Troubleshooting response time problems – why you cannot trust your system metrics
Production Monitoring is about ensuring the stability and health of our system, that also includes the application. A lot of times we encounter production systems that concentrate on System Monitoring, under the assumption that a stable system leads to stable and healthy applications. So let’s see what System Monitoring can tell us about our Application.
Let’s take a very simple two tier Web Application:
This is a simple multi-tier eCommerce solution. Users are concerned about bad performance when they do a search. Let’s see what we can find out about it if performance is not satisfactory. We start by looking at a couple of simple metrics.
The best known operating system metric is CPU utilization, but it is also the most misunderstood. This metric tells us how much time the CPU spent executing code in the last interval and how much more it could execute theoretically. Like all other utilization measures it tells us something about the capacity, but not about health, stability or even performance. Simply put: 99% CPU utilization can either be optimal or indicate impeding disaster depending on the application.
Let’s look at our setup. We see that the CPU utilization is well below 100%, so we do have capacity left. But does that mean the machine or the application can be considered healthy? Let’s look at another measure that is better suited for the job, the Load Average (System\Processor QueueLength on Windows ). The Load Average tells us how many threads or processes are currently executed or waiting to get CPU time.
Unix Top Output:
load average: 1.31, 1.13, 1.10
Linux systems display three sliding load averages for the last one, five and 15 minutes. The output above shows that in the last minute there were on average 1.3 processes that needed a CPU core at the same time.
If the Load Average is higher than the number of cores in the system we should either see near 100% CPU utilization, or the system has to wait for other resources and cannot max out the CPU. Examples would be Swapping or other I/O related tasks. So the Load Average tells us if we should trust the CPU usage on the one hand and if the machine is overloaded on the other. It does not tell us how well the application itself is performing, but whether the shortage of CPU might impact it negatively. If we do notice a problem we can identify the application that is causing the issue, but not why it is causing it.
In our case we see that neither the load average nor the CPU usage shines any light on our performance issue. If it were to show high CPU utilization or a high load average we could assume that the shortage in CPU is a problem, but we could not be certain.
Used memory is monitored because the lack of memory will lead to system instability. An important fact to note is that Unix and Linux operating systems will most always show close to 100% memory utilization over time. They fill the memory up with buffers and caches which get discarded, as opposed to swapped out, if that memory is needed otherwise. In order to get the “real” memory usage we need subtract these. In Linux we can do by using the free command.
If we do not have enough memory we can try to identify which application consumes the most by looking at the resident memory usage of a process. Once identified we will have to use other means to identify why the process uses up the memory and whether this is ok. When we look towards memory regarding Java/.NET performance we have to make sure that the application itself is never swapped out. This is especially important because Java accesses all its memory in a random-access fashion and if a portion were to be swapped out it would have serve performance penalties. We can monitor this via swapping measures on the process itself. So what we can learn here is whether the shortage of memory has a negative impact on application performance. As this is not the case, we are tempted to ignore memory as the issue.
We could look at other measures like network or disk, but in all cases the same thing would be true, the shortage of a resource might have impact, but we cannot say for sure. And if we don’t find a shortage it does not necessarily mean that everything is fine.
An especially good example of this problem is the database. Very often the database is considered the source of all performance problems, at least by the application people. From a DBA’s and operations point of view the database is often running fine though. Their reasoning is simple enough, the database is not running out of any resources, there are no especially long running or CPU consuming statements or processes running and most statements execute quite fast. So the database can not be the problem.
Let’s look at this from an application point of view
Looking at the Application
As users are reporting performance problems the first thing that we do is to look at the response time and its distribution within our system.
At first glance we don’t see anything particularly interesting when looking at the whole system. As users are complaining about specific requests lets go ahead and look at these in particular:
We see that the majority of the response time lies in the backend and the database layer. That the database contributes a major portion to the response time does not mean however that the DBA was wrong. We see that every single search executes 416 statements on average! That means that every statement is executing in under one millisecond and this is fast enough from the database point of view. The problem really lies within the application and its usage of the database. Let’s look at the backend next.
Looking at the JVM we immediately see that it does execute a lot of garbage collection (the red spikes), as you would probably see in every monitoring tool. Although this gives us a strong suspicion, we do not know how this is affecting our users. So let’s look at that impact:
A single transaction is hit by garbage collection several times and if we do the math we find out that garbage collection contributes 10% to the response time. While that is considerable it would not have made sense to spend a lot of time on tuning it just now. Even if we get it down to half it would only have saved us 5% of the response time. So while monitoring garbage collection is important, we should always analyze the impact before we jump to conclusions.
So let’s take a deeper look at where that particular transaction is spending time on the backend. To do this we need to have application centric monitoring in place which we can then use to isolate the root cause.
With the right measure points within our application we immediately see the root causes of the response time problem. At first we see that the WebService call done by the search takes up a large portion of the response time. It is also the largest CPU hotspot within that call. So while the host is not suffering CPU problems, we are in fact consuming a lot of it in that particular transaction. Secondly we see that an awful lot of EJB calls are done which in turn leads to the many database calls that we have already noticed.
That means we have identified a small memory-related issue; although there are no memory problems noticeable if we were to look only at system monitoring. We also found that we have a CPU hotspot, but the machine itself does not have a CPU problem. And finally we found that the biggest issue is squarely within the application; too many database and EJB calls, which we cannot see on a system monitoring level at all.
System metrics do a very good job at describing the environment, after all that is what they are meant for. If the environment itself has resource shortages we can almost assume that this has a negative impact on the applications, but we cannot be sure. If there is no obvious shortage this does not, however, imply that the application is running smoothly. A healthy and stable environment does not guarantee a healthy, stable and performing application.
Similar to the system, the application needs to be monitored in detail and with application-specific metrics in order to ensure its health and stability. There is no universal rule as to what these metrics are, but they should enable us to describe the health, stability and performance of the application itself.