Previously, I looked at some Top-like tools [1] that are useful when diagnosing misbehaving servers and you only have a shell to the node or a crash cart with no X  Window System. When this happens, you have to rely on ASCII tools.

However, I don't rely just on Top-like tools to help me diagnose a problem; rather, I use them as a starting point if I can get a login. Top tells me a little of what is happening, but not everything. To get more detail, I turn to "stat" tools, particularly vmstat and vmstat-like tools.

The cool thing about these tools is that not only are they good for diagnosing problems, they also are good for examining application performance. In this article, I will show how I use vmstat and a similar tool called dstat. As a bonus, I will also discuss mpstat, which can be of great use, particularly for multiprocessing code.

vmstat

More often than not, when a node is misbehaving (performing badly), I've found that the node is swapping. One way to discover this is to use top and look at the swap usage. Although this method works reasonably well, I prefer to use vmstat  [2](virtual memory statistics), because I can tell how actively the node is swapping – a little or a lot? Vmstat is already on virtually all Linux systems. I haven't seen one yet that didn't have it.

To begin, I'll start with a quick look of vmstat in action. Figure 1 shows the output of the command while a fairly computationally intensive Python application named test3.py was running.

Figure 1: Sample output from vmstat while running a user application.

Although the goal of vmstat is to inform the user about the status of the physical and virtual memory of the system, it also provides information on "…  information about processes, memory, paging, block IO, traps, and CPU activity" [3]. However, vmstat is a bit different from the top-like tools I mentioned previously: Rather than updating the screen so you see current values, vmstat prints a line of information at every time step, giving you a history of what is happening on the system. Generally, vmstat prints output to the screen until either you stop it or it has printed out the maximum number of lines of output you specify. Therefore, vmstat is often used to capture the state of the system over time. For example, you can use it to help track the effect of an application on the system, or you can use it to help diagnose system problems.

When I log in to a system that is misbehaving, I like to use the command vmstat 1, which tells vmstat to print out data every second and keep doing it forever (1 is the interval between data output). To do it for 10 seconds, I would use:

vmstat 1 10

For this command, 1 is the interval between printouts, and 10 is the number of lines of output. Vmstat has different modes you can invoke to get different or additional information. By default, vmstat runs in "virtual memory" mode, and no command-line options are needed; however, you can run it in other modes by adding command-line options.

• The -d flag runs vmstat in "disk" mode, displaying information about the disks in the system.
• The -p option focuses on a partition.
• The -m option runs vmstat in "slab" mode. Slabinfo refers to caches of frequently used objects in the kernel, such as buffer heads, inodes, dentries, and so on. Vmstat displays information about some of these caches.

Because I'm interested in what is happening with memory, particularly if the node is swapping, I tend to run vmstat in the default virtual memory mode. The output from vmstat is pretty simple, and a few column headers (Table 1) help understand the information displayed.

The first lines of output from vmstat are the averages since the last reboot, and unless you are interested in this information, it can be safely ignored. Subsequent lines are output according to the specified output interval; I usually specify one-second intervals if I'm searching for a problem.

You can change the units used in vmstat with the -S option. By default, vmstat uses kibibytes (KiB) [4], or 1024 bytes (-S K). You can change that to kilobytes (-S k), megabytes (-S m), or mebibytes (-S M). If you suspect server swapping (i.e., virtual memory usage), then look at the swap columns, labeled si and so. If you see non-zero numbers after the first line of output, you most likely have a node that is swapping.

From the screenshot in Figure 1, you can see that the swap columns are zero, so no swapping is taking place. The data in the next two columns, blocks in and out of the system, tell me if the application is doing a great deal of I/O, which could also be the cause of an apparent slowdown on the server.

If you want to get ambitious, you can plot vmstat output. VMSTAT Analyzer  [5] graphs data from the vmstat logfile, and VMLoad [6] runs vmstat and creates CSV files that can be plotted in a spreadsheet or with gnuplot. However, none of these tools really met my needs, so I wrote my own Python script, vmstat_plotter [7] that takes vmstat data and plots it. Vmstat plotter only plots data that can be gathered over time. This means it can plot the data for VM, disk, device, slabinfo, and partition modes. If you try to plot the data for any other mode, the script fails miserably. Also, you need to use the default units (KiB; -S K) and use the -n option, which only prints the header once.

dstat

As was the case with Top, people have developed tools to replace or supplement vmstat. Dstat [9] is one of the most popular tools of this class. The website claims it can replace vmstat, iostat, netstat, and ifstat, with the ability to monitor a number of processes, ranging from processors to disks to networks. I tend to run dstat with the defaults when I start debugging a node, so I can get a good overall view of the state of the server. Figure 2 shows

dstat --io -s --top-io --top-bio 1 10

Figure 2: Dstat output while running Python code.

output while I ran some computationally intensive code. As you can see, dstat monitors CPU, disk, network, and virtual memory (paging) metrics by default, as well as some system statistics (interrupts and context switches, just like vmstat). Dstat also presents the output in color.

Although dstat is very similar to vmstat – it was designed to be a replacement for vmstat, after all – it has several additional options for measuring all kinds of system metrics. In the interest of time and space, I won't list them here, but you can read the dstat man page [10] for more information. Moreover, dstat is written in Python, so you can write your own plugins to measure whatever you want. Table 2 describes the five metrics reported by dstat in the default mode.

Dstat can also export its output to a CSV file for plotting with various tools, such as spreadsheets or gnuplot. To demonstrate the flexibility of dstat, I will show some simple command-line examples I use when diagnosing problems by focusing on disk I/O, network activity, and CPU usage.

Dstat is a bit better than vmstat in investigating disk I/O, with its ability to present an overall view or watch specific partitions or drives. In Figure  3, the

dstat -cdl -D sdc 1 10

Figure 3: Dstat output with focus on CPU, load, and disk stats while running Python code.

output lets me watch the CPU (-c), load (-l), and disk (sdc) for 10 seconds at one-second increments.

Note that although the node isn't doing much reading, it's doing a reasonable amount of writing. In the fifth interval, the system wrote 885KB to sdc, and in the 10th interval, it wrote 897kB. In many installations, the greatest part of I/O occurs on network filesystems, not on local disks.

Therefore, if the I/O pattern to network storage seems reasonable, you should check local I/O, in case the application is writing to something like /tmp, or maybe swapping, causing an unexpected slowdown.

Diagnosing network issues is typically a tough issue, but with dstat, you can take a quick look at the overall network traffic  – TCP (Ethernet) not IB (InfiniBand) traffic. One place to begin is to watch the network traffic on a specific network device. For example, assuming you have data or computational traffic over eth0, you can get an idea of network activity:

dstat -tn -N eth0 1 10

I added the time option (-t) and the network stats option (-n), then monitored the network device of interest (-N eth0) for 10 seconds at one-second intervals (Figure 4). The application that was running wasn't using the network and all of the I/O was local, but you can see that you get a reasonable overview of the amount of send and receive data on the device. Be sure to look at the dstat man page to see other network options.

Figure 4: Dstat output with focus on eth0 network traffic while running computationally heavy Python code.

Before looking at either the I/O stats or the network stats, I like to look at the CPU stats of a misbehaving server using dstat. This can tell me a lot very quickly. For example, if the user load is low, maybe a user application is swapping or doing a great deal of I/O over the network.

Another possibility is that the system is experiencing some latency issues. A few dstat options can give me an overall picture of what is happening with the CPUs on the system. Figure 5 shows the results of the

dstat -c --top-cpu -d --top-bio --top-latency 1 10

Figure 5: Dstat output with focus on the CPU stats while running Python code.

command I use quite often to check on CPU stats.

The options I chose are pretty simple, but they illustrate the flexibility of dstat. For example, I use the -c option to check on CPU usage (the first six columns in Figure 5) and the --top-cpu option to see the process using the most CPU time (second group in Figure 5 labeled cpu process).

The first line of output in this group is Firefox (this is my desktop). After that, it's the test3.py application. I also used the -d option for disk stats, because I might as well check the I/O while I'm watching the CPU.

These stats are in the third group from the left in Figure 5. It shows the total read and write I/O to the system in each time interval. In combination with -d, I used --top-bio, which shows the process that is doing the most block I/O (fourth group from the left labeled block i/o process), even though the output is in terms of bytes (KB). In this group, the most active block I/O process, regardless of read or write activity, is listed along with the read and write I/O for that process. In the simple example in Figure 5, the first process listed is Firefox, then the program test3.py.

The final option, --top-latency, lists the process with the greatest latency. The last group, labeled latencyprocess shows latency in milliseconds. For the sample output, the process with the largest latency is kondemand, which is a kernel thread that helps conserve power by reducing CPU speed if the CPU doesn't need to run at maximum speed.

For this desktop, I have four real cores and four hyperthread cores, and I'm really only running one computational program, so the other cores are throttled down, which points to kondemand as having high latency.

mpstat

One more tool I want to mention is not exactly along the lines of vmstat or dstat, but it has helped me when I'm helping a user with an application, particularly one that uses more than one core. The tool is part of Systat [11], and is called mpstat [12]. Mpstat is more like iostat or nfsiostat, and it gives perhaps more and better information about the CPUs than vmstat or dstat. Because virtually all systems today have more than one core, mpstat can be very useful in tracking CPU usage. I use it when I'm writing OpenMP [12] code and want to see how much of the core capability I'm using. Figure 6 shows the output from

mpstat 1 10

Figure 6: Mpstat output for all processors combined while running Python code.

which outputs CPU statistics for 10 seconds at one-second intervals. The output first displays the system and the number of CPUs (eight, in this case), then starts printing the stats for all of the CPUs combined. The output is fairly similar to that of vmstat and dstat (Table 3).

The user applications are using a little over 13% of the total CPU capability, and the system is using a little less than 0.25%. The remaining roughly 86+% is idle (close to seven of eight cores). To see the details for each processor, use:

mpstat -P ALL

to show the same stats as in Figure 6, but for all of the CPU's combined and then for each core (Figure 7). I use this option to see if my programs using OpenMP code are using much of the CPU, which I hope reaches maximum core utilization.

Figure 7: Mpstat output for each processor while running Python code.