Modern enterprise applications are being pushed to become faster, with a higher scale. Consequently, the 3-tier architecture model is changing to move data closer to the applications and to integrate data from several sources.

• Hardware trends in 64-bit architecture, multi-core processors and inexpensive memory
• Ubiquitous data ‘in motion and everywhere'; and
• More and more enterprise applications require a faster and inexpensive scale, along with resilience to failure.

Welcome to the world of ‘mid-tier caching', also known as ‘cache tier' or simply ‘caching'.

The next generation applications are composite, aggregating data and business logic from sources that can be local, federated, or cloud-based. Data and applications can reside in different tiers with different semantics and access patterns. For example, data in back-end servers/clusters or in the cloud tends to be authoritative; data on the wire is message-oriented; data in the mid-tier is either cached data for performance or application session data; and data on the devices could be local data or data cached from back-end sources.

With the costs of memory going down, fairly large caches can be configured on the desktop and server machines. With the maturity of 64-bit hardware, 64-bit CPUs are becoming mainstream for client and server machines. True 64-bit architecture support dramatically increases memory limits (to bytes). For example, desktops can be configured with 16 GB (gigabyte) RAM, and servers can be configured with up to 2 TB (terabyte) of RAM. Large memory caches allow for data to be located close to the application, thereby providing significant performance benefits to applications. In addition, in a world where hundreds of gigabytes of storage is the norm, the ability to work with most data in memory (large caches) and easily shift from tables to trees and on to graphs of objects, is key to programmer productivity for next-generation applications. While the query-based access is a requirement, the data access, management requirements and semantics of caches are different from what commercial DBMSs support.

Caching in applications
As described earlier, data can reside in different tiers (in different service boundaries) with different semantics. For example, data stored in the backend database is authoritative and requires a high degree of data consistency and integrity. Typically, there tends to be a single authoritative source for any data instance. Most data in the mid-tier, being operated by the business logic, can tend to be a copy of the authoritative data. Such copies are suitable for caching. Understanding the different types of data and their semantics in different tiers, helps define the different degrees of caching that is possible.

Reference data

Reference data is a version of the authoritative data. It is either a direct copy (version) of the original data or aggregated and transformed from multiple data sources. Reference data is practically immutable-changing the reference data (or the corresponding authoritative data) creates a new version of the reference data. This means that every reference data version is unique. Reference data is an ideal candidate for caching; as the reference data does not change, it can be shared across multiple applications (users), thereby increasing the scale and performance.
Consider a product catalogue application aggregating product information across multiple backend applications and data sources.