What is Scalability in Cloud Computing

What is Scalability in Cloud Computing

This blog is the first in a series that introduces big data developers to Pepperdata Application Summary. Application Summary is the first in a series of Pepperdata guided application performance management (APM) user experiences. In these experiences, we solve a particular user problem (or use case) by providing all the relevant information, insights, and calls to action in one place so that the user can perform these tasks easily and quickly.

What is Application Summary?

Before I start the tour, let me first introduce Application Summary, a self-service performance solution created for application developers of Spark, MapReduce, and other big data applications.  When we talk about application performance, it could be in terms of running applications faster, using fewer resources, or, in the case of error resolution, mitigating these errors and quickly getting to the root cause. For developers who want to make their applications perform better, we target the following use cases:

  • Find my application(s) easily
  • Provide meaningful recommendations for improving application performance
  • Identify system bottlenecks that affect application performance
  • Help me to easily determine the root cause of application failures

Let’s start with finding applications by using the App Search function.

App Search

Based on user feedback, we simplified the search options so you can more easily search for all the applications running on your cluster or just specific ones that you are interested in. Either way, you can optionally specify a time range for your search, as well as an application’s full or partial name. If you want to narrow down your search to just one user or one queue, you can specify that as well. And, you can save your searches to use later so you don’t have to re-enter the same search criteria. Let’s see this in action. I’m going to specify “ScalaPageRank” as my specific app name, “prod” as the user name, and “root.prod” for the queue.