6.0 Release Notes

MemSQL 6.0 includes enhancements to query processing with up to 80X performance improvement for group-by/aggregate queries, and broader SQL support. It also introduces extensibility features, and enhanced manageability and resiliency.

MemSQL 6.0 contains the following new capabilities:


MemSQL 6 introduces extensibility features as part of the new MemSQL Procedural SQL (MPSQL) language. MemSQL developers can create user-defined:

SPs, UDFs, and UDAFs are compiled to machine code for high performance. Array and record types are supported in SPs and UDFs. SPs and UDFs also support exception handling. The MPSQL language will be familiar and straightforward to learn for database developers who have written functions and stored procedures in other database languages.

Query Processing

Query Language Features

The following new query language features are included in the release:

Query Optimization

Query Execution

Query execution improvements include:


Improvements to the columnstore include:

Data Loading

Data loading enhancements include:

Manageability and Resiliency

Manageability and resiliency has been improved for the 6.0 release, including:

Benefits include fewer situations that require manual intervention, simplified application development, and improved availability.

Streamliner Deprecation

MemSQL Streamliner was previously deprecated and is removed in MemSQL 6.0. For current Streamliner users, we recommend migrating to MemSQL Pipelines instead. MemSQL Pipelines provides increased stability, improved ingest performance, and exactly-once semantics. For more information about Pipelines, see the MemSQL Pipelines documentation.

What Do I Do With My Old Streamliner Spark Cluster?

To upgrade to MemSQL 6.0, you must first remove each of your Streamliner pipelines, and then uninstall the Spark Cluster co-located with your MemSQL cluster. Once each of the pipelines has been removed, simply run memsql-ops spark-uninstall to uninstall the Spark Cluster.

Can I Still Use Spark With MemSQL?

Yes, MemSQL supports Spark integration via the MemSQL Spark Connector. The connector allows you to leverage your existing Spark clusters to write data directly to MemSQL via a performant and easy-to-use API. For more information on the MemSQL Spark Connector, please see the MemSQL Spark 2.0 Connector GitHub page.

How Do I Get Started With Pipelines?

For more information on Pipelines, please see:

Maintenance Release Changelog

2018-07-16 Version 6.0.26

2018-07-02 Version 6.0.25

2018-06-18 Version 6.0.24

2018-06-04 Version 6.0.23

2018-05-21 Version 6.0.22

2018-05-01 Version 6.0.21

2018-04-02 Version 6.0.20

2018-03-12 Version 6.0.18

2018-03-02 Version 6.0.17

2018-02-16 Version 6.0.16

2018-02-12 Version 6.0.15

2018-01-29 Version 6.0.14

2018-01-11 Version 6.0.13

2017-12-13 Version 6.0.11

2017-11-28 Version 6.0.10

2017-11-15 Version 6.0.9

2017-11-07 Version 6.0.8

2017-10-25 Version 6.0.7

2017-10-18 Version 6.0.6

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