I am extremely leery about pushing product on this forum and have refrained from it in the past. But since you are talking about loading parquet data into Spark, run some aggregate queries and then write the results to a fast data store, and specifically asking for product options,  it makes absolute sense to consider SnappyData. SnappyData turns Spark into a fast read write store and you can do what you are trying to do with a single cluster which hosts Spark and the database. It is an in memory store that supports high concurrency, fast lookups and the ability to run queries via ODBC/JDBC/Thrift. The tables stored in the database are accessible as dataframes and you can use the Spark API to access the data.

Check it out here. Happy to answer any questions (there are tons of resources on the site and you can post questions on the slack channel)

On Thu, Mar 16, 2017 at 2:43 AM, yohann jardin <yohannjardin@hotmail.com> wrote:

Hello everyone,

I'm also really interested in the answers as I will be facing the same issue soon.
Muthu, if you evaluate again Apache Ignite, can you share your results? I also noticed Alluxio to store spark results in memory that you might want to investigate.

In my case I want to use them to have a real time dashboard (or like waiting very few seconds to refine a dashboard), and that use case seems similar to your filter/aggregate previously computed spark results.

Regards,
Yohann



De : Rick Moritz <rahvin@gmail.com>
Envoyé : jeudi 16 mars 2017 10:37
À : user
Objet : Re: RE: Fast write datastore...
 
If you have enough RAM/SSDs available, maybe tiered HDFS storage and Parquet might also be an option. Of course, management-wise it has much more overhead than using ES, since you need to manually define partitions and buckets, which is suboptimal. On the other hand, for querying, you can probably get some decent performance by hooking up Impala or Presto or LLAP-Hive, if Spark were too slow/cumbersome.
Depending on your particular access patterns, this may not be very practical, but as a general approach it might be one way to get intermediate results quicker, and with less of a storage-zoo than some alternatives.

On Thu, Mar 16, 2017 at 7:57 AM, Shiva Ramagopal <tr.shiv@gmail.com> wrote:
I do think Kafka is an overkill in this case. There are no streaming use- cases that needs a queue to do pub-sub.

On 16-Mar-2017 11:47 AM, "vvshvv" <vvshvv@gmail.com> wrote:
Hi,

>> A slightly over-kill solution may be Spark to Kafka to ElasticSearch?

I do not think so, in this case you will be able to process Parquet files as usual, but Kafka will allow your Elasticsearch cluster to be stable and survive regarding the number of rows.

Regards,
Uladzimir



On jasbir.sing@accenture.com, Mar 16, 2017 7:52 AM wrote:

Hi,

 

Will MongoDB not fit this solution?

 

 

 

From: Vova Shelgunov [mailto:vvshvv@gmail.com]
Sent: Wednesday, March 15, 2017 11:51 PM
To: Muthu Jayakumar <babloo80@gmail.com>
Cc: vincent gromakowski <vincent.gromakowski@gmail.com>; Richard Siebeling <rsiebeling@gmail.com>; user <user@spark.apache.org>; Shiva Ramagopal <tr.shiv@gmail.com>
Subject: Re: Fast write datastore...

 

Hi Muthu,.

 

I did not catch from your message, what performance do you expect from subsequent queries?

 

Regards,

Uladzimir

 

On Mar 15, 2017 9:03 PM, "Muthu Jayakumar" <babloo80@gmail.com> wrote:

Hello Uladzimir / Shiva,

 

From ElasticSearch documentation (i have to see the logical plan of a query to confirm), the richness of filters (like regex,..) is pretty good while comparing to Cassandra. As for aggregates, i think Spark Dataframes is quite rich enough to tackle.

Let me know your thoughts.

 

Thanks,

Muthu

 

 

On Wed, Mar 15, 2017 at 10:55 AM, vvshvv <vvshvv@gmail.com> wrote:

Hi muthu,

 

I agree with Shiva, Cassandra also supports SASI indexes, which can partially replace Elasticsearch functionality.

 

Regards,

Uladzimir

 

 

 

Sent from my Mi phone

On Shiva Ramagopal <tr.shiv@gmail.com>, Mar 15, 2017 5:57 PM wrote:

Probably Cassandra is a good choice if you are mainly looking for a datastore that supports fast writes. You can ingest the data into a table and define one or more materialized views on top of it to support your queries. Since you mention that your queries are going to be simple you can define your indexes in the materialized views according to how you want to query the data.

Thanks,

Shiva

 

 

On Wed, Mar 15, 2017 at 7:58 PM, Muthu Jayakumar <babloo80@gmail.com> wrote:

Hello Vincent,

 

Cassandra may not fit my bill if I need to define my partition and other indexes upfront. Is this right?

 

Hello Richard,

 

Let me evaluate Apache Ignite. I did evaluate it 3 months back and back then the connector to Apache Spark did not support Spark 2.0.

 

Another drastic thought may be repartition the result count to 1 (but have to be cautions on making sure I don't run into Heap issues if the result is too large to fit into an executor)  and write to a relational database like mysql / postgres. But, I believe I can do the same using ElasticSearch too.

 

A slightly over-kill solution may be Spark to Kafka to ElasticSearch?

 

More thoughts welcome please. 

 

Thanks,

Muthu

 

On Wed, Mar 15, 2017 at 4:53 AM, Richard Siebeling <rsiebeling@gmail.com> wrote:

maybe Apache Ignite does fit your requirements

 

On 15 March 2017 at 08:44, vincent gromakowski <vincent.gromakowski@gmail.com> wrote:

Hi

If queries are statics and filters are on the same columns, Cassandra is a good option. 

 

Le 15 mars 2017 7:04 AM, "muthu" <babloo80@gmail.com> a écrit :

Hello there,

I have one or more parquet files to read and perform some aggregate queries
using Spark Dataframe. I would like to find a reasonable fast datastore that
allows me to write the results for subsequent (simpler queries).
I did attempt to use ElasticSearch to write the query results using
ElasticSearch Hadoop connector. But I am running into connector write issues
if the number of Spark executors are too many for ElasticSearch to handle.
But in the schema sense, this seems a great fit as ElasticSearch has smartz
in place to discover the schema. Also in the query sense, I can perform
simple filters and sort using ElasticSearch and for more complex aggregate,
Spark Dataframe can come back to the rescue :).
Please advice on other possible data-stores I could use?

Thanks,
Muthu



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