First as I understand MapR-DB is a proprietary (not open source) NOSQL database that MapR offers. Similar to Hbase but better performance. There are some speculative statement as below:
https://hackernoon.com/mapr-db-spark-connector-with-secondary-indexes-df41909f28ea "MapR Data Platform offers significant advantages over any other tool on the big data space. MapR-DB is one of the core components of the platform and it offers state of the art capabilities that blow away most of the NoSQL databases out there" OK Spark has connectors for Hbase, Aerospike, Mongo etc. So no surprise here. However, as I understand within Map-R DB one can create secondary indexes and Spark can take advantages of these filters to reduce the load into RDD. val schema = StructType(Seq(StructField("_id", StringType), StructField("uid", StringType))) val data = sparkSession .loadFromMapRDB("/user/mapr/tables/data", schema) .filter("uid = '101'") .select("_id") So apparently this load will be more efficient as long as the secondary indexes are created in Map-R on the filtering column. Also see this doc https://mapr.com/docs/51/MapROverview/c_maprdb_new.html Sounds like MapR-DB tries to be a third part version of HBase and in some way mimics HDFS as well. I just don't understand when one can use Apache Phoenix with secondary indexes on Hbase that provide a relational view of Hbase. Has anyone used this product? There is some reference here as well https://stackoverflow.com/questions/30254134/difference-between-mapr-db-and-hbase Thanks Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <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.
