This is a response to a blog postby analyst and marketing consultant Curt Monash. Originally virtualization meant running one operating system in a window inside of another operating system, e.g. running a Linux on a Windows machine using Microsoft Virtual PC or VMWare. Then virtualization evolved to mean slicing a single server into many for more granular resource allocation (Curt’s ex uno plures, translated: out of one, many). It has since expanded to include e pluribus unum (from many, one) and e pluribus ad pluribus (from many to many). This is evidenced in the use of the term “virtualization” to create the compound words: server virtualization, storage virtualization, network virtualization and now database virtualization. Server [Read more...]
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 [Read more...]
The CAP Theorem has become a convenient excuse for throwing data consistency under the bus. It is automatically assumed that every distributed system falls prey to CAP and therefore must sacrifice one of the three objectives, with consistency being the consistent fall guy. This automatic assumption is simply false. I am not debating the validity of the CAP Theorem, but instead positing that the onset of CAP limitations—what I call the CAP event horizon—does not start as soon as you move to a second master database node. Certain approaches can, in fact, extend the CAP event horizon. Physics tells us that different properties apply at different scales. For example, quantum physics displays properties that do not apply at larger scale. We see similar nuances in scaling databases. For example, if you are running a master slave database, using synchronous replication with a single [Read more...]
ScaleDB and Oracle RAC are both clustered databases that use a shared-disk architecture. As I have mentioned previously, they both actually share data via a shared cache, so it might be more appropriate to call them shared-cache databases.
Whether it is called shared-disk or shared-cache, these databases must orchestrate the sharing of a single set of data amongst multiple nodes. This introduces two challenges: the physical sharing of the data and the logical sharing of the data.
Physical Sharing: Raw storage is meant to work on a 1:1 basis with a single server. In order to share that data amongst multiple servers, you need either a Network File System (NFS), which shares whole files, or a Cluster File System (CFS), which shares data blocks. [Read more...]
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