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Full Stack Tracing, Part 3 Interpreting traces

This is the third and last installment of our series about full stack tracing. If you haven’t read them yet, you’ll probably want to check out the introduction and tool setup portions first. In this blog, we’re going to focus on visualizing and interpreting full stack traces.

At this point, we’re going to assume that you’ve already captured a trace file for the problem you’re examining as a common trace format (CTF) file, a structured binary file format that LTTng natively generates. What do you do with this file?

Text output

The first option is to use the babeltrace utility to make the CTF files human readable. Babeltrace converts between trace formats and it has a ton of options. However, the default options and implicit sinks and sources make it trivial to get a text version of a trace:

 $ babeltrace2 ./mytracefile.ctf 

 

Nevertheless, the output is quite dense and requires a bit of wading through to make sense of it:

[16:21:53.959371489] (+0.001033737) irq_handler_entry: { cpu_id = 4 }, { irq = 26, name = "ahci[0000:00:1f.2]" }
[16:21:53.959376537] (+0.000005048) block_rq_complete: { cpu_id = 4 }, { dev = 8388640, sector = 412553920, nr_sector = 192, error = 0, rwbs = 12 }
[16:21:53.959383667] (+0.000007130) kmem_cache_free: { cpu_id = 4 }, { call_site = 0xFFFFFFFF997A02D6, ptr = 0xFFFF8E8D764FB000 }
[16:21:53.959384148] (+0.000000481) kmem_cache_free: { cpu_id = 4 }, { call_site = 0xFFFFFFFF997A9023, ptr = 0xFFFF8E8D78778D00 }
[16:21:53.959384386] (+0.000000238) sched_switch: { cpu_id = 6 }, { prev_comm = "swapper/6", prev_tid = 0, prev_prio = 20, prev_state = 0, next_comm = "samegame", next_tid = 12 7718, next_prio = 20 } 
[16:21:53.959385565] (+0.000001179) irq_handler_exit: { cpu_id = 4 }, { irq = 26, ret = 1 }
[16:21:53.959413189] (+0.000027624) qtcore:QObject_ctor: { cpu_id = 6 }, { object = 0x7FFD1B520660 }
[16:21:53.959419236] (+0.000006047) qtgui:QGuiApplicationPrivate_init_entry: { cpu_id = 6 }, { }
[16:21:53.959423690] (+0.000004454) qtcore:QCoreApplicationPrivate_init_entry: { cpu_id = 6 }, { }

...

[16:21:53.962944916] (+0.000023918) sched_switch: { cpu_id = 6 }, { prev_comm = "swapper/6", prev_tid = 0, prev_prio = 20, prev_state = 0, next_comm = "samegame", next_tid = 127718, next_prio = 20 } 
[16:21:53.962968468] (+0.000023552) syscall_entry_openat: { cpu_id = 6 }, { dfd = -100, filename = "/usr/lib/locale/locale-archive", flags = 524288, mode = 0 }
[16:21:53.962971371] (+0.000002903) kmem_cache_alloc: { cpu_id = 6 }, { call_site = 0xFFFFFFFF9967EC4B, ptr = 0xFFFF8E87FDFBA000, bytes_req = 4096, bytes_alloc = 4096, gfp_flags = 6291648 } 
[16:21:53.962976139] (+0.000004768) kmem_cache_alloc: { cpu_id = 6 }, { call_site = 0xFFFFFFFF9966FA13, ptr = 0xFFFF8E8874118D00, bytes_req = 256, bytes_alloc = 256, gfp_flags = 6324416 } 
[16:21:53.962986535] (+0.000010396) kmem_cache_free: { cpu_id = 6 }, { call_site = 0xFFFFFFFF9966B8EB, ptr = 0xFFFF8E87FDFBA000 }
[16:21:53.962988213] (+0.000001678) syscall_exit_openat: { cpu_id = 6 }, { ret = 41 }
[16:21:53.962990485] (+0.000002272) syscall_entry_newfstat: { cpu_id = 6 }, { fd = 41
[16:21:53.962993755] (+0.000003270) syscall_exit_newfstat: { cpu_id = 6 }, { ret = 0, statbuf = 140443163546912 }
[16:21:53.962995661] (+0.000001906) syscall_entry_mmap: { cpu_id = 6 }, { addr = 0x0, len = 3035216, prot = 1, flags = 2, fd = 41, offset = 0 }
[16:21:53.963002217] (+0.000006556) kmem_cache_alloc: { cpu_id = 6 }, { call_site = 0xFFFFFFFF99480ADA, ptr = 0xFFFF8E88743F8480, bytes_req = 192, bytes_alloc = 192, gfp_flags = 6291648 } 
[16:21:53.963007890] (+0.000005673) syscall_exit_mmap: { cpu_id = 6 }, { ret = 0x7FBB6E688000 }

 

It’s not like you couldn’t figure this out, but a bit of visualization and computer-aided assistance could help immensely. This leads us to the next option.

Trace Compass

Provided by the Eclipse foundation, Trace Compass provides a much better way to see what’s in your trace files.

Trace Compass analysis

This tool has a lot of options for analyzing kernel and syscall events, as well as sporting a great user interface. While it’s possible to visualize custom trace points in the timeline, it’s very unwieldy to do non-trivial custom analyses within its XML framework. You would need to write a Java plugin for Trace Compass, which I personally don’t feel comfortable doing. (However, if someone else wants to volunteer to add the features that we describe below in ctf2ctf to Trace Compass, I’m sure the community would appreciate having more tools at their disposal!)

Chrome traces

The Chrome browser (using chrome://tracing) can also display traces in Chrome Trace Format, a JSON file format defined by Google. Although Google created Chrome Trace Format for Android and browser tracing, it’s extensible enough to handle other tracing applications. And of course, rather confusingly (but understandably), Google abbreviates this as CTF even though it has nothing to do with the other CTF we’ve been discussing.

How do you get your CTF (Common Trace Format) into CTF (Chrome Trace Format)? Simple! Use ctf2ctf, which is a KDAB-created utility that uses babeltrace to convert from LTTng files to a Chrome-compatible format. It also adds a number of metadata annotations to make the interpretation of your trace files even easier, like adding file names to file descriptors and page faults, showing device names with interrupts, converting string formats as needed, and counting memory page allocations.

After using ctf2ctf and opening the resulting file in Chrome, we get something like this:

Chrome tracing scenegraph

Great! The biggest problem with Chrome trace viewing is that JavaScript is limited to 4GB of running memory. That means you are limited in the size of trace files you can view, something to be aware of if you load large files (100MB or larger) and the browser crashes or starts behaving abnormally.

Qt Creator

KDAB also started a project to add trace viewing capability to Qt Creator, which is available in the latest version of Qt. Like the Chrome viewer, this also imports Chrome JSON trace files from our ctf2ctf tool.

Qt Creator trace image

Although this feature is available now in the main Qt distribution, we’re still adding features and fixing; if you want to contribute, we would most welcome it.

Kernel areas to focus on

There are several key events that you need to look at to gain essential insights for interpreting a full stack trace. These let you understand the overall execution flow of the system and help you focus on an area of particular interest.

  • Scheduler trace points (sched_process_fork, sched_switch, sched_process_exec, sched_process_free) These are critical categories of trace points as they delineate application task switches: which process is running, when processes start and stop, and the currently running thread.
  • Syscalls (openat, close, mmap, read, write, nanosleep, futex, poll, ioctl) These are extremely important as they show when applications make OS calls. Not only do they help you correlate the trace to execution points within your code, they also give you a rough idea of the system interaction and explain various scheduler task switches. (And if you’ve been using ctf2ctf, the file descriptors will be annotated with file names, which is super handy for understanding what those file calls are actually doing.)
  • Block devices and interrupts Traces on block devices and interrupts are useful to see when the system is I/O bound and to ensure that hardware is operating as expected.

System load

One of the more important uses of a full stack trace is to estimate system load. An unexpectedly heavy load can be very detrimental to the system and it can be quite difficult to identify in any other way.

Examining sched_switch events lets you check the total number of concurrently running threads and processes at any point. CPU frequency can be tracked across the CPU cores, telling you if each core is running flat out (and consuming lots of battery). CPU utilization gives you an estimate as to the overall cycle consumption of the system.

Chrome tracing CPU

What about RAM? Tracking the kernel memory allocations (kmem_cache_alloc_allocated, kmem_cache_alloc_requested, and mm_page_alloc) shows you how much RAM is in use. If you’re using an x86 processor, watch the page faults. Paging can invalidate all kinds of timing assumptions if not taken into account. (You’ll want to also trace mmap and openat so you can see which file the addresses belong to.)

Chrome tracing memory

Interpreting full stack traces with Qt

When it comes to understanding Qt behavior, there are a large number of places that we instrumented useful to interpreting the system’s execution. QCoreApplication initialization, QLibrary loading, QDebug output, event handling, and signal/slot are all traced, as well as QImageReader reading, and QFontDatabase loading.

Note that, as you might expect, all QStrings written into the trace are UTF-16 encoded. That’s not a problem if you’re using ctf2ctf as it converts strings to UTF-8. It is something to be aware of, however, if you’re trying to use babeltrace or TraceCompass as they don’t understand UTF-16 format strings.

Chrome Trace Qt GUI

 

For QML applications, we’ve inserted tracepoints for creating QML object instances, QML V4 functions calls, binding execution, QML compilation, and signal handling. This covers a gamut of areas where QML code might need an extra bit of profiling.

Chrome Tracing Qt QML

 

And in QtQuick, you have the ability to monitor scene graphs, window and scene rendering, animations, texture uploads, and glyph caching.

Chrome Tracing Scenegraph

With all of these areas traceable, hopefully you’ll be able to extract enough clues from your traces to find out where critical time is going and how to make your application perform better.

What’s next?

Is this the end for visualizing and interpreting full stack traces? There’s lots more we’d like to see. Enhancements we’re considering include better annotation of signals/slots, making the trace APIs public so you can use them in your code, overall improvements to the trace point coverage, and better analysis and visualization tools in Qt Creator. We’d also like to bring this capability to other Linux-like operating systems like Android, macOS, and iOS. Did we mention that if you’re interested in helping, we’re looking for contributors?

We want to help you too! We offer hands-on workshops, as well as a Debugging and Profiling for Linux training class if you’re looking for some help with interpreting your full stack trace project. Leave a comment on this blog if you need a recommendation on what might work best for your team.

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created the Massif-Visualizer, heaptrack and hotspot tools now used widely to improve C++ and Qt applications performance. The co-maintainer of the KDevelop IDE, in 2015 he won the KDE Akademy Award for ongoing work in that. Milian has a Masters Degree in Physics and lives in Berlin. He is fluent in English and German.
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