Organizations are eager to capitalize on real-time data analysis, move beyond batch processing for time-critical insights, and excel at big data in a predictable, reliable way. But performance has been an issue for distributed systems like Hadoop, especially as use cases become more complex, such as in multi-tenant or multi-workload environments. The worst part? You may not even know you have a performance issue. 

In this report, Chad Carson and Sean Suchter of Pepperdata describe the performance challenges of running multi-tenant distributed computing environments. After examining pros and cons of current solutions for these problems, you’ll learn how to use real-time, intelligent software to track and dynamically adjust each application’s usage of physical hardware. Get ahead of your Hadoop operations for faster decision-making and better business ROI.

With this report, you’ll explore:

• How Hadoop and other multi-tenant distributed systems work, and why performance matters.
• Business-visible symptoms of performance problems: late jobs, inconsistent runtimes, and underutilized hardware.
• Scheduling challenges in multi-tenant systems.
• Symptoms and solutions for CPU performance limitations.
• Physical and virtual limits of node memory—and what happens when you run out.
• Identifying and solving performance problems due to disk and network performance limits and other typical bottlenecks.
• Solutions for monitoring performance and accurately allocating cluster costs among users and business units.