{"id":31534,"date":"2023-05-25T07:05:26","date_gmt":"2023-05-25T11:05:26","guid":{"rendered":"https:\/\/www.eginnovations.com\/blog\/?p=31534"},"modified":"2023-05-25T07:05:26","modified_gmt":"2023-05-25T11:05:26","slug":"how-apm-solutions-enhance-jmeter-load-testing-visibility-bridging-the-gap","status":"publish","type":"post","link":"https:\/\/www.eginnovations.com\/blog\/how-apm-solutions-enhance-jmeter-load-testing-visibility-bridging-the-gap\/","title":{"rendered":"How APM Solutions Enhance JMeter Load Testing Visibility – Bridging the Gap!"},"content":{"rendered":"
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As an SRE and DevOps evangelist, I talk to many customers and prospects, most of whom run load and stress testing as part of their application delivery chain, often using JMeter for load testing. Many of them have a misconception:<\/p>\n

“I have JMeter and I am all set from a performance\/ scalability perspective. I don’t need any other tools”.<\/div>\n

Where you could fall short by solely relying on JMeter for load testing<\/h2>\n

Take a look at a typical JMeter load testing report (I’ve included some screenshots below).<\/p>\n

You will get graph after graph of metrics such as response time, requests per second, error rates, CPU utilization, disk IO, throughput, etc.<\/p>\n

JMeter is great at answering the question: “How many simulated requests\/users can my web application support before it falls over?”<\/div>\n

However, JMeter metrics fall short in pinpointing the exact layer, tier or component(s) that may be causing performance issues in a full-stack application.<\/p>\n

As an SRE, you need an X-ray into pinpointing system bottlenecks to answer the question:<\/p>\n

\u201cWhy exactly is my application slow\u201d?<\/div>\n

In this article, we\u2019ll look at:<\/p>\n