Further, this architecture can dramatically impact the latency of the application as each page load results in operations that travel completely through to the data tier. As application load increases, this heavy frequency in database operations can pose severe scalability limitations and performance bottlenecks due to an increased contention for resources and the physical limitations of retrieving data from disk. This architecture, unfortunately, faces several key challenges in the areas of scalability, performance, and availability.įigure 1 Typical Data-based Web App Architectureįirst, there is a major scalability challenge inherent in that the application performs database operations upon every page load in the form of retrieval operations for catalog and top-seller data. This application architecture takes on the familiar separation of user interface, business logic, and data access as shown in Figure 1. Additionally, aggregated order data is utilized to display top-selling products as well as products commonly purchased together.
This application enables users to browse a product catalog and purchase products through a shopping cart experience. Your typical current data-powered application could be something along the lines of a simple storefront over a database. This article focuses on how key features of Velocity can be leveraged to deliver the next level of scalability, availability, and performance in new and existing distributed.
By leveraging distributed memory, Velocity enables these high-performance, available applications to flexibly scale out as application demand increases. The Velocity cluster offers high availability to further insulate applications from data loss as well as increased load on the data tier.
Through Velocity, client applications can enhance performance by bringing data closer to the logic that consumes it, thus reducing pressure on the underlying data store. Velocity enables you to create scalable, available, high-performance applications by exposing a unified view of distributed memory for client application consumption. Additionally, the underlying hardware trends and technological advances across processing, storage, memory, and connectivity have enabled application architectures to harness inexpensive, commodity hardware to effectively scale out these applications.Ī new Microsoft project, code-named Velocity, provides a distributed, in-memory cache. Application architectures continue to evolve to leverage this wealth of data accessibility. Data flows from a myriad of sources including relational sources, service-oriented applications, syndicated feeds, and data-centric documents and messages. This article uses the following technologies:ĭata-driven applications have become pervasive. Classifying data for distributed caching.This article is based on a prerelease version of Microsoft Velocity. Volume 24 Number 06 Velocity - Build Better Data-Driven Apps With Distributed Caching
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