First, Let's look on common principles that used for scalable event-driven and service-orientired applications
Let's take a look at some patterns and practices that can be leveraged to enhance the scalability and performance of your application.
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In many large-scale solutions, data is divided into separate partitions that can be managed and accessed separately. The partitioning strategy must be chosen carefully to maximize the benefits while minimizing adverse effects. Partitioning can help improve scalability, reduce contention, and optimize performance.
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Use caching in your architecture can help improve performance. Caching is a mechanism to store frequently used data or assets (web pages, images) for faster retrieval. Caching can be used at different layers of your application. You can use caching between your application servers and a database, to decrease data retrieval times. You could also use caching between your end users and your web servers, placing static content closer to the user and decreasing the time it takes to return web pages to the end user. This also has a secondary effect of offloading requests from your database or web servers, increasing the performance for other requests.
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Autoscaling is the process of dynamically allocating resources to match performance requirements. As the volume of work grows, an application may need additional resources to maintain the desired performance levels and satisfy service-level agreements (SLAs). As demand slackens and the additional resources are no longer needed, they can be de-allocated to minimize costs.
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Many types of applications require background tasks that run independently of the user interface (UI). Examples include batch jobs, intensive processing tasks, and long-running processes such as workflows. Background jobs can be executed without requiring user interaction--the application can start the job and then continue to process interactive requests from users. This can help to minimize the load on the application UI, which can improve availability and reduce interactive response times.
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Adding a messaging layer in between services can have a benefit to performance and scalability. Adding a messaging layer creates a buffer for requests between the services so that requests can continue to flow in without error if the application can’t keep up. As the application works through the requests, they will be answered in the order in which they were received.
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Scale as a unit. For each resource, determine the impact that a scaling activity may have on dependent systems. This makes applying scale-out operations easier, and less prone to negative impact on the application. For example, adding x number of web and worker roles might require y number of additional queues and z number of storage accounts to handle the additional workload generated by the roles. A scale unit could consist of x web and worker roles, y queues, and z storage accounts. Design the application so that it's easily scaled by adding one or more scale units.
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Distributed applications and services running in the cloud are, by their nature, complex pieces of software that comprise many moving parts. In a production environment, it's important to be able to track the way in which users utilize your system, trace resource utilization, and generally monitor the health and performance of your system. You can use this information as a diagnostic aid to detect and correct issues, and also to help spot potential problems and prevent them from occurring.
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Look across all layers of your application and identify and remediate performance bottlenecks in your application. These bottlenecks could be poor memory handling in your application, or even the process of adding indexes into your database. It may be an iterative process as you relieve one bottleneck and then uncover another that you were unaware of.
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