SingleStore DB (formerly MemSQL) backup Spark
August 02, 2016

SingleStore DB (formerly MemSQL) backup Spark

Tianwen Chu | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with MemSQL

SingleStore DB (formerly MemSQL) is used as a persistent storage solution for Spark. We use SingleStore DB (formerly MemSQL) spark connector (Scala code) to bridge two techs. I am leading projects of using spark and SingleStore DB (formerly MemSQL) to process life science data. It solved the spark storage issue.
  • Faster query speed than traditional SQL database.
  • It con server in the pipeline to deal with streaming data with Kafka, spark streaming and SingleStore DB (formerly MemSQL).
  • It is very scalable.
  • Better tuning of SingleStore DB (formerly MemSQL) performance on Scale-up server
  • SingleStore DB (formerly MemSQL) connection between spark failed when more than around 48 partitions data processing
  • Provide faster python API for invoking SingleStore DB (formerly MemSQL)
  • It offers me solution to solve spark storage problem.
  • It adds more complexity of my application since multiple tech softwares are involved.
  • More types of bugs will be encountered when doing streamliner, including hardware connection.
I have tried using CSV as a back-end storage, yet I/O is very heavy, direct transit from spark to SingleStore DB (formerly MemSQL) in memory really beats.
If data has too many joins necessities, then think of using graphdatabase technology rather than relational table.

Using MemSQL

I still want to see the performance about using latest version of spark and memsql. About renewal, if there is a new and better version of spark-memsql connector, then maybe.