MemSQL Documentation

MemSQL is a high-performance, in-memory database that combines the horizontal scalability of distributed systems with the familiarity of SQL.

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5.8 Release Notes

MemSQL 5.8 brings support for SAML 2.0 authentication, security audit logging capabilities, manageability enhancements, and other improvements.

Security Enhancements

A number of security enhancements have been added in MemSQL 5.8.

SAML 2.0 Authentication

Users can now authenticate to MemSQL by using SAML 2.0 third-party authentication. When properly configured, a MemSQL cluster can now receive and validate SAML 2.0 assertions about a user during authentication with the database. For more information, see the SAML Authentication documentation.

Audit Logging

MemSQL now provides audit logging capability that records all database activity and writes log files to a external location. Numerous logging levels are provided to allow system administrators to adjust both the verbosity and types of queries that can be logged. For more information, see the Audit Logging documentation.

Note

As of the time of this publication, audit logging feature is made available and licensed only as part of the MemSQL Advanced Security Option. Before using or implementing this functionality, please consult with your enterprise's licensing administrator to confirm that your enterprise has purchased the necessary Advanced Security Option license from MemSQL.

MemSQL Ops Agent Authentication

MemSQL Ops now supports inter-agent authentication using unique API tokens. When the primary agent generates a new token, agents on other hosts can follow the primary agent by specifying its unique API token. For more information, see the following topics in the documentation:

Workload Profiling and Management Views

MemSQL 5.8 introduces a new set of management views to help diagnose performance problems. These views allow you to determine where queries and system tasks are spending time, what resources are being consumed, and where bottlenecks may exist in the cluster. For more information, see the following topics in the documentation:

Streamliner Deprecation

MemSQL Streamliner will be deprecated 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:

Fixed Issues

  • Fixed an issue where recovering reference databases could be reattached to the cluster either automatically or explicitly via ATTACH LEAF.
  • Fixed an issue with SHOW LOAD ERRORS where CSV parse errors would not be reported if FIELDS ENCLOSED BY was specified when executing LOAD DATA SKIP ERRORS.
  • Fixed an issue with S3 Pipelines where a pipeline would become unresponsive if a corrupt zip file was loaded.
  • Due to the new Workload Profiling functionality, distributed_plancache and distributed_plancache_summary have been removed.

Maintenance Release Changelog

The changelog below contains MemSQL improvements and bug fixes introduced in maintenance or revision releases. For a similar list for MemSQL Ops, see MemSQL Ops Releases.

2017-07-19 Version 5.8.5 (LATEST)

  • The following built-in functions have been added for working with vectors with 32-bit floating-point numbers:
  • Fixed an issue where reference tables could be dropped under rare circumstances.
  • Fixed a possible crash when querying information_schema.OPTIMIZER_STATISTICS.

2017-06-07 Version 5.8.4

  • Memory overhead per database has been reduced by up to 30%, depending on whether or not the cluster's transaction_buffer system variable has been set to a low value. For more information, see Configuring Durability.
  • Performance has been improved for the CAST() function when executed on a CHAR data type.
  • Improved support for POLYGON types with a large number of vertices.

2017-05-04 Version 5.8.2

  • Kerberos Authentication now supports regular expressions when granting access to a Kerberos identity. For more information, see Granting a User Kerberos Authentication Permissions.
  • Memory overhead of empty tables has been reduced by as much as 40%.
  • Memory usage metrics exposed in management views is now more accurate.

5.8 Release Notes