More and more public cloud companies are moving to managed cloud services to improve their value-add (price premium) and the stickiness of their solution. However, the shift to a database as a service (DaaS) severely reduces the DBAs visibility into the business, thus limiting the ability to hand tune the database to the requirements of the application and the database. The solution is a cloud database that eliminates the hand-tuning of the database, thereby enabling the DBA to be equally effective even with limited visibility into the business and application needs. It is these unique needs, particularly for SQL databases, that is fueling the NewSQL movement.
DBAs traditionally have insight into the company, enabling them to hand tune the database in a collaborative basis with the development team, such as:
1. Performance Trade-offs/Tuning: The database is partitioned and tuned to address business requirements, maximizing performance of certain critical processes, while slowing less critical processes.
2. System Maintenance Planning: You need to shut down the database to do an application/database upgrade, repartition, etc. but you may need to coordinate with the development team, and the schedule may change up until the last moment.
3. Application Evolution: The database must be designed and tuned to accommodate the planned changes in the application.
4. Consulting with Application Developers: Since the database partitioning and performance are hand-tuned, the DBA must collaborate closely with the application development team on design, development and deployment.
5. Partitioning Requirements: When partitioning (or repartitioning) your database you’ll need to partition the data to suit application requirements avoiding things like cross partition joins, range scans, aggregates, etc., which can cause tremendous performance penalties if not implemented correctly.
6. Moving Processes to the Application Layer: Single server databases can handle joins, range scans, aggregates and the like internally. However, when you partition the database these functions are typically moved to the application layer. The application must add the logic to accomplish these things. It must also add the routing code to point to the correct partitions. As a result, an application written to a single server API does not work in a multi-server configuration.
When moving from a self-managed database—either in the cloud or on premise—to a DaaS, the “DBA-in-the-cloud” doesn’t have that visibility into the business requirements, performance requirements, development schedule, and more. This lack of visibility turns the already challenging task of hand-tuning the database into a near impossibility using traditional databases.
A real world example: You are the DBA-in-the-cloud. Your customer has been running on a single server, but they need to now scale-out across multiple database nodes to accommodate growth. How do you split the data? You don’t know which queries have higher performance priority. You don’t know the development plan for new version of the application. You don’t know when it is convenient to shut down the application to implement the partitioning. You need to inform the application developers how they should implement their routing code to send database requests to the right nodes. You need to inform the application developers that they need to handle joins, range scans and aggregates at the application level, since the database can no longer handle those.
DBAs have enough of a challenge scaling and maintaining databases when they have full visibility into the business. To address these challenges without that level of visibility is unrealistic. I refer to this problem as the “Blind DBA” challenge, because the DaaS DBA has a serious lack of visibility into the requirements and inner workings of the company.
ScaleDB is a NewSQL database uniquely designed to handle the Blind DBA challenge inherent in DaaS implementations. ScaleDB is based on a shared-disk architecture that scales in a manner that is invisible to the application. It eliminates the need to push functions (joins, range scans, aggregates) up to the application layer. It eliminates the need to coordinate your application and database design to work around partitions, because there are no partitions. It eliminates the performance tradeoffs in partitioning design, you get consistent performance across the database. It eliminates scheduling and coordinating application shut-down to repartition, because it doesn’t use partitions. In short, with ScaleDB the DBA needn’t have any visibility into the business to deliver optimal database performance via a single API. This is what makes ScaleDB an ideal solution for cloud companies implementing the database as a managed service or DaaS.