A MemSQL customer previously ran their business on two databases: the Amazon Web Services Relational Database Service (AWS RDS) for transactions and Druid.io for analytics, with a total bill which reached over $93,000 a month. They moved both functions to MemSQL, and their savings have been dramatic – about two-thirds of the total cost.
The customer’s new monthly cost is about $31,000, for a savings of $62,000 a month, or 66%. In addition, the customer gains increased performance, greater concurrency, and easier database management. Future projects can also claim these benefits, giving the customer lower costs for adding new features to their services and greater strategic flexibility.
The chart above shows the reduction in AWS RDS costs only – from a total of roughly $68,000 per month with AWS RDS and Druid.io, to roughly $5,700 a month with MemSQL replacing both. Licensing costs for Druid.io were roughly $25,000/month, the same as for MemSQL. So total costs dropped from roughly $93,000, with AWS RDS and Druid.io, to roughly $31,000, with MemSQL running in AWS.
In addition to the dramatic cost savings, strategic flexibility is enhanced by the fact that MemSQL can run in a variety of public and private clouds, on premises, in mixed on-premises and cloud environments, and in virtual machines as well as containers. “Cloud lock-in” becomes a problem of the past.
Moving from AWS RDS to MemSQL
AWS RDS is a flexible service that allows the use of multiple databases. In this case, the customer was using AWS Aurora. Aurora is a MySQL and PostgreSQL-compatible relational database offered by AWS as one of a wide range of database offerings. Because MemSQL is MySQL wire protocol-compatible, the move from Aurora to MemSQL was very easy.
And, because MemSQL is much more flexible than any one AWS database offering, it can accomplish many more tasks in a single database. For instance, MemSQL has strong support for both in-memory rowstore and disk-based columnstore data formats, with data moving flexibly between them. In AWS, by contrast, you might need to use either AWS ElastiCache for Memcached or AWS ElastiCache for Redis for in-memory performance, then transfer data into AWS Redshift for disk-based storage and analytics, making the analytics data stale.
MemSQL can also be used to augment, rather than replace, Aurora and other AWS offerings. Data stored in Aurora can be copied to MemSQL for analytics, for example. The Aurora database then runs faster because it no longer has to handle analytics inquiries; analytics queries and analytics apps run faster because they have a dedicated MemSQL database, and because of MemSQL’s faster query performance and greater concurrency support.
The customer’s ability to save so much money from such a simple change is somewhat ironic, as one of the claimed selling points of AWS Aurora is: “Performance and availability of commercial databases at 1/10th the cost.” Whereas this customer was able to save more than 66%, and gain better performance, greater concurrency, and strategic flexibility, through a simple and easy move from AWS Aurora and Druid.io to MemSQL.