the open-source world, 
especially non-hadoop based cluster environment. As we can see, 

the advantages obviously consist of :

1 the feasibility and scalability of spark datafram api, which can also make a 
perfect complement for Apache Cassandra native cql feature.

2 both streaming and batch process availability using the ALL-STACK thing, cool.

3 we can both achieve compacity and usability for spark with cassandra, 
including seemlessly integrating with job scheduling and resource management.

Only one concern goes to the OLAP query performance issue, which mainly caused 
by frequent aggregation work between daily increased large tables, for 

both spark sql and cassandra. I can see that the [1] use case facilitates 
FiloDB to achieve columnar storage and query performance, but we had nothing 
more 

knowledge. 

Question is : Any guy had such use case for now, especially using in your 
production environment ? Would be interested in your architeture for designing 
this 

OLAP engine using spark +  cassandra. What do you think the comparison between 
the scenario with traditional OLAP cube design? Like Apache Kylin or 

pentaho mondrian ? 

Best Regards,

Sun.


[1]  
http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark



fightf...@163.com

OLAP query using spark dataframe with cassandra

[fightf...@163.com]

Hi, community

We are specially interested about this featural integration according to some 
slides from [1]. The SMACK(Spark+Mesos+Akka+Cassandra+Kafka)

seems good implementation for lambda architecure in the open-source world, 
especially non-hadoop based cluster environment. As we can see, 

the advantages obviously consist of :

1 the feasibility and scalability of spark datafram api, which can also make a 
perfect complement for Apache Cassandra native cql feature.

2 both streaming and batch process availability using the ALL-STACK thing, cool.

3 we can both achieve compacity and usability for spark with cassandra, 
including seemlessly integrating with job scheduling and resource management.

Only one concern goes to the OLAP query performance issue, which mainly caused 
by frequent aggregation work between daily increased large tables, for 

both spark sql and cassandra. I can see that the [1] use case facilitates 
FiloDB to achieve columnar storage and query performance, but we had nothing 
more 

knowledge. 

Question is : Any guy had such use case for now, especially using in your 
production environment ? Would be interested in your architeture for designing 
this 

OLAP engine using spark +  cassandra. What do you think the comparison between 
the scenario with traditional OLAP cube design? Like Apache Kylin or 

pentaho mondrian ? 

Best Regards,

Sun.


[1]  
http://www.slideshare.net/planetcassandra/cassandra-summit-2014-interactive-olap-queries-using-apache-cassandra-and-spark



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