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 <http://www.snappydata.io/download>. Happy to answer any questions (there are tons of resources on the site and you can post questions on the slack <https://snappydata-public.slack.com> channel) On Thu, Mar 16, 2017 at 2:43 AM, yohann jardin <yohannjar...@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 <rah...@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.s...@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" <vvs...@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.s...@accenture.com, Mar 16, 2017 7:52 AM wrote: >>> >>> Hi, >>> >>> >>> >>> Will MongoDB not fit this solution? >>> >>> >>> >>> >>> >>> >>> >>> *From:* Vova Shelgunov [mailto:vvs...@gmail.com] >>> *Sent:* Wednesday, March 15, 2017 11:51 PM >>> *To:* Muthu Jayakumar <bablo...@gmail.com> >>> *Cc:* vincent gromakowski <vincent.gromakow...@gmail.com>; Richard >>> Siebeling <rsiebel...@gmail.com>; user <user@spark.apache.org>; Shiva >>> Ramagopal <tr.s...@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" <bablo...@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 <vvs...@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.s...@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 <bablo...@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 <rsiebel...@gmail.com> >>> wrote: >>> >>> maybe Apache Ignite does fit your requirements >>> >>> >>> >>> On 15 March 2017 at 08:44, vincent gromakowski < >>> vincent.gromakow...@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" <bablo...@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 >>> >>> >>> >>> -- >>> View this message in context: http://apache-spark-user-list. >>> 1001560.n3.nabble.com/Fast-write-datastore-tp28497.html >>> <https://urldefense.proofpoint.com/v2/url?u=http-3A__apache-2Dspark-2Duser-2Dlist.1001560.n3.nabble.com_Fast-2Dwrite-2Ddatastore-2Dtp28497.html&d=DwMFaQ&c=eIGjsITfXP_y-DLLX0uEHXJvU8nOHrUK8IrwNKOtkVU&r=7scIIjM0jY9x3fjvY6a_yERLxMA2NwA8l0DnuyrL6yA&m=9OzGCUHXXQLjuS_SpMHII54QWHNzFKrwMma4qV3ADxE&s=6305WvqHeyTC5S2ZSBXamJrcO03n3MQyoU4tkMQlM_k&e=> >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> ------------------------------ >>> >>> This message is for the designated recipient only and may contain >>> privileged, proprietary, or otherwise confidential information. 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