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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