Category: Architecture

James Serra describes a few architectures for multi-tenant databases in the cloud:

Separate Servers\VMs

You create VMs for each tenant, essentially doing a “lift and shift” of the current on-premise solution.  This provides the best isolation possible and it’s regularly done on-premises, but it’s also the one that doesn’t enable cutting costs, since each tenant has it’s own server, sql, license and so on.  Sometimes this is the only allowable option if you have in your client contract that their data will be hardware-isolated from other clients.  Some cons: table updates must be replicated across all the servers (i.e. updating reference tables), there is no resource sharing, and you need multiple backup strategies across all the servers.

Read on for a few other strategies.  There aren’t any cloud-only details here; you could implement the same strategies on-premises.

Kyle Kingsbury looks at VoltDB:

Unlike most SQL databases, which default to weaker isolation levels for performance reasons, VoltDB chooses to provide strong serializable isolation by default: the combination of serializability’s multi-object atomicity, and linearizability’s real-time constraints.

Serializability is the strongest of the four ANSI SQL isolation levels: transactions must appear to execute in some order, one at a time. It prohibits a number of consistency anomalies, including lost updates, dirty reads, fuzzy reads, and phantoms.

If you use VoltDB, it sounds like upgrading to 6.4 is a good idea.

Sebastiao Correia discusses Lambda architecture:

The batch layer stores all the data with no constraint on the schema. The schema-on-read is built in the batch views in the serving layer. Creating schema-on-read views requires algorithms to parse the data from the batch layer and convert them in a readable way. This allows input data to freely evolve as there is no constraint on their structure. But then, the algorithm that builds the view is responsible to manage the structural change in order to still deliver the same view as expected. 

This shows a coupling between the data and the algorithms used for serving the data. Focusing on data quality is therefore not enough and we may ask the question of the algorithm quality. As the system lives and evolves, the algorithms may become more and more complex. These algorithms must not be regarded as black boxes, but a clear understanding of what they are doing is important if we want to have a good data governance. Moreover, during the batch view creation, data quality transformations could be done so as to provide data of better quality to the consumer of the views.

Lambda is an interesting architectural concept, as it tries to solve the age-old “fast or accurate?” problem with “both.”  Get your fast estimates streamed through a speed layer, but your accurate, slow calculations handled through the serving layer.  Definitely check out this article.