Application performance monitoring system // IT Management Solutions

Application performance monitoring system

In a complex System average tier response times are not helpful. Tier response times need to be look at with a transaction context as provided by this transaction flow
Last time I explained logical and organizational prerequisites to a successful production level application performance monitoring. I originally wanted to look at the concrete metrics we need on every tier, but was asked how you can correlate data in a distributed environment, so this will be the first thing that we look into. So let’s take a look at the technical prerequisites of successful production monitoring.

Collecting data from distributed environment

The first problem that we have is the distributed nature of most applications. In order to isolate response time problems or errors we need to know which tier and component is responsible. The first step is to record response times on every entry and exit from a tier.

dynaTrace automatically traces calls across tiers (synchronous and asynchronous), captures contextual information per transactions and highlights which tiers contribute how much to the response time

The problem with this is twofold. First the externalJira tier will host multiple different Services which will have different characteristics. This is why we need to measure the response time on that service level and not just on the tier level. We need to do this on both sides of the fence, otherwise we will run into an averaging problem. The second problem is that externalJira is called from different other tiers and not just one.

When we look at the picture we see that externalJira is called from three different tiers. These tiers sometimes call the same services on externalJira, but with vastly different parameters which leads to different response times of externalJira.While Response time and Overall CPU Usage fluctuates the average CPU usage per transaction is stable We have a double averaging problem:

  • different tiers calling different services on externalJira skewing the average
  • different tiers calling the same service on externalJira with different parameters skewing the average

Let’s look at this in a little more detail with following example

Tier Entry Point Avg. Tier Response Time Service 1 Response Time The Database Statement impacts the response time by contributing 20%Service 2 Response Time Service 3 Response Time
100x Payment 1 1000ms 305ms 403ms 102ms
200x Payment 2 1500ms 607ms 151ms 202ms
300x Service 1 500ms 101ms
600x Service 2 166ms 52ms
400x Service 3 89ms

In this table we see which Tier Entry Point calls which Services on other tiers. The Payment 1 Service calls Services 1-3 and measures the response time on its side. The Payment 2 Service calls the same three services but with very different response times.Trying to corrlate log files from all the involved servers and databases is nearly impossible in bigger systems When we look at the times measured on Services 1-3 respectively we will see a completely different timing. We did measure the response time of Service 1-3 irrespective of their calling context and ended up with an average! Service 1 does not contribute 500ms to the response times of either Payment 1 or 2, but the overall average is 500 ms. This average becomes more and more useless the more tiers we add. One of our biggest customers hits 30 JVMs in every single transaction. In such complex environments quick root cause isolation is nearly impossible if you only measure on a tier by tier basis.

In order to correlate the response times in a complex system we need to retain the transaction context of the original caller. One way to solve this is to trace transactions, either by using a monitoring tool that can do that or by modifying code and build it into the application.

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